-
-
-## 
-
-
-**🐸TTS is a library for advanced Text-to-Speech generation.**
-
-🚀 Pretrained models in +1100 languages.
-
-🛠️ Tools for training new models and fine-tuning existing models in any language.
-
-📚 Utilities for dataset analysis and curation.
-______________________________________________________________________
-
-[](https://discord.gg/5eXr5seRrv)
-[![License]()](https://opensource.org/licenses/MPL-2.0)
-[](https://badge.fury.io/py/TTS)
-[](https://github.com/coqui-ai/TTS/blob/master/CODE_OF_CONDUCT.md)
-[](https://pepy.tech/project/tts)
-[](https://zenodo.org/badge/latestdoi/265612440)
-
-
-
-
-
-
-
-
-
-
-
-
-[![Docs]()](https://tts.readthedocs.io/en/latest/)
-
-
-
-______________________________________________________________________
-
-## 💬 Where to ask questions
-Please use our dedicated channels for questions and discussion. Help is much more valuable if it's shared publicly so that more people can benefit from it.
-
-| Type | Platforms |
-| ------------------------------- | --------------------------------------- |
-| 🚨 **Bug Reports** | [GitHub Issue Tracker] |
-| 🎁 **Feature Requests & Ideas** | [GitHub Issue Tracker] |
-| 👩💻 **Usage Questions** | [GitHub Discussions] |
-| 🗯 **General Discussion** | [GitHub Discussions] or [Discord] |
-
-[github issue tracker]: https://github.com/coqui-ai/tts/issues
-[github discussions]: https://github.com/coqui-ai/TTS/discussions
-[discord]: https://discord.gg/5eXr5seRrv
-[Tutorials and Examples]: https://github.com/coqui-ai/TTS/wiki/TTS-Notebooks-and-Tutorials
-
-
-## 🔗 Links and Resources
-| Type | Links |
-| ------------------------------- | --------------------------------------- |
-| 💼 **Documentation** | [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
-| 💾 **Installation** | [TTS/README.md](https://github.com/coqui-ai/TTS/tree/dev#install-tts)|
-| 👩💻 **Contributing** | [CONTRIBUTING.md](https://github.com/coqui-ai/TTS/blob/main/CONTRIBUTING.md)|
-| 📌 **Road Map** | [Main Development Plans](https://github.com/coqui-ai/TTS/issues/378)
-| 🚀 **Released Models** | [TTS Releases](https://github.com/coqui-ai/TTS/releases) and [Experimental Models](https://github.com/coqui-ai/TTS/wiki/Experimental-Released-Models)|
-| 📰 **Papers** | [TTS Papers](https://github.com/erogol/TTS-papers)|
-
-
-## 🥇 TTS Performance
-
-
-##
-
-
-**🐸TTS is a library for advanced Text-to-Speech generation.**
-
-🚀 Pretrained models in +1100 languages.
-
-🛠️ Tools for training new models and fine-tuning existing models in any language.
-
-📚 Utilities for dataset analysis and curation.
-______________________________________________________________________
-
-[](https://discord.gg/5eXr5seRrv)
-[
-
- {% if show_details == true %}
-
-
- Model details
-
-
-
-
-
-
-
-
-
- CLI arguments:
-| CLI key | -Value | -
| {{ key }} | -{{ value }} | -
-
- {% if model_config != None %}
-
-
-
-
-
-
-
-
-
-
- {% endif %}
-
- Model config:
- -| Key | -Value | -
| {{ key }} | -{{ value }} | -
- {% if vocoder_config != None %}
-
-
- {% else %}
-
-
-
-
- {% endif %}
- Vocoder model config:
- -| Key | -Value | -
| {{ key }} | -{{ value }} | -
- Please start server with --show_details=true to see details.
-
-
- {% endif %}
-
-
-
-
\ No newline at end of file
diff --git a/TTS/TTS/server/templates/index.html b/TTS/TTS/server/templates/index.html
deleted file mode 100644
index 6354d3919d9a1e9c1e22e9866c84c4eb8284bc13..0000000000000000000000000000000000000000
--- a/TTS/TTS/server/templates/index.html
+++ /dev/null
@@ -1,154 +0,0 @@
-
-
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diff --git a/TTS/TTS/tts/__init__.py b/TTS/TTS/tts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/TTS/tts/configs/__init__.py b/TTS/TTS/tts/configs/__init__.py
deleted file mode 100644
index 3146ac1c116cb807a81889b7a9ab223b9a051036..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import importlib
-import os
-from inspect import isclass
-
-# import all files under configs/
-# configs_dir = os.path.dirname(__file__)
-# for file in os.listdir(configs_dir):
-# path = os.path.join(configs_dir, file)
-# if not file.startswith("_") and not file.startswith(".") and (file.endswith(".py") or os.path.isdir(path)):
-# config_name = file[: file.find(".py")] if file.endswith(".py") else file
-# module = importlib.import_module("TTS.tts.configs." + config_name)
-# for attribute_name in dir(module):
-# attribute = getattr(module, attribute_name)
-
-# if isclass(attribute):
-# # Add the class to this package's variables
-# globals()[attribute_name] = attribute
diff --git a/TTS/TTS/tts/configs/align_tts_config.py b/TTS/TTS/tts/configs/align_tts_config.py
deleted file mode 100644
index 317a01af53ce26914d83610a913eb44b5836dac2..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/align_tts_config.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.align_tts import AlignTTSArgs
-
-
-@dataclass
-class AlignTTSConfig(BaseTTSConfig):
- """Defines parameters for AlignTTS model.
- Example:
-
- >>> from TTS.tts.configs.align_tts_config import AlignTTSConfig
- >>> config = AlignTTSConfig()
-
- Args:
- model(str):
- Model name used for selecting the right model at initialization. Defaults to `align_tts`.
- positional_encoding (bool):
- enable / disable positional encoding applied to the encoder output. Defaults to True.
- hidden_channels (int):
- Base number of hidden channels. Defines all the layers expect ones defined by the specific encoder or decoder
- parameters. Defaults to 256.
- hidden_channels_dp (int):
- Number of hidden channels of the duration predictor's layers. Defaults to 256.
- encoder_type (str):
- Type of the encoder used by the model. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
- Defaults to `fftransformer`.
- encoder_params (dict):
- Parameters used to define the encoder network. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
- Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
- decoder_type (str):
- Type of the decoder used by the model. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
- Defaults to `fftransformer`.
- decoder_params (dict):
- Parameters used to define the decoder network. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
- Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
- phase_start_steps (List[int]):
- A list of number of steps required to start the next training phase. AlignTTS has 4 different training
- phases. Thus you need to define 4 different values to enable phase based training. If None, it
- trains the whole model together. Defaults to None.
- ssim_alpha (float):
- Weight for the SSIM loss. If set <= 0, disables the SSIM loss. Defaults to 1.0.
- duration_loss_alpha (float):
- Weight for the duration predictor's loss. Defaults to 1.0.
- mdn_alpha (float):
- Weight for the MDN loss. Defaults to 1.0.
- spec_loss_alpha (float):
- Weight for the MSE spectrogram loss. If set <= 0, disables the L1 loss. Defaults to 1.0.
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
- noam_schedule (bool):
- enable / disable the use of Noam LR scheduler. Defaults to False.
- warmup_steps (int):
- Number of warm-up steps for the Noam scheduler. Defaults 4000.
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
- min_seq_len (int):
- Minimum input sequence length to be used at training.
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage."""
-
- model: str = "align_tts"
- # model specific params
- model_args: AlignTTSArgs = field(default_factory=AlignTTSArgs)
- phase_start_steps: List[int] = None
-
- ssim_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- dur_loss_alpha: float = 1.0
- mdn_alpha: float = 1.0
-
- # multi-speaker settings
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = None
- lr_scheduler_params: dict = None
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
diff --git a/TTS/TTS/tts/configs/bark_config.py b/TTS/TTS/tts/configs/bark_config.py
deleted file mode 100644
index 4d1cd1374afe8d5f0b9e87ed81db25d7e4032af9..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/bark_config.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import os
-from dataclasses import dataclass, field
-from typing import Dict
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.layers.bark.model import GPTConfig
-from TTS.tts.layers.bark.model_fine import FineGPTConfig
-from TTS.tts.models.bark import BarkAudioConfig
-from TTS.utils.generic_utils import get_user_data_dir
-
-
-@dataclass
-class BarkConfig(BaseTTSConfig):
- """Bark TTS configuration
-
- Args:
- model (str): model name that registers the model.
- audio (BarkAudioConfig): audio configuration. Defaults to BarkAudioConfig().
- num_chars (int): number of characters in the alphabet. Defaults to 0.
- semantic_config (GPTConfig): semantic configuration. Defaults to GPTConfig().
- fine_config (FineGPTConfig): fine configuration. Defaults to FineGPTConfig().
- coarse_config (GPTConfig): coarse configuration. Defaults to GPTConfig().
- CONTEXT_WINDOW_SIZE (int): GPT context window size. Defaults to 1024.
- SEMANTIC_RATE_HZ (float): semantic tokens rate in Hz. Defaults to 49.9.
- SEMANTIC_VOCAB_SIZE (int): semantic vocabulary size. Defaults to 10_000.
- CODEBOOK_SIZE (int): encodec codebook size. Defaults to 1024.
- N_COARSE_CODEBOOKS (int): number of coarse codebooks. Defaults to 2.
- N_FINE_CODEBOOKS (int): number of fine codebooks. Defaults to 8.
- COARSE_RATE_HZ (int): coarse tokens rate in Hz. Defaults to 75.
- SAMPLE_RATE (int): sample rate. Defaults to 24_000.
- USE_SMALLER_MODELS (bool): use smaller models. Defaults to False.
- TEXT_ENCODING_OFFSET (int): text encoding offset. Defaults to 10_048.
- SEMANTIC_PAD_TOKEN (int): semantic pad token. Defaults to 10_000.
- TEXT_PAD_TOKEN ([type]): text pad token. Defaults to 10_048.
- TEXT_EOS_TOKEN ([type]): text end of sentence token. Defaults to 10_049.
- TEXT_SOS_TOKEN ([type]): text start of sentence token. Defaults to 10_050.
- SEMANTIC_INFER_TOKEN (int): semantic infer token. Defaults to 10_051.
- COARSE_SEMANTIC_PAD_TOKEN (int): coarse semantic pad token. Defaults to 12_048.
- COARSE_INFER_TOKEN (int): coarse infer token. Defaults to 12_050.
- REMOTE_BASE_URL ([type]): remote base url. Defaults to "https://huggingface.co/erogol/bark/tree".
- REMOTE_MODEL_PATHS (Dict): remote model paths. Defaults to None.
- LOCAL_MODEL_PATHS (Dict): local model paths. Defaults to None.
- SMALL_REMOTE_MODEL_PATHS (Dict): small remote model paths. Defaults to None.
- CACHE_DIR (str): local cache directory. Defaults to get_user_data_dir().
- DEF_SPEAKER_DIR (str): default speaker directory to stoke speaker values for voice cloning. Defaults to get_user_data_dir().
- """
-
- model: str = "bark"
- audio: BarkAudioConfig = field(default_factory=BarkAudioConfig)
- num_chars: int = 0
- semantic_config: GPTConfig = field(default_factory=GPTConfig)
- fine_config: FineGPTConfig = field(default_factory=FineGPTConfig)
- coarse_config: GPTConfig = field(default_factory=GPTConfig)
- CONTEXT_WINDOW_SIZE: int = 1024
- SEMANTIC_RATE_HZ: float = 49.9
- SEMANTIC_VOCAB_SIZE: int = 10_000
- CODEBOOK_SIZE: int = 1024
- N_COARSE_CODEBOOKS: int = 2
- N_FINE_CODEBOOKS: int = 8
- COARSE_RATE_HZ: int = 75
- SAMPLE_RATE: int = 24_000
- USE_SMALLER_MODELS: bool = False
-
- TEXT_ENCODING_OFFSET: int = 10_048
- SEMANTIC_PAD_TOKEN: int = 10_000
- TEXT_PAD_TOKEN: int = 129_595
- SEMANTIC_INFER_TOKEN: int = 129_599
- COARSE_SEMANTIC_PAD_TOKEN: int = 12_048
- COARSE_INFER_TOKEN: int = 12_050
-
- REMOTE_BASE_URL = "https://huggingface.co/erogol/bark/tree/main/"
- REMOTE_MODEL_PATHS: Dict = None
- LOCAL_MODEL_PATHS: Dict = None
- SMALL_REMOTE_MODEL_PATHS: Dict = None
- CACHE_DIR: str = str(get_user_data_dir("tts/suno/bark_v0"))
- DEF_SPEAKER_DIR: str = str(get_user_data_dir("tts/bark_v0/speakers"))
-
- def __post_init__(self):
- self.REMOTE_MODEL_PATHS = {
- "text": {
- "path": os.path.join(self.REMOTE_BASE_URL, "text_2.pt"),
- "checksum": "54afa89d65e318d4f5f80e8e8799026a",
- },
- "coarse": {
- "path": os.path.join(self.REMOTE_BASE_URL, "coarse_2.pt"),
- "checksum": "8a98094e5e3a255a5c9c0ab7efe8fd28",
- },
- "fine": {
- "path": os.path.join(self.REMOTE_BASE_URL, "fine_2.pt"),
- "checksum": "59d184ed44e3650774a2f0503a48a97b",
- },
- }
- self.LOCAL_MODEL_PATHS = {
- "text": os.path.join(self.CACHE_DIR, "text_2.pt"),
- "coarse": os.path.join(self.CACHE_DIR, "coarse_2.pt"),
- "fine": os.path.join(self.CACHE_DIR, "fine_2.pt"),
- "hubert_tokenizer": os.path.join(self.CACHE_DIR, "tokenizer.pth"),
- "hubert": os.path.join(self.CACHE_DIR, "hubert.pt"),
- }
- self.SMALL_REMOTE_MODEL_PATHS = {
- "text": {"path": os.path.join(self.REMOTE_BASE_URL, "text.pt")},
- "coarse": {"path": os.path.join(self.REMOTE_BASE_URL, "coarse.pt")},
- "fine": {"path": os.path.join(self.REMOTE_BASE_URL, "fine.pt")},
- }
- self.sample_rate = self.SAMPLE_RATE # pylint: disable=attribute-defined-outside-init
diff --git a/TTS/TTS/tts/configs/delightful_tts_config.py b/TTS/TTS/tts/configs/delightful_tts_config.py
deleted file mode 100644
index 805d995369e29fce7d6aa87750356b21458cd64a..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/delightful_tts_config.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.delightful_tts import DelightfulTtsArgs, DelightfulTtsAudioConfig, VocoderConfig
-
-
-@dataclass
-class DelightfulTTSConfig(BaseTTSConfig):
- """
- Configuration class for the DelightfulTTS model.
-
- Attributes:
- model (str): Name of the model ("delightful_tts").
- audio (DelightfulTtsAudioConfig): Configuration for audio settings.
- model_args (DelightfulTtsArgs): Configuration for model arguments.
- use_attn_priors (bool): Whether to use attention priors.
- vocoder (VocoderConfig): Configuration for the vocoder.
- init_discriminator (bool): Whether to initialize the discriminator.
- steps_to_start_discriminator (int): Number of steps to start the discriminator.
- grad_clip (List[float]): Gradient clipping values.
- lr_gen (float): Learning rate for the gan generator.
- lr_disc (float): Learning rate for the gan discriminator.
- lr_scheduler_gen (str): Name of the learning rate scheduler for the generator.
- lr_scheduler_gen_params (dict): Parameters for the learning rate scheduler for the generator.
- lr_scheduler_disc (str): Name of the learning rate scheduler for the discriminator.
- lr_scheduler_disc_params (dict): Parameters for the learning rate scheduler for the discriminator.
- scheduler_after_epoch (bool): Whether to schedule after each epoch.
- optimizer (str): Name of the optimizer.
- optimizer_params (dict): Parameters for the optimizer.
- ssim_loss_alpha (float): Alpha value for the SSIM loss.
- mel_loss_alpha (float): Alpha value for the mel loss.
- aligner_loss_alpha (float): Alpha value for the aligner loss.
- pitch_loss_alpha (float): Alpha value for the pitch loss.
- energy_loss_alpha (float): Alpha value for the energy loss.
- u_prosody_loss_alpha (float): Alpha value for the utterance prosody loss.
- p_prosody_loss_alpha (float): Alpha value for the phoneme prosody loss.
- dur_loss_alpha (float): Alpha value for the duration loss.
- char_dur_loss_alpha (float): Alpha value for the character duration loss.
- binary_align_loss_alpha (float): Alpha value for the binary alignment loss.
- binary_loss_warmup_epochs (int): Number of warm-up epochs for the binary loss.
- disc_loss_alpha (float): Alpha value for the discriminator loss.
- gen_loss_alpha (float): Alpha value for the generator loss.
- feat_loss_alpha (float): Alpha value for the feature loss.
- vocoder_mel_loss_alpha (float): Alpha value for the vocoder mel loss.
- multi_scale_stft_loss_alpha (float): Alpha value for the multi-scale STFT loss.
- multi_scale_stft_loss_params (dict): Parameters for the multi-scale STFT loss.
- return_wav (bool): Whether to return audio waveforms.
- use_weighted_sampler (bool): Whether to use a weighted sampler.
- weighted_sampler_attrs (dict): Attributes for the weighted sampler.
- weighted_sampler_multipliers (dict): Multipliers for the weighted sampler.
- r (int): Value for the `r` override.
- compute_f0 (bool): Whether to compute F0 values.
- f0_cache_path (str): Path to the F0 cache.
- attn_prior_cache_path (str): Path to the attention prior cache.
- num_speakers (int): Number of speakers.
- use_speaker_embedding (bool): Whether to use speaker embedding.
- speakers_file (str): Path to the speaker file.
- speaker_embedding_channels (int): Number of channels for the speaker embedding.
- language_ids_file (str): Path to the language IDs file.
- """
-
- model: str = "delightful_tts"
-
- # model specific params
- audio: DelightfulTtsAudioConfig = field(default_factory=DelightfulTtsAudioConfig)
- model_args: DelightfulTtsArgs = field(default_factory=DelightfulTtsArgs)
- use_attn_priors: bool = True
-
- # vocoder
- vocoder: VocoderConfig = field(default_factory=VocoderConfig)
- init_discriminator: bool = True
-
- # optimizer
- steps_to_start_discriminator: int = 200000
- grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])
- lr_gen: float = 0.0002
- lr_disc: float = 0.0002
- lr_scheduler_gen: str = "ExponentialLR"
- lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
- lr_scheduler_disc: str = "ExponentialLR"
- lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
- scheduler_after_epoch: bool = True
- optimizer: str = "AdamW"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01})
-
- # acoustic model loss params
- ssim_loss_alpha: float = 1.0
- mel_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 1.0
- energy_loss_alpha: float = 1.0
- u_prosody_loss_alpha: float = 0.5
- p_prosody_loss_alpha: float = 0.5
- dur_loss_alpha: float = 1.0
- char_dur_loss_alpha: float = 0.01
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 10
-
- # vocoder loss params
- disc_loss_alpha: float = 1.0
- gen_loss_alpha: float = 1.0
- feat_loss_alpha: float = 1.0
- vocoder_mel_loss_alpha: float = 10.0
- multi_scale_stft_loss_alpha: float = 2.5
- multi_scale_stft_loss_params: dict = field(
- default_factory=lambda: {
- "n_ffts": [1024, 2048, 512],
- "hop_lengths": [120, 240, 50],
- "win_lengths": [600, 1200, 240],
- }
- )
-
- # data loader params
- return_wav: bool = True
- use_weighted_sampler: bool = False
- weighted_sampler_attrs: dict = field(default_factory=lambda: {})
- weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
-
- # overrides
- r: int = 1
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
- attn_prior_cache_path: str = None
-
- # multi-speaker settings
- # use speaker embedding layer
- num_speakers: int = 0
- use_speaker_embedding: bool = False
- speakers_file: str = None
- speaker_embedding_channels: int = 256
- language_ids_file: str = None
- use_language_embedding: bool = False
-
- # use d-vectors
- use_d_vector_file: bool = False
- d_vector_file: str = None
- d_vector_dim: int = None
-
- # testing
- test_sentences: List[List[str]] = field(
- default_factory=lambda: [
- ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."],
- ["Be a voice, not an echo."],
- ["I'm sorry Dave. I'm afraid I can't do that."],
- ["This cake is great. It's so delicious and moist."],
- ["Prior to November 22, 1963."],
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/TTS/tts/configs/fast_pitch_config.py b/TTS/TTS/tts/configs/fast_pitch_config.py
deleted file mode 100644
index d086d26564450c60fa04a7f3a068506f4147d3be..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/fast_pitch_config.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class FastPitchConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as FastPitch model.
-
- Example:
-
- >>> from TTS.tts.configs.fast_pitch_config import FastPitchConfig
- >>> config = FastPitchConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- binary_align_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
-
- # dataset configs
- compute_f0(bool):
- Compute pitch. defaults to True
-
- f0_cache_path(str):
- pith cache path. defaults to None
- """
-
- model: str = "fast_pitch"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=ForwardTTSArgs)
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.1
- dur_loss_alpha: float = 0.1
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/TTS/tts/configs/fast_speech_config.py b/TTS/TTS/tts/configs/fast_speech_config.py
deleted file mode 100644
index af6c2db6faf55ee2b15047fff86281d42dab1b87..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/fast_speech_config.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class FastSpeechConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as FastSpeech model.
-
- Example:
-
- >>> from TTS.tts.configs.fast_speech_config import FastSpeechConfig
- >>> config = FastSpeechConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastSpeechArgs` for more details. Defaults to `FastSpeechArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- binary_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "fast_speech"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=lambda: ForwardTTSArgs(use_pitch=False))
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- dur_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.0
- aligner_loss_alpha: float = 1.0
- binary_align_loss_alpha: float = 1.0
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = False
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/TTS/tts/configs/fastspeech2_config.py b/TTS/TTS/tts/configs/fastspeech2_config.py
deleted file mode 100644
index d179617fb034fff269355ce7e3d78b67db90aacd..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/fastspeech2_config.py
+++ /dev/null
@@ -1,198 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class Fastspeech2Config(BaseTTSConfig):
- """Configure `ForwardTTS` as FastPitch model.
-
- Example:
-
- >>> from TTS.tts.configs.fastspeech2_config import FastSpeech2Config
- >>> config = FastSpeech2Config()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- energy_loss_alpha (float):
- Weight for the energy predictor's loss. If set 0, disables the energy predictor. Defaults to 1.0.
-
- binary_align_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
-
- # dataset configs
- compute_f0(bool):
- Compute pitch. defaults to True
-
- f0_cache_path(str):
- pith cache path. defaults to None
-
- # dataset configs
- compute_energy(bool):
- Compute energy. defaults to True
-
- energy_cache_path(str):
- energy cache path. defaults to None
- """
-
- model: str = "fastspeech2"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=lambda: ForwardTTSArgs(use_pitch=True, use_energy=True))
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.1
- energy_loss_alpha: float = 0.1
- dur_loss_alpha: float = 0.1
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
-
- # dataset configs
- compute_energy: bool = True
- energy_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/TTS/tts/configs/glow_tts_config.py b/TTS/TTS/tts/configs/glow_tts_config.py
deleted file mode 100644
index f42f3e5a510bacf1b2312ccea7d46201bbcb774f..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/glow_tts_config.py
+++ /dev/null
@@ -1,182 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-
-
-@dataclass
-class GlowTTSConfig(BaseTTSConfig):
- """Defines parameters for GlowTTS model.
-
- Example:
-
- >>> from TTS.tts.configs.glow_tts_config import GlowTTSConfig
- >>> config = GlowTTSConfig()
-
- Args:
- model(str):
- Model name used for selecting the right model at initialization. Defaults to `glow_tts`.
- encoder_type (str):
- Type of the encoder used by the model. Look at `TTS.tts.layers.glow_tts.encoder` for more details.
- Defaults to `rel_pos_transformers`.
- encoder_params (dict):
- Parameters used to define the encoder network. Look at `TTS.tts.layers.glow_tts.encoder` for more details.
- Defaults to `{"kernel_size": 3, "dropout_p": 0.1, "num_layers": 6, "num_heads": 2, "hidden_channels_ffn": 768}`
- use_encoder_prenet (bool):
- enable / disable the use of a prenet for the encoder. Defaults to True.
- hidden_channels_enc (int):
- Number of base hidden channels used by the encoder network. It defines the input and the output channel sizes,
- and for some encoder types internal hidden channels sizes too. Defaults to 192.
- hidden_channels_dec (int):
- Number of base hidden channels used by the decoder WaveNet network. Defaults to 192 as in the original work.
- hidden_channels_dp (int):
- Number of layer channels of the duration predictor network. Defaults to 256 as in the original work.
- mean_only (bool):
- If true predict only the mean values by the decoder flow. Defaults to True.
- out_channels (int):
- Number of channels of the model output tensor. Defaults to 80.
- num_flow_blocks_dec (int):
- Number of decoder blocks. Defaults to 12.
- inference_noise_scale (float):
- Noise scale used at inference. Defaults to 0.33.
- kernel_size_dec (int):
- Decoder kernel size. Defaults to 5
- dilation_rate (int):
- Rate to increase dilation by each layer in a decoder block. Defaults to 1.
- num_block_layers (int):
- Number of decoder layers in each decoder block. Defaults to 4.
- dropout_p_dec (float):
- Dropout rate for decoder. Defaults to 0.1.
- num_speaker (int):
- Number of speaker to define the size of speaker embedding layer. Defaults to 0.
- c_in_channels (int):
- Number of speaker embedding channels. It is set to 512 if embeddings are learned. Defaults to 0.
- num_splits (int):
- Number of split levels in inversible conv1x1 operation. Defaults to 4.
- num_squeeze (int):
- Number of squeeze levels. When squeezing channels increases and time steps reduces by the factor
- 'num_squeeze'. Defaults to 2.
- sigmoid_scale (bool):
- enable/disable sigmoid scaling in decoder. Defaults to False.
- mean_only (bool):
- If True, encoder only computes mean value and uses constant variance for each time step. Defaults to true.
- encoder_type (str):
- Encoder module type. Possible values are`["rel_pos_transformer", "gated_conv", "residual_conv_bn", "time_depth_separable"]`
- Check `TTS.tts.layers.glow_tts.encoder` for more details. Defaults to `rel_pos_transformers` as in the original paper.
- encoder_params (dict):
- Encoder module parameters. Defaults to None.
- d_vector_dim (int):
- Channels of external speaker embedding vectors. Defaults to 0.
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
- style_wav_for_test (str):
- Path to the wav file used for changing the style of the speech. Defaults to None.
- inference_noise_scale (float):
- Variance used for sampling the random noise added to the decoder's input at inference. Defaults to 0.0.
- length_scale (float):
- Multiply the predicted durations with this value to change the speech speed. Defaults to 1.
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
- noam_schedule (bool):
- enable / disable the use of Noam LR scheduler. Defaults to False.
- warmup_steps (int):
- Number of warm-up steps for the Noam scheduler. Defaults 4000.
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
- min_seq_len (int):
- Minimum input sequence length to be used at training.
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "glow_tts"
-
- # model params
- num_chars: int = None
- encoder_type: str = "rel_pos_transformer"
- encoder_params: dict = field(
- default_factory=lambda: {
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 6,
- "num_heads": 2,
- "hidden_channels_ffn": 768,
- }
- )
- use_encoder_prenet: bool = True
- hidden_channels_enc: int = 192
- hidden_channels_dec: int = 192
- hidden_channels_dp: int = 256
- dropout_p_dp: float = 0.1
- dropout_p_dec: float = 0.05
- mean_only: bool = True
- out_channels: int = 80
- num_flow_blocks_dec: int = 12
- inference_noise_scale: float = 0.33
- kernel_size_dec: int = 5
- dilation_rate: int = 1
- num_block_layers: int = 4
- num_speakers: int = 0
- c_in_channels: int = 0
- num_splits: int = 4
- num_squeeze: int = 2
- sigmoid_scale: bool = False
- encoder_type: str = "rel_pos_transformer"
- encoder_params: dict = field(
- default_factory=lambda: {
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 6,
- "num_heads": 2,
- "hidden_channels_ffn": 768,
- "input_length": None,
- }
- )
- d_vector_dim: int = 0
-
- # training params
- data_dep_init_steps: int = 10
-
- # inference params
- style_wav_for_test: str = None
- inference_noise_scale: float = 0.0
- length_scale: float = 1.0
-
- # multi-speaker settings
- use_speaker_embedding: bool = False
- speakers_file: str = None
- use_d_vector_file: bool = False
- d_vector_file: str = False
-
- # optimizer parameters
- optimizer: str = "RAdam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- grad_clip: float = 5.0
- lr: float = 1e-3
-
- # overrides
- min_seq_len: int = 3
- max_seq_len: int = 500
- r: int = 1 # DO NOT CHANGE - TODO: make this immutable once coqpit implements it.
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
diff --git a/TTS/TTS/tts/configs/neuralhmm_tts_config.py b/TTS/TTS/tts/configs/neuralhmm_tts_config.py
deleted file mode 100644
index 50f72847ed3e1c7089915ef8fd77ae5775c5b260..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/neuralhmm_tts_config.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-
-
-@dataclass
-class NeuralhmmTTSConfig(BaseTTSConfig):
- """
- Define parameters for Neural HMM TTS model.
-
- Example:
-
- >>> from TTS.tts.configs.overflow_config import OverflowConfig
- >>> config = OverflowConfig()
-
- Args:
- model (str):
- Model name used to select the right model class to initilize. Defaults to `Overflow`.
- run_eval_steps (int):
- Run evalulation epoch after N steps. If None, waits until training epoch is completed. Defaults to None.
- save_step (int):
- Save local checkpoint every save_step steps. Defaults to 500.
- plot_step (int):
- Plot training stats on the logger every plot_step steps. Defaults to 1.
- model_param_stats (bool):
- Log model parameters stats on the logger dashboard. Defaults to False.
- force_generate_statistics (bool):
- Force generate mel normalization statistics. Defaults to False.
- mel_statistics_parameter_path (str):
- Path to the mel normalization statistics.If the model doesn't finds a file there it will generate statistics.
- Defaults to None.
- num_chars (int):
- Number of characters used by the model. It must be defined before initializing the model. Defaults to None.
- state_per_phone (int):
- Generates N states per phone. Similar, to `add_blank` parameter in GlowTTS but in Overflow it is upsampled by model's encoder. Defaults to 2.
- encoder_in_out_features (int):
- Channels of encoder input and character embedding tensors. Defaults to 512.
- encoder_n_convolutions (int):
- Number of convolution layers in the encoder. Defaults to 3.
- out_channels (int):
- Channels of the final model output. It must match the spectragram size. Defaults to 80.
- ar_order (int):
- Autoregressive order of the model. Defaults to 1. In ablations of Neural HMM it was found that more autoregression while giving more variation hurts naturalness of the synthesised audio.
- sampling_temp (float):
- Variation added to the sample from the latent space of neural HMM. Defaults to 0.334.
- deterministic_transition (bool):
- deterministic duration generation based on duration quantiles as defiend in "S. Ronanki, O. Watts, S. King, and G. E. Henter, “Medianbased generation of synthetic speech durations using a nonparametric approach,” in Proc. SLT, 2016.". Defaults to True.
- duration_threshold (float):
- Threshold for duration quantiles. Defaults to 0.55. Tune this to change the speaking rate of the synthesis, where lower values defines a slower speaking rate and higher values defines a faster speaking rate.
- use_grad_checkpointing (bool):
- Use gradient checkpointing to save memory. In a multi-GPU setting currently pytorch does not supports gradient checkpoint inside a loop so we will have to turn it off then.Adjust depending on whatever get more batch size either by using a single GPU or multi-GPU. Defaults to True.
- max_sampling_time (int):
- Maximum sampling time while synthesising latents from neural HMM. Defaults to 1000.
- prenet_type (str):
- `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the
- Prenet. Defaults to `original`.
- prenet_dim (int):
- Dimension of the Prenet. Defaults to 256.
- prenet_n_layers (int):
- Number of layers in the Prenet. Defaults to 2.
- prenet_dropout (float):
- Dropout rate of the Prenet. Defaults to 0.5.
- prenet_dropout_at_inference (bool):
- Use dropout at inference time. Defaults to False.
- memory_rnn_dim (int):
- Dimension of the memory LSTM to process the prenet output. Defaults to 1024.
- outputnet_size (list[int]):
- Size of the output network inside the neural HMM. Defaults to [1024].
- flat_start_params (dict):
- Parameters for the flat start initialization of the neural HMM. Defaults to `{"mean": 0.0, "std": 1.0, "transition_p": 0.14}`.
- It will be recomputed when you pass the dataset.
- std_floor (float):
- Floor value for the standard deviation of the neural HMM. Prevents model cheating by putting point mass and getting infinite likelihood at any datapoint. Defaults to 0.01.
- It is called `variance flooring` in standard HMM literature.
- optimizer (str):
- Optimizer to use for training. Defaults to `adam`.
- optimizer_params (dict):
- Parameters for the optimizer. Defaults to `{"weight_decay": 1e-6}`.
- grad_clip (float):
- Gradient clipping threshold. Defaults to 40_000.
- lr (float):
- Learning rate. Defaults to 1e-3.
- lr_scheduler (str):
- Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
- `TTS.utils.training`. Defaults to `None`.
- min_seq_len (int):
- Minimum input sequence length to be used at training.
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "NeuralHMM_TTS"
-
- # Training and Checkpoint configs
- run_eval_steps: int = 100
- save_step: int = 500
- plot_step: int = 1
- model_param_stats: bool = False
-
- # data parameters
- force_generate_statistics: bool = False
- mel_statistics_parameter_path: str = None
-
- # Encoder parameters
- num_chars: int = None
- state_per_phone: int = 2
- encoder_in_out_features: int = 512
- encoder_n_convolutions: int = 3
-
- # HMM parameters
- out_channels: int = 80
- ar_order: int = 1
- sampling_temp: float = 0
- deterministic_transition: bool = True
- duration_threshold: float = 0.43
- use_grad_checkpointing: bool = True
- max_sampling_time: int = 1000
-
- ## Prenet parameters
- prenet_type: str = "original"
- prenet_dim: int = 256
- prenet_n_layers: int = 2
- prenet_dropout: float = 0.5
- prenet_dropout_at_inference: bool = True
- memory_rnn_dim: int = 1024
-
- ## Outputnet parameters
- outputnet_size: List[int] = field(default_factory=lambda: [1024])
- flat_start_params: dict = field(default_factory=lambda: {"mean": 0.0, "std": 1.0, "transition_p": 0.14})
- std_floor: float = 0.001
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"weight_decay": 1e-6})
- grad_clip: float = 40000.0
- lr: float = 1e-3
- lr_scheduler: str = None
-
- # overrides
- min_text_len: int = 10
- max_text_len: int = 500
- min_audio_len: int = 512
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "Be a voice, not an echo.",
- ]
- )
-
- # Extra needed config
- r: int = 1
- use_d_vector_file: bool = False
- use_speaker_embedding: bool = False
-
- def check_values(self):
- """Validate the hyperparameters.
-
- Raises:
- AssertionError: when the parameters network is not defined
- AssertionError: transition probability is not between 0 and 1
- """
- assert self.ar_order > 0, "AR order must be greater than 0 it is an autoregressive model."
- assert (
- len(self.outputnet_size) >= 1
- ), f"Parameter Network must have atleast one layer check the config file for parameter network. Provided: {self.parameternetwork}"
- assert (
- 0 < self.flat_start_params["transition_p"] < 1
- ), f"Transition probability must be between 0 and 1. Provided: {self.flat_start_params['transition_p']}"
diff --git a/TTS/TTS/tts/configs/overflow_config.py b/TTS/TTS/tts/configs/overflow_config.py
deleted file mode 100644
index dc3e5548b8f62f76c88acca85d19e2cee8687ebd..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/overflow_config.py
+++ /dev/null
@@ -1,201 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-
-
-@dataclass
-class OverflowConfig(BaseTTSConfig): # The classname has to be camel case
- """
- Define parameters for OverFlow model.
-
- Example:
-
- >>> from TTS.tts.configs.overflow_config import OverflowConfig
- >>> config = OverflowConfig()
-
- Args:
- model (str):
- Model name used to select the right model class to initilize. Defaults to `Overflow`.
- run_eval_steps (int):
- Run evalulation epoch after N steps. If None, waits until training epoch is completed. Defaults to None.
- save_step (int):
- Save local checkpoint every save_step steps. Defaults to 500.
- plot_step (int):
- Plot training stats on the logger every plot_step steps. Defaults to 1.
- model_param_stats (bool):
- Log model parameters stats on the logger dashboard. Defaults to False.
- force_generate_statistics (bool):
- Force generate mel normalization statistics. Defaults to False.
- mel_statistics_parameter_path (str):
- Path to the mel normalization statistics.If the model doesn't finds a file there it will generate statistics.
- Defaults to None.
- num_chars (int):
- Number of characters used by the model. It must be defined before initializing the model. Defaults to None.
- state_per_phone (int):
- Generates N states per phone. Similar, to `add_blank` parameter in GlowTTS but in Overflow it is upsampled by model's encoder. Defaults to 2.
- encoder_in_out_features (int):
- Channels of encoder input and character embedding tensors. Defaults to 512.
- encoder_n_convolutions (int):
- Number of convolution layers in the encoder. Defaults to 3.
- out_channels (int):
- Channels of the final model output. It must match the spectragram size. Defaults to 80.
- ar_order (int):
- Autoregressive order of the model. Defaults to 1. In ablations of Neural HMM it was found that more autoregression while giving more variation hurts naturalness of the synthesised audio.
- sampling_temp (float):
- Variation added to the sample from the latent space of neural HMM. Defaults to 0.334.
- deterministic_transition (bool):
- deterministic duration generation based on duration quantiles as defiend in "S. Ronanki, O. Watts, S. King, and G. E. Henter, “Medianbased generation of synthetic speech durations using a nonparametric approach,” in Proc. SLT, 2016.". Defaults to True.
- duration_threshold (float):
- Threshold for duration quantiles. Defaults to 0.55. Tune this to change the speaking rate of the synthesis, where lower values defines a slower speaking rate and higher values defines a faster speaking rate.
- use_grad_checkpointing (bool):
- Use gradient checkpointing to save memory. In a multi-GPU setting currently pytorch does not supports gradient checkpoint inside a loop so we will have to turn it off then.Adjust depending on whatever get more batch size either by using a single GPU or multi-GPU. Defaults to True.
- max_sampling_time (int):
- Maximum sampling time while synthesising latents from neural HMM. Defaults to 1000.
- prenet_type (str):
- `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the
- Prenet. Defaults to `original`.
- prenet_dim (int):
- Dimension of the Prenet. Defaults to 256.
- prenet_n_layers (int):
- Number of layers in the Prenet. Defaults to 2.
- prenet_dropout (float):
- Dropout rate of the Prenet. Defaults to 0.5.
- prenet_dropout_at_inference (bool):
- Use dropout at inference time. Defaults to False.
- memory_rnn_dim (int):
- Dimension of the memory LSTM to process the prenet output. Defaults to 1024.
- outputnet_size (list[int]):
- Size of the output network inside the neural HMM. Defaults to [1024].
- flat_start_params (dict):
- Parameters for the flat start initialization of the neural HMM. Defaults to `{"mean": 0.0, "std": 1.0, "transition_p": 0.14}`.
- It will be recomputed when you pass the dataset.
- std_floor (float):
- Floor value for the standard deviation of the neural HMM. Prevents model cheating by putting point mass and getting infinite likelihood at any datapoint. Defaults to 0.01.
- It is called `variance flooring` in standard HMM literature.
- hidden_channels_dec (int):
- Number of base hidden channels used by the decoder WaveNet network. Defaults to 150.
- kernel_size_dec (int):
- Decoder kernel size. Defaults to 5
- dilation_rate (int):
- Rate to increase dilation by each layer in a decoder block. Defaults to 1.
- num_flow_blocks_dec (int):
- Number of decoder layers in each decoder block. Defaults to 4.
- dropout_p_dec (float):
- Dropout rate of the decoder. Defaults to 0.05.
- num_splits (int):
- Number of split levels in inversible conv1x1 operation. Defaults to 4.
- num_squeeze (int):
- Number of squeeze levels. When squeezing channels increases and time steps reduces by the factor
- 'num_squeeze'. Defaults to 2.
- sigmoid_scale (bool):
- enable/disable sigmoid scaling in decoder. Defaults to False.
- c_in_channels (int):
- Unused parameter from GlowTTS's decoder. Defaults to 0.
- optimizer (str):
- Optimizer to use for training. Defaults to `adam`.
- optimizer_params (dict):
- Parameters for the optimizer. Defaults to `{"weight_decay": 1e-6}`.
- grad_clip (float):
- Gradient clipping threshold. Defaults to 40_000.
- lr (float):
- Learning rate. Defaults to 1e-3.
- lr_scheduler (str):
- Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
- `TTS.utils.training`. Defaults to `None`.
- min_seq_len (int):
- Minimum input sequence length to be used at training.
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "Overflow"
-
- # Training and Checkpoint configs
- run_eval_steps: int = 100
- save_step: int = 500
- plot_step: int = 1
- model_param_stats: bool = False
-
- # data parameters
- force_generate_statistics: bool = False
- mel_statistics_parameter_path: str = None
-
- # Encoder parameters
- num_chars: int = None
- state_per_phone: int = 2
- encoder_in_out_features: int = 512
- encoder_n_convolutions: int = 3
-
- # HMM parameters
- out_channels: int = 80
- ar_order: int = 1
- sampling_temp: float = 0.334
- deterministic_transition: bool = True
- duration_threshold: float = 0.55
- use_grad_checkpointing: bool = True
- max_sampling_time: int = 1000
-
- ## Prenet parameters
- prenet_type: str = "original"
- prenet_dim: int = 256
- prenet_n_layers: int = 2
- prenet_dropout: float = 0.5
- prenet_dropout_at_inference: bool = False
- memory_rnn_dim: int = 1024
-
- ## Outputnet parameters
- outputnet_size: List[int] = field(default_factory=lambda: [1024])
- flat_start_params: dict = field(default_factory=lambda: {"mean": 0.0, "std": 1.0, "transition_p": 0.14})
- std_floor: float = 0.01
-
- # Decoder parameters
- hidden_channels_dec: int = 150
- kernel_size_dec: int = 5
- dilation_rate: int = 1
- num_flow_blocks_dec: int = 12
- num_block_layers: int = 4
- dropout_p_dec: float = 0.05
- num_splits: int = 4
- num_squeeze: int = 2
- sigmoid_scale: bool = False
- c_in_channels: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"weight_decay": 1e-6})
- grad_clip: float = 40000.0
- lr: float = 1e-3
- lr_scheduler: str = None
-
- # overrides
- min_text_len: int = 10
- max_text_len: int = 500
- min_audio_len: int = 512
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "Be a voice, not an echo.",
- ]
- )
-
- # Extra needed config
- r: int = 1
- use_d_vector_file: bool = False
- use_speaker_embedding: bool = False
-
- def check_values(self):
- """Validate the hyperparameters.
-
- Raises:
- AssertionError: when the parameters network is not defined
- AssertionError: transition probability is not between 0 and 1
- """
- assert self.ar_order > 0, "AR order must be greater than 0 it is an autoregressive model."
- assert (
- len(self.outputnet_size) >= 1
- ), f"Parameter Network must have atleast one layer check the config file for parameter network. Provided: {self.parameternetwork}"
- assert (
- 0 < self.flat_start_params["transition_p"] < 1
- ), f"Transition probability must be between 0 and 1. Provided: {self.flat_start_params['transition_p']}"
diff --git a/TTS/TTS/tts/configs/shared_configs.py b/TTS/TTS/tts/configs/shared_configs.py
deleted file mode 100644
index bf17322c190bb234d4e27c6196e53b276fb5f09d..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/shared_configs.py
+++ /dev/null
@@ -1,344 +0,0 @@
-from dataclasses import asdict, dataclass, field
-from typing import Dict, List
-
-from coqpit import Coqpit, check_argument
-
-from TTS.config import BaseAudioConfig, BaseDatasetConfig, BaseTrainingConfig
-
-
-@dataclass
-class GSTConfig(Coqpit):
- """Defines the Global Style Token Module
-
- Args:
- gst_style_input_wav (str):
- Path to the wav file used to define the style of the output speech at inference. Defaults to None.
-
- gst_style_input_weights (dict):
- Defines the weights for each style token used at inference. Defaults to None.
-
- gst_embedding_dim (int):
- Defines the size of the GST embedding vector dimensions. Defaults to 256.
-
- gst_num_heads (int):
- Number of attention heads used by the multi-head attention. Defaults to 4.
-
- gst_num_style_tokens (int):
- Number of style token vectors. Defaults to 10.
- """
-
- gst_style_input_wav: str = None
- gst_style_input_weights: dict = None
- gst_embedding_dim: int = 256
- gst_use_speaker_embedding: bool = False
- gst_num_heads: int = 4
- gst_num_style_tokens: int = 10
-
- def check_values(
- self,
- ):
- """Check config fields"""
- c = asdict(self)
- super().check_values()
- check_argument("gst_style_input_weights", c, restricted=False)
- check_argument("gst_style_input_wav", c, restricted=False)
- check_argument("gst_embedding_dim", c, restricted=True, min_val=0, max_val=1000)
- check_argument("gst_use_speaker_embedding", c, restricted=False)
- check_argument("gst_num_heads", c, restricted=True, min_val=2, max_val=10)
- check_argument("gst_num_style_tokens", c, restricted=True, min_val=1, max_val=1000)
-
-
-@dataclass
-class CapacitronVAEConfig(Coqpit):
- """Defines the capacitron VAE Module
- Args:
- capacitron_capacity (int):
- Defines the variational capacity limit of the prosody embeddings. Defaults to 150.
- capacitron_VAE_embedding_dim (int):
- Defines the size of the Capacitron embedding vector dimension. Defaults to 128.
- capacitron_use_text_summary_embeddings (bool):
- If True, use a text summary embedding in Capacitron. Defaults to True.
- capacitron_text_summary_embedding_dim (int):
- Defines the size of the capacitron text embedding vector dimension. Defaults to 128.
- capacitron_use_speaker_embedding (bool):
- if True use speaker embeddings in Capacitron. Defaults to False.
- capacitron_VAE_loss_alpha (float):
- Weight for the VAE loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- capacitron_grad_clip (float):
- Gradient clipping value for all gradients except beta. Defaults to 5.0
- """
-
- capacitron_loss_alpha: int = 1
- capacitron_capacity: int = 150
- capacitron_VAE_embedding_dim: int = 128
- capacitron_use_text_summary_embeddings: bool = True
- capacitron_text_summary_embedding_dim: int = 128
- capacitron_use_speaker_embedding: bool = False
- capacitron_VAE_loss_alpha: float = 0.25
- capacitron_grad_clip: float = 5.0
-
- def check_values(
- self,
- ):
- """Check config fields"""
- c = asdict(self)
- super().check_values()
- check_argument("capacitron_capacity", c, restricted=True, min_val=10, max_val=500)
- check_argument("capacitron_VAE_embedding_dim", c, restricted=True, min_val=16, max_val=1024)
- check_argument("capacitron_use_speaker_embedding", c, restricted=False)
- check_argument("capacitron_text_summary_embedding_dim", c, restricted=False, min_val=16, max_val=512)
- check_argument("capacitron_VAE_loss_alpha", c, restricted=False)
- check_argument("capacitron_grad_clip", c, restricted=False)
-
-
-@dataclass
-class CharactersConfig(Coqpit):
- """Defines arguments for the `BaseCharacters` or `BaseVocabulary` and their subclasses.
-
- Args:
- characters_class (str):
- Defines the class of the characters used. If None, we pick ```Phonemes``` or ```Graphemes``` based on
- the configuration. Defaults to None.
-
- vocab_dict (dict):
- Defines the vocabulary dictionary used to encode the characters. Defaults to None.
-
- pad (str):
- characters in place of empty padding. Defaults to None.
-
- eos (str):
- characters showing the end of a sentence. Defaults to None.
-
- bos (str):
- characters showing the beginning of a sentence. Defaults to None.
-
- blank (str):
- Optional character used between characters by some models for better prosody. Defaults to `_blank`.
-
- characters (str):
- character set used by the model. Characters not in this list are ignored when converting input text to
- a list of sequence IDs. Defaults to None.
-
- punctuations (str):
- characters considered as punctuation as parsing the input sentence. Defaults to None.
-
- phonemes (str):
- characters considered as parsing phonemes. This is only for backwards compat. Use `characters` for new
- models. Defaults to None.
-
- is_unique (bool):
- remove any duplicate characters in the character lists. It is a bandaid for compatibility with the old
- models trained with character lists with duplicates. Defaults to True.
-
- is_sorted (bool):
- Sort the characters in alphabetical order. Defaults to True.
- """
-
- characters_class: str = None
-
- # using BaseVocabulary
- vocab_dict: Dict = None
-
- # using on BaseCharacters
- pad: str = None
- eos: str = None
- bos: str = None
- blank: str = None
- characters: str = None
- punctuations: str = None
- phonemes: str = None
- is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates
- is_sorted: bool = True
-
-
-@dataclass
-class BaseTTSConfig(BaseTrainingConfig):
- """Shared parameters among all the tts models.
-
- Args:
-
- audio (BaseAudioConfig):
- Audio processor config object instance.
-
- use_phonemes (bool):
- enable / disable phoneme use.
-
- phonemizer (str):
- Name of the phonemizer to use. If set None, the phonemizer will be selected by `phoneme_language`.
- Defaults to None.
-
- phoneme_language (str):
- Language code for the phonemizer. You can check the list of supported languages by running
- `python TTS/tts/utils/text/phonemizers/__init__.py`. Defaults to None.
-
- compute_input_seq_cache (bool):
- enable / disable precomputation of the phoneme sequences. At the expense of some delay at the beginning of
- the training, It allows faster data loader time and precise limitation with `max_seq_len` and
- `min_seq_len`.
-
- text_cleaner (str):
- Name of the text cleaner used for cleaning and formatting transcripts.
-
- enable_eos_bos_chars (bool):
- enable / disable the use of eos and bos characters.
-
- test_senteces_file (str):
- Path to a txt file that has sentences used at test time. The file must have a sentence per line.
-
- phoneme_cache_path (str):
- Path to the output folder caching the computed phonemes for each sample.
-
- characters (CharactersConfig):
- Instance of a CharactersConfig class.
-
- batch_group_size (int):
- Size of the batch groups used for bucketing. By default, the dataloader orders samples by the sequence
- length for a more efficient and stable training. If `batch_group_size > 1` then it performs bucketing to
- prevent using the same batches for each epoch.
-
- loss_masking (bool):
- enable / disable masking loss values against padded segments of samples in a batch.
-
- min_text_len (int):
- Minimum length of input text to be used. All shorter samples will be ignored. Defaults to 0.
-
- max_text_len (int):
- Maximum length of input text to be used. All longer samples will be ignored. Defaults to float("inf").
-
- min_audio_len (int):
- Minimum length of input audio to be used. All shorter samples will be ignored. Defaults to 0.
-
- max_audio_len (int):
- Maximum length of input audio to be used. All longer samples will be ignored. The maximum length in the
- dataset defines the VRAM used in the training. Hence, pay attention to this value if you encounter an
- OOM error in training. Defaults to float("inf").
-
- compute_f0 (int):
- (Not in use yet).
-
- compute_energy (int):
- (Not in use yet).
-
- compute_linear_spec (bool):
- If True data loader computes and returns linear spectrograms alongside the other data.
-
- precompute_num_workers (int):
- Number of workers to precompute features. Defaults to 0.
-
- use_noise_augment (bool):
- Augment the input audio with random noise.
-
- start_by_longest (bool):
- If True, the data loader will start loading the longest batch first. It is useful for checking OOM issues.
- Defaults to False.
-
- shuffle (bool):
- If True, the data loader will shuffle the dataset when there is not sampler defined. Defaults to True.
-
- drop_last (bool):
- If True, the data loader will drop the last batch if it is not complete. It helps to prevent
- issues that emerge from the partial batch statistics. Defaults to True.
-
- add_blank (bool):
- Add blank characters between each other two characters. It improves performance for some models at expense
- of slower run-time due to the longer input sequence.
-
- datasets (List[BaseDatasetConfig]):
- List of datasets used for training. If multiple datasets are provided, they are merged and used together
- for training.
-
- optimizer (str):
- Optimizer used for the training. Set one from `torch.optim.Optimizer` or `TTS.utils.training`.
- Defaults to ``.
-
- optimizer_params (dict):
- Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}`
-
- lr_scheduler (str):
- Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
- `TTS.utils.training`. Defaults to ``.
-
- lr_scheduler_params (dict):
- Parameters for the generator learning rate scheduler. Defaults to `{"warmup": 4000}`.
-
- test_sentences (List[str]):
- List of sentences to be used at testing. Defaults to '[]'
-
- eval_split_max_size (int):
- Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
-
- eval_split_size (float):
- If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
- If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
-
- use_speaker_weighted_sampler (bool):
- Enable / Disable the batch balancer by speaker. Defaults to ```False```.
-
- speaker_weighted_sampler_alpha (float):
- Number that control the influence of the speaker sampler weights. Defaults to ```1.0```.
-
- use_language_weighted_sampler (bool):
- Enable / Disable the batch balancer by language. Defaults to ```False```.
-
- language_weighted_sampler_alpha (float):
- Number that control the influence of the language sampler weights. Defaults to ```1.0```.
-
- use_length_weighted_sampler (bool):
- Enable / Disable the batch balancer by audio length. If enabled the dataset will be divided
- into 10 buckets considering the min and max audio of the dataset. The sampler weights will be
- computed forcing to have the same quantity of data for each bucket in each training batch. Defaults to ```False```.
-
- length_weighted_sampler_alpha (float):
- Number that control the influence of the length sampler weights. Defaults to ```1.0```.
- """
-
- audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
- # phoneme settings
- use_phonemes: bool = False
- phonemizer: str = None
- phoneme_language: str = None
- compute_input_seq_cache: bool = False
- text_cleaner: str = None
- enable_eos_bos_chars: bool = False
- test_sentences_file: str = ""
- phoneme_cache_path: str = None
- # vocabulary parameters
- characters: CharactersConfig = None
- add_blank: bool = False
- # training params
- batch_group_size: int = 0
- loss_masking: bool = None
- # dataloading
- min_audio_len: int = 1
- max_audio_len: int = float("inf")
- min_text_len: int = 1
- max_text_len: int = float("inf")
- compute_f0: bool = False
- compute_energy: bool = False
- compute_linear_spec: bool = False
- precompute_num_workers: int = 0
- use_noise_augment: bool = False
- start_by_longest: bool = False
- shuffle: bool = False
- drop_last: bool = False
- # dataset
- datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
- # optimizer
- optimizer: str = "radam"
- optimizer_params: dict = None
- # scheduler
- lr_scheduler: str = None
- lr_scheduler_params: dict = field(default_factory=lambda: {})
- # testing
- test_sentences: List[str] = field(default_factory=lambda: [])
- # evaluation
- eval_split_max_size: int = None
- eval_split_size: float = 0.01
- # weighted samplers
- use_speaker_weighted_sampler: bool = False
- speaker_weighted_sampler_alpha: float = 1.0
- use_language_weighted_sampler: bool = False
- language_weighted_sampler_alpha: float = 1.0
- use_length_weighted_sampler: bool = False
- length_weighted_sampler_alpha: float = 1.0
diff --git a/TTS/TTS/tts/configs/speedy_speech_config.py b/TTS/TTS/tts/configs/speedy_speech_config.py
deleted file mode 100644
index bf8517dfc478a135978df19f3126313a616c14c2..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/speedy_speech_config.py
+++ /dev/null
@@ -1,194 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class SpeedySpeechConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as SpeedySpeech model.
-
- Example:
-
- >>> from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
- >>> config = SpeedySpeechConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `speedy_speech`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `RAdam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `l1`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `huber`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- binary_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "speedy_speech"
- base_model: str = "forward_tts"
-
- # set model args as SpeedySpeech
- model_args: ForwardTTSArgs = field(
- default_factory=lambda: ForwardTTSArgs(
- use_pitch=False,
- encoder_type="residual_conv_bn",
- encoder_params={
- "kernel_size": 4,
- "dilations": 4 * [1, 2, 4] + [1],
- "num_conv_blocks": 2,
- "num_res_blocks": 13,
- },
- decoder_type="residual_conv_bn",
- decoder_params={
- "kernel_size": 4,
- "dilations": 4 * [1, 2, 4, 8] + [1],
- "num_conv_blocks": 2,
- "num_res_blocks": 17,
- },
- out_channels=80,
- hidden_channels=128,
- positional_encoding=True,
- detach_duration_predictor=True,
- )
- )
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "l1"
- duration_loss_type: str = "huber"
- use_ssim_loss: bool = False
- ssim_loss_alpha: float = 1.0
- dur_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- binary_align_loss_alpha: float = 0.3
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = False
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/TTS/tts/configs/tacotron2_config.py b/TTS/TTS/tts/configs/tacotron2_config.py
deleted file mode 100644
index 95b65202218cf3aa0dd70c8d8cd55a3f913ed308..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/tacotron2_config.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from dataclasses import dataclass
-
-from TTS.tts.configs.tacotron_config import TacotronConfig
-
-
-@dataclass
-class Tacotron2Config(TacotronConfig):
- """Defines parameters for Tacotron2 based models.
-
- Example:
-
- >>> from TTS.tts.configs.tacotron2_config import Tacotron2Config
- >>> config = Tacotron2Config()
-
- Check `TacotronConfig` for argument descriptions.
- """
-
- model: str = "tacotron2"
- out_channels: int = 80
- encoder_in_features: int = 512
- decoder_in_features: int = 512
diff --git a/TTS/TTS/tts/configs/tacotron_config.py b/TTS/TTS/tts/configs/tacotron_config.py
deleted file mode 100644
index 350b5ea99633569d6977851875d5d8d83175ac36..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/tacotron_config.py
+++ /dev/null
@@ -1,235 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig, CapacitronVAEConfig, GSTConfig
-
-
-@dataclass
-class TacotronConfig(BaseTTSConfig):
- """Defines parameters for Tacotron based models.
-
- Example:
-
- >>> from TTS.tts.configs.tacotron_config import TacotronConfig
- >>> config = TacotronConfig()
-
- Args:
- model (str):
- Model name used to select the right model class to initilize. Defaults to `Tacotron`.
- use_gst (bool):
- enable / disable the use of Global Style Token modules. Defaults to False.
- gst (GSTConfig):
- Instance of `GSTConfig` class.
- gst_style_input (str):
- Path to the wav file used at inference to set the speech style through GST. If `GST` is enabled and
- this is not defined, the model uses a zero vector as an input. Defaults to None.
- use_capacitron_vae (bool):
- enable / disable the use of Capacitron modules. Defaults to False.
- capacitron_vae (CapacitronConfig):
- Instance of `CapacitronConfig` class.
- num_chars (int):
- Number of characters used by the model. It must be defined before initializing the model. Defaults to None.
- num_speakers (int):
- Number of speakers for multi-speaker models. Defaults to 1.
- r (int):
- Initial number of output frames that the decoder computed per iteration. Larger values makes training and inference
- faster but reduces the quality of the output frames. This must be equal to the largest `r` value used in
- `gradual_training` schedule. Defaults to 1.
- gradual_training (List[List]):
- Parameters for the gradual training schedule. It is in the form `[[a, b, c], [d ,e ,f] ..]` where `a` is
- the step number to start using the rest of the values, `b` is the `r` value and `c` is the batch size.
- If sets None, no gradual training is used. Defaults to None.
- memory_size (int):
- Defines the number of previous frames used by the Prenet. If set to < 0, then it uses only the last frame.
- Defaults to -1.
- prenet_type (str):
- `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the
- Prenet. Defaults to `original`.
- prenet_dropout (bool):
- enables / disables the use of dropout in the Prenet. Defaults to True.
- prenet_dropout_at_inference (bool):
- enable / disable the use of dropout in the Prenet at the inference time. Defaults to False.
- stopnet (bool):
- enable /disable the Stopnet that predicts the end of the decoder sequence. Defaults to True.
- stopnet_pos_weight (float):
- Weight that is applied to over-weight positive instances in the Stopnet loss. Use larger values with
- datasets with longer sentences. Defaults to 0.2.
- max_decoder_steps (int):
- Max number of steps allowed for the decoder. Defaults to 50.
- encoder_in_features (int):
- Channels of encoder input and character embedding tensors. Defaults to 256.
- decoder_in_features (int):
- Channels of decoder input and encoder output tensors. Defaults to 256.
- out_channels (int):
- Channels of the final model output. It must match the spectragram size. Defaults to 80.
- separate_stopnet (bool):
- Use a distinct Stopnet which is trained separately from the rest of the model. Defaults to True.
- attention_type (str):
- attention type. Check ```TTS.tts.layers.attentions.init_attn```. Defaults to 'original'.
- attention_heads (int):
- Number of attention heads for GMM attention. Defaults to 5.
- windowing (bool):
- It especially useful at inference to keep attention alignment diagonal. Defaults to False.
- use_forward_attn (bool):
- It is only valid if ```attn_type``` is ```original```. Defaults to False.
- forward_attn_mask (bool):
- enable/disable extra masking over forward attention. It is useful at inference to prevent
- possible attention failures. Defaults to False.
- transition_agent (bool):
- enable/disable transition agent in forward attention. Defaults to False.
- location_attn (bool):
- enable/disable location sensitive attention as in the original Tacotron2 paper.
- It is only valid if ```attn_type``` is ```original```. Defaults to True.
- bidirectional_decoder (bool):
- enable/disable bidirectional decoding. Defaults to False.
- double_decoder_consistency (bool):
- enable/disable double decoder consistency. Defaults to False.
- ddc_r (int):
- reduction rate used by the coarse decoder when `double_decoder_consistency` is in use. Set this
- as a multiple of the `r` value. Defaults to 6.
- speakers_file (str):
- Path to the speaker mapping file for the Speaker Manager. Defaults to None.
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
- optimizer (str):
- Optimizer used for the training. Set one from `torch.optim.Optimizer` or `TTS.utils.training`.
- Defaults to `RAdam`.
- optimizer_params (dict):
- Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}`
- lr_scheduler (str):
- Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
- `TTS.utils.training`. Defaults to `NoamLR`.
- lr_scheduler_params (dict):
- Parameters for the generator learning rate scheduler. Defaults to `{"warmup": 4000}`.
- lr (float):
- Initial learning rate. Defaults to `1e-4`.
- wd (float):
- Weight decay coefficient. Defaults to `1e-6`.
- grad_clip (float):
- Gradient clipping threshold. Defaults to `5`.
- seq_len_norm (bool):
- enable / disable the sequnce length normalization in the loss functions. If set True, loss of a sample
- is divided by the sequence length. Defaults to False.
- loss_masking (bool):
- enable / disable masking the paddings of the samples in loss computation. Defaults to True.
- decoder_loss_alpha (float):
- Weight for the decoder loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- postnet_loss_alpha (float):
- Weight for the postnet loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- postnet_diff_spec_alpha (float):
- Weight for the postnet differential loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- decoder_diff_spec_alpha (float):
-
- Weight for the decoder differential loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- decoder_ssim_alpha (float):
- Weight for the decoder SSIM loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- postnet_ssim_alpha (float):
- Weight for the postnet SSIM loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- ga_alpha (float):
- Weight for the guided attention loss. If set less than or equal to zero, it disables the corresponding loss
- function. Defaults to 5.
- """
-
- model: str = "tacotron"
- # model_params: TacotronArgs = field(default_factory=lambda: TacotronArgs())
- use_gst: bool = False
- gst: GSTConfig = None
- gst_style_input: str = None
-
- use_capacitron_vae: bool = False
- capacitron_vae: CapacitronVAEConfig = None
-
- # model specific params
- num_speakers: int = 1
- num_chars: int = 0
- r: int = 2
- gradual_training: List[List[int]] = None
- memory_size: int = -1
- prenet_type: str = "original"
- prenet_dropout: bool = True
- prenet_dropout_at_inference: bool = False
- stopnet: bool = True
- separate_stopnet: bool = True
- stopnet_pos_weight: float = 0.2
- max_decoder_steps: int = 10000
- encoder_in_features: int = 256
- decoder_in_features: int = 256
- decoder_output_dim: int = 80
- out_channels: int = 513
-
- # attention layers
- attention_type: str = "original"
- attention_heads: int = None
- attention_norm: str = "sigmoid"
- attention_win: bool = False
- windowing: bool = False
- use_forward_attn: bool = False
- forward_attn_mask: bool = False
- transition_agent: bool = False
- location_attn: bool = True
-
- # advance methods
- bidirectional_decoder: bool = False
- double_decoder_consistency: bool = False
- ddc_r: int = 6
-
- # multi-speaker settings
- speakers_file: str = None
- use_speaker_embedding: bool = False
- speaker_embedding_dim: int = 512
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = None
-
- # optimizer parameters
- optimizer: str = "RAdam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
- seq_len_norm: bool = False
- loss_masking: bool = True
-
- # loss params
- decoder_loss_alpha: float = 0.25
- postnet_loss_alpha: float = 0.25
- postnet_diff_spec_alpha: float = 0.25
- decoder_diff_spec_alpha: float = 0.25
- decoder_ssim_alpha: float = 0.25
- postnet_ssim_alpha: float = 0.25
- ga_alpha: float = 5.0
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def check_values(self):
- if self.gradual_training:
- assert (
- self.gradual_training[0][1] == self.r
- ), f"[!] the first scheduled gradual training `r` must be equal to the model's `r` value. {self.gradual_training[0][1]} vs {self.r}"
- if self.model == "tacotron" and self.audio is not None:
- assert self.out_channels == (
- self.audio.fft_size // 2 + 1
- ), f"{self.out_channels} vs {self.audio.fft_size // 2 + 1}"
- if self.model == "tacotron2" and self.audio is not None:
- assert self.out_channels == self.audio.num_mels
diff --git a/TTS/TTS/tts/configs/tortoise_config.py b/TTS/TTS/tts/configs/tortoise_config.py
deleted file mode 100644
index d60e43d71280bfa085988e31a52acfeef015c5f0..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/tortoise_config.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from dataclasses import dataclass, field
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.tortoise import TortoiseArgs, TortoiseAudioConfig
-
-
-@dataclass
-class TortoiseConfig(BaseTTSConfig):
- """Defines parameters for Tortoise TTS model.
-
- Args:
- model (str):
- Model name. Do not change unless you know what you are doing.
-
- model_args (TortoiseArgs):
- Model architecture arguments. Defaults to `TortoiseArgs()`.
-
- audio (TortoiseAudioConfig):
- Audio processing configuration. Defaults to `TortoiseAudioConfig()`.
-
- model_dir (str):
- Path to the folder that has all the Tortoise models. Defaults to None.
-
- temperature (float):
- Temperature for the autoregressive model inference. Larger values makes predictions more creative sacrificing stability. Defaults to `0.2`.
-
- length_penalty (float):
- Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to the sequence length,
- which in turn is used to divide the score of the sequence. Since the score is the log likelihood of the sequence (i.e. negative),
- length_penalty > 0.0 promotes longer sequences, while length_penalty < 0.0 encourages shorter sequences.
-
- reperation_penalty (float):
- The parameter for repetition penalty. 1.0 means no penalty. Defaults to `2.0`.
-
- top_p (float):
- If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
- Defaults to `0.8`.
-
- cond_free_k (float):
- Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
- As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
- Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k. Defaults to `2.0`.
-
- diffusion_temperature (float):
- Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
- are the "mean" prediction of the diffusion network and will sound bland and smeared.
- Defaults to `1.0`.
-
- num_autoregressive_samples (int):
- Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
- As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
- Defaults to `16`.
-
- diffusion_iterations (int):
- Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
- the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
- however. Defaults to `30`.
-
- sampler (str):
- Diffusion sampler to be used. `ddim` or `dpm++2m`. Defaults to `ddim`.
- Note:
- Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
-
- Example:
-
- >>> from TTS.tts.configs.tortoise_config import TortoiseConfig
- >>> config = TortoiseConfig()
- """
-
- model: str = "tortoise"
- # model specific params
- model_args: TortoiseArgs = field(default_factory=TortoiseArgs)
- audio: TortoiseAudioConfig = field(default_factory=TortoiseAudioConfig)
- model_dir: str = None
-
- # settings
- temperature: float = 0.2
- length_penalty: float = 1.0
- repetition_penalty: float = 2.0
- top_p: float = 0.8
- cond_free_k: float = 2.0
- diffusion_temperature: float = 1.0
-
- # inference params
- num_autoregressive_samples: int = 16
- diffusion_iterations: int = 30
- sampler: str = "ddim"
diff --git a/TTS/TTS/tts/configs/vits_config.py b/TTS/TTS/tts/configs/vits_config.py
deleted file mode 100644
index 2d0242bf131a25d6b2cef7a297a3c32b283f908a..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/configs/vits_config.py
+++ /dev/null
@@ -1,176 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.vits import VitsArgs, VitsAudioConfig
-
-
-@dataclass
-class VitsConfig(BaseTTSConfig):
- """Defines parameters for VITS End2End TTS model.
-
- Args:
- model (str):
- Model name. Do not change unless you know what you are doing.
-
- model_args (VitsArgs):
- Model architecture arguments. Defaults to `VitsArgs()`.
-
- audio (VitsAudioConfig):
- Audio processing configuration. Defaults to `VitsAudioConfig()`.
-
- grad_clip (List):
- Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
-
- lr_gen (float):
- Initial learning rate for the generator. Defaults to 0.0002.
-
- lr_disc (float):
- Initial learning rate for the discriminator. Defaults to 0.0002.
-
- lr_scheduler_gen (str):
- Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to
- `ExponentialLR`.
-
- lr_scheduler_gen_params (dict):
- Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
-
- lr_scheduler_disc (str):
- Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to
- `ExponentialLR`.
-
- lr_scheduler_disc_params (dict):
- Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
-
- scheduler_after_epoch (bool):
- If true, step the schedulers after each epoch else after each step. Defaults to `False`.
-
- optimizer (str):
- Name of the optimizer to use with both the generator and the discriminator networks. One of the
- `torch.optim.*`. Defaults to `AdamW`.
-
- kl_loss_alpha (float):
- Loss weight for KL loss. Defaults to 1.0.
-
- disc_loss_alpha (float):
- Loss weight for the discriminator loss. Defaults to 1.0.
-
- gen_loss_alpha (float):
- Loss weight for the generator loss. Defaults to 1.0.
-
- feat_loss_alpha (float):
- Loss weight for the feature matching loss. Defaults to 1.0.
-
- mel_loss_alpha (float):
- Loss weight for the mel loss. Defaults to 45.0.
-
- return_wav (bool):
- If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`.
-
- compute_linear_spec (bool):
- If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`.
-
- use_weighted_sampler (bool):
- If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`.
-
- weighted_sampler_attrs (dict):
- Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities
- by overweighting `root_path` by 2.0. Defaults to `{}`.
-
- weighted_sampler_multipliers (dict):
- Weight each unique value of a key returned by the formatter for weighted sampling.
- For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`.
- It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`.
-
- r (int):
- Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`.
-
- add_blank (bool):
- If true, a blank token is added in between every character. Defaults to `True`.
-
- test_sentences (List[List]):
- List of sentences with speaker and language information to be used for testing.
-
- language_ids_file (str):
- Path to the language ids file.
-
- use_language_embedding (bool):
- If true, language embedding is used. Defaults to `False`.
-
- Note:
- Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
-
- Example:
-
- >>> from TTS.tts.configs.vits_config import VitsConfig
- >>> config = VitsConfig()
- """
-
- model: str = "vits"
- # model specific params
- model_args: VitsArgs = field(default_factory=VitsArgs)
- audio: VitsAudioConfig = field(default_factory=VitsAudioConfig)
-
- # optimizer
- grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])
- lr_gen: float = 0.0002
- lr_disc: float = 0.0002
- lr_scheduler_gen: str = "ExponentialLR"
- lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
- lr_scheduler_disc: str = "ExponentialLR"
- lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
- scheduler_after_epoch: bool = True
- optimizer: str = "AdamW"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01})
-
- # loss params
- kl_loss_alpha: float = 1.0
- disc_loss_alpha: float = 1.0
- gen_loss_alpha: float = 1.0
- feat_loss_alpha: float = 1.0
- mel_loss_alpha: float = 45.0
- dur_loss_alpha: float = 1.0
- speaker_encoder_loss_alpha: float = 1.0
-
- # data loader params
- return_wav: bool = True
- compute_linear_spec: bool = True
-
- # sampler params
- use_weighted_sampler: bool = False # TODO: move it to the base config
- weighted_sampler_attrs: dict = field(default_factory=lambda: {})
- weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
-
- # overrides
- r: int = 1 # DO NOT CHANGE
- add_blank: bool = True
-
- # testing
- test_sentences: List[List] = field(
- default_factory=lambda: [
- ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."],
- ["Be a voice, not an echo."],
- ["I'm sorry Dave. I'm afraid I can't do that."],
- ["This cake is great. It's so delicious and moist."],
- ["Prior to November 22, 1963."],
- ]
- )
-
- # multi-speaker settings
- # use speaker embedding layer
- num_speakers: int = 0
- use_speaker_embedding: bool = False
- speakers_file: str = None
- speaker_embedding_channels: int = 256
- language_ids_file: str = None
- use_language_embedding: bool = False
-
- # use d-vectors
- use_d_vector_file: bool = False
- d_vector_file: List[str] = None
- d_vector_dim: int = None
-
- def __post_init__(self):
- for key, val in self.model_args.items():
- if hasattr(self, key):
- self[key] = val
diff --git a/TTS/TTS/tts/datasets/__init__.py b/TTS/TTS/tts/datasets/__init__.py
deleted file mode 100644
index 192138561fdb4e85978fe8beb52eae2edf73888e..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/datasets/__init__.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import os
-import sys
-from collections import Counter
-from pathlib import Path
-from typing import Callable, Dict, List, Tuple, Union
-
-import numpy as np
-
-from TTS.tts.datasets.dataset import *
-from TTS.tts.datasets.formatters import *
-
-
-def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
- """Split a dataset into train and eval. Consider speaker distribution in multi-speaker training.
-
- Args:
- items (List[List]):
- A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`.
-
- eval_split_max_size (int):
- Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
-
- eval_split_size (float):
- If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
- If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
- """
- speakers = [item["speaker_name"] for item in items]
- is_multi_speaker = len(set(speakers)) > 1
- if eval_split_size > 1:
- eval_split_size = int(eval_split_size)
- else:
- if eval_split_max_size:
- eval_split_size = min(eval_split_max_size, int(len(items) * eval_split_size))
- else:
- eval_split_size = int(len(items) * eval_split_size)
-
- assert (
- eval_split_size > 0
- ), " [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}".format(
- 1 / len(items)
- )
- np.random.seed(0)
- np.random.shuffle(items)
- if is_multi_speaker:
- items_eval = []
- speakers = [item["speaker_name"] for item in items]
- speaker_counter = Counter(speakers)
- while len(items_eval) < eval_split_size:
- item_idx = np.random.randint(0, len(items))
- speaker_to_be_removed = items[item_idx]["speaker_name"]
- if speaker_counter[speaker_to_be_removed] > 1:
- items_eval.append(items[item_idx])
- speaker_counter[speaker_to_be_removed] -= 1
- del items[item_idx]
- return items_eval, items
- return items[:eval_split_size], items[eval_split_size:]
-
-
-def add_extra_keys(metadata, language, dataset_name):
- for item in metadata:
- # add language name
- item["language"] = language
- # add unique audio name
- relfilepath = os.path.splitext(os.path.relpath(item["audio_file"], item["root_path"]))[0]
- audio_unique_name = f"{dataset_name}#{relfilepath}"
- item["audio_unique_name"] = audio_unique_name
- return metadata
-
-
-def load_tts_samples(
- datasets: Union[List[Dict], Dict],
- eval_split=True,
- formatter: Callable = None,
- eval_split_max_size=None,
- eval_split_size=0.01,
-) -> Tuple[List[List], List[List]]:
- """Parse the dataset from the datasets config, load the samples as a List and load the attention alignments if provided.
- If `formatter` is not None, apply the formatter to the samples else pick the formatter from the available ones based
- on the dataset name.
-
- Args:
- datasets (List[Dict], Dict): A list of datasets or a single dataset dictionary. If multiple datasets are
- in the list, they are all merged.
-
- eval_split (bool, optional): If true, create a evaluation split. If an eval split provided explicitly, generate
- an eval split automatically. Defaults to True.
-
- formatter (Callable, optional): The preprocessing function to be applied to create the list of samples. It
- must take the root_path and the meta_file name and return a list of samples in the format of
- `[[text, audio_path, speaker_id], ...]]`. See the available formatters in `TTS.tts.dataset.formatter` as
- example. Defaults to None.
-
- eval_split_max_size (int):
- Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
-
- eval_split_size (float):
- If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
- If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
-
- Returns:
- Tuple[List[List], List[List]: training and evaluation splits of the dataset.
- """
- meta_data_train_all = []
- meta_data_eval_all = [] if eval_split else None
- if not isinstance(datasets, list):
- datasets = [datasets]
- for dataset in datasets:
- formatter_name = dataset["formatter"]
- dataset_name = dataset["dataset_name"]
- root_path = dataset["path"]
- meta_file_train = dataset["meta_file_train"]
- meta_file_val = dataset["meta_file_val"]
- ignored_speakers = dataset["ignored_speakers"]
- language = dataset["language"]
-
- # setup the right data processor
- if formatter is None:
- formatter = _get_formatter_by_name(formatter_name)
- # load train set
- meta_data_train = formatter(root_path, meta_file_train, ignored_speakers=ignored_speakers)
- assert len(meta_data_train) > 0, f" [!] No training samples found in {root_path}/{meta_file_train}"
-
- meta_data_train = add_extra_keys(meta_data_train, language, dataset_name)
-
- print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
- # load evaluation split if set
- if eval_split:
- if meta_file_val:
- meta_data_eval = formatter(root_path, meta_file_val, ignored_speakers=ignored_speakers)
- meta_data_eval = add_extra_keys(meta_data_eval, language, dataset_name)
- else:
- eval_size_per_dataset = eval_split_max_size // len(datasets) if eval_split_max_size else None
- meta_data_eval, meta_data_train = split_dataset(meta_data_train, eval_size_per_dataset, eval_split_size)
- meta_data_eval_all += meta_data_eval
- meta_data_train_all += meta_data_train
- # load attention masks for the duration predictor training
- if dataset.meta_file_attn_mask:
- meta_data = dict(load_attention_mask_meta_data(dataset["meta_file_attn_mask"]))
- for idx, ins in enumerate(meta_data_train_all):
- attn_file = meta_data[ins["audio_file"]].strip()
- meta_data_train_all[idx].update({"alignment_file": attn_file})
- if meta_data_eval_all:
- for idx, ins in enumerate(meta_data_eval_all):
- attn_file = meta_data[ins["audio_file"]].strip()
- meta_data_eval_all[idx].update({"alignment_file": attn_file})
- # set none for the next iter
- formatter = None
- return meta_data_train_all, meta_data_eval_all
-
-
-def load_attention_mask_meta_data(metafile_path):
- """Load meta data file created by compute_attention_masks.py"""
- with open(metafile_path, "r", encoding="utf-8") as f:
- lines = f.readlines()
-
- meta_data = []
- for line in lines:
- wav_file, attn_file = line.split("|")
- meta_data.append([wav_file, attn_file])
- return meta_data
-
-
-def _get_formatter_by_name(name):
- """Returns the respective preprocessing function."""
- thismodule = sys.modules[__name__]
- return getattr(thismodule, name.lower())
-
-
-def find_unique_chars(data_samples, verbose=True):
- texts = "".join(item[0] for item in data_samples)
- chars = set(texts)
- lower_chars = filter(lambda c: c.islower(), chars)
- chars_force_lower = [c.lower() for c in chars]
- chars_force_lower = set(chars_force_lower)
-
- if verbose:
- print(f" > Number of unique characters: {len(chars)}")
- print(f" > Unique characters: {''.join(sorted(chars))}")
- print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
- print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
- return chars_force_lower
diff --git a/TTS/TTS/tts/datasets/dataset.py b/TTS/TTS/tts/datasets/dataset.py
deleted file mode 100644
index c673c963b65398b624688b630962efa0baf71732..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/datasets/dataset.py
+++ /dev/null
@@ -1,962 +0,0 @@
-import base64
-import collections
-import os
-import random
-from typing import Dict, List, Union
-
-import numpy as np
-import torch
-import tqdm
-from torch.utils.data import Dataset
-
-from TTS.tts.utils.data import prepare_data, prepare_stop_target, prepare_tensor
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.audio.numpy_transforms import compute_energy as calculate_energy
-
-# to prevent too many open files error as suggested here
-# https://github.com/pytorch/pytorch/issues/11201#issuecomment-421146936
-torch.multiprocessing.set_sharing_strategy("file_system")
-
-
-def _parse_sample(item):
- language_name = None
- attn_file = None
- if len(item) == 5:
- text, wav_file, speaker_name, language_name, attn_file = item
- elif len(item) == 4:
- text, wav_file, speaker_name, language_name = item
- elif len(item) == 3:
- text, wav_file, speaker_name = item
- else:
- raise ValueError(" [!] Dataset cannot parse the sample.")
- return text, wav_file, speaker_name, language_name, attn_file
-
-
-def noise_augment_audio(wav):
- return wav + (1.0 / 32768.0) * np.random.rand(*wav.shape)
-
-
-def string2filename(string):
- # generate a safe and reversible filename based on a string
- filename = base64.urlsafe_b64encode(string.encode("utf-8")).decode("utf-8", "ignore")
- return filename
-
-
-class TTSDataset(Dataset):
- def __init__(
- self,
- outputs_per_step: int = 1,
- compute_linear_spec: bool = False,
- ap: AudioProcessor = None,
- samples: List[Dict] = None,
- tokenizer: "TTSTokenizer" = None,
- compute_f0: bool = False,
- compute_energy: bool = False,
- f0_cache_path: str = None,
- energy_cache_path: str = None,
- return_wav: bool = False,
- batch_group_size: int = 0,
- min_text_len: int = 0,
- max_text_len: int = float("inf"),
- min_audio_len: int = 0,
- max_audio_len: int = float("inf"),
- phoneme_cache_path: str = None,
- precompute_num_workers: int = 0,
- speaker_id_mapping: Dict = None,
- d_vector_mapping: Dict = None,
- language_id_mapping: Dict = None,
- use_noise_augment: bool = False,
- start_by_longest: bool = False,
- verbose: bool = False,
- ):
- """Generic 📂 data loader for `tts` models. It is configurable for different outputs and needs.
-
- If you need something different, you can subclass and override.
-
- Args:
- outputs_per_step (int): Number of time frames predicted per step.
-
- compute_linear_spec (bool): compute linear spectrogram if True.
-
- ap (TTS.tts.utils.AudioProcessor): Audio processor object.
-
- samples (list): List of dataset samples.
-
- tokenizer (TTSTokenizer): tokenizer to convert text to sequence IDs. If None init internally else
- use the given. Defaults to None.
-
- compute_f0 (bool): compute f0 if True. Defaults to False.
-
- compute_energy (bool): compute energy if True. Defaults to False.
-
- f0_cache_path (str): Path to store f0 cache. Defaults to None.
-
- energy_cache_path (str): Path to store energy cache. Defaults to None.
-
- return_wav (bool): Return the waveform of the sample. Defaults to False.
-
- batch_group_size (int): Range of batch randomization after sorting
- sequences by length. It shuffles each batch with bucketing to gather similar lenght sequences in a
- batch. Set 0 to disable. Defaults to 0.
-
- min_text_len (int): Minimum length of input text to be used. All shorter samples will be ignored.
- Defaults to 0.
-
- max_text_len (int): Maximum length of input text to be used. All longer samples will be ignored.
- Defaults to float("inf").
-
- min_audio_len (int): Minimum length of input audio to be used. All shorter samples will be ignored.
- Defaults to 0.
-
- max_audio_len (int): Maximum length of input audio to be used. All longer samples will be ignored.
- The maximum length in the dataset defines the VRAM used in the training. Hence, pay attention to
- this value if you encounter an OOM error in training. Defaults to float("inf").
-
- phoneme_cache_path (str): Path to cache computed phonemes. It writes phonemes of each sample to a
- separate file. Defaults to None.
-
- precompute_num_workers (int): Number of workers to precompute features. Defaults to 0.
-
- speaker_id_mapping (dict): Mapping of speaker names to IDs used to compute embedding vectors by the
- embedding layer. Defaults to None.
-
- d_vector_mapping (dict): Mapping of wav files to computed d-vectors. Defaults to None.
-
- use_noise_augment (bool): Enable adding random noise to wav for augmentation. Defaults to False.
-
- start_by_longest (bool): Start by longest sequence. It is especially useful to check OOM. Defaults to False.
-
- verbose (bool): Print diagnostic information. Defaults to false.
- """
- super().__init__()
- self.batch_group_size = batch_group_size
- self._samples = samples
- self.outputs_per_step = outputs_per_step
- self.compute_linear_spec = compute_linear_spec
- self.return_wav = return_wav
- self.compute_f0 = compute_f0
- self.compute_energy = compute_energy
- self.f0_cache_path = f0_cache_path
- self.energy_cache_path = energy_cache_path
- self.min_audio_len = min_audio_len
- self.max_audio_len = max_audio_len
- self.min_text_len = min_text_len
- self.max_text_len = max_text_len
- self.ap = ap
- self.phoneme_cache_path = phoneme_cache_path
- self.speaker_id_mapping = speaker_id_mapping
- self.d_vector_mapping = d_vector_mapping
- self.language_id_mapping = language_id_mapping
- self.use_noise_augment = use_noise_augment
- self.start_by_longest = start_by_longest
-
- self.verbose = verbose
- self.rescue_item_idx = 1
- self.pitch_computed = False
- self.tokenizer = tokenizer
-
- if self.tokenizer.use_phonemes:
- self.phoneme_dataset = PhonemeDataset(
- self.samples, self.tokenizer, phoneme_cache_path, precompute_num_workers=precompute_num_workers
- )
-
- if compute_f0:
- self.f0_dataset = F0Dataset(
- self.samples, self.ap, cache_path=f0_cache_path, precompute_num_workers=precompute_num_workers
- )
- if compute_energy:
- self.energy_dataset = EnergyDataset(
- self.samples, self.ap, cache_path=energy_cache_path, precompute_num_workers=precompute_num_workers
- )
- if self.verbose:
- self.print_logs()
-
- @property
- def lengths(self):
- lens = []
- for item in self.samples:
- _, wav_file, *_ = _parse_sample(item)
- audio_len = os.path.getsize(wav_file) / 16 * 8 # assuming 16bit audio
- lens.append(audio_len)
- return lens
-
- @property
- def samples(self):
- return self._samples
-
- @samples.setter
- def samples(self, new_samples):
- self._samples = new_samples
- if hasattr(self, "f0_dataset"):
- self.f0_dataset.samples = new_samples
- if hasattr(self, "energy_dataset"):
- self.energy_dataset.samples = new_samples
- if hasattr(self, "phoneme_dataset"):
- self.phoneme_dataset.samples = new_samples
-
- def __len__(self):
- return len(self.samples)
-
- def __getitem__(self, idx):
- return self.load_data(idx)
-
- def print_logs(self, level: int = 0) -> None:
- indent = "\t" * level
- print("\n")
- print(f"{indent}> DataLoader initialization")
- print(f"{indent}| > Tokenizer:")
- self.tokenizer.print_logs(level + 1)
- print(f"{indent}| > Number of instances : {len(self.samples)}")
-
- def load_wav(self, filename):
- waveform = self.ap.load_wav(filename)
- assert waveform.size > 0
- return waveform
-
- def get_phonemes(self, idx, text):
- out_dict = self.phoneme_dataset[idx]
- assert text == out_dict["text"], f"{text} != {out_dict['text']}"
- assert len(out_dict["token_ids"]) > 0
- return out_dict
-
- def get_f0(self, idx):
- out_dict = self.f0_dataset[idx]
- item = self.samples[idx]
- assert item["audio_unique_name"] == out_dict["audio_unique_name"]
- return out_dict
-
- def get_energy(self, idx):
- out_dict = self.energy_dataset[idx]
- item = self.samples[idx]
- assert item["audio_unique_name"] == out_dict["audio_unique_name"]
- return out_dict
-
- @staticmethod
- def get_attn_mask(attn_file):
- return np.load(attn_file)
-
- def get_token_ids(self, idx, text):
- if self.tokenizer.use_phonemes:
- token_ids = self.get_phonemes(idx, text)["token_ids"]
- else:
- token_ids = self.tokenizer.text_to_ids(text)
- return np.array(token_ids, dtype=np.int32)
-
- def load_data(self, idx):
- item = self.samples[idx]
-
- raw_text = item["text"]
-
- wav = np.asarray(self.load_wav(item["audio_file"]), dtype=np.float32)
-
- # apply noise for augmentation
- if self.use_noise_augment:
- wav = noise_augment_audio(wav)
-
- # get token ids
- token_ids = self.get_token_ids(idx, item["text"])
-
- # get pre-computed attention maps
- attn = None
- if "alignment_file" in item:
- attn = self.get_attn_mask(item["alignment_file"])
-
- # after phonemization the text length may change
- # this is a shareful 🤭 hack to prevent longer phonemes
- # TODO: find a better fix
- if len(token_ids) > self.max_text_len or len(wav) < self.min_audio_len:
- self.rescue_item_idx += 1
- return self.load_data(self.rescue_item_idx)
-
- # get f0 values
- f0 = None
- if self.compute_f0:
- f0 = self.get_f0(idx)["f0"]
- energy = None
- if self.compute_energy:
- energy = self.get_energy(idx)["energy"]
-
- sample = {
- "raw_text": raw_text,
- "token_ids": token_ids,
- "wav": wav,
- "pitch": f0,
- "energy": energy,
- "attn": attn,
- "item_idx": item["audio_file"],
- "speaker_name": item["speaker_name"],
- "language_name": item["language"],
- "wav_file_name": os.path.basename(item["audio_file"]),
- "audio_unique_name": item["audio_unique_name"],
- }
- return sample
-
- @staticmethod
- def _compute_lengths(samples):
- new_samples = []
- for item in samples:
- audio_length = os.path.getsize(item["audio_file"]) / 16 * 8 # assuming 16bit audio
- text_lenght = len(item["text"])
- item["audio_length"] = audio_length
- item["text_length"] = text_lenght
- new_samples += [item]
- return new_samples
-
- @staticmethod
- def filter_by_length(lengths: List[int], min_len: int, max_len: int):
- idxs = np.argsort(lengths) # ascending order
- ignore_idx = []
- keep_idx = []
- for idx in idxs:
- length = lengths[idx]
- if length < min_len or length > max_len:
- ignore_idx.append(idx)
- else:
- keep_idx.append(idx)
- return ignore_idx, keep_idx
-
- @staticmethod
- def sort_by_length(samples: List[List]):
- audio_lengths = [s["audio_length"] for s in samples]
- idxs = np.argsort(audio_lengths) # ascending order
- return idxs
-
- @staticmethod
- def create_buckets(samples, batch_group_size: int):
- assert batch_group_size > 0
- for i in range(len(samples) // batch_group_size):
- offset = i * batch_group_size
- end_offset = offset + batch_group_size
- temp_items = samples[offset:end_offset]
- random.shuffle(temp_items)
- samples[offset:end_offset] = temp_items
- return samples
-
- @staticmethod
- def _select_samples_by_idx(idxs, samples):
- samples_new = []
- for idx in idxs:
- samples_new.append(samples[idx])
- return samples_new
-
- def preprocess_samples(self):
- r"""Sort `items` based on text length or audio length in ascending order. Filter out samples out or the length
- range.
- """
- samples = self._compute_lengths(self.samples)
-
- # sort items based on the sequence length in ascending order
- text_lengths = [i["text_length"] for i in samples]
- audio_lengths = [i["audio_length"] for i in samples]
- text_ignore_idx, text_keep_idx = self.filter_by_length(text_lengths, self.min_text_len, self.max_text_len)
- audio_ignore_idx, audio_keep_idx = self.filter_by_length(audio_lengths, self.min_audio_len, self.max_audio_len)
- keep_idx = list(set(audio_keep_idx) & set(text_keep_idx))
- ignore_idx = list(set(audio_ignore_idx) | set(text_ignore_idx))
-
- samples = self._select_samples_by_idx(keep_idx, samples)
-
- sorted_idxs = self.sort_by_length(samples)
-
- if self.start_by_longest:
- longest_idxs = sorted_idxs[-1]
- sorted_idxs[-1] = sorted_idxs[0]
- sorted_idxs[0] = longest_idxs
-
- samples = self._select_samples_by_idx(sorted_idxs, samples)
-
- if len(samples) == 0:
- raise RuntimeError(" [!] No samples left")
-
- # shuffle batch groups
- # create batches with similar length items
- # the larger the `batch_group_size`, the higher the length variety in a batch.
- if self.batch_group_size > 0:
- samples = self.create_buckets(samples, self.batch_group_size)
-
- # update items to the new sorted items
- audio_lengths = [s["audio_length"] for s in samples]
- text_lengths = [s["text_length"] for s in samples]
- self.samples = samples
-
- if self.verbose:
- print(" | > Preprocessing samples")
- print(" | > Max text length: {}".format(np.max(text_lengths)))
- print(" | > Min text length: {}".format(np.min(text_lengths)))
- print(" | > Avg text length: {}".format(np.mean(text_lengths)))
- print(" | ")
- print(" | > Max audio length: {}".format(np.max(audio_lengths)))
- print(" | > Min audio length: {}".format(np.min(audio_lengths)))
- print(" | > Avg audio length: {}".format(np.mean(audio_lengths)))
- print(f" | > Num. instances discarded samples: {len(ignore_idx)}")
- print(" | > Batch group size: {}.".format(self.batch_group_size))
-
- @staticmethod
- def _sort_batch(batch, text_lengths):
- """Sort the batch by the input text length for RNN efficiency.
-
- Args:
- batch (Dict): Batch returned by `__getitem__`.
- text_lengths (List[int]): Lengths of the input character sequences.
- """
- text_lengths, ids_sorted_decreasing = torch.sort(torch.LongTensor(text_lengths), dim=0, descending=True)
- batch = [batch[idx] for idx in ids_sorted_decreasing]
- return batch, text_lengths, ids_sorted_decreasing
-
- def collate_fn(self, batch):
- r"""
- Perform preprocessing and create a final data batch:
- 1. Sort batch instances by text-length
- 2. Convert Audio signal to features.
- 3. PAD sequences wrt r.
- 4. Load to Torch.
- """
-
- # Puts each data field into a tensor with outer dimension batch size
- if isinstance(batch[0], collections.abc.Mapping):
- token_ids_lengths = np.array([len(d["token_ids"]) for d in batch])
-
- # sort items with text input length for RNN efficiency
- batch, token_ids_lengths, ids_sorted_decreasing = self._sort_batch(batch, token_ids_lengths)
-
- # convert list of dicts to dict of lists
- batch = {k: [dic[k] for dic in batch] for k in batch[0]}
-
- # get language ids from language names
- if self.language_id_mapping is not None:
- language_ids = [self.language_id_mapping[ln] for ln in batch["language_name"]]
- else:
- language_ids = None
- # get pre-computed d-vectors
- if self.d_vector_mapping is not None:
- embedding_keys = list(batch["audio_unique_name"])
- d_vectors = [self.d_vector_mapping[w]["embedding"] for w in embedding_keys]
- else:
- d_vectors = None
-
- # get numerical speaker ids from speaker names
- if self.speaker_id_mapping:
- speaker_ids = [self.speaker_id_mapping[sn] for sn in batch["speaker_name"]]
- else:
- speaker_ids = None
- # compute features
- mel = [self.ap.melspectrogram(w).astype("float32") for w in batch["wav"]]
-
- mel_lengths = [m.shape[1] for m in mel]
-
- # lengths adjusted by the reduction factor
- mel_lengths_adjusted = [
- m.shape[1] + (self.outputs_per_step - (m.shape[1] % self.outputs_per_step))
- if m.shape[1] % self.outputs_per_step
- else m.shape[1]
- for m in mel
- ]
-
- # compute 'stop token' targets
- stop_targets = [np.array([0.0] * (mel_len - 1) + [1.0]) for mel_len in mel_lengths]
-
- # PAD stop targets
- stop_targets = prepare_stop_target(stop_targets, self.outputs_per_step)
-
- # PAD sequences with longest instance in the batch
- token_ids = prepare_data(batch["token_ids"]).astype(np.int32)
-
- # PAD features with longest instance
- mel = prepare_tensor(mel, self.outputs_per_step)
-
- # B x D x T --> B x T x D
- mel = mel.transpose(0, 2, 1)
-
- # convert things to pytorch
- token_ids_lengths = torch.LongTensor(token_ids_lengths)
- token_ids = torch.LongTensor(token_ids)
- mel = torch.FloatTensor(mel).contiguous()
- mel_lengths = torch.LongTensor(mel_lengths)
- stop_targets = torch.FloatTensor(stop_targets)
-
- # speaker vectors
- if d_vectors is not None:
- d_vectors = torch.FloatTensor(d_vectors)
-
- if speaker_ids is not None:
- speaker_ids = torch.LongTensor(speaker_ids)
-
- if language_ids is not None:
- language_ids = torch.LongTensor(language_ids)
-
- # compute linear spectrogram
- linear = None
- if self.compute_linear_spec:
- linear = [self.ap.spectrogram(w).astype("float32") for w in batch["wav"]]
- linear = prepare_tensor(linear, self.outputs_per_step)
- linear = linear.transpose(0, 2, 1)
- assert mel.shape[1] == linear.shape[1]
- linear = torch.FloatTensor(linear).contiguous()
-
- # format waveforms
- wav_padded = None
- if self.return_wav:
- wav_lengths = [w.shape[0] for w in batch["wav"]]
- max_wav_len = max(mel_lengths_adjusted) * self.ap.hop_length
- wav_lengths = torch.LongTensor(wav_lengths)
- wav_padded = torch.zeros(len(batch["wav"]), 1, max_wav_len)
- for i, w in enumerate(batch["wav"]):
- mel_length = mel_lengths_adjusted[i]
- w = np.pad(w, (0, self.ap.hop_length * self.outputs_per_step), mode="edge")
- w = w[: mel_length * self.ap.hop_length]
- wav_padded[i, :, : w.shape[0]] = torch.from_numpy(w)
- wav_padded.transpose_(1, 2)
-
- # format F0
- if self.compute_f0:
- pitch = prepare_data(batch["pitch"])
- assert mel.shape[1] == pitch.shape[1], f"[!] {mel.shape} vs {pitch.shape}"
- pitch = torch.FloatTensor(pitch)[:, None, :].contiguous() # B x 1 xT
- else:
- pitch = None
- # format energy
- if self.compute_energy:
- energy = prepare_data(batch["energy"])
- assert mel.shape[1] == energy.shape[1], f"[!] {mel.shape} vs {energy.shape}"
- energy = torch.FloatTensor(energy)[:, None, :].contiguous() # B x 1 xT
- else:
- energy = None
- # format attention masks
- attns = None
- if batch["attn"][0] is not None:
- attns = [batch["attn"][idx].T for idx in ids_sorted_decreasing]
- for idx, attn in enumerate(attns):
- pad2 = mel.shape[1] - attn.shape[1]
- pad1 = token_ids.shape[1] - attn.shape[0]
- assert pad1 >= 0 and pad2 >= 0, f"[!] Negative padding - {pad1} and {pad2}"
- attn = np.pad(attn, [[0, pad1], [0, pad2]])
- attns[idx] = attn
- attns = prepare_tensor(attns, self.outputs_per_step)
- attns = torch.FloatTensor(attns).unsqueeze(1)
-
- return {
- "token_id": token_ids,
- "token_id_lengths": token_ids_lengths,
- "speaker_names": batch["speaker_name"],
- "linear": linear,
- "mel": mel,
- "mel_lengths": mel_lengths,
- "stop_targets": stop_targets,
- "item_idxs": batch["item_idx"],
- "d_vectors": d_vectors,
- "speaker_ids": speaker_ids,
- "attns": attns,
- "waveform": wav_padded,
- "raw_text": batch["raw_text"],
- "pitch": pitch,
- "energy": energy,
- "language_ids": language_ids,
- "audio_unique_names": batch["audio_unique_name"],
- }
-
- raise TypeError(
- (
- "batch must contain tensors, numbers, dicts or lists;\
- found {}".format(
- type(batch[0])
- )
- )
- )
-
-
-class PhonemeDataset(Dataset):
- """Phoneme Dataset for converting input text to phonemes and then token IDs
-
- At initialization, it pre-computes the phonemes under `cache_path` and loads them in training to reduce data
- loading latency. If `cache_path` is already present, it skips the pre-computation.
-
- Args:
- samples (Union[List[List], List[Dict]]):
- List of samples. Each sample is a list or a dict.
-
- tokenizer (TTSTokenizer):
- Tokenizer to convert input text to phonemes.
-
- cache_path (str):
- Path to cache phonemes. If `cache_path` is already present or None, it skips the pre-computation.
-
- precompute_num_workers (int):
- Number of workers used for pre-computing the phonemes. Defaults to 0.
- """
-
- def __init__(
- self,
- samples: Union[List[Dict], List[List]],
- tokenizer: "TTSTokenizer",
- cache_path: str,
- precompute_num_workers=0,
- ):
- self.samples = samples
- self.tokenizer = tokenizer
- self.cache_path = cache_path
- if cache_path is not None and not os.path.exists(cache_path):
- os.makedirs(cache_path)
- self.precompute(precompute_num_workers)
-
- def __getitem__(self, index):
- item = self.samples[index]
- ids = self.compute_or_load(string2filename(item["audio_unique_name"]), item["text"], item["language"])
- ph_hat = self.tokenizer.ids_to_text(ids)
- return {"text": item["text"], "ph_hat": ph_hat, "token_ids": ids, "token_ids_len": len(ids)}
-
- def __len__(self):
- return len(self.samples)
-
- def compute_or_load(self, file_name, text, language):
- """Compute phonemes for the given text.
-
- If the phonemes are already cached, load them from cache.
- """
- file_ext = "_phoneme.npy"
- cache_path = os.path.join(self.cache_path, file_name + file_ext)
- try:
- ids = np.load(cache_path)
- except FileNotFoundError:
- ids = self.tokenizer.text_to_ids(text, language=language)
- np.save(cache_path, ids)
- return ids
-
- def get_pad_id(self):
- """Get pad token ID for sequence padding"""
- return self.tokenizer.pad_id
-
- def precompute(self, num_workers=1):
- """Precompute phonemes for all samples.
-
- We use pytorch dataloader because we are lazy.
- """
- print("[*] Pre-computing phonemes...")
- with tqdm.tqdm(total=len(self)) as pbar:
- batch_size = num_workers if num_workers > 0 else 1
- dataloder = torch.utils.data.DataLoader(
- batch_size=batch_size, dataset=self, shuffle=False, num_workers=num_workers, collate_fn=self.collate_fn
- )
- for _ in dataloder:
- pbar.update(batch_size)
-
- def collate_fn(self, batch):
- ids = [item["token_ids"] for item in batch]
- ids_lens = [item["token_ids_len"] for item in batch]
- texts = [item["text"] for item in batch]
- texts_hat = [item["ph_hat"] for item in batch]
- ids_lens_max = max(ids_lens)
- ids_torch = torch.LongTensor(len(ids), ids_lens_max).fill_(self.get_pad_id())
- for i, ids_len in enumerate(ids_lens):
- ids_torch[i, :ids_len] = torch.LongTensor(ids[i])
- return {"text": texts, "ph_hat": texts_hat, "token_ids": ids_torch}
-
- def print_logs(self, level: int = 0) -> None:
- indent = "\t" * level
- print("\n")
- print(f"{indent}> PhonemeDataset ")
- print(f"{indent}| > Tokenizer:")
- self.tokenizer.print_logs(level + 1)
- print(f"{indent}| > Number of instances : {len(self.samples)}")
-
-
-class F0Dataset:
- """F0 Dataset for computing F0 from wav files in CPU
-
- Pre-compute F0 values for all the samples at initialization if `cache_path` is not None or already present. It
- also computes the mean and std of F0 values if `normalize_f0` is True.
-
- Args:
- samples (Union[List[List], List[Dict]]):
- List of samples. Each sample is a list or a dict.
-
- ap (AudioProcessor):
- AudioProcessor to compute F0 from wav files.
-
- cache_path (str):
- Path to cache F0 values. If `cache_path` is already present or None, it skips the pre-computation.
- Defaults to None.
-
- precompute_num_workers (int):
- Number of workers used for pre-computing the F0 values. Defaults to 0.
-
- normalize_f0 (bool):
- Whether to normalize F0 values by mean and std. Defaults to True.
- """
-
- def __init__(
- self,
- samples: Union[List[List], List[Dict]],
- ap: "AudioProcessor",
- audio_config=None, # pylint: disable=unused-argument
- verbose=False,
- cache_path: str = None,
- precompute_num_workers=0,
- normalize_f0=True,
- ):
- self.samples = samples
- self.ap = ap
- self.verbose = verbose
- self.cache_path = cache_path
- self.normalize_f0 = normalize_f0
- self.pad_id = 0.0
- self.mean = None
- self.std = None
- if cache_path is not None and not os.path.exists(cache_path):
- os.makedirs(cache_path)
- self.precompute(precompute_num_workers)
- if normalize_f0:
- self.load_stats(cache_path)
-
- def __getitem__(self, idx):
- item = self.samples[idx]
- f0 = self.compute_or_load(item["audio_file"], string2filename(item["audio_unique_name"]))
- if self.normalize_f0:
- assert self.mean is not None and self.std is not None, " [!] Mean and STD is not available"
- f0 = self.normalize(f0)
- return {"audio_unique_name": item["audio_unique_name"], "f0": f0}
-
- def __len__(self):
- return len(self.samples)
-
- def precompute(self, num_workers=0):
- print("[*] Pre-computing F0s...")
- with tqdm.tqdm(total=len(self)) as pbar:
- batch_size = num_workers if num_workers > 0 else 1
- # we do not normalize at preproessing
- normalize_f0 = self.normalize_f0
- self.normalize_f0 = False
- dataloder = torch.utils.data.DataLoader(
- batch_size=batch_size, dataset=self, shuffle=False, num_workers=num_workers, collate_fn=self.collate_fn
- )
- computed_data = []
- for batch in dataloder:
- f0 = batch["f0"]
- computed_data.append(f for f in f0)
- pbar.update(batch_size)
- self.normalize_f0 = normalize_f0
-
- if self.normalize_f0:
- computed_data = [tensor for batch in computed_data for tensor in batch] # flatten
- pitch_mean, pitch_std = self.compute_pitch_stats(computed_data)
- pitch_stats = {"mean": pitch_mean, "std": pitch_std}
- np.save(os.path.join(self.cache_path, "pitch_stats"), pitch_stats, allow_pickle=True)
-
- def get_pad_id(self):
- return self.pad_id
-
- @staticmethod
- def create_pitch_file_path(file_name, cache_path):
- pitch_file = os.path.join(cache_path, file_name + "_pitch.npy")
- return pitch_file
-
- @staticmethod
- def _compute_and_save_pitch(ap, wav_file, pitch_file=None):
- wav = ap.load_wav(wav_file)
- pitch = ap.compute_f0(wav)
- if pitch_file:
- np.save(pitch_file, pitch)
- return pitch
-
- @staticmethod
- def compute_pitch_stats(pitch_vecs):
- nonzeros = np.concatenate([v[np.where(v != 0.0)[0]] for v in pitch_vecs])
- mean, std = np.mean(nonzeros), np.std(nonzeros)
- return mean, std
-
- def load_stats(self, cache_path):
- stats_path = os.path.join(cache_path, "pitch_stats.npy")
- stats = np.load(stats_path, allow_pickle=True).item()
- self.mean = stats["mean"].astype(np.float32)
- self.std = stats["std"].astype(np.float32)
-
- def normalize(self, pitch):
- zero_idxs = np.where(pitch == 0.0)[0]
- pitch = pitch - self.mean
- pitch = pitch / self.std
- pitch[zero_idxs] = 0.0
- return pitch
-
- def denormalize(self, pitch):
- zero_idxs = np.where(pitch == 0.0)[0]
- pitch *= self.std
- pitch += self.mean
- pitch[zero_idxs] = 0.0
- return pitch
-
- def compute_or_load(self, wav_file, audio_unique_name):
- """
- compute pitch and return a numpy array of pitch values
- """
- pitch_file = self.create_pitch_file_path(audio_unique_name, self.cache_path)
- if not os.path.exists(pitch_file):
- pitch = self._compute_and_save_pitch(self.ap, wav_file, pitch_file)
- else:
- pitch = np.load(pitch_file)
- return pitch.astype(np.float32)
-
- def collate_fn(self, batch):
- audio_unique_name = [item["audio_unique_name"] for item in batch]
- f0s = [item["f0"] for item in batch]
- f0_lens = [len(item["f0"]) for item in batch]
- f0_lens_max = max(f0_lens)
- f0s_torch = torch.LongTensor(len(f0s), f0_lens_max).fill_(self.get_pad_id())
- for i, f0_len in enumerate(f0_lens):
- f0s_torch[i, :f0_len] = torch.LongTensor(f0s[i])
- return {"audio_unique_name": audio_unique_name, "f0": f0s_torch, "f0_lens": f0_lens}
-
- def print_logs(self, level: int = 0) -> None:
- indent = "\t" * level
- print("\n")
- print(f"{indent}> F0Dataset ")
- print(f"{indent}| > Number of instances : {len(self.samples)}")
-
-
-class EnergyDataset:
- """Energy Dataset for computing Energy from wav files in CPU
-
- Pre-compute Energy values for all the samples at initialization if `cache_path` is not None or already present. It
- also computes the mean and std of Energy values if `normalize_Energy` is True.
-
- Args:
- samples (Union[List[List], List[Dict]]):
- List of samples. Each sample is a list or a dict.
-
- ap (AudioProcessor):
- AudioProcessor to compute Energy from wav files.
-
- cache_path (str):
- Path to cache Energy values. If `cache_path` is already present or None, it skips the pre-computation.
- Defaults to None.
-
- precompute_num_workers (int):
- Number of workers used for pre-computing the Energy values. Defaults to 0.
-
- normalize_Energy (bool):
- Whether to normalize Energy values by mean and std. Defaults to True.
- """
-
- def __init__(
- self,
- samples: Union[List[List], List[Dict]],
- ap: "AudioProcessor",
- verbose=False,
- cache_path: str = None,
- precompute_num_workers=0,
- normalize_energy=True,
- ):
- self.samples = samples
- self.ap = ap
- self.verbose = verbose
- self.cache_path = cache_path
- self.normalize_energy = normalize_energy
- self.pad_id = 0.0
- self.mean = None
- self.std = None
- if cache_path is not None and not os.path.exists(cache_path):
- os.makedirs(cache_path)
- self.precompute(precompute_num_workers)
- if normalize_energy:
- self.load_stats(cache_path)
-
- def __getitem__(self, idx):
- item = self.samples[idx]
- energy = self.compute_or_load(item["audio_file"], string2filename(item["audio_unique_name"]))
- if self.normalize_energy:
- assert self.mean is not None and self.std is not None, " [!] Mean and STD is not available"
- energy = self.normalize(energy)
- return {"audio_unique_name": item["audio_unique_name"], "energy": energy}
-
- def __len__(self):
- return len(self.samples)
-
- def precompute(self, num_workers=0):
- print("[*] Pre-computing energys...")
- with tqdm.tqdm(total=len(self)) as pbar:
- batch_size = num_workers if num_workers > 0 else 1
- # we do not normalize at preproessing
- normalize_energy = self.normalize_energy
- self.normalize_energy = False
- dataloder = torch.utils.data.DataLoader(
- batch_size=batch_size, dataset=self, shuffle=False, num_workers=num_workers, collate_fn=self.collate_fn
- )
- computed_data = []
- for batch in dataloder:
- energy = batch["energy"]
- computed_data.append(e for e in energy)
- pbar.update(batch_size)
- self.normalize_energy = normalize_energy
-
- if self.normalize_energy:
- computed_data = [tensor for batch in computed_data for tensor in batch] # flatten
- energy_mean, energy_std = self.compute_energy_stats(computed_data)
- energy_stats = {"mean": energy_mean, "std": energy_std}
- np.save(os.path.join(self.cache_path, "energy_stats"), energy_stats, allow_pickle=True)
-
- def get_pad_id(self):
- return self.pad_id
-
- @staticmethod
- def create_energy_file_path(wav_file, cache_path):
- file_name = os.path.splitext(os.path.basename(wav_file))[0]
- energy_file = os.path.join(cache_path, file_name + "_energy.npy")
- return energy_file
-
- @staticmethod
- def _compute_and_save_energy(ap, wav_file, energy_file=None):
- wav = ap.load_wav(wav_file)
- energy = calculate_energy(wav, fft_size=ap.fft_size, hop_length=ap.hop_length, win_length=ap.win_length)
- if energy_file:
- np.save(energy_file, energy)
- return energy
-
- @staticmethod
- def compute_energy_stats(energy_vecs):
- nonzeros = np.concatenate([v[np.where(v != 0.0)[0]] for v in energy_vecs])
- mean, std = np.mean(nonzeros), np.std(nonzeros)
- return mean, std
-
- def load_stats(self, cache_path):
- stats_path = os.path.join(cache_path, "energy_stats.npy")
- stats = np.load(stats_path, allow_pickle=True).item()
- self.mean = stats["mean"].astype(np.float32)
- self.std = stats["std"].astype(np.float32)
-
- def normalize(self, energy):
- zero_idxs = np.where(energy == 0.0)[0]
- energy = energy - self.mean
- energy = energy / self.std
- energy[zero_idxs] = 0.0
- return energy
-
- def denormalize(self, energy):
- zero_idxs = np.where(energy == 0.0)[0]
- energy *= self.std
- energy += self.mean
- energy[zero_idxs] = 0.0
- return energy
-
- def compute_or_load(self, wav_file, audio_unique_name):
- """
- compute energy and return a numpy array of energy values
- """
- energy_file = self.create_energy_file_path(audio_unique_name, self.cache_path)
- if not os.path.exists(energy_file):
- energy = self._compute_and_save_energy(self.ap, wav_file, energy_file)
- else:
- energy = np.load(energy_file)
- return energy.astype(np.float32)
-
- def collate_fn(self, batch):
- audio_unique_name = [item["audio_unique_name"] for item in batch]
- energys = [item["energy"] for item in batch]
- energy_lens = [len(item["energy"]) for item in batch]
- energy_lens_max = max(energy_lens)
- energys_torch = torch.LongTensor(len(energys), energy_lens_max).fill_(self.get_pad_id())
- for i, energy_len in enumerate(energy_lens):
- energys_torch[i, :energy_len] = torch.LongTensor(energys[i])
- return {"audio_unique_name": audio_unique_name, "energy": energys_torch, "energy_lens": energy_lens}
-
- def print_logs(self, level: int = 0) -> None:
- indent = "\t" * level
- print("\n")
- print(f"{indent}> energyDataset ")
- print(f"{indent}| > Number of instances : {len(self.samples)}")
diff --git a/TTS/TTS/tts/datasets/formatters.py b/TTS/TTS/tts/datasets/formatters.py
deleted file mode 100644
index 0eac29c8f7b6af90b5bfcff6c59a7f4597709633..0000000000000000000000000000000000000000
--- a/TTS/TTS/tts/datasets/formatters.py
+++ /dev/null
@@ -1,616 +0,0 @@
-import os
-import re
-import xml.etree.ElementTree as ET
-from glob import glob
-from pathlib import Path
-from typing import List
-
-import pandas as pd
-from tqdm import tqdm
-
-########################
-# DATASETS
-########################
-
-
-def coqui(root_path, meta_file, ignored_speakers=None):
- """Interal dataset formatter."""
- filepath = os.path.join(root_path, meta_file)
- # ensure there are 4 columns for every line
- with open(filepath, "r", encoding="utf8") as f:
- lines = f.readlines()
- num_cols = len(lines[0].split("|")) # take the first row as reference
- for idx, line in enumerate(lines[1:]):
- if len(line.split("|")) != num_cols:
- print(f" > Missing column in line {idx + 1} -> {line.strip()}")
- # load metadata
- metadata = pd.read_csv(os.path.join(root_path, meta_file), sep="|")
- assert all(x in metadata.columns for x in ["audio_file", "text"])
- speaker_name = None if "speaker_name" in metadata.columns else "coqui"
- emotion_name = None if "emotion_name" in metadata.columns else "neutral"
- items = []
- not_found_counter = 0
- for row in metadata.itertuples():
- if speaker_name is None and ignored_speakers is not None and row.speaker_name in ignored_speakers:
- continue
- audio_path = os.path.join(root_path, row.audio_file)
- if not os.path.exists(audio_path):
- not_found_counter += 1
- continue
- items.append(
- {
- "text": row.text,
- "audio_file": audio_path,
- "speaker_name": speaker_name if speaker_name is not None else row.speaker_name,
- "emotion_name": emotion_name if emotion_name is not None else row.emotion_name,
- "root_path": root_path,
- }
- )
- if not_found_counter > 0:
- print(f" | > [!] {not_found_counter} files not found")
- return items
-
-
-def tweb(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalize TWEB dataset.
- https://www.kaggle.com/bryanpark/the-world-english-bible-speech-dataset
- """
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "tweb"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("\t")
- wav_file = os.path.join(root_path, cols[0] + ".wav")
- text = cols[1]
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def mozilla(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes Mozilla meta data files to TTS format"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "mozilla"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = cols[1].strip()
- text = cols[0].strip()
- wav_file = os.path.join(root_path, "wavs", wav_file)
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def mozilla_de(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes Mozilla meta data files to TTS format"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "mozilla"
- with open(txt_file, "r", encoding="ISO 8859-1") as ttf:
- for line in ttf:
- cols = line.strip().split("|")
- wav_file = cols[0].strip()
- text = cols[1].strip()
- folder_name = f"BATCH_{wav_file.split('_')[0]}_FINAL"
- wav_file = os.path.join(root_path, folder_name, wav_file)
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def mailabs(root_path, meta_files=None, ignored_speakers=None):
- """Normalizes M-AI-Labs meta data files to TTS format
-
- Args:
- root_path (str): root folder of the MAILAB language folder.
- meta_files (str): list of meta files to be used in the training. If None, finds all the csv files
- recursively. Defaults to None
- """
- speaker_regex = re.compile(f"by_book{os.sep}(male|female){os.sep}(?P
-
-
- }})
-
-
- - {%if use_multi_speaker%} - Choose a speaker: -
- {%endif%} - - {%if use_multi_language%} - Choose a language: -
- {%endif%} - - - {%if show_details%} -
- {%endif%} - - -
-
-
- -
-
- - {%if use_multi_speaker%} - Choose a speaker: -
- {%endif%} - - {%if use_multi_language%} - Choose a language: -
- {%endif%} - - - {%if show_details%} -
- {%endif%} - - -
\n", - " | - audio1.wav
\n", - " | - audio2.wav
\n", - " | - audio3.wav
\n", - " ...
\n", - "\n", - "\n", - "and a **metadata.csv** file will have the audio file name in parallel to the transcript, delimited by `|`: \n", - " \n", - "\n", - "# metadata.csv
\n", - "audio1|This is my sentence.
\n", - "audio2|This is maybe my sentence.
\n", - "audio3|This is certainly my sentence.
\n", - "audio4|Let this be your sentence.
\n", - "...\n", - "\n", - "\n", - "In the end, we should have the following **folder structure**:\n", - "\n", - "\n", - "/MyTTSDataset
\n", - " |
\n", - " | -> metadata.csv
\n", - " | -> /wavs
\n", - " | -> audio1.wav
\n", - " | -> audio2.wav
\n", - " | ...
\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "69501a10-3b53-4e75-ae66-90221d6f2271", - "metadata": {}, - "source": [ - "🐸TTS already provides tooling for the _LJSpeech_. if you use the same format, you can start training your models right away.
\n", - "\n", - "After you collect and format your dataset, you need to check two things. Whether you need a **_formatter_** and a **_text_cleaner_**.
The **_formatter_** loads the text file (created above) as a list and the **_text_cleaner_** performs a sequence of text normalization operations that converts the raw text into the spoken representation (e.g. converting numbers to text, acronyms, and symbols to the spoken format).\n", - "\n", - "If you use a different dataset format then the LJSpeech or the other public datasets that 🐸TTS supports, then you need to write your own **_formatter_** and **_text_cleaner_**." - ] - }, - { - "cell_type": "markdown", - "id": "e7f226c8-4e55-48fa-937b-8415d539b17c", - "metadata": {}, - "source": [ - "## ⏳️ Loading your dataset\n", - "Load one of the dataset supported by 🐸TTS.\n", - "\n", - "We will start by defining dataset config and setting LJSpeech as our target dataset and define its path.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3cb0191-b8fc-4158-bd26-8423c2a8ba66", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "# BaseDatasetConfig: defines name, formatter and path of the dataset.\n", - "from TTS.tts.configs.shared_configs import BaseDatasetConfig\n", - "\n", - "output_path = \"tts_train_dir\"\n", - "if not os.path.exists(output_path):\n", - " os.makedirs(output_path)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae6b7019-3685-4b48-8917-c152e288d7e3", - "metadata": {}, - "outputs": [], - "source": [ - "# Download and extract LJSpeech dataset.\n", - "\n", - "!wget -O $output_path/LJSpeech-1.1.tar.bz2 https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2 \n", - "!tar -xf $output_path/LJSpeech-1.1.tar.bz2 -C $output_path" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76cd3ab5-6387-45f1-b488-24734cc1beb5", - "metadata": {}, - "outputs": [], - "source": [ - "dataset_config = BaseDatasetConfig(\n", - " formatter=\"ljspeech\", meta_file_train=\"metadata.csv\", path=os.path.join(output_path, \"LJSpeech-1.1/\")\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ae82fd75", - "metadata": {}, - "source": [ - "## ✅ Train a new model\n", - "\n", - "Let's kick off a training run 🚀🚀🚀.\n", - "\n", - "Deciding on the model architecture you'd want to use is based on your needs and available resources. Each model architecture has it's pros and cons that define the run-time efficiency and the voice quality.\n", - "We have many recipes under `TTS/recipes/` that provide a good starting point. For this tutorial, we will be using `GlowTTS`." - ] - }, - { - "cell_type": "markdown", - "id": "f5876e46-2aee-4bcf-b6b3-9e3c535c553f", - "metadata": {}, - "source": [ - "We will begin by initializing the model training configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5483ca28-39d6-49f8-a18e-4fb53c50ad84", - "metadata": {}, - "outputs": [], - "source": [ - "# GlowTTSConfig: all model related values for training, validating and testing.\n", - "from TTS.tts.configs.glow_tts_config import GlowTTSConfig\n", - "config = GlowTTSConfig(\n", - " batch_size=32,\n", - " eval_batch_size=16,\n", - " num_loader_workers=4,\n", - " num_eval_loader_workers=4,\n", - " run_eval=True,\n", - " test_delay_epochs=-1,\n", - " epochs=100,\n", - " text_cleaner=\"phoneme_cleaners\",\n", - " use_phonemes=True,\n", - " phoneme_language=\"en-us\",\n", - " phoneme_cache_path=os.path.join(output_path, \"phoneme_cache\"),\n", - " print_step=25,\n", - " print_eval=False,\n", - " mixed_precision=True,\n", - " output_path=output_path,\n", - " datasets=[dataset_config],\n", - " save_step=1000,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b93ed377-80b7-447b-bd92-106bffa777ee", - "metadata": {}, - "source": [ - "Next we will initialize the audio processor which is used for feature extraction and audio I/O." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1b12f61-f851-4565-84dd-7640947e04ab", - "metadata": {}, - "outputs": [], - "source": [ - "from TTS.utils.audio import AudioProcessor\n", - "ap = AudioProcessor.init_from_config(config)\n", - "# Modify sample rate if for a custom audio dataset:\n", - "# ap.sample_rate = 22050\n" - ] - }, - { - "cell_type": "markdown", - "id": "1d461683-b05e-403f-815f-8007bda08c38", - "metadata": {}, - "source": [ - "Next we will initialize the tokenizer which is used to convert text to sequences of token IDs. If characters are not defined in the config, default characters are passed to the config." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "014879b7-f18d-44c0-b24a-e10f8002113a", - "metadata": {}, - "outputs": [], - "source": [ - "from TTS.tts.utils.text.tokenizer import TTSTokenizer\n", - "tokenizer, config = TTSTokenizer.init_from_config(config)" - ] - }, - { - "cell_type": "markdown", - "id": "df3016e1-9e99-4c4f-94e3-fa89231fd978", - "metadata": {}, - "source": [ - "Next we will load data samples. Each sample is a list of ```[text, audio_file_path, speaker_name]```. You can define your custom sample loader returning the list of samples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cadd6ada-c8eb-4f79-b8fe-6d72850af5a7", - "metadata": {}, - "outputs": [], - "source": [ - "from TTS.tts.datasets import load_tts_samples\n", - "train_samples, eval_samples = load_tts_samples(\n", - " dataset_config,\n", - " eval_split=True,\n", - " eval_split_max_size=config.eval_split_max_size,\n", - " eval_split_size=config.eval_split_size,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "db8b451e-1fe1-4aa3-b69e-ab22b925bd19", - "metadata": {}, - "source": [ - "Now we're ready to initialize the model.\n", - "\n", - "Models take a config object and a speaker manager as input. Config defines the details of the model like the number of layers, the size of the embedding, etc. Speaker manager is used by multi-speaker models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac2ffe3e-ad0c-443e-800c-9b076ee811b4", - "metadata": {}, - "outputs": [], - "source": [ - "from TTS.tts.models.glow_tts import GlowTTS\n", - "model = GlowTTS(config, ap, tokenizer, speaker_manager=None)" - ] - }, - { - "cell_type": "markdown", - "id": "e2832c56-889d-49a6-95b6-eb231892ecc6", - "metadata": {}, - "source": [ - "Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training, distributed training, etc." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f609945-4fe0-4d0d-b95e-11d7bfb63ebe", - "metadata": {}, - "outputs": [], - "source": [ - "from trainer import Trainer, TrainerArgs\n", - "trainer = Trainer(\n", - " TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5b320831-dd83-429b-bb6a-473f9d49d321", - "metadata": {}, - "source": [ - "### AND... 3,2,1... START TRAINING 🚀🚀🚀" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4c07f99-3d1d-4bea-801e-9f33bbff0e9f", - "metadata": {}, - "outputs": [], - "source": [ - "trainer.fit()" - ] - }, - { - "cell_type": "markdown", - "id": "4cff0c40-2734-40a6-a905-e945a9fb3e98", - "metadata": {}, - "source": [ - "#### 🚀 Run the Tensorboard. 🚀\n", - "On the notebook and Tensorboard, you can monitor the progress of your model. Also Tensorboard provides certain figures and sample outputs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a85cd3b-1646-40ad-a6c2-49323e08eeec", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install tensorboard\n", - "!tensorboard --logdir=tts_train_dir" - ] - }, - { - "cell_type": "markdown", - "id": "9f6dc959", - "metadata": {}, - "source": [ - "## ✅ Test the model\n", - "\n", - "We made it! 🙌\n", - "\n", - "Let's kick off the testing run, which displays performance metrics.\n", - "\n", - "We're committing the cardinal sin of ML 😈 (aka - testing on our training data) so you don't want to deploy this model into production. In this notebook we're focusing on the workflow itself, so it's forgivable 😇\n", - "\n", - "You can see from the test output that our tiny model has overfit to the data, and basically memorized this one sentence.\n", - "\n", - "When you start training your own models, make sure your testing data doesn't include your training data 😅" - ] - }, - { - "cell_type": "markdown", - "id": "99fada7a-592f-4a09-9369-e6f3d82de3a0", - "metadata": {}, - "source": [ - "Let's get the latest saved checkpoint. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6dd47ed5-da8e-4bf9-b524-d686630d6961", - "metadata": {}, - "outputs": [], - "source": [ - "import glob, os\n", - "output_path = \"tts_train_dir\"\n", - "ckpts = sorted([f for f in glob.glob(output_path+\"/*/*.pth\")])\n", - "configs = sorted([f for f in glob.glob(output_path+\"/*/*.json\")])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd42bc7a", - "metadata": {}, - "outputs": [], - "source": [ - " !tts --text \"Text for TTS\" \\\n", - " --model_path $test_ckpt \\\n", - " --config_path $test_config \\\n", - " --out_path out.wav" - ] - }, - { - "cell_type": "markdown", - "id": "81cbcb3f-d952-469b-a0d8-8941cd7af670", - "metadata": {}, - "source": [ - "## 📣 Listen to the synthesized wave 📣" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0000bd6-6763-4a10-a74d-911dd08ebcff", - "metadata": {}, - "outputs": [], - "source": [ - "import IPython\n", - "IPython.display.Audio(\"out.wav\")" - ] - }, - { - "cell_type": "markdown", - "id": "13914401-cad1-494a-b701-474e52829138", - "metadata": {}, - "source": [ - "## 🎉 Congratulations! 🎉 You now have trained your first TTS model! \n", - "Follow up with the next tutorials to learn more advanced material." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "950d9fc6-896f-4a2c-86fd-8fd1fcbbb3f7", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/TTS/notebooks/dataset_analysis/AnalyzeDataset.ipynb b/TTS/notebooks/dataset_analysis/AnalyzeDataset.ipynb deleted file mode 100644 index 4f0dbb8e40ed78530c287feadd9168ac2afc07c4..0000000000000000000000000000000000000000 --- a/TTS/notebooks/dataset_analysis/AnalyzeDataset.ipynb +++ /dev/null @@ -1,427 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "# TTS_PATH = \"/home/erogol/projects/\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import librosa\n", - "import numpy as np\n", - "import pandas as pd\n", - "from scipy.stats import norm\n", - "from tqdm import tqdm_notebook as tqdm\n", - "from multiprocessing import Pool\n", - "from matplotlib import pylab as plt\n", - "from collections import Counter\n", - "from TTS.config.shared_configs import BaseDatasetConfig\n", - "from TTS.tts.datasets import load_tts_samples\n", - "from TTS.tts.datasets.formatters import *\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "NUM_PROC = 8\n", - "DATASET_CONFIG = BaseDatasetConfig(\n", - " formatter=\"ljspeech\", meta_file_train=\"metadata.csv\", path=\"/absolute/path/to/your/dataset/\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def formatter(root_path, meta_file, **kwargs): # pylint: disable=unused-argument\n", - " txt_file = os.path.join(root_path, meta_file)\n", - " items = []\n", - " speaker_name = \"myspeaker\"\n", - " with open(txt_file, \"r\", encoding=\"utf-8\") as ttf:\n", - " for line in ttf:\n", - " cols = line.split(\"|\")\n", - " wav_file = os.path.join(root_path, \"wavs\", cols[0] + \".wav\") \n", - " text = cols[1]\n", - " items.append({\"text\": text, \"audio_file\": wav_file, \"speaker_name\": speaker_name, \"root_path\": root_path})\n", - " return items" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "# use your own preprocessor at this stage - TTS/datasets/proprocess.py\n", - "train_samples, eval_samples = load_tts_samples(DATASET_CONFIG, eval_split=True, formatter=formatter)\n", - "if eval_samples is not None:\n", - " items = train_samples + eval_samples\n", - "else:\n", - " items = train_samples\n", - "print(\" > Number of audio files: {}\".format(len(items)))\n", - "print(items[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "# check wavs if exist\n", - "wav_files = []\n", - "for item in items:\n", - " wav_file = item[\"audio_file\"].strip()\n", - " wav_files.append(wav_file)\n", - " if not os.path.exists(wav_file):\n", - " print(waf_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "# show duplicate items\n", - "c = Counter(wav_files)\n", - "print([item for item, count in c.items() if count > 1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "item" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "def load_item(item):\n", - " text = item[\"text\"].strip()\n", - " file_name = item[\"audio_file\"].strip()\n", - " audio, sr = librosa.load(file_name, sr=None)\n", - " audio_len = len(audio) / sr\n", - " text_len = len(text)\n", - " return file_name, text, text_len, audio, audio_len\n", - "\n", - "# This will take a while depending on size of dataset\n", - "if NUM_PROC == 1:\n", - " data = []\n", - " for m in tqdm(items):\n", - " data += [load_item(m)]\n", - "else:\n", - " with Pool(8) as p:\n", - " data = list(tqdm(p.imap(load_item, items), total=len(items)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "# count words in the dataset\n", - "w_count = Counter()\n", - "for item in tqdm(data):\n", - " text = item[1].lower().strip()\n", - " for word in text.split():\n", - " w_count[word] += 1\n", - "print(\" > Number of words: {}\".format(len(w_count)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "text_vs_durs = {} # text length vs audio duration\n", - "text_len_counter = Counter() # number of sentences with the keyed length\n", - "for item in tqdm(data):\n", - " text = item[1].lower().strip()\n", - " text_len = len(text)\n", - " text_len_counter[text_len] += 1\n", - " audio_len = item[-1]\n", - " try:\n", - " text_vs_durs[text_len] += [audio_len]\n", - " except:\n", - " text_vs_durs[text_len] = [audio_len]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "# text_len vs avg_audio_len, median_audio_len, std_audio_len\n", - "text_vs_avg = {}\n", - "text_vs_median = {}\n", - "text_vs_std = {}\n", - "for key, durs in text_vs_durs.items():\n", - " text_vs_avg[key] = np.mean(durs)\n", - " text_vs_median[key] = np.median(durs)\n", - " text_vs_std[key] = np.std(durs)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "Collapsed": "false" - }, - "source": [ - "### Avg audio length per char" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "for item in data:\n", - " if item[-1] < 2:\n", - " print(item)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "sec_per_chars = []\n", - "for item in data:\n", - " text = item[1]\n", - " dur = item[-1]\n", - " sec_per_char = dur / len(text)\n", - " sec_per_chars.append(sec_per_char)\n", - "# sec_per_char /= len(data)\n", - "# print(sec_per_char)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "mean = np.mean(sec_per_chars)\n", - "std = np.std(sec_per_chars)\n", - "print(mean)\n", - "print(std)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "dist = norm(mean, std)\n", - "\n", - "# find irregular instances long or short voice durations\n", - "for item in data:\n", - " text = item[1]\n", - " dur = item[-1]\n", - " sec_per_char = dur / len(text)\n", - " pdf =norm.pdf(sec_per_char)\n", - " if pdf < 0.39:\n", - " print(item)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "Collapsed": "false" - }, - "source": [ - "### Plot Dataset Statistics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "plt.title(\"text length vs mean audio duration\")\n", - "plt.scatter(list(text_vs_avg.keys()), list(text_vs_avg.values()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "plt.title(\"text length vs median audio duration\")\n", - "plt.scatter(list(text_vs_median.keys()), list(text_vs_median.values()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "plt.title(\"text length vs STD\")\n", - "plt.scatter(list(text_vs_std.keys()), list(text_vs_std.values()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "plt.title(\"text length vs # instances\")\n", - "plt.scatter(list(text_len_counter.keys()), list(text_len_counter.values()))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "Collapsed": "false" - }, - "source": [ - "### Check words frequencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "w_count_df = pd.DataFrame.from_dict(w_count, orient='index')\n", - "w_count_df.sort_values(0, ascending=False, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false", - "scrolled": true - }, - "outputs": [], - "source": [ - "w_count_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "# check a certain word\n", - "w_count_df.at['minute', 0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "Collapsed": "false" - }, - "outputs": [], - "source": [ - "# fequency bar plot - it takes time!!\n", - "w_count_df.plot.bar()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/TTS/notebooks/dataset_analysis/CheckDatasetSNR.ipynb b/TTS/notebooks/dataset_analysis/CheckDatasetSNR.ipynb deleted file mode 100644 index 18c48d0bd181830fe096852dfdfe30cba1bc47b4..0000000000000000000000000000000000000000 --- a/TTS/notebooks/dataset_analysis/CheckDatasetSNR.ipynb +++ /dev/null @@ -1,210 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook computes the average SNR a given Voice Dataset. If the SNR is too low, that might reduce the performance or prevent model to learn. SNR paper can be seen here: https://www.cs.cmu.edu/~robust/Papers/KimSternIS08.pdf\n", - "\n", - "To use this notebook, you need:\n", - "- WADA SNR estimation: http://www.cs.cmu.edu/~robust/archive/algorithms/WADA_SNR_IS_2008/\n", - " 1. extract in the same folder as this notebook\n", - " 2. under MacOS you'll have to rebuild the executable. In the build folder: 1) remove existing .o files and 2) run make\n", - "\n", - "\n", - "- FFMPEG: ```sudo apt-get install ffmpeg ``` \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import glob\n", - "import subprocess\n", - "import IPython\n", - "import soundfile as sf\n", - "import numpy as np\n", - "from tqdm import tqdm\n", - "from multiprocessing import Pool\n", - "from matplotlib import pylab as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the meta parameters\n", - "DATA_PATH = \"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/eva_k/\"\n", - "NUM_PROC = 1\n", - "CURRENT_PATH = os.getcwd()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def compute_file_snr(file_path):\n", - " \"\"\" Convert given file to required format with FFMPEG and process with WADA.\"\"\"\n", - " _, sr = sf.read(file_path)\n", - " new_file = file_path.replace(\".wav\", \"_tmp.wav\")\n", - " if sr != 16000:\n", - " command = f'ffmpeg -i \"{file_path}\" -ac 1 -acodec pcm_s16le -y -ar 16000 \"{new_file}\"'\n", - " else:\n", - " command = f'cp \"{file_path}\" \"{new_file}\"'\n", - " os.system(command)\n", - " command = [f'\"{CURRENT_PATH}/WadaSNR/Exe/WADASNR\"', f'-i \"{new_file}\"', f'-t \"{CURRENT_PATH}/WadaSNR/Exe/Alpha0.400000.txt\"', '-ifmt mswav']\n", - " output = subprocess.check_output(\" \".join(command), shell=True)\n", - " try:\n", - " output = float(output.split()[-3].decode(\"utf-8\"))\n", - " except:\n", - " raise RuntimeError(\" \".join(command))\n", - " os.system(f'rm \"{new_file}\"')\n", - " return output, file_path\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "wav_file = \"/home/erogol/Data/LJSpeech-1.1/wavs/LJ001-0001.wav\"\n", - "output = compute_file_snr(wav_file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "wav_files = glob.glob(f\"{DATA_PATH}/**/*.wav\", recursive=True)\n", - "print(f\" > Number of wav files {len(wav_files)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if NUM_PROC == 1:\n", - " file_snrs = [None] * len(wav_files) \n", - " for idx, wav_file in tqdm(enumerate(wav_files)):\n", - " tup = compute_file_snr(wav_file)\n", - " file_snrs[idx] = tup\n", - "else:\n", - " with Pool(NUM_PROC) as pool:\n", - " file_snrs = list(tqdm(pool.imap(compute_file_snr, wav_files), total=len(wav_files)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "snrs = [tup[0] for tup in file_snrs]\n", - "\n", - "error_idxs = np.where(np.isnan(snrs) == True)[0]\n", - "error_files = [wav_files[idx] for idx in error_idxs]\n", - "\n", - "file_snrs = [i for j, i in enumerate(file_snrs) if j not in error_idxs]\n", - "file_names = [tup[1] for tup in file_snrs]\n", - "snrs = [tup[0] for tup in file_snrs]\n", - "file_idxs = np.argsort(snrs)\n", - "\n", - "\n", - "print(f\" > Average SNR of the dataset:{np.mean(snrs)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def output_snr_with_audio(idx):\n", - " file_idx = file_idxs[idx]\n", - " file_name = file_names[file_idx]\n", - " wav, sr = sf.read(file_name)\n", - " # multi channel to single channel\n", - " if len(wav.shape) == 2:\n", - " wav = wav[:, 0]\n", - " print(f\" > {file_name} - snr:{snrs[file_idx]}\")\n", - " IPython.display.display(IPython.display.Audio(wav, rate=sr))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# find worse SNR files\n", - "N = 10 # number of files to fetch\n", - "for i in range(N):\n", - " output_snr_with_audio(i)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# find best recordings\n", - "N = 10 # number of files to fetch\n", - "for i in range(N):\n", - " output_snr_with_audio(-i-1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.hist(snrs, bins=100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/TTS/notebooks/dataset_analysis/CheckPitch.ipynb b/TTS/notebooks/dataset_analysis/CheckPitch.ipynb deleted file mode 100644 index 72afbc64a17e2a46d1d2d5336990f01eb620ca20..0000000000000000000000000000000000000000 --- a/TTS/notebooks/dataset_analysis/CheckPitch.ipynb +++ /dev/null @@ -1,179 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 9, - "source": [ - "import numpy as np\n", - "import glob\n", - "from TTS.utils.audio import AudioProcessor\n", - "from TTS.config.shared_configs import BaseAudioConfig\n", - "from TTS.tts.utils.visual import plot_pitch" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 13, - "source": [ - "pitch_path = \"/home/ubuntu/TTS/recipes/ljspeech/fast_pitch/f0_cache\"\n", - "wav_path = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/wavs\"\n", - "wav_files = glob.glob(\"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/wavs/*.wav\")\n", - "print(len(wav_files))" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "13100\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 20, - "source": [ - "ap = AudioProcessor(**BaseAudioConfig( sample_rate=22050,\n", - " do_trim_silence=True,\n", - " trim_db=60.0,\n", - " signal_norm=False,\n", - " mel_fmin=0.0,\n", - " mel_fmax=8000,\n", - " spec_gain=1.0,\n", - " log_func=\"np.log\",\n", - " ref_level_db=20,\n", - " preemphasis=0.0,))" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " > Setting up Audio Processor...\n", - " | > sample_rate:22050\n", - " | > resample:False\n", - " | > num_mels:80\n", - " | > log_func:np.log\n", - " | > min_level_db:-100\n", - " | > frame_shift_ms:None\n", - " | > frame_length_ms:None\n", - " | > ref_level_db:20\n", - " | > fft_size:1024\n", - " | > power:1.5\n", - " | > preemphasis:0.0\n", - " | > griffin_lim_iters:60\n", - " | > signal_norm:False\n", - " | > symmetric_norm:True\n", - " | > mel_fmin:0\n", - " | > mel_fmax:8000\n", - " | > spec_gain:1.0\n", - " | > stft_pad_mode:reflect\n", - " | > max_norm:4.0\n", - " | > clip_norm:True\n", - " | > do_trim_silence:True\n", - " | > trim_db:60.0\n", - " | > do_sound_norm:False\n", - " | > do_amp_to_db_linear:True\n", - " | > do_amp_to_db_mel:True\n", - " | > stats_path:None\n", - " | > base:2.718281828459045\n", - " | > hop_length:256\n", - " | > win_length:1024\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 15, - "source": [ - "pitch_files = [wf.replace(\".wav\", \"_pitch.npy\").replace(wav_path, pitch_path) for wf in wav_files]" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 30, - "source": [ - "idx = 100\n", - "# wav_file = wav_files[idx]\n", - "# pitch_file = pitch_files[idx]\n", - "wav_file = \"/home/ubuntu/TTS/recipes/ljspeech/fast_pitch/../LJSpeech-1.1/wavs/LJ011-0097.wav\"\n", - "pitch_file = \"/home/ubuntu/TTS/recipes/ljspeech/fast_pitch/f0_cache/LJ011-0097_pitch.npy\"\n", - "pitch = np.load(pitch_file)\n", - "wav = ap.load_wav(wav_file)\n", - "spec = ap.melspectrogram(wav)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 31, - "source": [ - "plot_pitch(pitch, spec.T)" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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", 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diff --git a/TTS/notebooks/dataset_analysis/CheckSpectrograms.ipynb b/TTS/notebooks/dataset_analysis/CheckSpectrograms.ipynb
deleted file mode 100644
index 47e5c4cf4dab2db8511b694c39bce113618d65da..0000000000000000000000000000000000000000
--- a/TTS/notebooks/dataset_analysis/CheckSpectrograms.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "\n",
- "from TTS.utils.audio import AudioProcessor\n",
- "from TTS.tts.utils.visual import plot_spectrogram\n",
- "from TTS.config import load_config\n",
- "\n",
- "import IPython.display as ipd\n",
- "import glob"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "from TTS.config.shared_configs import BaseAudioConfig\n",
- "CONFIG = BaseAudioConfig()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## ✍️ Set these values "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_path = \"/root/wav48_silence_trimmed/\"\n",
- "file_ext = \".flac\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Read audio files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "file_paths = glob.glob(data_path + f\"/**/*{file_ext}\", recursive=True)\n",
- "\n",
- "# Change this to the index of the desired file listed below\n",
- "sample_file_index = 10\n",
- "\n",
- "SAMPLE_FILE_PATH = file_paths[sample_file_index]\n",
- "\n",
- "print(\"File list, by index:\")\n",
- "dict(enumerate(file_paths))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Collapsed": "false"
- },
- "source": [
- "## ✍️ Set Audio Processor\n",
- "Play with the AP parameters until you find a good fit with the synthesis speech below.\n",
- "\n",
- "The default values are loaded from your config.json file, so you only need to\n",
- "uncomment and modify values below that you'd like to tune."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "tune_params={\n",
- " 'num_mels': 80, # In general, you don't need to change this. \n",
- " 'fft_size': 2400, # In general, you don't need to change this.\n",
- " 'frame_length_ms': 50, \n",
- " 'frame_shift_ms': 12.5,\n",
- " 'sample_rate': 48000, # This must match the sample rate of the dataset.\n",
- " 'hop_length': None, # In general, you don't need to change this.\n",
- " 'win_length': 1024, # In general, you don't need to change this.\n",
- " 'preemphasis': 0.98, # In general, 0 gives better voice recovery but makes training harder. If your model does not train, try 0.97 - 0.99.\n",
- " 'min_level_db': -100,\n",
- " 'ref_level_db': 0, # The base DB; increase until all background noise is removed in the spectrogram, then lower until you hear better speech below.\n",
- " 'power': 1.5, # Change this value and listen to the synthesized voice. 1.2 - 1.5 are resonable values.\n",
- " 'griffin_lim_iters': 60, # Quality does not improve for values > 60\n",
- " 'mel_fmin': 0.0, # Adjust this and check mel-spectrogram-based voice synthesis below.\n",
- " 'mel_fmax': 8000.0, # Adjust this and check mel-spectrogram-based voice synthesis below.\n",
- " 'do_trim_silence': True # If you dataset has some silience at the beginning or end, this trims it. Check the AP.load_wav() below,if it causes any difference for the loaded audio file.\n",
- "}\n",
- "\n",
- "# These options have to be forced off in order to avoid errors about the \n",
- "# pre-calculated not matching the options being tuned.\n",
- "reset={\n",
- " 'signal_norm': True, # check this if you want to test normalization parameters.\n",
- " 'stats_path': None,\n",
- " 'symmetric_norm': False,\n",
- " 'max_norm': 1,\n",
- " 'clip_norm': True,\n",
- "}\n",
- "\n",
- "# Override select parts of loaded config with parameters above\n",
- "tuned_config = CONFIG.copy()\n",
- "tuned_config.update(reset)\n",
- "tuned_config.update(tune_params)\n",
- "\n",
- "AP = AudioProcessor(**tuned_config);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Collapsed": "false"
- },
- "source": [
- "### Check audio loading "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "wav = AP.load_wav(SAMPLE_FILE_PATH)\n",
- "ipd.Audio(data=wav, rate=AP.sample_rate) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Collapsed": "false"
- },
- "source": [
- "### Generate Mel-Spectrogram and Re-synthesis with GL"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "AP.power = 1.5"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mel = AP.melspectrogram(wav)\n",
- "print(\"Max:\", mel.max())\n",
- "print(\"Min:\", mel.min())\n",
- "print(\"Mean:\", mel.mean())\n",
- "plot_spectrogram(mel.T, AP, output_fig=True)\n",
- "\n",
- "wav_gen = AP.inv_melspectrogram(mel)\n",
- "ipd.Audio(wav_gen, rate=AP.sample_rate)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Collapsed": "false"
- },
- "source": [
- "### Generate Linear-Spectrogram and Re-synthesis with GL"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "spec = AP.spectrogram(wav)\n",
- "print(\"Max:\", spec.max())\n",
- "print(\"Min:\", spec.min())\n",
- "print(\"Mean:\", spec.mean())\n",
- "plot_spectrogram(spec.T, AP, output_fig=True)\n",
- "\n",
- "wav_gen = AP.inv_spectrogram(spec)\n",
- "ipd.Audio(wav_gen, rate=AP.sample_rate)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Collapsed": "false"
- },
- "source": [
- "### Compare values for a certain parameter\n",
- "\n",
- "Optimize your parameters by comparing different values per parameter at a time."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "from librosa import display\n",
- "from matplotlib import pylab as plt\n",
- "import IPython\n",
- "plt.rcParams['figure.figsize'] = (20.0, 16.0)\n",
- "\n",
- "def compare_values(attribute, values):\n",
- " \"\"\"\n",
- " attributes (str): the names of the attribute you like to test.\n",
- " values (list): list of values to compare.\n",
- " \"\"\"\n",
- " file = SAMPLE_FILE_PATH\n",
- " wavs = []\n",
- " for idx, val in enumerate(values):\n",
- " set_val_cmd = \"AP.{}={}\".format(attribute, val)\n",
- " exec(set_val_cmd)\n",
- " wav = AP.load_wav(file)\n",
- " spec = AP.spectrogram(wav)\n",
- " spec_norm = AP.denormalize(spec.T)\n",
- " plt.subplot(len(values), 2, 2*idx + 1)\n",
- " plt.imshow(spec_norm.T, aspect=\"auto\", origin=\"lower\")\n",
- " # plt.colorbar()\n",
- " plt.tight_layout()\n",
- " wav_gen = AP.inv_spectrogram(spec)\n",
- " wavs.append(wav_gen)\n",
- " plt.subplot(len(values), 2, 2*idx + 2)\n",
- " display.waveplot(wav, alpha=0.5)\n",
- " display.waveplot(wav_gen, alpha=0.25)\n",
- " plt.title(\"{}={}\".format(attribute, val))\n",
- " plt.tight_layout()\n",
- " \n",
- " wav = AP.load_wav(file)\n",
- " print(\" > Ground-truth\")\n",
- " IPython.display.display(IPython.display.Audio(wav, rate=AP.sample_rate))\n",
- " \n",
- " for idx, wav_gen in enumerate(wavs):\n",
- " val = values[idx]\n",
- " print(\" > {} = {}\".format(attribute, val))\n",
- " IPython.display.display(IPython.display.Audio(wav_gen, rate=AP.sample_rate))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "compare_values(\"preemphasis\", [0, 0.5, 0.97, 0.98, 0.99])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "compare_values(\"ref_level_db\", [2, 5, 10, 15, 20, 25, 30, 35, 40, 1000])"
- ]
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "27648abe09795c3a768a281b31f7524fcf66a207e733f8ecda3a4e1fd1059fb0"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/TTS/notebooks/dataset_analysis/PhonemeCoverage.ipynb b/TTS/notebooks/dataset_analysis/PhonemeCoverage.ipynb
deleted file mode 100644
index a2268317fbdefad63e69a839edfa2cb0c5d9960c..0000000000000000000000000000000000000000
--- a/TTS/notebooks/dataset_analysis/PhonemeCoverage.ipynb
+++ /dev/null
@@ -1,256 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "Collapsed": "false"
- },
- "source": [
- "# Jupyter Notbook for phoneme coverage analysis\n",
- "\n",
- "This jupyter notebook checks dataset configured in config.json for phoneme coverage.\n",
- "As mentioned here https://github.com/mozilla/TTS/wiki/Dataset#what-makes-a-good-dataset a good phoneme coverage is recommended.\n",
- "\n",
- "Most parameters will be taken from config.json file in mozilla tts repo so please ensure it's configured correctly for your dataset.\n",
- "This notebook used lots of existring code from the TTS repo to ensure future compatibility.\n",
- "\n",
- "Many thanks to Neil Stoker supporting me on this topic :-).\n",
- "\n",
- "I provide this notebook without any warrenty but it's hopefully useful for your dataset analysis.\n",
- "\n",
- "Happy TTS'ing :-)\n",
- "\n",
- "Thorsten Müller\n",
- "\n",
- "* https://github.com/thorstenMueller/deep-learning-german-tts\n",
- "* https://discourse.mozilla.org/t/contributing-my-german-voice-for-tts/"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "# set some vars\n",
- "# TTS_PATH = \"/home/thorsten/___dev/tts/mozilla/TTS\"\n",
- "CONFIG_FILE = \"/path/to/config/config.json\"\n",
- "CHARS_TO_REMOVE = \".,:!?'\"\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "# import stuff\n",
- "from TTS.config import load_config\n",
- "from TTS.tts.datasets import load_tts_samples\n",
- "from TTS.tts.utils.text.tokenizer import TTSTokenizer\n",
- "from tqdm import tqdm\n",
- "from matplotlib import pylab as plt\n",
- "from multiprocessing import Pool, cpu_count\n",
- "\n",
- "# extra imports that might not be included in requirements.txt\n",
- "import collections\n",
- "import operator\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false",
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Load config.json properties\n",
- "CONFIG = load_config(CONFIG_FILE)\n",
- "\n",
- "# Load some properties from config.json\n",
- "CONFIG_METADATA = load_tts_samples(CONFIG.datasets)[0]\n",
- "CONFIG_METADATA = CONFIG_METADATA\n",
- "CONFIG_DATASET = CONFIG.datasets[0]\n",
- "CONFIG_PHONEME_LANGUAGE = CONFIG.phoneme_language\n",
- "CONFIG_TEXT_CLEANER = CONFIG.text_cleaner\n",
- "CONFIG_ENABLE_EOS_BOS_CHARS = CONFIG.enable_eos_bos_chars\n",
- "\n",
- "# Will be printed on generated output graph\n",
- "CONFIG_RUN_NAME = CONFIG.run_name\n",
- "CONFIG_RUN_DESC = CONFIG.run_description\n",
- "\n",
- "# Needed to convert text to phonemes and phonemes to ids\n",
- "tokenizer, config = TTSTokenizer.init_from_config(CONFIG)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false",
- "tags": []
- },
- "outputs": [],
- "source": [
- "# print some debug information on loaded config values\n",
- "print(\" > Run name: \" + CONFIG_RUN_NAME + \" (\" + CONFIG_RUN_DESC + \")\")\n",
- "print(\" > Dataset files: \" + str(len(CONFIG_METADATA)))\n",
- "print(\" > Phoneme language: \" + CONFIG_PHONEME_LANGUAGE)\n",
- "print(\" > Used text cleaner: \" + CONFIG_TEXT_CLEANER)\n",
- "print(\" > Enable eos bos chars: \" + str(CONFIG_ENABLE_EOS_BOS_CHARS))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_phoneme_from_sequence(text):\n",
- " temp_list = []\n",
- " if len(text[\"text\"]) > 0:\n",
- " #temp_text = text[0].rstrip('\\n')\n",
- " temp_text = text[\"text\"].rstrip('\\n')\n",
- " for rm_bad_chars in CHARS_TO_REMOVE:\n",
- " temp_text = temp_text.replace(rm_bad_chars,\"\")\n",
- " seq = tokenizer.text_to_ids(temp_text)\n",
- " text = tokenizer.ids_to_text(seq)\n",
- " text = text.replace(\" \",\"\")\n",
- " temp_list.append(text)\n",
- " return temp_list"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false",
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Get phonemes from metadata\n",
- "phonemes = []\n",
- "\n",
- "with Pool(cpu_count()-1) as p:\n",
- " \n",
- " phonemes = list(tqdm(p.imap(get_phoneme_from_sequence, CONFIG_METADATA), total=len(CONFIG_METADATA)))\n",
- " phonemes = [i for sub in phonemes for i in sub]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false",
- "tags": []
- },
- "outputs": [],
- "source": [
- "s = \"\"\n",
- "phonemeString = s.join(phonemes)\n",
- "\n",
- "d = {}\n",
- "collections._count_elements(d, phonemeString)\n",
- "sorted_d = dict(sorted(d.items(), key=operator.itemgetter(1),reverse=True))\n",
- "\n",
- "# remove useless keys\n",
- "sorted_d.pop(' ', None)\n",
- "sorted_d.pop('ˈ', None)\n",
- "\n",
- "phonemesSum = len(phonemeString.replace(\" \",\"\"))\n",
- "\n",
- "print(\"Dataset contains \" + str(len(sorted_d)) + \" different ipa phonemes.\")\n",
- "print(\"Dataset consists of \" + str(phonemesSum) + \" phonemes\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false",
- "tags": []
- },
- "outputs": [],
- "source": [
- "print(\"5 rarest phonemes\")\n",
- "\n",
- "rareList = dict(sorted(sorted_d.items(), key=operator.itemgetter(1), reverse=False)[:5])\n",
- "for key, value in rareList.items():\n",
- " print(key + \" --> \" + str(value) + \" occurrences\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": [
- "# create plot from analysis result\n",
- "\n",
- "x = []\n",
- "y = []\n",
- "\n",
- "for key, value in sorted_d.items():\n",
- " x.append(key)\n",
- " y.append(value)\n",
- "\n",
- "plt.figure(figsize=(50,50))\n",
- "plt.title(\"Phoneme coverage for \" + CONFIG_RUN_NAME + \" (\" + CONFIG_RUN_DESC + \")\", fontsize=50)\n",
- "plt.xticks(fontsize=50)\n",
- "plt.yticks(fontsize=50)\n",
- "plt.barh(x,y, align='center', alpha=1.0)\n",
- "plt.gca().invert_yaxis()\n",
- "plt.ylabel('phoneme', fontsize=50)\n",
- "plt.xlabel('occurrences', fontsize=50)\n",
- "\n",
- "for i, v in enumerate(y):\n",
- " plt.text(v + 2, i - .2, str(v), fontsize=20)\n",
- " plt.text(v + 2, i + .2, \"(\" + str(round(100/phonemesSum * v,2)) + \"%)\", fontsize=20)\n",
- " \n",
- " \n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Collapsed": "false"
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/TTS/notebooks/dataset_analysis/README.md b/TTS/notebooks/dataset_analysis/README.md
deleted file mode 100644
index 79faf5215951c996e7b15cc960a93195fd9034a8..0000000000000000000000000000000000000000
--- a/TTS/notebooks/dataset_analysis/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-## Simple Notebook to Analyze a Dataset
-
-By the use of this notebook, you can easily analyze a brand new dataset, find exceptional cases and define your training set.
-
-What we are looking in here is reasonable distribution of instances in terms of sequence-length, audio-length and word-coverage.
-
-This notebook is inspired from https://github.com/MycroftAI/mimic2
diff --git a/TTS/notebooks/dataset_analysis/analyze.py b/TTS/notebooks/dataset_analysis/analyze.py
deleted file mode 100644
index d34ce4c1b6a16c2acea81aa14451307cb4f0f0d0..0000000000000000000000000000000000000000
--- a/TTS/notebooks/dataset_analysis/analyze.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# visualisation tools for mimic2
-import argparse
-import csv
-import os
-import random
-from statistics import StatisticsError, mean, median, mode, stdev
-
-import matplotlib.pyplot as plt
-import seaborn as sns
-from text.cmudict import CMUDict
-
-
-def get_audio_seconds(frames):
- return (frames * 12.5) / 1000
-
-
-def append_data_statistics(meta_data):
- # get data statistics
- for char_cnt in meta_data:
- data = meta_data[char_cnt]["data"]
- audio_len_list = [d["audio_len"] for d in data]
- mean_audio_len = mean(audio_len_list)
- try:
- mode_audio_list = [round(d["audio_len"], 2) for d in data]
- mode_audio_len = mode(mode_audio_list)
- except StatisticsError:
- mode_audio_len = audio_len_list[0]
- median_audio_len = median(audio_len_list)
-
- try:
- std = stdev(d["audio_len"] for d in data)
- except StatisticsError:
- std = 0
-
- meta_data[char_cnt]["mean"] = mean_audio_len
- meta_data[char_cnt]["median"] = median_audio_len
- meta_data[char_cnt]["mode"] = mode_audio_len
- meta_data[char_cnt]["std"] = std
- return meta_data
-
-
-def process_meta_data(path):
- meta_data = {}
-
- # load meta data
- with open(path, "r", encoding="utf-8") as f:
- data = csv.reader(f, delimiter="|")
- for row in data:
- frames = int(row[2])
- utt = row[3]
- audio_len = get_audio_seconds(frames)
- char_count = len(utt)
- if not meta_data.get(char_count):
- meta_data[char_count] = {"data": []}
-
- meta_data[char_count]["data"].append(
- {
- "utt": utt,
- "frames": frames,
- "audio_len": audio_len,
- "row": "{}|{}|{}|{}".format(row[0], row[1], row[2], row[3]),
- }
- )
-
- meta_data = append_data_statistics(meta_data)
-
- return meta_data
-
-
-def get_data_points(meta_data):
- x = meta_data
- y_avg = [meta_data[d]["mean"] for d in meta_data]
- y_mode = [meta_data[d]["mode"] for d in meta_data]
- y_median = [meta_data[d]["median"] for d in meta_data]
- y_std = [meta_data[d]["std"] for d in meta_data]
- y_num_samples = [len(meta_data[d]["data"]) for d in meta_data]
- return {
- "x": x,
- "y_avg": y_avg,
- "y_mode": y_mode,
- "y_median": y_median,
- "y_std": y_std,
- "y_num_samples": y_num_samples,
- }
-
-
-def save_training(file_path, meta_data):
- rows = []
- for char_cnt in meta_data:
- data = meta_data[char_cnt]["data"]
- for d in data:
- rows.append(d["row"] + "\n")
-
- random.shuffle(rows)
- with open(file_path, "w+", encoding="utf-8") as f:
- for row in rows:
- f.write(row)
-
-
-def plot(meta_data, save_path=None):
- save = False
- if save_path:
- save = True
-
- graph_data = get_data_points(meta_data)
- x = graph_data["x"]
- y_avg = graph_data["y_avg"]
- y_std = graph_data["y_std"]
- y_mode = graph_data["y_mode"]
- y_median = graph_data["y_median"]
- y_num_samples = graph_data["y_num_samples"]
-
- plt.figure()
- plt.plot(x, y_avg, "ro")
- plt.xlabel("character lengths", fontsize=30)
- plt.ylabel("avg seconds", fontsize=30)
- if save:
- name = "char_len_vs_avg_secs"
- plt.savefig(os.path.join(save_path, name))
-
- plt.figure()
- plt.plot(x, y_mode, "ro")
- plt.xlabel("character lengths", fontsize=30)
- plt.ylabel("mode seconds", fontsize=30)
- if save:
- name = "char_len_vs_mode_secs"
- plt.savefig(os.path.join(save_path, name))
-
- plt.figure()
- plt.plot(x, y_median, "ro")
- plt.xlabel("character lengths", fontsize=30)
- plt.ylabel("median seconds", fontsize=30)
- if save:
- name = "char_len_vs_med_secs"
- plt.savefig(os.path.join(save_path, name))
-
- plt.figure()
- plt.plot(x, y_std, "ro")
- plt.xlabel("character lengths", fontsize=30)
- plt.ylabel("standard deviation", fontsize=30)
- if save:
- name = "char_len_vs_std"
- plt.savefig(os.path.join(save_path, name))
-
- plt.figure()
- plt.plot(x, y_num_samples, "ro")
- plt.xlabel("character lengths", fontsize=30)
- plt.ylabel("number of samples", fontsize=30)
- if save:
- name = "char_len_vs_num_samples"
- plt.savefig(os.path.join(save_path, name))
-
-
-def plot_phonemes(train_path, cmu_dict_path, save_path):
- cmudict = CMUDict(cmu_dict_path)
-
- phonemes = {}
-
- with open(train_path, "r", encoding="utf-8") as f:
- data = csv.reader(f, delimiter="|")
- phonemes["None"] = 0
- for row in data:
- words = row[3].split()
- for word in words:
- pho = cmudict.lookup(word)
- if pho:
- indie = pho[0].split()
- for nemes in indie:
- if phonemes.get(nemes):
- phonemes[nemes] += 1
- else:
- phonemes[nemes] = 1
- else:
- phonemes["None"] += 1
-
- x, y = [], []
- for k, v in phonemes.items():
- x.append(k)
- y.append(v)
-
- plt.figure()
- plt.rcParams["figure.figsize"] = (50, 20)
- barplot = sns.barplot(x=x, y=y)
- if save_path:
- fig = barplot.get_figure()
- fig.savefig(os.path.join(save_path, "phoneme_dist"))
-
-
-def main():
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--train_file_path",
- required=True,
- help="this is the path to the train.txt file that the preprocess.py script creates",
- )
- parser.add_argument("--save_to", help="path to save charts of data to")
- parser.add_argument("--cmu_dict_path", help="give cmudict-0.7b to see phoneme distribution")
- args = parser.parse_args()
- meta_data = process_meta_data(args.train_file_path)
- plt.rcParams["figure.figsize"] = (10, 5)
- plot(meta_data, save_path=args.save_to)
- if args.cmu_dict_path:
- plt.rcParams["figure.figsize"] = (30, 10)
- plot_phonemes(args.train_file_path, args.cmu_dict_path, args.save_to)
-
- plt.show()
-
-
-if __name__ == "__main__":
- main()
diff --git a/TTS/pyproject.toml b/TTS/pyproject.toml
deleted file mode 100644
index 8544bb202a0a86f0a3845834702e23cad41037a0..0000000000000000000000000000000000000000
--- a/TTS/pyproject.toml
+++ /dev/null
@@ -1,33 +0,0 @@
-[build-system]
-requires = ["setuptools", "wheel", "cython==0.29.30", "numpy==1.22.0", "packaging"]
-
-[flake8]
-max-line-length=120
-
-[tool.black]
-line-length = 120
-target-version = ['py39']
-exclude = '''
-
-(
- /(
- \.eggs # exclude a few common directories in the
- | \.git # root of the project
- | \.hg
- | \.mypy_cache
- | \.tox
- | \.venv
- | _build
- | buck-out
- | build
- | dist
- )/
- | foo.py # also separately exclude a file named foo.py in
- # the root of the project
-)
-'''
-
-[tool.isort]
-line_length = 120
-profile = "black"
-multi_line_output = 3
diff --git a/TTS/recipes/README.md b/TTS/recipes/README.md
deleted file mode 100644
index 21a6727d8bffb9a16c9b053aaae1aab25c1805fa..0000000000000000000000000000000000000000
--- a/TTS/recipes/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# 🐸💬 TTS Training Recipes
-
-TTS recipes intended to host scripts running all the necessary steps to train a TTS model on a particular dataset.
-
-For each dataset, you need to download the dataset once. Then you run the training for the model you want.
-
-Run each script from the root TTS folder as follows.
-
-```console
-$ sh ./recipes//download_.sh
-$ python recipes///train.py
-```
-
-For some datasets you might need to resample the audio files. For example, VCTK dataset can be resampled to 22050Hz as follows.
-
-```console
-python TTS/bin/resample.py --input_dir recipes/vctk/VCTK/wav48_silence_trimmed --output_sr 22050 --output_dir recipes/vctk/VCTK/wav48_silence_trimmed --n_jobs 8 --file_ext flac
-```
-
-If you train a new model using TTS, feel free to share your training to expand the list of recipes.
-
-You can also open a new discussion and share your progress with the 🐸 community.
\ No newline at end of file
diff --git a/TTS/recipes/bel-alex73/.gitignore b/TTS/recipes/bel-alex73/.gitignore
deleted file mode 100644
index 87a23ae8374ac1d323545ef1b2d7b266e83aea4e..0000000000000000000000000000000000000000
--- a/TTS/recipes/bel-alex73/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-/docker-prepare/*.txt
diff --git a/TTS/recipes/bel-alex73/README.md b/TTS/recipes/bel-alex73/README.md
deleted file mode 100644
index ad378dd9984b3fa94e1be7a0c479f9e51d88e1a6..0000000000000000000000000000000000000000
--- a/TTS/recipes/bel-alex73/README.md
+++ /dev/null
@@ -1,62 +0,0 @@
-This description was created based on [jhlfrfufyfn/ml-bel-tts](https://github.com/jhlfrfufyfn/ml-bel-tts). Thanks a lot to jhlfrfufyfn for advices, configuration, code and ideas.
-
-# Training
-
-This recipe uses [CommonVoice](https://commonvoice.mozilla.org/en/datasets) dataset. It has format mp3/32kHz/48kbps format and contains multiple speakers because it was created for voice recognition. Looks like it's the best voice corpus of Belarussian language for today. But for creating better voice synthesis it will require to record some specific corpus with good pronunciation and good record quality.
-
-Looks like for Belarusian Common Voice corpus there is no sense to train full big dataset (90 hours). It's enough 30 hours dataset, that makes very good progress for 350 epochs(24000 steps on 24GiB GPU). The quality of dataset is more important that size.
-
-To train a model, you need to:
-- download code and data
-- prepare training data and generate scale_stats file
-- change configuration settings
-- train TTS model (GlowTTS in this example)
-- train Vocoder model (HiFiGAN in this example)
-
-We recommend to prepare all things locally, then train models on the external computer with fast GPU. Text below describes all these steps.
-
-## Download code and data
-
-It would be good to place all things into local folder like /mycomputer/. You need files:
-
-- Coqui-TTS - code from this git. For example, to /mycomputer/TTS/. *Expected result: you have /mycomputer/TTS/setup.py and other files from git.*
-- [Common voice dataset](https://commonvoice.mozilla.org/en/datasets) into cv-corpus/ directory near Coqui-TTS. *Expected result: you have /mycomputer/cv-corpus/be/validated.tsv and more than 1 mln .mp3 files in the /mycomputer/cv-corpus/be/clips/.*
-- Belarusian text to phonemes converter - fanetyka.jar from the [https://github.com/alex73/Software-Korpus/releases](https://github.com/alex73/Software-Korpus/releases), then place it to fanetyka/ near Coqui-TTS. *Expected result: you have file /mycomputer/fanetyka/fanetyka.jar*
-
-Prepared data will be stored into storage/ directory near Coqui-TTS, like /mycomputer/storage/.
-
-## Prepare to training - locally
-
-Docker container was created for simplify local running. You can run `docker-prepare-start.sh` to start environment. All commands below should be started in docker console.
-
-* Start jupyter by the command `jupyter notebook --no-browser --allow-root --port=2525 --ip=0.0.0.0`. It will display link to http. You need to open this link, then choose `recipes/bel-alex73/choose_speaker.ipynb` notebook. You should run cells one-by-one, listen different speakers and select speaker that you want to use. After all commands in notebook, you can press Ctrl+C in docker console to stop jupyter. *Expected result: directory /mycomputer/storage/filtered_dataset/ with df_speaker.csv file and many *.wav files.*
-
-* Convert text to phonemes: `java -cp /a/fanetyka/fanetyka.jar org.alex73.fanetyka.impl.FanetykaTTSPrepare /storage/filtered_dataset/df_speaker.csv /storage/filtered_dataset/ipa_final_dataset.csv`. It will display all used characters at the end. You can use these characters to modify config in train_glowtts.py. *Expected result: file /mycomputer/storage/filtered_dataset/ipa_final_dataset.csv*
-
-* Modify configs(if you need) in the train_glowtts.py and train_hifigan.py. Then export config to old json format to create scale_stats.npy by the command `python3 recipes/bel-alex73/dump_config.py > recipes/bel-alex73/config.json`. *Expected result: file /mycomputer/TTS/recipes/bel-alex73/config.json exists.*
-
-* Start scale_stats.npy, that will the model to learn better: `mkdir -p /storage/TTS/; python3 TTS/bin/compute_statistics.py --config_path recipes/bel-alex73/config.json --out_path /storage/TTS/scale_stats.npy`. *Expected result: file /mycomputer/storage/TTS/scale_stats.npy exists.*
-
-## Training - with GPU
-
-You need to upload Coqui-TTS(/mycomputer/TTS/) and storage/ directory(/mycomputer/storage/) to some computer with GPU. We don't need cv-corpus/ and fanetyka/ directories for training. Install gcc, then run `pip install -e .[all,dev,notebooks]` to prepare modules. GlowTTS and HifiGan models should be learned separately based on /storage/filtered_dataset only, i.e. they are not dependent from each other. below means list of GPU ids from zero("0,1,2,3" for systems with 4 GPU). See details on the https://tts.readthedocs.io/en/latest/tutorial_for_nervous_beginners.html(multi-gpu training).
-
-Current setup created for 24GiB GPU. You need to change batch_size if you have more or less GPU memory. Also, you can try to set lr(learning rate) to lower value in the end of training GlowTTS.
-
-* Start GlowTTS model training by the command `OMP_NUM_THREADS=2 CUDA_VISIBLE_DEVICES= python3 -m trainer.distribute --script recipes/bel-alex73/train_glowtts.py`. It will produce training data into storage/output/ directory. Usually 100.000 global steps required. *Expected behavior: You will see /storage/output-glowtts//best_model_.pth files.*
-
-* Start HiFiGAN model training by the command `OMP_NUM_THREADS=2 CUDA_VISIBLE_DEVICES= python3 -m trainer.distribute --script recipes/bel-alex73/train_hifigan.py`. *Expected behavior: You will see /storage/output-hifigan//best_model_.pth files.*
-
-## How to monitor training
-
-* Run `nvidia-smi` to be sure that training uses all GPUs and to be sure that you are using more than 90% GPU memory and utilization.
-
-* Run `tensorboard --logdir=/storage/output-/` to see alignment, avg_loss metrics and check audio evaluation. You need only events.out.tfevents.\* files for that.
-
-## Synthesizing speech
-
- tts --text "" --out_path output.wav \
- --config_path /storage/output-glowtts/run/config.json \
- --model_path /storage/output-glowtts/run/best_model.pth \
- --vocoder_config_path /storage/output-hifigan/run/config.json \
- --vocoder_path /storage/output-hifigan/run/best_model.pth
diff --git a/TTS/recipes/bel-alex73/choose_speaker.ipynb b/TTS/recipes/bel-alex73/choose_speaker.ipynb
deleted file mode 100644
index 4941f6020775b988fe4f588ddc1976654d0f6c7a..0000000000000000000000000000000000000000
--- a/TTS/recipes/bel-alex73/choose_speaker.ipynb
+++ /dev/null
@@ -1,346 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# This file was created by jhlfrfufyfn for choose speaker from the Belarusian Mozilla Voice corpus\n",
- "#\n",
- "#\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "import os\n",
- "import librosa"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# unpackage tar gz file cv-corpus-12.0-2022-12-07-be.tar.gz\n",
- "# import tarfile\n",
- "# tar = tarfile.open(\"cv-corpus-12.0-2022-12-07-be.tar.gz\", \"r:gz\")\n",
- "# tar.extractall()\n",
- "# tar.close()\n",
- "\n",
- "corpuspath = '/a/cv-corpus'\n",
- "outputpath = '/storage/filtered_dataset'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# open validated.tsv\n",
- "df = pd.read_csv(corpuspath+'/be/validated.tsv', sep='\\t' ,low_memory=False)\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# drop from df columns age, accents\n",
- "df = df.drop(['age', 'accents', 'gender', 'variant', 'locale', 'segment'], axis=1)\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# count number of recordes with down_votes > 0\n",
- "df[df['down_votes'] > 0].count()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# count number of recordes with up_votes == 0\n",
- "df[df['up_votes'] == 0].count()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# drop all rows with down_votes > 0 and up_votes == 0\n",
- "df = df[df['down_votes'] == 0]\n",
- "df = df[df['up_votes'] > 0]\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# drop column down_votes and up_votes\n",
- "df = df.drop(['down_votes', 'up_votes'], axis=1)\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# sort by count\n",
- "df_sorted = df.groupby('client_id').count().sort_values(by='path', ascending=False)\n",
- "df_sorted"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# get top 10 speakers\n",
- "top_10_speakers = df_sorted.head(10)\n",
- "top_10_speakers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# get for the first speaker ten random paths to audio files\n",
- "def get_speaker_audio_list(speaker_id, n=10):\n",
- " return df[df['client_id'] == speaker_id].sample(n)['path'].values.tolist()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# CHOOSE : which speaker will we use\n",
- "speaker_index = 0\n",
- "speaker_audio_list = get_speaker_audio_list(top_10_speakers.index[speaker_index])\n",
- "print(speaker_audio_list)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# open audio files from speaker_audio_list and play them\n",
- "# audio files lie in cv-corpus-12.0-2022-12-07/be/clips\n",
- "import IPython.display as ipd\n",
- "for audio in speaker_audio_list:\n",
- " audio = corpuspath+'/be/clips/' + audio\n",
- " audio_data = ipd.Audio(audio)\n",
- " display(audio_data)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 0 is pretty good\n",
- "# 1 is bad\n",
- "# 2 is partly 0, other are different\n",
- "# 3 is bad\n",
- "# 4 is pretty fast and clear, but not good\n",
- "# 5 is echoing, sometimes mic cracks\n",
- "# 6 is really slow and clear, but accent?\n",
- "# 7 has a lot of intonation, but is pretty clear\n",
- "# 8 is clear and slow, sometimes little mic crack\n",
- "# 9 has background noise, whispering\n",
- "\n",
- "# options: 0, 6, 8"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# calculate speech rate in words per minute for each speaker\n",
- "def get_speech_rate(speaker_id):\n",
- " df_speaker = df[df['client_id'] == speaker_id]\n",
- " # get 1000 random samples to calculate speech rate\n",
- " df_speaker = df_speaker.sample(1000)\n",
- " # get duration of each audio file\n",
- " df_speaker['duration'] = df_speaker['path'].apply(lambda x: librosa.get_duration(path=corpuspath+'/be/clips/' + x))\n",
- " # get number of words in each audio file\n",
- " df_speaker['words'] = df_speaker['sentence'].apply(lambda x: len(x.split()))\n",
- " # calculate speech rate\n",
- " df_speaker['speech_rate'] = df_speaker['words'] / df_speaker['duration'] * 60\n",
- " # return mean speech rate\n",
- " return df_speaker['speech_rate'].mean()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# calculate speech rate for each speaker\n",
- "print(f'Speech rate for speaker {speaker_index}: ', get_speech_rate(top_10_speakers.index[speaker_index]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_average_duration(df_speaker):\n",
- " # get 1000 random samples to calculate speech rate\n",
- " df_speaker = df_speaker.sample(1000)\n",
- " # get duration of each audio file\n",
- " df_speaker['duration'] = df_speaker['path'].apply(lambda x: librosa.get_duration(path=corpuspath+'/be/clips/' + x))\n",
- " return df_speaker['duration'].mean()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_speaker = df[df['client_id'] == top_10_speakers.index[speaker_index]]\n",
- "\n",
- "avg_duration = get_average_duration(df_speaker)\n",
- "avg_total_duration = avg_duration * len(df_speaker.index)\n",
- "print(f'Average duration for speaker {speaker_index}: ', avg_duration, \", average total duration(hours): \",(avg_total_duration/60.0/60.0))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# get df with speaker_index speaker \n",
- "df_speaker = df[df['client_id'] == top_10_speakers.index[speaker_index]]\n",
- "df_speaker = df_speaker.drop(['client_id'], axis=1)\n",
- "\n",
- "# get only x latest hours\n",
- "limit_hours = 30\n",
- "limit_files = round(limit_hours*60*60 / avg_duration)\n",
- "df_speaker = df_speaker.tail(limit_files)\n",
- "\n",
- "df_speaker"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# # move all files of that speaker to another folder\n",
- "# # use multiprocessing to speed up\n",
- "# # add progress bar\n",
- "# from tqdm import tqdm\n",
- "# import multiprocessing\n",
- "# from multiprocessing import Pool\n",
- "# import shutil\n",
- "\n",
- "# def move_file(file):\n",
- "# shutil.move(corpuspath+'/be/clips/' + file, corpuspath+'/be/speaker_0/' + file)\n",
- "\n",
- "# # get list of files to move\n",
- "# files = df_speaker['path'].values.tolist()\n",
- "\n",
- "# # move files\n",
- "# with Pool(multiprocessing.cpu_count()) as p:\n",
- "# r = list(tqdm(p.imap(move_file, files), total=len(files)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# cleanup output and save text lines to csv\n",
- "if os.path.isdir(outputpath):\n",
- " for file in os.scandir(outputpath):\n",
- " os.remove(file.path)\n",
- "else:\n",
- " os.mkdir(outputpath)\n",
- "\n",
- "df_speaker['path2'] = df_speaker['path'].str.replace('\\.mp3$','.wav', regex=True)\n",
- "df_speaker[['path2','sentence']].to_csv(outputpath+'/df_speaker.csv', sep='|', header=False, index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# make rate=22050 of all mp3 files in speaker_0 folder with multiprocessing and tqdm\n",
- "import multiprocessing\n",
- "from multiprocessing import Pool\n",
- "from tqdm import tqdm\n",
- "from pydub import AudioSegment\n",
- "\n",
- "def convert_mp3_to_wav(file):\n",
- " sound = AudioSegment.from_mp3(corpuspath+'/be/clips/' + file)\n",
- " sound = sound.set_frame_rate(22050)\n",
- " sound.export(outputpath+'/' + file[:-4] + '.wav', format='wav')\n",
- "\n",
- "# get list of files to convert\n",
- "files = df_speaker['path'].values.tolist()\n",
- "\n",
- "# convert files\n",
- "with Pool(multiprocessing.cpu_count()) as p:\n",
- " r = list(tqdm(p.imap(convert_mp3_to_wav, files), total=len(files)))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/TTS/recipes/bel-alex73/docker-prepare-start.sh b/TTS/recipes/bel-alex73/docker-prepare-start.sh
deleted file mode 100644
index a4ce3c6dcca3abced93bd6c80d863061d8d86486..0000000000000000000000000000000000000000
--- a/TTS/recipes/bel-alex73/docker-prepare-start.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-set -x
-
-cd $( dirname -- "$0"; )
-
-cp ../../requirements*.txt docker-prepare/
-
-docker build -t tts-learn -f docker-prepare/Dockerfile docker-prepare/
-
-mkdir -p ../../../storage
-docker run --rm -it \
- -p 2525:2525 \
- --shm-size=256M \
- --name tts-learn-run \
- -v $(pwd)/../../:/a/TTS \
- -v $(pwd)/../../../cv-corpus:/a/cv-corpus \
- -v $(pwd)/../../../fanetyka/:/a/fanetyka/ \
- -v $(pwd)/../../../storage:/storage \
- tts-learn
diff --git a/TTS/recipes/bel-alex73/docker-prepare/Dockerfile b/TTS/recipes/bel-alex73/docker-prepare/Dockerfile
deleted file mode 100644
index fd9b745386da8319ddb9ed2cbb7d3db720e12bb9..0000000000000000000000000000000000000000
--- a/TTS/recipes/bel-alex73/docker-prepare/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM ubuntu:22.04
-
-RUN apt -y update
-RUN apt -y upgrade
-RUN apt -y install --no-install-recommends pip ffmpeg openjdk-19-jre-headless
-
-RUN mkdir /a/
-ADD requirements*.txt /a/
-WORKDIR /a/
-RUN pip install -r requirements.txt -r requirements.dev.txt -r requirements.notebooks.txt
-RUN pip install seaborn pydub notebook
-
-RUN apt -y install --no-install-recommends gcc libpython3.10-dev
-
-ADD runtime.sh /a/
-
-WORKDIR /a/TTS/
-CMD /a/runtime.sh
diff --git a/TTS/recipes/bel-alex73/docker-prepare/runtime.sh b/TTS/recipes/bel-alex73/docker-prepare/runtime.sh
deleted file mode 100644
index 27b723bc0fe56388674d33e2c8839b7fda68c776..0000000000000000000000000000000000000000
--- a/TTS/recipes/bel-alex73/docker-prepare/runtime.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/bash
-
-cd /a/TTS
-pip install -e .[all,dev,notebooks]
-
-LANG=C.utf8 bash
diff --git a/TTS/recipes/bel-alex73/dump_config.py b/TTS/recipes/bel-alex73/dump_config.py
deleted file mode 100644
index c4d307231cbdd36c88ecae29009bab7c48bc1d7e..0000000000000000000000000000000000000000
--- a/TTS/recipes/bel-alex73/dump_config.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import json
-import re
-
-from train_glowtts import config
-
-s = json.dumps(config, default=vars, indent=2)
-s = re.sub(r'"test_sentences":\s*\[\],', "", s)
-print(s)
diff --git a/TTS/recipes/bel-alex73/train_glowtts.py b/TTS/recipes/bel-alex73/train_glowtts.py
deleted file mode 100644
index e0827cdc44d85d1b7f378c12fc1d5e190607f5a3..0000000000000000000000000000000000000000
--- a/TTS/recipes/bel-alex73/train_glowtts.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import os
-
-# Trainer: Where the ✨️ happens.
-# TrainingArgs: Defines the set of arguments of the Trainer.
-from trainer import Trainer, TrainerArgs
-
-# GlowTTSConfig: all model related values for training, validating and testing.
-from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-
-# BaseDatasetConfig: defines name, formatter and path of the dataset.
-from TTS.tts.configs.shared_configs import BaseAudioConfig, BaseDatasetConfig, CharactersConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.glow_tts import GlowTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-# we use the same path as this script as our training folder.
-output_path = "/storage/output-glowtts/"
-
-
-# DEFINE DATASET CONFIG
-# Set LJSpeech as our target dataset and define its path.
-# You can also use a simple Dict to define the dataset and pass it to your custom formatter.
-dataset_config = BaseDatasetConfig(
- formatter="bel_tts_formatter",
- meta_file_train="ipa_final_dataset.csv",
- path=os.path.join(output_path, "/storage/filtered_dataset/"),
-)
-
-characters = CharactersConfig(
- characters_class="TTS.tts.utils.text.characters.Graphemes",
- pad="_",
- eos="~",
- bos="^",
- blank="@",
- characters="Iabdfgijklmnprstuvxzɔɛɣɨɫɱʂʐʲˈː̯͡β",
- punctuations="!,.?: -‒–—…",
-)
-
-audio_config = BaseAudioConfig(
- mel_fmin=50,
- mel_fmax=8000,
- hop_length=256,
- stats_path="/storage/TTS/scale_stats.npy",
-)
-
-# INITIALIZE THE TRAINING CONFIGURATION
-# Configure the model. Every config class inherits the BaseTTSConfig.
-config = GlowTTSConfig(
- batch_size=96,
- eval_batch_size=32,
- num_loader_workers=8,
- num_eval_loader_workers=8,
- use_noise_augment=True,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- print_step=50,
- print_eval=True,
- output_path=output_path,
- add_blank=True,
- datasets=[dataset_config],
- characters=characters,
- enable_eos_bos_chars=True,
- mixed_precision=False,
- save_step=10000,
- save_n_checkpoints=2,
- save_best_after=5000,
- text_cleaner="no_cleaners",
- audio=audio_config,
- test_sentences=[],
-)
-
-if __name__ == "__main__":
- # INITIALIZE THE AUDIO PROCESSOR
- # Audio processor is used for feature extraction and audio I/O.
- # It mainly serves to the dataloader and the training loggers.
- ap = AudioProcessor.init_from_config(config)
-
- # INITIALIZE THE TOKENIZER
- # Tokenizer is used to convert text to sequences of token IDs.
- # If characters are not defined in the config, default characters are passed to the config
- tokenizer, config = TTSTokenizer.init_from_config(config)
-
- # LOAD DATA SAMPLES
- # Each sample is a list of ```[text, audio_file_path, speaker_name]```
- # You can define your custom sample loader returning the list of samples.
- # Or define your custom formatter and pass it to the `load_tts_samples`.
- # Check `TTS.tts.datasets.load_tts_samples` for more details.
- train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
- )
-
- # INITIALIZE THE MODEL
- # Models take a config object and a speaker manager as input
- # Config defines the details of the model like the number of layers, the size of the embedding, etc.
- # Speaker manager is used by multi-speaker models.
- model = GlowTTS(config, ap, tokenizer, speaker_manager=None)
-
- # INITIALIZE THE TRAINER
- # Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
- # distributed training, etc.
- trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
- )
-
- # AND... 3,2,1... 🚀
- trainer.fit()
diff --git a/TTS/recipes/bel-alex73/train_hifigan.py b/TTS/recipes/bel-alex73/train_hifigan.py
deleted file mode 100644
index 3e740b2ff400ab8f8815d3958bae9d6664c49142..0000000000000000000000000000000000000000
--- a/TTS/recipes/bel-alex73/train_hifigan.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import os
-
-from coqpit import Coqpit
-from trainer import Trainer, TrainerArgs
-
-from TTS.tts.configs.shared_configs import BaseAudioConfig
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.configs.hifigan_config import *
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.gan import GAN
-
-output_path = "/storage/output-hifigan/"
-
-audio_config = BaseAudioConfig(
- mel_fmin=50,
- mel_fmax=8000,
- hop_length=256,
- stats_path="/storage/TTS/scale_stats.npy",
-)
-
-config = HifiganConfig(
- batch_size=74,
- eval_batch_size=16,
- num_loader_workers=8,
- num_eval_loader_workers=8,
- lr_disc=0.0002,
- lr_gen=0.0002,
- run_eval=True,
- test_delay_epochs=5,
- epochs=1000,
- use_noise_augment=True,
- seq_len=8192,
- pad_short=2000,
- save_step=5000,
- print_step=50,
- print_eval=True,
- mixed_precision=False,
- eval_split_size=30,
- save_n_checkpoints=2,
- save_best_after=5000,
- data_path="/storage/filtered_dataset",
- output_path=output_path,
- audio=audio_config,
-)
-
-# init audio processor
-ap = AudioProcessor.init_from_config(config)
-
-# load training samples
-print("config.eval_split_size = ", config.eval_split_size)
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = GAN(config, ap)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/blizzard2013/README.md b/TTS/recipes/blizzard2013/README.md
deleted file mode 100644
index 9dcb73972802686dba80b83e798ab1466f2b26a0..0000000000000000000000000000000000000000
--- a/TTS/recipes/blizzard2013/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# How to get the Blizzard 2013 Dataset
-
-The Capacitron model is a variational encoder extension of standard Tacotron based models to model prosody.
-
-To take full advantage of the model, it is advised to train the model with a dataset that contains a significant amount of prosodic information in the utterances. A tested candidate for such applications is the blizzard2013 dataset from the Blizzard Challenge, containing many hours of high quality audio book recordings.
-
-To get a license and download link for this dataset, you need to visit the [website](https://www.cstr.ed.ac.uk/projects/blizzard/2013/lessac_blizzard2013/license.html) of the Centre for Speech Technology Research of the University of Edinburgh.
-
-You get access to the raw dataset in a couple of days. There are a few preprocessing steps you need to do to be able to use the high fidelity dataset.
-
-1. Get the forced time alignments for the blizzard dataset from [here](https://github.com/mueller91/tts_alignments).
-2. Segment the high fidelity audio-book files based on the instructions [here](https://github.com/Tomiinek/Blizzard2013_Segmentation).
\ No newline at end of file
diff --git a/TTS/recipes/blizzard2013/tacotron1-Capacitron/train_capacitron_t1.py b/TTS/recipes/blizzard2013/tacotron1-Capacitron/train_capacitron_t1.py
deleted file mode 100644
index 0243735dde4c1b87434087ddc01ad6b80e53cc27..0000000000000000000000000000000000000000
--- a/TTS/recipes/blizzard2013/tacotron1-Capacitron/train_capacitron_t1.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig, CapacitronVAEConfig
-from TTS.tts.configs.tacotron_config import TacotronConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron import Tacotron
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-data_path = "/srv/data/"
-
-# Using LJSpeech like dataset processing for the blizzard dataset
-dataset_config = BaseDatasetConfig(formatter="ljspeech", meta_file_train="metadata.csv", path=data_path)
-
-audio_config = BaseAudioConfig(
- sample_rate=24000,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=True,
- mel_fmin=80.0,
- mel_fmax=12000,
- spec_gain=20.0,
- log_func="np.log10",
- ref_level_db=20,
- preemphasis=0.0,
- min_level_db=-100,
-)
-
-# Using the standard Capacitron config
-capacitron_config = CapacitronVAEConfig(capacitron_VAE_loss_alpha=1.0)
-
-config = TacotronConfig(
- run_name="Blizzard-Capacitron-T1",
- audio=audio_config,
- capacitron_vae=capacitron_config,
- use_capacitron_vae=True,
- batch_size=128, # Tune this to your gpu
- max_audio_len=6 * 24000, # Tune this to your gpu
- min_audio_len=0.5 * 24000,
- eval_batch_size=16,
- num_loader_workers=12,
- num_eval_loader_workers=8,
- precompute_num_workers=24,
- run_eval=True,
- test_delay_epochs=5,
- r=2,
- optimizer="CapacitronOptimizer",
- optimizer_params={"RAdam": {"betas": [0.9, 0.998], "weight_decay": 1e-6}, "SGD": {"lr": 1e-5, "momentum": 0.9}},
- attention_type="graves",
- attention_heads=5,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phonemizer="espeak",
- phoneme_cache_path=os.path.join(data_path, "phoneme_cache"),
- stopnet_pos_weight=15,
- print_step=50,
- print_eval=True,
- mixed_precision=False,
- output_path=output_path,
- datasets=[dataset_config],
- lr=1e-3,
- lr_scheduler="StepwiseGradualLR",
- lr_scheduler_params={"gradual_learning_rates": [[0, 1e-3], [2e4, 5e-4], [4e4, 3e-4], [6e4, 1e-4], [8e4, 5e-5]]},
- scheduler_after_epoch=False, # scheduler doesn't work without this flag
- loss_masking=False,
- decoder_loss_alpha=1.0,
- postnet_loss_alpha=1.0,
- postnet_diff_spec_alpha=1.0,
- decoder_diff_spec_alpha=1.0,
- decoder_ssim_alpha=1.0,
- postnet_ssim_alpha=1.0,
-)
-
-ap = AudioProcessor(**config.audio.to_dict())
-
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
-
-model = Tacotron(config, ap, tokenizer, speaker_manager=None)
-
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
-)
-
-# 🚀
-trainer.fit()
diff --git a/TTS/recipes/blizzard2013/tacotron2-Capacitron/train_capacitron_t2.py b/TTS/recipes/blizzard2013/tacotron2-Capacitron/train_capacitron_t2.py
deleted file mode 100644
index b41676d8e7dccce6f513118ac2d22adbd892d2f2..0000000000000000000000000000000000000000
--- a/TTS/recipes/blizzard2013/tacotron2-Capacitron/train_capacitron_t2.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig, CapacitronVAEConfig
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron2 import Tacotron2
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-data_path = "/srv/data/blizzard2013/segmented"
-
-# Using LJSpeech like dataset processing for the blizzard dataset
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- path=data_path,
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=24000,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=True,
- mel_fmin=80.0,
- mel_fmax=12000,
- spec_gain=25.0,
- log_func="np.log10",
- ref_level_db=20,
- preemphasis=0.0,
- min_level_db=-100,
-)
-
-# Using the standard Capacitron config
-capacitron_config = CapacitronVAEConfig(capacitron_VAE_loss_alpha=1.0)
-
-config = Tacotron2Config(
- run_name="Blizzard-Capacitron-T2",
- audio=audio_config,
- capacitron_vae=capacitron_config,
- use_capacitron_vae=True,
- batch_size=246, # Tune this to your gpu
- max_audio_len=6 * 24000, # Tune this to your gpu
- min_audio_len=1 * 24000,
- eval_batch_size=16,
- num_loader_workers=12,
- num_eval_loader_workers=8,
- precompute_num_workers=24,
- run_eval=True,
- test_delay_epochs=5,
- r=2,
- optimizer="CapacitronOptimizer",
- optimizer_params={"RAdam": {"betas": [0.9, 0.998], "weight_decay": 1e-6}, "SGD": {"lr": 1e-5, "momentum": 0.9}},
- attention_type="dynamic_convolution",
- grad_clip=0.0, # Important! We overwrite the standard grad_clip with capacitron_grad_clip
- double_decoder_consistency=False,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phonemizer="espeak",
- phoneme_cache_path=os.path.join(data_path, "phoneme_cache"),
- stopnet_pos_weight=15,
- print_step=25,
- print_eval=True,
- mixed_precision=False,
- output_path=output_path,
- datasets=[dataset_config],
- lr=1e-3,
- lr_scheduler="StepwiseGradualLR",
- lr_scheduler_params={
- "gradual_learning_rates": [
- [0, 1e-3],
- [2e4, 5e-4],
- [4e4, 3e-4],
- [6e4, 1e-4],
- [8e4, 5e-5],
- ]
- },
- scheduler_after_epoch=False, # scheduler doesn't work without this flag
- seq_len_norm=True,
- loss_masking=False,
- decoder_loss_alpha=1.0,
- postnet_loss_alpha=1.0,
- postnet_diff_spec_alpha=1.0,
- decoder_diff_spec_alpha=1.0,
- decoder_ssim_alpha=1.0,
- postnet_ssim_alpha=1.0,
-)
-
-ap = AudioProcessor(**config.audio.to_dict())
-
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
-
-model = Tacotron2(config, ap, tokenizer, speaker_manager=None)
-
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
- training_assets={"audio_processor": ap},
-)
-
-trainer.fit()
diff --git a/TTS/recipes/kokoro/tacotron2-DDC/run.sh b/TTS/recipes/kokoro/tacotron2-DDC/run.sh
deleted file mode 100644
index 69800cf7b4e9b518a352191498ec50e44af86f90..0000000000000000000000000000000000000000
--- a/TTS/recipes/kokoro/tacotron2-DDC/run.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/bin/bash
-# take the scripts's parent's directory to prefix all the output paths.
-RUN_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
-CORPUS=kokoro-speech-v1_1-small
-echo $RUN_DIR
-if [ \! -d $RUN_DIR/$CORPUS ] ; then
- echo "$RUN_DIR/$CORPUS doesn't exist."
- echo "Follow the instruction of https://github.com/kaiidams/Kokoro-Speech-Dataset to make the corpus."
- exit 1
-fi
-# create train-val splits
-shuf $RUN_DIR/$CORPUS/metadata.csv > $RUN_DIR/$CORPUS/metadata_shuf.csv
-head -n 8000 $RUN_DIR/$CORPUS/metadata_shuf.csv > $RUN_DIR/$CORPUS/metadata_train.csv
-tail -n 812 $RUN_DIR/$CORPUS/metadata_shuf.csv > $RUN_DIR/$CORPUS/metadata_val.csv
-# compute dataset mean and variance for normalization
-python TTS/bin/compute_statistics.py $RUN_DIR/tacotron2-DDC.json $RUN_DIR/scale_stats.npy --data_path $RUN_DIR/$CORPUS/wavs/
-# training ....
-# change the GPU id if needed
-CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py --config_path $RUN_DIR/tacotron2-DDC.json \
- --coqpit.output_path $RUN_DIR \
- --coqpit.datasets.0.path $RUN_DIR/$CORPUS \
- --coqpit.audio.stats_path $RUN_DIR/scale_stats.npy \
- --coqpit.phoneme_cache_path $RUN_DIR/phoneme_cache \
\ No newline at end of file
diff --git a/TTS/recipes/kokoro/tacotron2-DDC/tacotron2-DDC.json b/TTS/recipes/kokoro/tacotron2-DDC/tacotron2-DDC.json
deleted file mode 100644
index c2e526f46c53dc6342bc67b74740a55358c33652..0000000000000000000000000000000000000000
--- a/TTS/recipes/kokoro/tacotron2-DDC/tacotron2-DDC.json
+++ /dev/null
@@ -1,125 +0,0 @@
-{
- "datasets": [
- {
- "formatter": "kokoro",
- "path": "DEFINE THIS",
- "meta_file_train": "metadata.csv",
- "meta_file_val": null
- }
- ],
- "audio": {
- "fft_size": 1024,
- "win_length": 1024,
- "hop_length": 256,
- "frame_length_ms": null,
- "frame_shift_ms": null,
- "sample_rate": 22050,
- "preemphasis": 0.0,
- "ref_level_db": 20,
- "do_trim_silence": true,
- "trim_db": 60,
- "power": 1.5,
- "griffin_lim_iters": 60,
- "num_mels": 80,
- "mel_fmin": 50.0,
- "mel_fmax": 7600.0,
- "spec_gain": 1,
- "signal_norm": true,
- "min_level_db": -100,
- "symmetric_norm": true,
- "max_norm": 4.0,
- "clip_norm": true,
- "stats_path": "scale_stats.npy"
- },
- "gst":{
- "gst_style_input": null,
-
-
-
- "gst_embedding_dim": 512,
- "gst_num_heads": 4,
- "gst_style_tokens": 10,
- "gst_use_speaker_embedding": false
- },
- "model": "Tacotron2",
- "run_name": "kokoro-ddc",
- "run_description": "tacotron2 with DDC and differential spectral loss.",
- "batch_size": 32,
- "eval_batch_size": 16,
- "mixed_precision": true,
- "distributed": {
- "backend": "nccl",
- "url": "tcp:\/\/localhost:54321"
- },
- "reinit_layers": [],
- "loss_masking": true,
- "decoder_loss_alpha": 0.5,
- "postnet_loss_alpha": 0.25,
- "postnet_diff_spec_alpha": 0.25,
- "decoder_diff_spec_alpha": 0.25,
- "decoder_ssim_alpha": 0.5,
- "postnet_ssim_alpha": 0.25,
- "ga_alpha": 5.0,
- "stopnet_pos_weight": 15.0,
- "run_eval": true,
- "test_delay_epochs": 10,
- "test_sentences_file": null,
- "noam_schedule": false,
- "grad_clip": 1.0,
- "epochs": 1000,
- "lr": 0.0001,
- "wd": 0.000001,
- "warmup_steps": 4000,
- "seq_len_norm": false,
- "memory_size": -1,
- "prenet_type": "original",
- "prenet_dropout": true,
- "attention_type": "original",
- "windowing": false,
- "use_forward_attn": false,
- "forward_attn_mask": false,
- "transition_agent": false,
- "location_attn": true,
- "bidirectional_decoder": false,
- "double_decoder_consistency": true,
- "ddc_r": 7,
- "attention_heads": 4,
- "attention_norm": "sigmoid",
- "r": 7,
- "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]],
- "stopnet": true,
- "separate_stopnet": true,
- "print_step": 25,
- "tb_plot_step": 100,
- "print_eval": false,
- "save_step": 10000,
- "checkpoint": true,
- "keep_all_best": false,
- "keep_after": 10000,
- "tb_model_param_stats": false,
- "text_cleaner": "basic_cleaners",
- "enable_eos_bos_chars": false,
- "num_loader_workers": 4,
- "num_val_loader_workers": 4,
- "batch_group_size": 4,
- "min_seq_len": 6,
- "max_seq_len": 153,
- "compute_input_seq_cache": false,
- "use_noise_augment": true,
- "output_path": "DEFINE THIS",
- "phoneme_cache_path": "DEFINE THIS",
- "use_phonemes": true,
- "phoneme_language": "ja-jp",
- "characters": {
- "pad": "_",
- "eos": "~",
- "bos": "^",
- "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
- "punctuations": "!'(),-.:;? ",
- "phonemes": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
- },
- "use_speaker_embedding": false,
- "use_gst": false,
- "use_external_speaker_embedding_file": false,
- "external_speaker_embedding_file": "../../speakers-vctk-en.json"
-}
\ No newline at end of file
diff --git a/TTS/recipes/ljspeech/README.md b/TTS/recipes/ljspeech/README.md
deleted file mode 100644
index 94508a7f2ecd7d161b16997e415ed4c4935a39f2..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# 🐸💬 TTS LJspeech Recipes
-
-For running the recipes
-
-1. Download the LJSpeech dataset here either manually from [its official website](https://keithito.com/LJ-Speech-Dataset/) or using ```download_ljspeech.sh```.
-2. Go to your desired model folder and run the training.
-
- Running Python files. (Choose the desired GPU ID for your run and set ```CUDA_VISIBLE_DEVICES```)
- ```terminal
- CUDA_VISIBLE_DEVICES="0" python train_modelX.py
- ```
-
- Running bash scripts.
- ```terminal
- bash run.sh
- ```
-
-💡 Note that these runs are just templates to help you start training your first model. They are not optimized for the best
-result. Double-check the configurations and feel free to share your experiments to find better parameters together 💪.
diff --git a/TTS/recipes/ljspeech/align_tts/train_aligntts.py b/TTS/recipes/ljspeech/align_tts/train_aligntts.py
deleted file mode 100644
index 451114add2eb40d4d466be637d862ac8f7eb897d..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/align_tts/train_aligntts.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.tts.configs.align_tts_config import AlignTTSConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.align_tts import AlignTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
-)
-config = AlignTTSConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=False,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=25,
- print_eval=True,
- mixed_precision=False,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init model
-model = AlignTTS(config, ap, tokenizer)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/delightful_tts/train_delightful_tts.py b/TTS/recipes/ljspeech/delightful_tts/train_delightful_tts.py
deleted file mode 100644
index 81e40c84aed772f768f91218b45446e669c2aa91..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/delightful_tts/train_delightful_tts.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.delightful_tts_config import DelightfulTtsAudioConfig, DelightfulTTSConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.delightful_tts import DelightfulTTS, DelightfulTtsArgs, VocoderConfig
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio.processor import AudioProcessor
-
-data_path = ""
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-dataset_config = BaseDatasetConfig(
- dataset_name="ljspeech", formatter="ljspeech", meta_file_train="metadata.csv", path=data_path
-)
-
-audio_config = DelightfulTtsAudioConfig()
-model_args = DelightfulTtsArgs()
-
-vocoder_config = VocoderConfig()
-
-delightful_tts_config = DelightfulTTSConfig(
- run_name="delightful_tts_ljspeech",
- run_description="Train like in delightful tts paper.",
- model_args=model_args,
- audio=audio_config,
- vocoder=vocoder_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=10,
- num_eval_loader_workers=10,
- precompute_num_workers=10,
- batch_group_size=2,
- compute_input_seq_cache=True,
- compute_f0=True,
- f0_cache_path=os.path.join(output_path, "f0_cache"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=50,
- print_eval=False,
- mixed_precision=True,
- output_path=output_path,
- datasets=[dataset_config],
- start_by_longest=False,
- eval_split_size=0.1,
- binary_align_loss_alpha=0.0,
- use_attn_priors=False,
- lr_gen=4e-1,
- lr=4e-1,
- lr_disc=4e-1,
- max_text_len=130,
-)
-
-tokenizer, config = TTSTokenizer.init_from_config(delightful_tts_config)
-
-ap = AudioProcessor.init_from_config(config)
-
-
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-model = DelightfulTTS(ap=ap, config=config, tokenizer=tokenizer, speaker_manager=None)
-
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
-)
-
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/download_ljspeech.sh b/TTS/recipes/ljspeech/download_ljspeech.sh
deleted file mode 100644
index 9468988a9928708d2d1792afeacebd6e0c4cb64a..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/download_ljspeech.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-# take the scripts's parent's directory to prefix all the output paths.
-RUN_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
-echo $RUN_DIR
-# download LJSpeech dataset
-wget http://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
-# extract
-tar -xjf LJSpeech-1.1.tar.bz2
-# create train-val splits
-shuf LJSpeech-1.1/metadata.csv > LJSpeech-1.1/metadata_shuf.csv
-head -n 12000 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_train.csv
-tail -n 1100 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_val.csv
-mv LJSpeech-1.1 $RUN_DIR/recipes/ljspeech/
-rm LJSpeech-1.1.tar.bz2
\ No newline at end of file
diff --git a/TTS/recipes/ljspeech/fast_pitch/train_fast_pitch.py b/TTS/recipes/ljspeech/fast_pitch/train_fast_pitch.py
deleted file mode 100644
index 055526b1bcea41c646e841baa556b71a71da7487..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/fast_pitch/train_fast_pitch.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig, BaseDatasetConfig
-from TTS.tts.configs.fast_pitch_config import FastPitchConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.forward_tts import ForwardTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.manage import ModelManager
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"),
- path=os.path.join(output_path, "../LJSpeech-1.1/"),
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = FastPitchConfig(
- run_name="fast_pitch_ljspeech",
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=8,
- num_eval_loader_workers=4,
- compute_input_seq_cache=True,
- compute_f0=True,
- f0_cache_path=os.path.join(output_path, "f0_cache"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- precompute_num_workers=4,
- print_step=50,
- print_eval=False,
- mixed_precision=False,
- max_seq_len=500000,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# compute alignments
-if not config.model_args.use_aligner:
- manager = ModelManager()
- model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA")
- # TODO: make compute_attention python callable
- os.system(
- f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true"
- )
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init the model
-model = ForwardTTS(config, ap, tokenizer, speaker_manager=None)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/fast_speech/train_fast_speech.py b/TTS/recipes/ljspeech/fast_speech/train_fast_speech.py
deleted file mode 100644
index 8c9a272e81655dddf7543a8574e8969836115c3a..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/fast_speech/train_fast_speech.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config import BaseAudioConfig, BaseDatasetConfig
-from TTS.tts.configs.fast_speech_config import FastSpeechConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.forward_tts import ForwardTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.manage import ModelManager
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"),
- path=os.path.join(output_path, "../LJSpeech-1.1/"),
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = FastSpeechConfig(
- run_name="fast_speech_ljspeech",
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=8,
- num_eval_loader_workers=4,
- compute_input_seq_cache=True,
- compute_f0=False,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- precompute_num_workers=8,
- print_step=50,
- print_eval=False,
- mixed_precision=False,
- max_seq_len=500000,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# compute alignments
-if not config.model_args.use_aligner:
- manager = ModelManager()
- model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA")
- # TODO: make compute_attention python callable
- os.system(
- f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true"
- )
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init the model
-model = ForwardTTS(config, ap, tokenizer)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/fastspeech2/train_fastspeech2.py b/TTS/recipes/ljspeech/fastspeech2/train_fastspeech2.py
deleted file mode 100644
index 93737dba7f25e1383c8238e9a39ed47465cfdb48..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/fastspeech2/train_fastspeech2.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig, BaseDatasetConfig
-from TTS.tts.configs.fastspeech2_config import Fastspeech2Config
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.forward_tts import ForwardTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.manage import ModelManager
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"),
- path=os.path.join(output_path, "../LJSpeech-1.1/"),
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = Fastspeech2Config(
- run_name="fastspeech2_ljspeech",
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=8,
- num_eval_loader_workers=4,
- compute_input_seq_cache=True,
- compute_f0=True,
- f0_cache_path=os.path.join(output_path, "f0_cache"),
- compute_energy=True,
- energy_cache_path=os.path.join(output_path, "energy_cache"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- precompute_num_workers=4,
- print_step=50,
- print_eval=False,
- mixed_precision=False,
- max_seq_len=500000,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# compute alignments
-if not config.model_args.use_aligner:
- manager = ModelManager()
- model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA")
- # TODO: make compute_attention python callable
- os.system(
- f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true"
- )
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init the model
-model = ForwardTTS(config, ap, tokenizer, speaker_manager=None)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/glow_tts/train_glowtts.py b/TTS/recipes/ljspeech/glow_tts/train_glowtts.py
deleted file mode 100644
index 9eb188f8a4db42c6868bdf2e2cf8bbbeedb97cdd..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/glow_tts/train_glowtts.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-
-# Trainer: Where the ✨️ happens.
-# TrainingArgs: Defines the set of arguments of the Trainer.
-from trainer import Trainer, TrainerArgs
-
-# GlowTTSConfig: all model related values for training, validating and testing.
-from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-
-# BaseDatasetConfig: defines name, formatter and path of the dataset.
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.glow_tts import GlowTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-# we use the same path as this script as our training folder.
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# DEFINE DATASET CONFIG
-# Set LJSpeech as our target dataset and define its path.
-# You can also use a simple Dict to define the dataset and pass it to your custom formatter.
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
-)
-
-# INITIALIZE THE TRAINING CONFIGURATION
-# Configure the model. Every config class inherits the BaseTTSConfig.
-config = GlowTTSConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=25,
- print_eval=False,
- mixed_precision=True,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# INITIALIZE THE MODEL
-# Models take a config object and a speaker manager as input
-# Config defines the details of the model like the number of layers, the size of the embedding, etc.
-# Speaker manager is used by multi-speaker models.
-model = GlowTTS(config, ap, tokenizer, speaker_manager=None)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/hifigan/train_hifigan.py b/TTS/recipes/ljspeech/hifigan/train_hifigan.py
deleted file mode 100644
index b4cbae63edc228f755375d38e7a117ff76f2a785..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/hifigan/train_hifigan.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.configs import HifiganConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.gan import GAN
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-config = HifiganConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=5,
- epochs=1000,
- seq_len=8192,
- pad_short=2000,
- use_noise_augment=True,
- eval_split_size=10,
- print_step=25,
- print_eval=False,
- mixed_precision=False,
- lr_gen=1e-4,
- lr_disc=1e-4,
- data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
- output_path=output_path,
-)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = GAN(config, ap)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/multiband_melgan/train_multiband_melgan.py b/TTS/recipes/ljspeech/multiband_melgan/train_multiband_melgan.py
deleted file mode 100644
index 225f5a302f349be2f2069eeb10cd4b8ab6645eb0..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/multiband_melgan/train_multiband_melgan.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.configs import MultibandMelganConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.gan import GAN
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-config = MultibandMelganConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=5,
- epochs=1000,
- seq_len=8192,
- pad_short=2000,
- use_noise_augment=True,
- eval_split_size=10,
- print_step=25,
- print_eval=False,
- mixed_precision=False,
- lr_gen=1e-4,
- lr_disc=1e-4,
- data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
- output_path=output_path,
-)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = GAN(config, ap)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/neuralhmm_tts/train_neuralhmmtts.py b/TTS/recipes/ljspeech/neuralhmm_tts/train_neuralhmmtts.py
deleted file mode 100644
index 28d37799750b7115be9a24c4a947526fed9429fe..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/neuralhmm_tts/train_neuralhmmtts.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.neuralhmm_tts_config import NeuralhmmTTSConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.neuralhmm_tts import NeuralhmmTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join("data", "LJSpeech-1.1/")
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = NeuralhmmTTSConfig( # This is the config that is saved for the future use
- run_name="neuralhmmtts_ljspeech",
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- precompute_num_workers=8,
- mel_statistics_parameter_path=os.path.join(output_path, "lj_parameters.pt"),
- force_generate_statistics=False,
- print_step=1,
- print_eval=True,
- mixed_precision=True,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# INITIALIZE THE MODEL
-# Models take a config object and a speaker manager as input
-# Config defines the details of the model like the number of layers, the size of the embedding, etc.
-# Speaker manager is used by multi-speaker models.
-model = NeuralhmmTTS(config, ap, tokenizer)
-
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
- gpu=1,
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/overflow/lj_parameters.pt b/TTS/recipes/ljspeech/overflow/lj_parameters.pt
deleted file mode 100644
index 625d0e4b475641e304487f911996d21ba0ad0e10..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/overflow/lj_parameters.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:67dee2d73e7df7b1d8621a06447580cbb604823d665f4fd125596865f79b296a
-size 507
diff --git a/TTS/recipes/ljspeech/overflow/train_overflow.py b/TTS/recipes/ljspeech/overflow/train_overflow.py
deleted file mode 100644
index e05e399d19b1d0005df9496169fd190749d12876..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/overflow/train_overflow.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.overflow_config import OverflowConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.overflow import Overflow
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join("data", "LJSpeech-1.1/")
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = OverflowConfig( # This is the config that is saved for the future use
- run_name="overflow_ljspeech",
- audio=audio_config,
- batch_size=30,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- precompute_num_workers=8,
- mel_statistics_parameter_path=os.path.join(output_path, "lj_parameters.pt"),
- force_generate_statistics=False,
- print_step=1,
- print_eval=True,
- mixed_precision=True,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# INITIALIZE THE MODEL
-# Models take a config object and a speaker manager as input
-# Config defines the details of the model like the number of layers, the size of the embedding, etc.
-# Speaker manager is used by multi-speaker models.
-model = Overflow(config, ap, tokenizer)
-
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
- gpu=1,
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/speedy_speech/train_speedy_speech.py b/TTS/recipes/ljspeech/speedy_speech/train_speedy_speech.py
deleted file mode 100644
index ed282aed962ddcea64b395c206fca5659f72e3b2..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/speedy_speech/train_speedy_speech.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config import BaseAudioConfig, BaseDatasetConfig
-from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.forward_tts import ForwardTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = SpeedySpeechConfig(
- run_name="speedy_speech_ljspeech",
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- compute_input_seq_cache=True,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- precompute_num_workers=4,
- print_step=50,
- print_eval=False,
- mixed_precision=False,
- max_seq_len=500000,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init model
-model = ForwardTTS(config, ap, tokenizer)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/tacotron2-Capacitron/train_capacitron_t2.py b/TTS/recipes/ljspeech/tacotron2-Capacitron/train_capacitron_t2.py
deleted file mode 100644
index f1ae2bd5c584ff5d10d19ca7ed3a5154c49cf9b7..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/tacotron2-Capacitron/train_capacitron_t2.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig, CapacitronVAEConfig
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron2 import Tacotron2
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-data_path = "/srv/data/"
-
-# Using LJSpeech like dataset processing for the blizzard dataset
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- path=data_path,
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=11025,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-# Using the standard Capacitron config
-capacitron_config = CapacitronVAEConfig(capacitron_VAE_loss_alpha=1.0, capacitron_capacity=50)
-
-config = Tacotron2Config(
- run_name="Capacitron-Tacotron2",
- audio=audio_config,
- capacitron_vae=capacitron_config,
- use_capacitron_vae=True,
- batch_size=128, # Tune this to your gpu
- max_audio_len=8 * 22050, # Tune this to your gpu
- min_audio_len=1 * 22050,
- eval_batch_size=16,
- num_loader_workers=8,
- num_eval_loader_workers=8,
- precompute_num_workers=24,
- run_eval=True,
- test_delay_epochs=25,
- ga_alpha=0.0,
- r=2,
- optimizer="CapacitronOptimizer",
- optimizer_params={"RAdam": {"betas": [0.9, 0.998], "weight_decay": 1e-6}, "SGD": {"lr": 1e-5, "momentum": 0.9}},
- attention_type="dynamic_convolution",
- grad_clip=0.0, # Important! We overwrite the standard grad_clip with capacitron_grad_clip
- double_decoder_consistency=False,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phonemizer="espeak",
- phoneme_cache_path=os.path.join(data_path, "phoneme_cache"),
- stopnet_pos_weight=15,
- print_step=25,
- print_eval=True,
- mixed_precision=False,
- seq_len_norm=True,
- output_path=output_path,
- datasets=[dataset_config],
- lr=1e-3,
- lr_scheduler="StepwiseGradualLR",
- lr_scheduler_params={
- "gradual_learning_rates": [
- [0, 1e-3],
- [2e4, 5e-4],
- [4e5, 3e-4],
- [6e4, 1e-4],
- [8e4, 5e-5],
- ]
- },
- scheduler_after_epoch=False, # scheduler doesn't work without this flag
- # Need to experiment with these below for capacitron
- loss_masking=False,
- decoder_loss_alpha=1.0,
- postnet_loss_alpha=1.0,
- postnet_diff_spec_alpha=0.0,
- decoder_diff_spec_alpha=0.0,
- decoder_ssim_alpha=0.0,
- postnet_ssim_alpha=0.0,
-)
-
-ap = AudioProcessor(**config.audio.to_dict())
-
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
-
-model = Tacotron2(config, ap, tokenizer, speaker_manager=None)
-
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
- training_assets={"audio_processor": ap},
-)
-
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py b/TTS/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py
deleted file mode 100644
index d9836f56ad5d47f801d7fc4a2fdf08dd0c78c7a1..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron2 import Tacotron2
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-# from TTS.tts.datasets.tokenizer import Tokenizer
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = Tacotron2Config( # This is the config that is saved for the future use
- audio=audio_config,
- batch_size=64,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- ga_alpha=0.0,
- decoder_loss_alpha=0.25,
- postnet_loss_alpha=0.25,
- postnet_diff_spec_alpha=0,
- decoder_diff_spec_alpha=0,
- decoder_ssim_alpha=0,
- postnet_ssim_alpha=0,
- r=2,
- attention_type="dynamic_convolution",
- double_decoder_consistency=False,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=25,
- print_eval=True,
- mixed_precision=False,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# INITIALIZE THE MODEL
-# Models take a config object and a speaker manager as input
-# Config defines the details of the model like the number of layers, the size of the embedding, etc.
-# Speaker manager is used by multi-speaker models.
-model = Tacotron2(config, ap, tokenizer)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py b/TTS/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py
deleted file mode 100644
index f050ae3222ed4fd2fc0cd7e4d3c873c5ae4557c4..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron2 import Tacotron2
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-# from TTS.tts.datasets.tokenizer import Tokenizer
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
-)
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = Tacotron2Config( # This is the config that is saved for the future use
- audio=audio_config,
- batch_size=64,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- r=6,
- gradual_training=[[0, 6, 64], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
- double_decoder_consistency=True,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- precompute_num_workers=8,
- print_step=25,
- print_eval=True,
- mixed_precision=False,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# INITIALIZE THE MODEL
-# Models take a config object and a speaker manager as input
-# Config defines the details of the model like the number of layers, the size of the embedding, etc.
-# Speaker manager is used by multi-speaker models.
-model = Tacotron2(config, ap, tokenizer, speaker_manager=None)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/univnet/train.py b/TTS/recipes/ljspeech/univnet/train.py
deleted file mode 100644
index 81d2b889b90cb888084d7229c424986f0e3118d4..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/univnet/train.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.configs import UnivnetConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.gan import GAN
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-config = UnivnetConfig(
- batch_size=64,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- seq_len=8192,
- pad_short=2000,
- use_noise_augment=True,
- eval_split_size=10,
- print_step=25,
- print_eval=False,
- mixed_precision=False,
- lr_gen=1e-4,
- lr_disc=1e-4,
- data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
- output_path=output_path,
-)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = GAN(config, ap)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/vits_tts/train_vits.py b/TTS/recipes/ljspeech/vits_tts/train_vits.py
deleted file mode 100644
index ba3265e3056e8cceb4f749d8d09554fdee83e13e..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/vits_tts/train_vits.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import Vits, VitsAudioConfig
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
-)
-audio_config = VitsAudioConfig(
- sample_rate=22050, win_length=1024, hop_length=256, num_mels=80, mel_fmin=0, mel_fmax=None
-)
-
-config = VitsConfig(
- audio=audio_config,
- run_name="vits_ljspeech",
- batch_size=32,
- eval_batch_size=16,
- batch_group_size=5,
- num_loader_workers=8,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- compute_input_seq_cache=True,
- print_step=25,
- print_eval=True,
- mixed_precision=True,
- output_path=output_path,
- datasets=[dataset_config],
- cudnn_benchmark=False,
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# config is updated with the default characters if not defined in the config.
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init model
-model = Vits(config, ap, tokenizer, speaker_manager=None)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/wavegrad/train_wavegrad.py b/TTS/recipes/ljspeech/wavegrad/train_wavegrad.py
deleted file mode 100644
index 1abdf45d8759de249eafdd479c5e96b7f5f59b33..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/wavegrad/train_wavegrad.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.configs import WavegradConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.wavegrad import Wavegrad
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-config = WavegradConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- seq_len=6144,
- pad_short=2000,
- use_noise_augment=True,
- eval_split_size=50,
- print_step=50,
- print_eval=True,
- mixed_precision=False,
- data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
- output_path=output_path,
-)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = Wavegrad(config)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
- training_assets={"audio_processor": ap},
-)
-trainer.fit()
diff --git a/TTS/recipes/ljspeech/wavernn/train_wavernn.py b/TTS/recipes/ljspeech/wavernn/train_wavernn.py
deleted file mode 100644
index 640f50921888f4fd4a33a32e725b280a639170b6..0000000000000000000000000000000000000000
--- a/TTS/recipes/ljspeech/wavernn/train_wavernn.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.configs import WavernnConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.wavernn import Wavernn
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-config = WavernnConfig(
- batch_size=64,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=10000,
- seq_len=1280,
- pad_short=2000,
- use_noise_augment=False,
- eval_split_size=10,
- print_step=25,
- print_eval=True,
- mixed_precision=False,
- lr=1e-4,
- grad_clip=4,
- data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
- output_path=output_path,
-)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = Wavernn(config)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
- training_assets={"audio_processor": ap},
-)
-trainer.fit()
diff --git a/TTS/recipes/multilingual/vits_tts/train_vits_tts.py b/TTS/recipes/multilingual/vits_tts/train_vits_tts.py
deleted file mode 100644
index 89119aa1ff9321b8e8d185c8021f4a4c75b113c5..0000000000000000000000000000000000000000
--- a/TTS/recipes/multilingual/vits_tts/train_vits_tts.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import os
-from glob import glob
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import CharactersConfig, Vits, VitsArgs, VitsAudioConfig
-from TTS.tts.utils.languages import LanguageManager
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-mailabs_path = "/home/julian/workspace/mailabs/**"
-dataset_paths = glob(mailabs_path)
-dataset_config = [
- BaseDatasetConfig(formatter="mailabs", meta_file_train=None, path=path, language=path.split("/")[-1])
- for path in dataset_paths
-]
-
-audio_config = VitsAudioConfig(
- sample_rate=16000,
- win_length=1024,
- hop_length=256,
- num_mels=80,
- mel_fmin=0,
- mel_fmax=None,
-)
-
-vitsArgs = VitsArgs(
- use_language_embedding=True,
- embedded_language_dim=4,
- use_speaker_embedding=True,
- use_sdp=False,
-)
-
-config = VitsConfig(
- model_args=vitsArgs,
- audio=audio_config,
- run_name="vits_vctk",
- use_speaker_embedding=True,
- batch_size=32,
- eval_batch_size=16,
- batch_group_size=0,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="multilingual_cleaners",
- use_phonemes=False,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- compute_input_seq_cache=True,
- print_step=25,
- use_language_weighted_sampler=True,
- print_eval=False,
- mixed_precision=False,
- min_audio_len=32 * 256 * 4,
- max_audio_len=160000,
- output_path=output_path,
- datasets=dataset_config,
- characters=CharactersConfig(
- characters_class="TTS.tts.models.vits.VitsCharacters",
- pad="",
- eos="",
- bos="",
- blank="",
- characters="!¡'(),-.:;¿?abcdefghijklmnopqrstuvwxyzµßàáâäåæçèéêëìíîïñòóôöùúûüąćęłńœśşźżƒабвгдежзийклмнопрстуфхцчшщъыьэюяёєіїґӧ «°±µ»$%&‘’‚“`”„",
- punctuations="!¡'(),-.:;¿? ",
- phonemes=None,
- ),
- test_sentences=[
- [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "mary_ann",
- None,
- "en_US",
- ],
- [
- "Il m'a fallu beaucoup de temps pour d\u00e9velopper une voix, et maintenant que je l'ai, je ne vais pas me taire.",
- "ezwa",
- None,
- "fr_FR",
- ],
- ["Ich finde, dieses Startup ist wirklich unglaublich.", "eva_k", None, "de_DE"],
- ["Я думаю, что этот стартап действительно удивительный.", "oblomov", None, "ru_RU"],
- ],
-)
-
-# force the convertion of the custom characters to a config attribute
-config.from_dict(config.to_dict())
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it maps speaker-id to speaker-name in the model and data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-config.model_args.num_speakers = speaker_manager.num_speakers
-
-language_manager = LanguageManager(config=config)
-config.model_args.num_languages = language_manager.num_languages
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# config is updated with the default characters if not defined in the config.
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# init model
-model = Vits(config, ap, tokenizer, speaker_manager, language_manager)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/multilingual/vits_tts/train_vits_tts_phonemes.py b/TTS/recipes/multilingual/vits_tts/train_vits_tts_phonemes.py
deleted file mode 100644
index 24e9e51a9428022cbc60750dcf1f6216a018a247..0000000000000000000000000000000000000000
--- a/TTS/recipes/multilingual/vits_tts/train_vits_tts_phonemes.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-from glob import glob
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import Vits, VitsArgs, VitsAudioConfig
-from TTS.tts.utils.languages import LanguageManager
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = "/media/julian/Workdisk/train"
-
-mailabs_path = "/home/julian/workspace/mailabs/**"
-dataset_paths = glob(mailabs_path)
-dataset_config = [
- BaseDatasetConfig(
- formatter="mailabs",
- meta_file_train=None,
- path=path,
- language=path.split("/")[-1], # language code is the folder name
- )
- for path in dataset_paths
-]
-
-audio_config = VitsAudioConfig(
- sample_rate=16000,
- win_length=1024,
- hop_length=256,
- num_mels=80,
- mel_fmin=0,
- mel_fmax=None,
-)
-
-vitsArgs = VitsArgs(
- use_language_embedding=True,
- embedded_language_dim=4,
- use_speaker_embedding=True,
- use_sdp=False,
-)
-
-config = VitsConfig(
- model_args=vitsArgs,
- audio=audio_config,
- run_name="vits_vctk",
- use_speaker_embedding=True,
- batch_size=32,
- eval_batch_size=16,
- batch_group_size=0,
- num_loader_workers=12,
- num_eval_loader_workers=12,
- precompute_num_workers=12,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="multilingual_cleaners",
- use_phonemes=True,
- phoneme_language=None,
- phonemizer="multi_phonemizer",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- compute_input_seq_cache=True,
- print_step=25,
- use_language_weighted_sampler=True,
- print_eval=False,
- mixed_precision=False,
- min_audio_len=audio_config.sample_rate,
- max_audio_len=audio_config.sample_rate * 10,
- output_path=output_path,
- datasets=dataset_config,
- test_sentences=[
- [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "mary_ann",
- None,
- "en-us",
- ],
- [
- "Il m'a fallu beaucoup de temps pour d\u00e9velopper une voix, et maintenant que je l'ai, je ne vais pas me taire.",
- "ezwa",
- None,
- "fr-fr",
- ],
- ["Ich finde, dieses Startup ist wirklich unglaublich.", "eva_k", None, "de-de"],
- ["Я думаю, что этот стартап действительно удивительный.", "nikolaev", None, "ru"],
- ],
-)
-
-# force the convertion of the custom characters to a config attribute
-config.from_dict(config.to_dict())
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it maps speaker-id to speaker-name in the model and data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-config.model_args.num_speakers = speaker_manager.num_speakers
-
-language_manager = LanguageManager(config=config)
-config.model_args.num_languages = language_manager.num_languages
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# config is updated with the default characters if not defined in the config.
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# init model
-model = Vits(config, ap, tokenizer, speaker_manager, language_manager)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/README.md b/TTS/recipes/thorsten_DE/README.md
deleted file mode 100644
index 3ef0dbaa8b631f8fc0e5e4d38422dcead94799eb..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# 🐸💬 TTS Thorsten Recipes
-
-For running the recipes you need the [Thorsten-Voice](https://github.com/thorstenMueller/Thorsten-Voice) dataset.
-
-You can download it manually from [the official website](https://www.thorsten-voice.de/) or use ```download_thorsten_de.sh``` alternatively running any of the **train_modelX.py**scripts will download the dataset if not already present.
-
-Then, go to your desired model folder and run the training.
-
- Running Python files. (Choose the desired GPU ID for your run and set ```CUDA_VISIBLE_DEVICES```)
- ```terminal
- CUDA_VISIBLE_DEVICES="0" python train_modelX.py
- ```
-
-💡 Note that these runs are just templates to help you start training your first model. They are not optimized for the best
-result. Double-check the configurations and feel free to share your experiments to find better parameters together 💪.
diff --git a/TTS/recipes/thorsten_DE/align_tts/train_aligntts.py b/TTS/recipes/thorsten_DE/align_tts/train_aligntts.py
deleted file mode 100644
index 32cfd9967fa55a3bd60992f7bb409d6d388efa1b..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/align_tts/train_aligntts.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.tts.configs.align_tts_config import AlignTTSConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.align_tts import AlignTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
-)
-
-# download dataset if not already present
-if not os.path.exists(dataset_config.path):
- print("Downloading dataset")
- download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
-
-config = AlignTTSConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=False,
- phoneme_language="de",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=25,
- print_eval=True,
- mixed_precision=False,
- test_sentences=[
- "Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.",
- "Sei eine Stimme, kein Echo.",
- "Es tut mir Leid David. Das kann ich leider nicht machen.",
- "Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
- "Vor dem 22. November 1963.",
- ],
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init model
-model = AlignTTS(config, ap, tokenizer)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/download_thorsten_DE.sh b/TTS/recipes/thorsten_DE/download_thorsten_DE.sh
deleted file mode 100644
index 27809ce50741e4491338f1cf04cbff52df1e26d9..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/download_thorsten_DE.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-# create venv
-python3 -m venv env
-source .env/bin/activate
-pip install pip --upgrade
-
-# download Thorsten_DE dataset
-pip install gdown
-gdown --id 1yKJM1LAOQpRVojKunD9r8WN_p5KzBxjc -O dataset.tgz
-tar -xzf dataset.tgz
-
-# create train-val splits
-shuf LJSpeech-1.1/metadata.csv > LJSpeech-1.1/metadata_shuf.csv
-head -n 20668 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_train.csv
-tail -n 2000 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_val.csv
-
-# rename dataset and remove archive
-mv LJSpeech-1.1 thorsten-de
-rm dataset.tgz
-
-# destry venv
-rm -rf env
diff --git a/TTS/recipes/thorsten_DE/glow_tts/train_glowtts.py b/TTS/recipes/thorsten_DE/glow_tts/train_glowtts.py
deleted file mode 100644
index 00c67fb5d8c75afb80e6ed860dc0817525db8ff3..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/glow_tts/train_glowtts.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import os
-
-# Trainer: Where the ✨️ happens.
-# TrainingArgs: Defines the set of arguments of the Trainer.
-from trainer import Trainer, TrainerArgs
-
-# GlowTTSConfig: all model related values for training, validating and testing.
-from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-
-# BaseDatasetConfig: defines name, formatter and path of the dataset.
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.glow_tts import GlowTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-
-# we use the same path as this script as our training folder.
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# DEFINE DATASET CONFIG
-# Set LJSpeech as our target dataset and define its path.
-# You can also use a simple Dict to define the dataset and pass it to your custom formatter.
-dataset_config = BaseDatasetConfig(
- formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
-)
-
-# download dataset if not already present
-if not os.path.exists(dataset_config.path):
- print("Downloading dataset")
- download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
-
-# INITIALIZE THE TRAINING CONFIGURATION
-# Configure the model. Every config class inherits the BaseTTSConfig.
-config = GlowTTSConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="de",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=25,
- print_eval=False,
- mixed_precision=True,
- test_sentences=[
- "Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.",
- "Sei eine Stimme, kein Echo.",
- "Es tut mir Leid David. Das kann ich leider nicht machen.",
- "Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
- "Vor dem 22. November 1963.",
- ],
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# INITIALIZE THE MODEL
-# Models take a config object and a speaker manager as input
-# Config defines the details of the model like the number of layers, the size of the embedding, etc.
-# Speaker manager is used by multi-speaker models.
-model = GlowTTS(config, ap, tokenizer, speaker_manager=None)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/hifigan/train_hifigan.py b/TTS/recipes/thorsten_DE/hifigan/train_hifigan.py
deleted file mode 100644
index b476780211154266bf3683b8657b40481bba1366..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/hifigan/train_hifigan.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-from TTS.vocoder.configs import HifiganConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.gan import GAN
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-config = HifiganConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=5,
- epochs=1000,
- seq_len=8192,
- pad_short=2000,
- use_noise_augment=True,
- eval_split_size=10,
- print_step=25,
- print_eval=False,
- mixed_precision=False,
- lr_gen=1e-4,
- lr_disc=1e-4,
- data_path=os.path.join(output_path, "../thorsten-de/wavs/"),
- output_path=output_path,
-)
-
-# download dataset if not already present
-if not os.path.exists(config.data_path):
- print("Downloading dataset")
- download_path = os.path.abspath(os.path.join(os.path.abspath(config.data_path), "../../"))
- download_thorsten_de(download_path)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = GAN(config, ap)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/multiband_melgan/train_multiband_melgan.py b/TTS/recipes/thorsten_DE/multiband_melgan/train_multiband_melgan.py
deleted file mode 100644
index 2951b1495a78fa7f0ded9dbd4201af88206267cf..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/multiband_melgan/train_multiband_melgan.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-from TTS.vocoder.configs import MultibandMelganConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.gan import GAN
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-config = MultibandMelganConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=5,
- epochs=1000,
- seq_len=8192,
- pad_short=2000,
- use_noise_augment=True,
- eval_split_size=10,
- print_step=25,
- print_eval=False,
- mixed_precision=False,
- lr_gen=1e-4,
- lr_disc=1e-4,
- data_path=os.path.join(output_path, "../thorsten-de/wavs/"),
- output_path=output_path,
-)
-
-# download dataset if not already present
-if not os.path.exists(config.data_path):
- print("Downloading dataset")
- download_path = os.path.abspath(os.path.join(os.path.abspath(config.data_path), "../../"))
- download_thorsten_de(download_path)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = GAN(config, ap)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/speedy_speech/train_speedy_speech.py b/TTS/recipes/thorsten_DE/speedy_speech/train_speedy_speech.py
deleted file mode 100644
index a3d0b9db2b3b2c2edeaf40b90b120117e79f5077..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/speedy_speech/train_speedy_speech.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config import BaseAudioConfig, BaseDatasetConfig
-from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.forward_tts import ForwardTTS
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(
- formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
-)
-
-# download dataset if not already present
-if not os.path.exists(dataset_config.path):
- print("Downloading dataset")
- download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = SpeedySpeechConfig(
- run_name="speedy_speech_thorsten-de",
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- compute_input_seq_cache=True,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- min_audio_len=11050, # need to up min_audio_len to avois speedy speech error
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="de",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- precompute_num_workers=4,
- print_step=50,
- print_eval=False,
- mixed_precision=False,
- test_sentences=[
- "Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.",
- "Sei eine Stimme, kein Echo.",
- "Es tut mir Leid David. Das kann ich leider nicht machen.",
- "Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
- "Vor dem 22. November 1963.",
- ],
- max_seq_len=500000,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init model
-model = ForwardTTS(config, ap, tokenizer)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/tacotron2-DDC/train_tacotron_ddc.py b/TTS/recipes/thorsten_DE/tacotron2-DDC/train_tacotron_ddc.py
deleted file mode 100644
index bc0274f5af2a6c1096c89e41d8b2e359fe5432f6..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/tacotron2-DDC/train_tacotron_ddc.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron2 import Tacotron2
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-
-# from TTS.tts.datasets.tokenizer import Tokenizer
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
-dataset_config = BaseDatasetConfig(
- formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
-)
-
-# download dataset if not already present
-if not os.path.exists(dataset_config.path):
- print("Downloading dataset")
- download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = Tacotron2Config( # This is the config that is saved for the future use
- audio=audio_config,
- batch_size=40, # BS of 40 and max length of 10s will use about 20GB of GPU memory
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- r=6,
- gradual_training=[[0, 6, 64], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
- double_decoder_consistency=True,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="de",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- precompute_num_workers=8,
- print_step=25,
- print_eval=True,
- mixed_precision=False,
- test_sentences=[
- "Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.",
- "Sei eine Stimme, kein Echo.",
- "Es tut mir Leid David. Das kann ich leider nicht machen.",
- "Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
- "Vor dem 22. November 1963.",
- ],
- # max audio length of 10 seconds, feel free to increase if you got more than 20GB GPU memory
- max_audio_len=22050 * 10,
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# INITIALIZE THE MODEL
-# Models take a config object and a speaker manager as input
-# Config defines the details of the model like the number of layers, the size of the embedding, etc.
-# Speaker manager is used by multi-speaker models.
-model = Tacotron2(config, ap, tokenizer, speaker_manager=None)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/univnet/train_univnet.py b/TTS/recipes/thorsten_DE/univnet/train_univnet.py
deleted file mode 100644
index 7d82093d627cd6eea19df00f8828b1abc90aca27..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/univnet/train_univnet.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-from TTS.vocoder.configs import UnivnetConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.gan import GAN
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-config = UnivnetConfig(
- batch_size=64,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- seq_len=8192,
- pad_short=2000,
- use_noise_augment=True,
- eval_split_size=10,
- print_step=25,
- print_eval=False,
- mixed_precision=False,
- lr_gen=1e-4,
- lr_disc=1e-4,
- data_path=os.path.join(output_path, "../thorsten-de/wavs/"),
- output_path=output_path,
-)
-
-# download dataset if not already present
-if not os.path.exists(config.data_path):
- print("Downloading dataset")
- download_path = os.path.abspath(os.path.join(os.path.abspath(config.data_path), "../../"))
- download_thorsten_de(download_path)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = GAN(config, ap)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/vits_tts/train_vits.py b/TTS/recipes/thorsten_DE/vits_tts/train_vits.py
deleted file mode 100644
index 4ffa0f30f6d028f3b844144df6d3abf29835afaa..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/vits_tts/train_vits.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import Vits, VitsAudioConfig
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(
- formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
-)
-
-# download dataset if not already present
-if not os.path.exists(dataset_config.path):
- print("Downloading dataset")
- download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
-
-audio_config = VitsAudioConfig(
- sample_rate=22050,
- win_length=1024,
- hop_length=256,
- num_mels=80,
- mel_fmin=0,
- mel_fmax=None,
-)
-
-config = VitsConfig(
- audio=audio_config,
- run_name="vits_thorsten-de",
- batch_size=32,
- eval_batch_size=16,
- batch_group_size=5,
- num_loader_workers=0,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="de",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- compute_input_seq_cache=True,
- print_step=25,
- print_eval=True,
- mixed_precision=True,
- test_sentences=[
- "Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.",
- "Sei eine Stimme, kein Echo.",
- "Es tut mir Leid David. Das kann ich leider nicht machen.",
- "Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
- "Vor dem 22. November 1963.",
- ],
- output_path=output_path,
- datasets=[dataset_config],
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# config is updated with the default characters if not defined in the config.
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init model
-model = Vits(config, ap, tokenizer, speaker_manager=None)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
-)
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/wavegrad/train_wavegrad.py b/TTS/recipes/thorsten_DE/wavegrad/train_wavegrad.py
deleted file mode 100644
index e9d2c95c006f332bb05cb6b33577dece2285809f..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/wavegrad/train_wavegrad.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-from TTS.vocoder.configs import WavegradConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.wavegrad import Wavegrad
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-config = WavegradConfig(
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- seq_len=6144,
- pad_short=2000,
- use_noise_augment=True,
- eval_split_size=50,
- print_step=50,
- print_eval=True,
- mixed_precision=False,
- data_path=os.path.join(output_path, "../thorsten-de/wavs/"),
- output_path=output_path,
-)
-
-# download dataset if not already present
-if not os.path.exists(config.data_path):
- print("Downloading dataset")
- download_path = os.path.abspath(os.path.join(os.path.abspath(config.data_path), "../../"))
- download_thorsten_de(download_path)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = Wavegrad(config)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
- training_assets={"audio_processor": ap},
-)
-trainer.fit()
diff --git a/TTS/recipes/thorsten_DE/wavernn/train_wavernn.py b/TTS/recipes/thorsten_DE/wavernn/train_wavernn.py
deleted file mode 100644
index f2a283f745e9772856dd605798e87bd167053de5..0000000000000000000000000000000000000000
--- a/TTS/recipes/thorsten_DE/wavernn/train_wavernn.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.downloaders import download_thorsten_de
-from TTS.vocoder.configs import WavernnConfig
-from TTS.vocoder.datasets.preprocess import load_wav_data
-from TTS.vocoder.models.wavernn import Wavernn
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-config = WavernnConfig(
- batch_size=64,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=10000,
- seq_len=1280,
- pad_short=2000,
- use_noise_augment=False,
- eval_split_size=10,
- print_step=25,
- print_eval=True,
- mixed_precision=False,
- lr=1e-4,
- grad_clip=4,
- data_path=os.path.join(output_path, "../thorsten-de/wavs/"),
- output_path=output_path,
-)
-
-# download dataset if not already present
-if not os.path.exists(config.data_path):
- print("Downloading dataset")
- download_path = os.path.abspath(os.path.join(os.path.abspath(config.data_path), "../../"))
- download_thorsten_de(download_path)
-
-# init audio processor
-ap = AudioProcessor(**config.audio.to_dict())
-
-# load training samples
-eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
-
-# init model
-model = Wavernn(config)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
- training_assets={"audio_processor": ap},
-)
-trainer.fit()
diff --git a/TTS/recipes/vctk/delightful_tts/train_delightful_tts.py b/TTS/recipes/vctk/delightful_tts/train_delightful_tts.py
deleted file mode 100644
index eebf408bf12d0896dfd2059d4614a1a4929cba6a..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/delightful_tts/train_delightful_tts.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.delightful_tts_config import DelightfulTtsAudioConfig, DelightfulTTSConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.delightful_tts import DelightfulTTS, DelightfulTtsArgs, VocoderConfig
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio.processor import AudioProcessor
-
-data_path = "/raid/datasets/vctk_v092_48khz_removed_silence_silero_vad"
-output_path = os.path.dirname(os.path.abspath(__file__))
-
-
-dataset_config = BaseDatasetConfig(
- dataset_name="vctk", formatter="vctk", meta_file_train="", path=data_path, language="en-us"
-)
-
-audio_config = DelightfulTtsAudioConfig()
-
-model_args = DelightfulTtsArgs()
-
-vocoder_config = VocoderConfig()
-
-something_tts_config = DelightfulTTSConfig(
- run_name="delightful_tts_vctk",
- run_description="Train like in delightful tts paper.",
- model_args=model_args,
- audio=audio_config,
- vocoder=vocoder_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=10,
- num_eval_loader_workers=10,
- precompute_num_workers=40,
- compute_input_seq_cache=True,
- compute_f0=True,
- f0_cache_path=os.path.join(output_path, "f0_cache"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=50,
- print_eval=False,
- mixed_precision=True,
- output_path=output_path,
- datasets=[dataset_config],
- start_by_longest=True,
- binary_align_loss_alpha=0.0,
- use_attn_priors=False,
- max_text_len=60,
- steps_to_start_discriminator=10000,
-)
-
-tokenizer, config = TTSTokenizer.init_from_config(something_tts_config)
-
-ap = AudioProcessor.init_from_config(config)
-
-
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-config.model_args.num_speakers = speaker_manager.num_speakers
-
-
-model = DelightfulTTS(ap=ap, config=config, tokenizer=tokenizer, speaker_manager=speaker_manager, emotion_manager=None)
-
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-trainer.fit()
diff --git a/TTS/recipes/vctk/download_vctk.sh b/TTS/recipes/vctk/download_vctk.sh
deleted file mode 100644
index d08a53c61cf5e4c5f3f0154c55a3677c177a366c..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/download_vctk.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/usr/bin/env bash
-# take the scripts's parent's directory to prefix all the output paths.
-RUN_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
-echo $RUN_DIR
-# download VCTK dataset
-wget https://datashare.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip -O VCTK-Corpus-0.92.zip
-# extract
-mkdir VCTK
-unzip VCTK-Corpus-0.92 -d VCTK
-# create train-val splits
-mv VCTK $RUN_DIR/recipes/vctk/
-rm VCTK-Corpus-0.92.zip
diff --git a/TTS/recipes/vctk/fast_pitch/train_fast_pitch.py b/TTS/recipes/vctk/fast_pitch/train_fast_pitch.py
deleted file mode 100644
index 70b4578906e5254e0d9659ad07a1e675f1cdf6e2..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/fast_pitch/train_fast_pitch.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config import BaseAudioConfig, BaseDatasetConfig
-from TTS.tts.configs.fast_pitch_config import FastPitchConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.forward_tts import ForwardTTS
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=23.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = FastPitchConfig(
- run_name="fast_pitch_ljspeech",
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=8,
- num_eval_loader_workers=4,
- compute_input_seq_cache=True,
- precompute_num_workers=4,
- compute_f0=True,
- f0_cache_path=os.path.join(output_path, "f0_cache"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=50,
- print_eval=False,
- mixed_precision=False,
- min_text_len=0,
- max_text_len=500,
- min_audio_len=0,
- max_audio_len=500000,
- output_path=output_path,
- datasets=[dataset_config],
- use_speaker_embedding=True,
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it maps speaker-id to speaker-name in the model and data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-config.model_args.num_speakers = speaker_manager.num_speakers
-
-# init model
-model = ForwardTTS(config, ap, tokenizer, speaker_manager=speaker_manager)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/vctk/fast_speech/train_fast_speech.py b/TTS/recipes/vctk/fast_speech/train_fast_speech.py
deleted file mode 100644
index 3db7ff7afe7770ed6489650f24db5567eaaadb4f..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/fast_speech/train_fast_speech.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config import BaseAudioConfig, BaseDatasetConfig
-from TTS.tts.configs.fast_speech_config import FastSpeechConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.forward_tts import ForwardTTS
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=23.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = FastSpeechConfig(
- run_name="fast_speech_vctk",
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=8,
- num_eval_loader_workers=4,
- compute_input_seq_cache=True,
- precompute_num_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=50,
- print_eval=False,
- mixed_precision=False,
- min_text_len=0,
- max_text_len=500,
- min_audio_len=0,
- max_audio_len=500000,
- output_path=output_path,
- datasets=[dataset_config],
- use_speaker_embedding=True,
-)
-
-## INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it maps speaker-id to speaker-name in the model and data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-config.model_args.num_speakers = speaker_manager.num_speakers
-
-# init model
-model = ForwardTTS(config, ap, tokenizer, speaker_manager=speaker_manager)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/vctk/glow_tts/train_glow_tts.py b/TTS/recipes/vctk/glow_tts/train_glow_tts.py
deleted file mode 100644
index ae26029b9151217164fa6c4d5c592fc26fb44ee2..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/glow_tts/train_glow_tts.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.glow_tts import GlowTTS
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-# set experiment paths
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_path = os.path.join(output_path, "../VCTK/")
-
-# download the dataset if not downloaded
-if not os.path.exists(dataset_path):
- from TTS.utils.downloaders import download_vctk
-
- download_vctk(dataset_path)
-
-# define dataset config
-dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=dataset_path)
-
-# define audio config
-# ❗ resample the dataset externally using `TTS/bin/resample.py` and set `resample=False` for faster training
-audio_config = BaseAudioConfig(sample_rate=22050, resample=True, do_trim_silence=True, trim_db=23.0)
-
-# define model config
-config = GlowTTSConfig(
- batch_size=64,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- precompute_num_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=25,
- print_eval=False,
- mixed_precision=True,
- output_path=output_path,
- datasets=[dataset_config],
- use_speaker_embedding=True,
- min_text_len=0,
- max_text_len=500,
- min_audio_len=0,
- max_audio_len=500000,
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it maps speaker-id to speaker-name in the model and data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-config.num_speakers = speaker_manager.num_speakers
-
-# init model
-model = GlowTTS(config, ap, tokenizer, speaker_manager=speaker_manager)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/vctk/resnet_speaker_encoder/train_encoder.py b/TTS/recipes/vctk/resnet_speaker_encoder/train_encoder.py
deleted file mode 100644
index cd01754b72ef8e59a93412c5e31e2dfe5b58b90e..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/resnet_speaker_encoder/train_encoder.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import os
-
-from TTS.encoder.configs.speaker_encoder_config import SpeakerEncoderConfig
-
-# from TTS.encoder.configs.emotion_encoder_config import EmotionEncoderConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-
-CURRENT_PATH = os.getcwd()
-# change the root path to the TTS root path
-os.chdir("../../../")
-
-### Definitions ###
-# dataset
-VCTK_PATH = "/raid/datasets/VCTK_NEW_16khz_removed_silence_silero_vad/" # download: https://datashare.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zipdddddddddd
-RIR_SIMULATED_PATH = "/raid/datasets/DA/RIRS_NOISES/simulated_rirs/" # download: https://www.openslr.org/17/
-MUSAN_PATH = "/raid/datasets/DA/musan/" # download: https://www.openslr.org/17/
-
-# training
-OUTPUT_PATH = os.path.join(
- CURRENT_PATH, "resnet_speaker_encoder_training_output/"
-) # path to save the train logs and checkpoint
-CONFIG_OUT_PATH = os.path.join(OUTPUT_PATH, "config_se.json")
-RESTORE_PATH = None # Checkpoint to use for transfer learning if None ignore
-
-# instance the config
-# to speaker encoder
-config = SpeakerEncoderConfig()
-# to emotion encoder
-# config = EmotionEncoderConfig()
-
-
-#### DATASET CONFIG ####
-# The formatter need to return the key "speaker_name" for the speaker encoder and the "emotion_name" for the emotion encoder
-dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", language="en-us", path=VCTK_PATH)
-
-# add the dataset to the config
-config.datasets = [dataset_config]
-
-
-#### TRAINING CONFIG ####
-# The encoder data loader balancer the dataset item equally to guarantee better training and to attend the losses requirements
-# It have two parameters to control the final batch size the number total of speaker used in each batch and the number of samples for each speaker
-
-# number total of speaker in batch in training
-config.num_classes_in_batch = 100
-# number of utterance per class/speaker in the batch in training
-config.num_utter_per_class = 4
-# final batch size = config.num_classes_in_batch * config.num_utter_per_class
-
-# number total of speaker in batch in evaluation
-config.eval_num_classes_in_batch = 100
-# number of utterance per class/speaker in the batch in evaluation
-config.eval_num_utter_per_class = 4
-
-# number of data loader workers
-config.num_loader_workers = 8
-config.num_val_loader_workers = 8
-
-# number of epochs
-config.epochs = 10000
-# loss to be used in training
-config.loss = "softmaxproto"
-
-# run eval
-config.run_eval = False
-
-# output path for the checkpoints
-config.output_path = OUTPUT_PATH
-
-# Save local checkpoint every save_step steps
-config.save_step = 2000
-
-### Model Config ###
-config.model_params = {
- "model_name": "resnet", # supported "lstm" and "resnet"
- "input_dim": 64,
- "use_torch_spec": True,
- "log_input": True,
- "proj_dim": 512, # embedding dim
-}
-
-### Audio Config ###
-# To fast train the model divides the audio in small parts. it parameter defines the length in seconds of these "parts"
-config.voice_len = 2.0
-# all others configs
-config.audio = {
- "fft_size": 512,
- "win_length": 400,
- "hop_length": 160,
- "frame_shift_ms": None,
- "frame_length_ms": None,
- "stft_pad_mode": "reflect",
- "sample_rate": 16000,
- "resample": False,
- "preemphasis": 0.97,
- "ref_level_db": 20,
- "do_sound_norm": False,
- "do_trim_silence": False,
- "trim_db": 60,
- "power": 1.5,
- "griffin_lim_iters": 60,
- "num_mels": 64,
- "mel_fmin": 0.0,
- "mel_fmax": 8000.0,
- "spec_gain": 20,
- "signal_norm": False,
- "min_level_db": -100,
- "symmetric_norm": False,
- "max_norm": 4.0,
- "clip_norm": False,
- "stats_path": None,
- "do_rms_norm": True,
- "db_level": -27.0,
-}
-
-
-### Augmentation Config ###
-config.audio_augmentation = {
- # additive noise and room impulse response (RIR) simulation similar to: https://arxiv.org/pdf/2009.14153.pdf
- "p": 0.5, # probability to the use of one of the augmentation - 0 means disabled
- "rir": {"rir_path": RIR_SIMULATED_PATH, "conv_mode": "full"}, # download: https://www.openslr.org/17/
- "additive": {
- "sounds_path": MUSAN_PATH,
- "speech": {"min_snr_in_db": 13, "max_snr_in_db": 20, "min_num_noises": 1, "max_num_noises": 1},
- "noise": {"min_snr_in_db": 0, "max_snr_in_db": 15, "min_num_noises": 1, "max_num_noises": 1},
- "music": {"min_snr_in_db": 5, "max_snr_in_db": 15, "min_num_noises": 1, "max_num_noises": 1},
- },
- "gaussian": {"p": 0.7, "min_amplitude": 0.0, "max_amplitude": 1e-05},
-}
-
-config.save_json(CONFIG_OUT_PATH)
-
-print(CONFIG_OUT_PATH)
-if RESTORE_PATH is not None:
- command = f"python TTS/bin/train_encoder.py --config_path {CONFIG_OUT_PATH} --restore_path {RESTORE_PATH}"
-else:
- command = f"python TTS/bin/train_encoder.py --config_path {CONFIG_OUT_PATH}"
-
-os.system(command)
diff --git a/TTS/recipes/vctk/speedy_speech/train_speedy_speech.py b/TTS/recipes/vctk/speedy_speech/train_speedy_speech.py
deleted file mode 100644
index 04caa6d25ac1814ed04eeeefe0090d6f11556142..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/speedy_speech/train_speedy_speech.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config import BaseAudioConfig, BaseDatasetConfig
-from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.forward_tts import ForwardTTS
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=23.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = SpeedySpeechConfig(
- run_name="fast_pitch_ljspeech",
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=8,
- num_eval_loader_workers=4,
- compute_input_seq_cache=True,
- precompute_num_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=50,
- print_eval=False,
- mixed_precision=False,
- min_text_len=0,
- max_text_len=500,
- min_audio_len=0,
- max_audio_len=500000,
- output_path=output_path,
- datasets=[dataset_config],
- use_speaker_embedding=True,
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it maps speaker-id to speaker-name in the model and data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-config.model_args.num_speakers = speaker_manager.num_speakers
-
-# init model
-model = ForwardTTS(config, ap, tokenizer, speaker_manager)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py b/TTS/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py
deleted file mode 100644
index 7607a1675a3fbf1e7115df58c2bde8e5ad1a9f95..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.tacotron_config import TacotronConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron import Tacotron
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- resample=True, # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training.
- do_trim_silence=True,
- trim_db=23.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = TacotronConfig( # This is the config that is saved for the future use
- audio=audio_config,
- batch_size=48,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- precompute_num_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- r=6,
- gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
- double_decoder_consistency=True,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=25,
- print_eval=False,
- mixed_precision=True,
- min_text_len=0,
- max_text_len=500,
- min_audio_len=0,
- max_audio_len=44000 * 10, # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio
- output_path=output_path,
- datasets=[dataset_config],
- use_speaker_embedding=True, # set this to enable multi-sepeaker training
-)
-
-## INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it mainly handles speaker-id to speaker-name for the model and the data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-
-# init model
-model = Tacotron(config, ap, tokenizer, speaker_manager)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py b/TTS/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py
deleted file mode 100644
index 8623018ae230d2b38ceb0ebfb8c8d5f84bf8271d..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron2 import Tacotron2
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- resample=False, # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training.
- do_trim_silence=True,
- trim_db=23.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- preemphasis=0.0,
-)
-
-config = Tacotron2Config( # This is the config that is saved for the future use
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- r=2,
- # gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
- double_decoder_consistency=True,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=150,
- print_eval=False,
- mixed_precision=True,
- min_text_len=0,
- max_text_len=500,
- min_audio_len=0,
- max_audio_len=44000 * 10,
- output_path=output_path,
- datasets=[dataset_config],
- use_speaker_embedding=True, # set this to enable multi-sepeaker training
- decoder_ssim_alpha=0.0, # disable ssim losses that causes NaN for some runs.
- postnet_ssim_alpha=0.0,
- postnet_diff_spec_alpha=0.0,
- decoder_diff_spec_alpha=0.0,
- attention_norm="softmax",
- optimizer="Adam",
- lr_scheduler=None,
- lr=3e-5,
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it mainly handles speaker-id to speaker-name for the model and the data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-
-# init model
-model = Tacotron2(config, ap, tokenizer, speaker_manager)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/vctk/tacotron2/train_tacotron2.py b/TTS/recipes/vctk/tacotron2/train_tacotron2.py
deleted file mode 100644
index d3f66348df4eb2e6c13be2ea9e6ba3cdf51ec9d0..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/tacotron2/train_tacotron2.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.tacotron2 import Tacotron2
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- resample=False, # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training.
- do_trim_silence=True,
- trim_db=23.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- preemphasis=0.0,
-)
-
-config = Tacotron2Config( # This is the config that is saved for the future use
- audio=audio_config,
- batch_size=32,
- eval_batch_size=16,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- r=2,
- # gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
- double_decoder_consistency=False,
- epochs=1000,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- print_step=150,
- print_eval=False,
- mixed_precision=True,
- min_text_len=0,
- max_text_len=500,
- min_audio_len=0,
- max_audio_len=44000 * 10,
- output_path=output_path,
- datasets=[dataset_config],
- use_speaker_embedding=True, # set this to enable multi-sepeaker training
- decoder_ssim_alpha=0.0, # disable ssim losses that causes NaN for some runs.
- postnet_ssim_alpha=0.0,
- postnet_diff_spec_alpha=0.0,
- decoder_diff_spec_alpha=0.0,
- attention_norm="softmax",
- optimizer="Adam",
- lr_scheduler=None,
- lr=3e-5,
-)
-
-## INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# If characters are not defined in the config, default characters are passed to the config
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it mainly handles speaker-id to speaker-name for the model and the data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-
-# init model
-model = Tacotron2(config, ap, tokenizer, speaker_manager)
-
-# INITIALIZE THE TRAINER
-# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
-# distributed training, etc.
-trainer = Trainer(
- TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
-)
-
-# AND... 3,2,1... 🚀
-trainer.fit()
diff --git a/TTS/recipes/vctk/vits/train_vits.py b/TTS/recipes/vctk/vits/train_vits.py
deleted file mode 100644
index dbc06eecdcfcb75802e6b31cd56c6dd73068b5cd..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/vits/train_vits.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import os
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.tts.configs.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import Vits, VitsArgs, VitsAudioConfig
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(
- formatter="vctk", meta_file_train="", language="en-us", path=os.path.join(output_path, "../VCTK/")
-)
-
-
-audio_config = VitsAudioConfig(
- sample_rate=22050, win_length=1024, hop_length=256, num_mels=80, mel_fmin=0, mel_fmax=None
-)
-
-vitsArgs = VitsArgs(
- use_speaker_embedding=True,
-)
-
-config = VitsConfig(
- model_args=vitsArgs,
- audio=audio_config,
- run_name="vits_vctk",
- batch_size=32,
- eval_batch_size=16,
- batch_group_size=5,
- num_loader_workers=4,
- num_eval_loader_workers=4,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1000,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en",
- phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
- compute_input_seq_cache=True,
- print_step=25,
- print_eval=False,
- mixed_precision=True,
- max_text_len=325, # change this if you have a larger VRAM than 16GB
- output_path=output_path,
- datasets=[dataset_config],
- cudnn_benchmark=False,
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# config is updated with the default characters if not defined in the config.
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
- dataset_config,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it maps speaker-id to speaker-name in the model and data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-config.model_args.num_speakers = speaker_manager.num_speakers
-
-# init model
-model = Vits(config, ap, tokenizer, speaker_manager)
-
-# init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(),
- config,
- output_path,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
-)
-trainer.fit()
diff --git a/TTS/recipes/vctk/yourtts/train_yourtts.py b/TTS/recipes/vctk/yourtts/train_yourtts.py
deleted file mode 100644
index b9cf10fa8eb3c37451d1a95c95d17680b81642d7..0000000000000000000000000000000000000000
--- a/TTS/recipes/vctk/yourtts/train_yourtts.py
+++ /dev/null
@@ -1,253 +0,0 @@
-import os
-
-import torch
-from trainer import Trainer, TrainerArgs
-
-from TTS.bin.compute_embeddings import compute_embeddings
-from TTS.bin.resample import resample_files
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import CharactersConfig, Vits, VitsArgs, VitsAudioConfig
-from TTS.utils.downloaders import download_vctk
-
-torch.set_num_threads(24)
-
-# pylint: disable=W0105
-"""
- This recipe replicates the first experiment proposed in the YourTTS paper (https://arxiv.org/abs/2112.02418).
- YourTTS model is based on the VITS model however it uses external speaker embeddings extracted from a pre-trained speaker encoder and has small architecture changes.
- In addition, YourTTS can be trained in multilingual data, however, this recipe replicates the single language training using the VCTK dataset.
- If you are interested in multilingual training, we have commented on parameters on the VitsArgs class instance that should be enabled for multilingual training.
- In addition, you will need to add the extra datasets following the VCTK as an example.
-"""
-CURRENT_PATH = os.path.dirname(os.path.abspath(__file__))
-
-# Name of the run for the Trainer
-RUN_NAME = "YourTTS-EN-VCTK"
-
-# Path where you want to save the models outputs (configs, checkpoints and tensorboard logs)
-OUT_PATH = os.path.dirname(os.path.abspath(__file__)) # "/raid/coqui/Checkpoints/original-YourTTS/"
-
-# If you want to do transfer learning and speedup your training you can set here the path to the original YourTTS model
-RESTORE_PATH = None # "/root/.local/share/tts/tts_models--multilingual--multi-dataset--your_tts/model_file.pth"
-
-# This paramter is useful to debug, it skips the training epochs and just do the evaluation and produce the test sentences
-SKIP_TRAIN_EPOCH = False
-
-# Set here the batch size to be used in training and evaluation
-BATCH_SIZE = 32
-
-# Training Sampling rate and the target sampling rate for resampling the downloaded dataset (Note: If you change this you might need to redownload the dataset !!)
-# Note: If you add new datasets, please make sure that the dataset sampling rate and this parameter are matching, otherwise resample your audios
-SAMPLE_RATE = 16000
-
-# Max audio length in seconds to be used in training (every audio bigger than it will be ignored)
-MAX_AUDIO_LEN_IN_SECONDS = 10
-
-### Download VCTK dataset
-VCTK_DOWNLOAD_PATH = os.path.join(CURRENT_PATH, "VCTK")
-# Define the number of threads used during the audio resampling
-NUM_RESAMPLE_THREADS = 10
-# Check if VCTK dataset is not already downloaded, if not download it
-if not os.path.exists(VCTK_DOWNLOAD_PATH):
- print(">>> Downloading VCTK dataset:")
- download_vctk(VCTK_DOWNLOAD_PATH)
- resample_files(VCTK_DOWNLOAD_PATH, SAMPLE_RATE, file_ext="flac", n_jobs=NUM_RESAMPLE_THREADS)
-
-# init configs
-vctk_config = BaseDatasetConfig(
- formatter="vctk",
- dataset_name="vctk",
- meta_file_train="",
- meta_file_val="",
- path=VCTK_DOWNLOAD_PATH,
- language="en",
- ignored_speakers=[
- "p261",
- "p225",
- "p294",
- "p347",
- "p238",
- "p234",
- "p248",
- "p335",
- "p245",
- "p326",
- "p302",
- ], # Ignore the test speakers to full replicate the paper experiment
-)
-
-# Add here all datasets configs, in our case we just want to train with the VCTK dataset then we need to add just VCTK. Note: If you want to add new datasets, just add them here and it will automatically compute the speaker embeddings (d-vectors) for this new dataset :)
-DATASETS_CONFIG_LIST = [vctk_config]
-
-### Extract speaker embeddings
-SPEAKER_ENCODER_CHECKPOINT_PATH = (
- "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar"
-)
-SPEAKER_ENCODER_CONFIG_PATH = "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json"
-
-D_VECTOR_FILES = [] # List of speaker embeddings/d-vectors to be used during the training
-
-# Iterates all the dataset configs checking if the speakers embeddings are already computated, if not compute it
-for dataset_conf in DATASETS_CONFIG_LIST:
- # Check if the embeddings weren't already computed, if not compute it
- embeddings_file = os.path.join(dataset_conf.path, "speakers.pth")
- if not os.path.isfile(embeddings_file):
- print(f">>> Computing the speaker embeddings for the {dataset_conf.dataset_name} dataset")
- compute_embeddings(
- SPEAKER_ENCODER_CHECKPOINT_PATH,
- SPEAKER_ENCODER_CONFIG_PATH,
- embeddings_file,
- old_speakers_file=None,
- config_dataset_path=None,
- formatter_name=dataset_conf.formatter,
- dataset_name=dataset_conf.dataset_name,
- dataset_path=dataset_conf.path,
- meta_file_train=dataset_conf.meta_file_train,
- meta_file_val=dataset_conf.meta_file_val,
- disable_cuda=False,
- no_eval=False,
- )
- D_VECTOR_FILES.append(embeddings_file)
-
-
-# Audio config used in training.
-audio_config = VitsAudioConfig(
- sample_rate=SAMPLE_RATE,
- hop_length=256,
- win_length=1024,
- fft_size=1024,
- mel_fmin=0.0,
- mel_fmax=None,
- num_mels=80,
-)
-
-# Init VITSArgs setting the arguments that are needed for the YourTTS model
-model_args = VitsArgs(
- d_vector_file=D_VECTOR_FILES,
- use_d_vector_file=True,
- d_vector_dim=512,
- num_layers_text_encoder=10,
- speaker_encoder_model_path=SPEAKER_ENCODER_CHECKPOINT_PATH,
- speaker_encoder_config_path=SPEAKER_ENCODER_CONFIG_PATH,
- resblock_type_decoder="2", # In the paper, we accidentally trained the YourTTS using ResNet blocks type 2, if you like you can use the ResNet blocks type 1 like the VITS model
- # Useful parameters to enable the Speaker Consistency Loss (SCL) described in the paper
- # use_speaker_encoder_as_loss=True,
- # Useful parameters to enable multilingual training
- # use_language_embedding=True,
- # embedded_language_dim=4,
-)
-
-# General training config, here you can change the batch size and others useful parameters
-config = VitsConfig(
- output_path=OUT_PATH,
- model_args=model_args,
- run_name=RUN_NAME,
- project_name="YourTTS",
- run_description="""
- - Original YourTTS trained using VCTK dataset
- """,
- dashboard_logger="tensorboard",
- logger_uri=None,
- audio=audio_config,
- batch_size=BATCH_SIZE,
- batch_group_size=48,
- eval_batch_size=BATCH_SIZE,
- num_loader_workers=8,
- eval_split_max_size=256,
- print_step=50,
- plot_step=100,
- log_model_step=1000,
- save_step=5000,
- save_n_checkpoints=2,
- save_checkpoints=True,
- target_loss="loss_1",
- print_eval=False,
- use_phonemes=False,
- phonemizer="espeak",
- phoneme_language="en",
- compute_input_seq_cache=True,
- add_blank=True,
- text_cleaner="multilingual_cleaners",
- characters=CharactersConfig(
- characters_class="TTS.tts.models.vits.VitsCharacters",
- pad="_",
- eos="&",
- bos="*",
- blank=None,
- characters="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\u00af\u00b7\u00df\u00e0\u00e1\u00e2\u00e3\u00e4\u00e6\u00e7\u00e8\u00e9\u00ea\u00eb\u00ec\u00ed\u00ee\u00ef\u00f1\u00f2\u00f3\u00f4\u00f5\u00f6\u00f9\u00fa\u00fb\u00fc\u00ff\u0101\u0105\u0107\u0113\u0119\u011b\u012b\u0131\u0142\u0144\u014d\u0151\u0153\u015b\u016b\u0171\u017a\u017c\u01ce\u01d0\u01d2\u01d4\u0430\u0431\u0432\u0433\u0434\u0435\u0436\u0437\u0438\u0439\u043a\u043b\u043c\u043d\u043e\u043f\u0440\u0441\u0442\u0443\u0444\u0445\u0446\u0447\u0448\u0449\u044a\u044b\u044c\u044d\u044e\u044f\u0451\u0454\u0456\u0457\u0491\u2013!'(),-.:;? ",
- punctuations="!'(),-.:;? ",
- phonemes="",
- is_unique=True,
- is_sorted=True,
- ),
- phoneme_cache_path=None,
- precompute_num_workers=12,
- start_by_longest=True,
- datasets=DATASETS_CONFIG_LIST,
- cudnn_benchmark=False,
- max_audio_len=SAMPLE_RATE * MAX_AUDIO_LEN_IN_SECONDS,
- mixed_precision=False,
- test_sentences=[
- [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "VCTK_p277",
- None,
- "en",
- ],
- [
- "Be a voice, not an echo.",
- "VCTK_p239",
- None,
- "en",
- ],
- [
- "I'm sorry Dave. I'm afraid I can't do that.",
- "VCTK_p258",
- None,
- "en",
- ],
- [
- "This cake is great. It's so delicious and moist.",
- "VCTK_p244",
- None,
- "en",
- ],
- [
- "Prior to November 22, 1963.",
- "VCTK_p305",
- None,
- "en",
- ],
- ],
- # Enable the weighted sampler
- use_weighted_sampler=True,
- # Ensures that all speakers are seen in the training batch equally no matter how many samples each speaker has
- weighted_sampler_attrs={"speaker_name": 1.0},
- weighted_sampler_multipliers={},
- # It defines the Speaker Consistency Loss (SCL) α to 9 like the paper
- speaker_encoder_loss_alpha=9.0,
-)
-
-# Load all the datasets samples and split traning and evaluation sets
-train_samples, eval_samples = load_tts_samples(
- config.datasets,
- eval_split=True,
- eval_split_max_size=config.eval_split_max_size,
- eval_split_size=config.eval_split_size,
-)
-
-# Init the model
-model = Vits.init_from_config(config)
-
-# Init the trainer and 🚀
-trainer = Trainer(
- TrainerArgs(restore_path=RESTORE_PATH, skip_train_epoch=SKIP_TRAIN_EPOCH),
- config,
- output_path=OUT_PATH,
- model=model,
- train_samples=train_samples,
- eval_samples=eval_samples,
-)
-trainer.fit()
diff --git a/TTS/requirements.dev.txt b/TTS/requirements.dev.txt
deleted file mode 100644
index 8c674727d3da0bd22788be40496d7578a315c2eb..0000000000000000000000000000000000000000
--- a/TTS/requirements.dev.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-black
-coverage
-isort
-nose2
-pylint==2.10.2
diff --git a/TTS/requirements.ja.txt b/TTS/requirements.ja.txt
deleted file mode 100644
index d6a04ca4b29ee87f1ae689472bc3dff6dde92ec5..0000000000000000000000000000000000000000
--- a/TTS/requirements.ja.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-# These cause some compatibility issues on some systems and are not strictly necessary
-# japanese g2p deps
-mecab-python3==1.0.6
-unidic-lite==1.0.8
diff --git a/TTS/requirements.notebooks.txt b/TTS/requirements.notebooks.txt
deleted file mode 100644
index 65d3f642c9dcaf109cd8697beb8672f53a81dd59..0000000000000000000000000000000000000000
--- a/TTS/requirements.notebooks.txt
+++ /dev/null
@@ -1 +0,0 @@
-bokeh==1.4.0
\ No newline at end of file
diff --git a/TTS/requirements.txt b/TTS/requirements.txt
deleted file mode 100644
index 90d238b7ba95d664e906205fd4e729b98a85093f..0000000000000000000000000000000000000000
--- a/TTS/requirements.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-# core deps
-numpy==1.22.0;python_version<="3.10"
-numpy==1.24.3;python_version>"3.10"
-cython==0.29.30
-scipy>=1.4.0
-torch>=1.7
-torchaudio
-soundfile
-librosa==0.10.0.*
-numba==0.55.1;python_version<"3.9"
-numba==0.57.0;python_version>="3.9"
-inflect==5.6.0
-tqdm
-anyascii
-pyyaml
-fsspec>=2021.04.0
-aiohttp
-packaging
-# deps for examples
-flask
-# deps for inference
-pysbd
-# deps for notebooks
-umap-learn==0.5.1
-pandas
-# deps for training
-matplotlib
-# coqui stack
-trainer
-# config management
-coqpit>=0.0.16
-# chinese g2p deps
-jieba
-pypinyin
-# gruut+supported langs
-gruut[de,es,fr]==2.2.3
-# deps for korean
-jamo
-nltk
-g2pkk>=0.1.1
-# deps for bangla
-bangla==0.0.2
-bnnumerizer
-bnunicodenormalizer==0.1.1
-#deps for tortoise
-k_diffusion
-einops
-transformers
-#deps for bark
-encodec
diff --git a/TTS/run_bash_tests.sh b/TTS/run_bash_tests.sh
deleted file mode 100644
index 2f5ba889343a2d188c0f914063cc24cd0205d05c..0000000000000000000000000000000000000000
--- a/TTS/run_bash_tests.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-set -e
-TF_CPP_MIN_LOG_LEVEL=3
-
-# runtime bash based tests
-# TODO: move these to python
-./tests/bash_tests/test_demo_server.sh && \
-./tests/bash_tests/test_compute_statistics.sh
diff --git a/TTS/server/README.md b/TTS/server/README.md
deleted file mode 100644
index 270656c4e39dc11636efbb1ba51eba7c9b4a8f04..0000000000000000000000000000000000000000
--- a/TTS/server/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# :frog: TTS demo server
-Before you use the server, make sure you [install](https://github.com/coqui-ai/TTS/tree/dev#install-tts)) :frog: TTS properly. Then, you can follow the steps below.
-
-**Note:** If you install :frog:TTS using ```pip```, you can also use the ```tts-server``` end point on the terminal.
-
-Examples runs:
-
-List officially released models.
-```python TTS/server/server.py --list_models ```
-
-Run the server with the official models.
-```python TTS/server/server.py --model_name tts_models/en/ljspeech/tacotron2-DCA --vocoder_name vocoder_models/en/ljspeech/multiband-melgan```
-
-Run the server with the official models on a GPU.
-```CUDA_VISIBLE_DEVICES="0" python TTS/server/server.py --model_name tts_models/en/ljspeech/tacotron2-DCA --vocoder_name vocoder_models/en/ljspeech/multiband-melgan --use_cuda True```
-
-Run the server with a custom models.
-```python TTS/server/server.py --tts_checkpoint /path/to/tts/model.pth --tts_config /path/to/tts/config.json --vocoder_checkpoint /path/to/vocoder/model.pth --vocoder_config /path/to/vocoder/config.json```
diff --git a/TTS/server/__init__.py b/TTS/server/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/server/conf.json b/TTS/server/conf.json
deleted file mode 100644
index 49b6c09c3848a224dfb39a1f653aa1b289a4b6e5..0000000000000000000000000000000000000000
--- a/TTS/server/conf.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
- "tts_path":"/media/erogol/data_ssd/Models/libri_tts/5049/", // tts model root folder
- "tts_file":"best_model.pth", // tts checkpoint file
- "tts_config":"config.json", // tts config.json file
- "tts_speakers": null, // json file listing speaker ids. null if no speaker embedding.
- "vocoder_config":null,
- "vocoder_file": null,
- "is_wavernn_batched":true,
- "port": 5002,
- "use_cuda": true,
- "debug": true
-}
diff --git a/TTS/server/server.py b/TTS/server/server.py
deleted file mode 100644
index 6b2141a9aa419b9095956ccae317621fa3a604da..0000000000000000000000000000000000000000
--- a/TTS/server/server.py
+++ /dev/null
@@ -1,258 +0,0 @@
-#!flask/bin/python
-import argparse
-import io
-import json
-import os
-import sys
-from pathlib import Path
-from threading import Lock
-from typing import Union
-from urllib.parse import parse_qs
-
-from flask import Flask, render_template, render_template_string, request, send_file
-
-from TTS.config import load_config
-from TTS.utils.manage import ModelManager
-from TTS.utils.synthesizer import Synthesizer
-
-
-def create_argparser():
- def convert_boolean(x):
- return x.lower() in ["true", "1", "yes"]
-
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--list_models",
- type=convert_boolean,
- nargs="?",
- const=True,
- default=False,
- help="list available pre-trained tts and vocoder models.",
- )
- parser.add_argument(
- "--model_name",
- type=str,
- default="tts_models/en/ljspeech/tacotron2-DDC",
- help="Name of one of the pre-trained tts models in format //",
- )
- parser.add_argument("--vocoder_name", type=str, default=None, help="name of one of the released vocoder models.")
-
- # Args for running custom models
- parser.add_argument("--config_path", default=None, type=str, help="Path to model config file.")
- parser.add_argument(
- "--model_path",
- type=str,
- default=None,
- help="Path to model file.",
- )
- parser.add_argument(
- "--vocoder_path",
- type=str,
- help="Path to vocoder model file. If it is not defined, model uses GL as vocoder. Please make sure that you installed vocoder library before (WaveRNN).",
- default=None,
- )
- parser.add_argument("--vocoder_config_path", type=str, help="Path to vocoder model config file.", default=None)
- parser.add_argument("--speakers_file_path", type=str, help="JSON file for multi-speaker model.", default=None)
- parser.add_argument("--port", type=int, default=5002, help="port to listen on.")
- parser.add_argument("--use_cuda", type=convert_boolean, default=False, help="true to use CUDA.")
- parser.add_argument("--debug", type=convert_boolean, default=False, help="true to enable Flask debug mode.")
- parser.add_argument("--show_details", type=convert_boolean, default=False, help="Generate model detail page.")
- return parser
-
-
-# parse the args
-args = create_argparser().parse_args()
-
-path = Path(__file__).parent / "../.models.json"
-manager = ModelManager(path)
-
-if args.list_models:
- manager.list_models()
- sys.exit()
-
-# update in-use models to the specified released models.
-model_path = None
-config_path = None
-speakers_file_path = None
-vocoder_path = None
-vocoder_config_path = None
-
-# CASE1: list pre-trained TTS models
-if args.list_models:
- manager.list_models()
- sys.exit()
-
-# CASE2: load pre-trained model paths
-if args.model_name is not None and not args.model_path:
- model_path, config_path, model_item = manager.download_model(args.model_name)
- args.vocoder_name = model_item["default_vocoder"] if args.vocoder_name is None else args.vocoder_name
-
-if args.vocoder_name is not None and not args.vocoder_path:
- vocoder_path, vocoder_config_path, _ = manager.download_model(args.vocoder_name)
-
-# CASE3: set custom model paths
-if args.model_path is not None:
- model_path = args.model_path
- config_path = args.config_path
- speakers_file_path = args.speakers_file_path
-
-if args.vocoder_path is not None:
- vocoder_path = args.vocoder_path
- vocoder_config_path = args.vocoder_config_path
-
-# load models
-synthesizer = Synthesizer(
- tts_checkpoint=model_path,
- tts_config_path=config_path,
- tts_speakers_file=speakers_file_path,
- tts_languages_file=None,
- vocoder_checkpoint=vocoder_path,
- vocoder_config=vocoder_config_path,
- encoder_checkpoint="",
- encoder_config="",
- use_cuda=args.use_cuda,
-)
-
-use_multi_speaker = hasattr(synthesizer.tts_model, "num_speakers") and (
- synthesizer.tts_model.num_speakers > 1 or synthesizer.tts_speakers_file is not None
-)
-speaker_manager = getattr(synthesizer.tts_model, "speaker_manager", None)
-
-use_multi_language = hasattr(synthesizer.tts_model, "num_languages") and (
- synthesizer.tts_model.num_languages > 1 or synthesizer.tts_languages_file is not None
-)
-language_manager = getattr(synthesizer.tts_model, "language_manager", None)
-
-# TODO: set this from SpeakerManager
-use_gst = synthesizer.tts_config.get("use_gst", False)
-app = Flask(__name__)
-
-
-def style_wav_uri_to_dict(style_wav: str) -> Union[str, dict]:
- """Transform an uri style_wav, in either a string (path to wav file to be use for style transfer)
- or a dict (gst tokens/values to be use for styling)
-
- Args:
- style_wav (str): uri
-
- Returns:
- Union[str, dict]: path to file (str) or gst style (dict)
- """
- if style_wav:
- if os.path.isfile(style_wav) and style_wav.endswith(".wav"):
- return style_wav # style_wav is a .wav file located on the server
-
- style_wav = json.loads(style_wav)
- return style_wav # style_wav is a gst dictionary with {token1_id : token1_weigth, ...}
- return None
-
-
-@app.route("/")
-def index():
- return render_template(
- "index.html",
- show_details=args.show_details,
- use_multi_speaker=use_multi_speaker,
- use_multi_language=use_multi_language,
- speaker_ids=speaker_manager.name_to_id if speaker_manager is not None else None,
- language_ids=language_manager.name_to_id if language_manager is not None else None,
- use_gst=use_gst,
- )
-
-
-@app.route("/details")
-def details():
- if args.config_path is not None and os.path.isfile(args.config_path):
- model_config = load_config(args.config_path)
- else:
- if args.model_name is not None:
- model_config = load_config(config_path)
-
- if args.vocoder_config_path is not None and os.path.isfile(args.vocoder_config_path):
- vocoder_config = load_config(args.vocoder_config_path)
- else:
- if args.vocoder_name is not None:
- vocoder_config = load_config(vocoder_config_path)
- else:
- vocoder_config = None
-
- return render_template(
- "details.html",
- show_details=args.show_details,
- model_config=model_config,
- vocoder_config=vocoder_config,
- args=args.__dict__,
- )
-
-
-lock = Lock()
-
-
-@app.route("/api/tts", methods=["GET", "POST"])
-def tts():
- with lock:
- text = request.headers.get("text") or request.values.get("text", "")
- speaker_idx = request.headers.get("speaker-id") or request.values.get("speaker_id", "")
- language_idx = request.headers.get("language-id") or request.values.get("language_id", "")
- style_wav = request.headers.get("style-wav") or request.values.get("style_wav", "")
- style_wav = style_wav_uri_to_dict(style_wav)
-
- print(f" > Model input: {text}")
- print(f" > Speaker Idx: {speaker_idx}")
- print(f" > Language Idx: {language_idx}")
- wavs = synthesizer.tts(text, speaker_name=speaker_idx, language_name=language_idx, style_wav=style_wav)
- out = io.BytesIO()
- synthesizer.save_wav(wavs, out)
- return send_file(out, mimetype="audio/wav")
-
-
-# Basic MaryTTS compatibility layer
-
-
-@app.route("/locales", methods=["GET"])
-def mary_tts_api_locales():
- """MaryTTS-compatible /locales endpoint"""
- # NOTE: We currently assume there is only one model active at the same time
- if args.model_name is not None:
- model_details = args.model_name.split("/")
- else:
- model_details = ["", "en", "", "default"]
- return render_template_string("{{ locale }}\n", locale=model_details[1])
-
-
-@app.route("/voices", methods=["GET"])
-def mary_tts_api_voices():
- """MaryTTS-compatible /voices endpoint"""
- # NOTE: We currently assume there is only one model active at the same time
- if args.model_name is not None:
- model_details = args.model_name.split("/")
- else:
- model_details = ["", "en", "", "default"]
- return render_template_string(
- "{{ name }} {{ locale }} {{ gender }}\n", name=model_details[3], locale=model_details[1], gender="u"
- )
-
-
-@app.route("/process", methods=["GET", "POST"])
-def mary_tts_api_process():
- """MaryTTS-compatible /process endpoint"""
- with lock:
- if request.method == "POST":
- data = parse_qs(request.get_data(as_text=True))
- # NOTE: we ignore param. LOCALE and VOICE for now since we have only one active model
- text = data.get("INPUT_TEXT", [""])[0]
- else:
- text = request.args.get("INPUT_TEXT", "")
- print(f" > Model input: {text}")
- wavs = synthesizer.tts(text)
- out = io.BytesIO()
- synthesizer.save_wav(wavs, out)
- return send_file(out, mimetype="audio/wav")
-
-
-def main():
- app.run(debug=args.debug, host="::", port=args.port)
-
-
-if __name__ == "__main__":
- main()
diff --git a/TTS/server/static/coqui-log-green-TTS.png b/TTS/server/static/coqui-log-green-TTS.png
deleted file mode 100644
index 6ad188b8c03a170097c0393c6769996f03cf9054..0000000000000000000000000000000000000000
Binary files a/TTS/server/static/coqui-log-green-TTS.png and /dev/null differ
diff --git a/TTS/server/templates/details.html b/TTS/server/templates/details.html
deleted file mode 100644
index 51c9ed85a83ac0aab045623ee1e6c430fbe51b9d..0000000000000000000000000000000000000000
--- a/TTS/server/templates/details.html
+++ /dev/null
@@ -1,131 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- TTS engine
-
-
-
-
-
-
-
-
-
-
-
- {% if show_details == true %}
-
- TTS engine
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- bʊ bɪr œrnˈɛc."
- ids = self.tokenizer_ph.text_to_ids(text)
- text_hat = self.tokenizer_ph.ids_to_text(ids)
- self.assertEqual(text_ph, text_hat)
-
- def test_print_logs(self):
- self.tokenizer.print_logs()
- self.tokenizer_ph.print_logs()
-
- def test_not_found_characters(self):
- self.ph = ESpeak("en-us")
- tokenizer_local = TTSTokenizer(use_phonemes=True, characters=IPAPhonemes(), phonemizer=self.ph)
- self.assertEqual(len(self.tokenizer.not_found_characters), 0)
- text = "Yolk of one egg beaten light"
- ids = tokenizer_local.text_to_ids(text)
- text_hat = tokenizer_local.ids_to_text(ids)
- self.assertEqual(tokenizer_local.not_found_characters, ["̩"])
- self.assertEqual(text_hat, "jˈoʊk ʌv wˈʌn ˈɛɡ bˈiːʔn lˈaɪt")
-
- def test_init_from_config(self):
- @dataclass
- class Characters(Coqpit):
- characters_class: str = None
- characters: str = _phonemes
- punctuations: str = _punctuations
- pad: str = _pad
- eos: str = _eos
- bos: str = _bos
- blank: str = _blank
- is_unique: bool = True
- is_sorted: bool = True
-
- @dataclass
- class TokenizerConfig(Coqpit):
- enable_eos_bos_chars: bool = True
- use_phonemes: bool = True
- add_blank: bool = False
- characters: str = field(default_factory=Characters)
- phonemizer: str = "espeak"
- phoneme_language: str = "tr"
- text_cleaner: str = "phoneme_cleaners"
- characters = field(default_factory=Characters)
-
- tokenizer_ph, _ = TTSTokenizer.init_from_config(TokenizerConfig())
- tokenizer_ph.phonemizer.backend = "espeak"
- text = "Bu bir Örnek."
- text_ph = "" + self.ph.phonemize(text, separator="") + ""
- ids = tokenizer_ph.text_to_ids(text)
- test_hat = tokenizer_ph.ids_to_text(ids)
- self.assertEqual(text_ph, test_hat)
diff --git a/TTS/tests/tts_tests/__init__.py b/TTS/tests/tts_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/tts_tests/test_helpers.py b/TTS/tests/tts_tests/test_helpers.py
deleted file mode 100644
index 23bb440a0af77b443e847b1c80620887bef485bb..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_helpers.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import torch as T
-
-from TTS.tts.utils.helpers import average_over_durations, generate_path, rand_segments, segment, sequence_mask
-
-
-def average_over_durations_test(): # pylint: disable=no-self-use
- pitch = T.rand(1, 1, 128)
-
- durations = T.randint(1, 5, (1, 21))
- coeff = 128.0 / durations.sum()
- durations = T.floor(durations * coeff)
- diff = 128.0 - durations.sum()
- durations[0, -1] += diff
- durations = durations.long()
-
- pitch_avg = average_over_durations(pitch, durations)
-
- index = 0
- for idx, dur in enumerate(durations[0]):
- assert abs(pitch_avg[0, 0, idx] - pitch[0, 0, index : index + dur.item()].mean()) < 1e-5
- index += dur
-
-
-def seqeunce_mask_test():
- lengths = T.randint(10, 15, (8,))
- mask = sequence_mask(lengths)
- for i in range(8):
- l = lengths[i].item()
- assert mask[i, :l].sum() == l
- assert mask[i, l:].sum() == 0
-
-
-def segment_test():
- x = T.range(0, 11)
- x = x.repeat(8, 1).unsqueeze(1)
- segment_ids = T.randint(0, 7, (8,))
-
- segments = segment(x, segment_ids, segment_size=4)
- for idx, start_indx in enumerate(segment_ids):
- assert x[idx, :, start_indx : start_indx + 4].sum() == segments[idx, :, :].sum()
-
- try:
- segments = segment(x, segment_ids, segment_size=10)
- raise Exception("Should have failed")
- except:
- pass
-
- segments = segment(x, segment_ids, segment_size=10, pad_short=True)
- for idx, start_indx in enumerate(segment_ids):
- assert x[idx, :, start_indx : start_indx + 10].sum() == segments[idx, :, :].sum()
-
-
-def rand_segments_test():
- x = T.rand(2, 3, 4)
- x_lens = T.randint(3, 4, (2,))
- segments, seg_idxs = rand_segments(x, x_lens, segment_size=3)
- assert segments.shape == (2, 3, 3)
- assert all(seg_idxs >= 0), seg_idxs
- try:
- segments, _ = rand_segments(x, x_lens, segment_size=5)
- raise Exception("Should have failed")
- except:
- pass
- x_lens_back = x_lens.clone()
- segments, seg_idxs = rand_segments(x, x_lens.clone(), segment_size=5, pad_short=True, let_short_samples=True)
- assert segments.shape == (2, 3, 5)
- assert all(seg_idxs >= 0), seg_idxs
- assert all(x_lens_back == x_lens)
-
-
-def generate_path_test():
- durations = T.randint(1, 4, (10, 21))
- x_length = T.randint(18, 22, (10,))
- x_mask = sequence_mask(x_length).unsqueeze(1).long()
- durations = durations * x_mask.squeeze(1)
- y_length = durations.sum(1)
- y_mask = sequence_mask(y_length).unsqueeze(1).long()
- attn_mask = (T.unsqueeze(x_mask, -1) * T.unsqueeze(y_mask, 2)).squeeze(1).long()
- print(attn_mask.shape)
- path = generate_path(durations, attn_mask)
- assert path.shape == (10, 21, durations.sum(1).max().item())
- for b in range(durations.shape[0]):
- current_idx = 0
- for t in range(durations.shape[1]):
- assert all(path[b, t, current_idx : current_idx + durations[b, t].item()] == 1.0)
- assert all(path[b, t, :current_idx] == 0.0)
- assert all(path[b, t, current_idx + durations[b, t].item() :] == 0.0)
- current_idx += durations[b, t].item()
diff --git a/TTS/tests/tts_tests/test_losses.py b/TTS/tests/tts_tests/test_losses.py
deleted file mode 100644
index 522b7bb17ca59ee62d2b1de7245a3eab91339417..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_losses.py
+++ /dev/null
@@ -1,239 +0,0 @@
-import unittest
-
-import torch as T
-
-from TTS.tts.layers.losses import BCELossMasked, L1LossMasked, MSELossMasked, SSIMLoss
-from TTS.tts.utils.helpers import sequence_mask
-
-
-class L1LossMaskedTests(unittest.TestCase):
- def test_in_out(self): # pylint: disable=no-self-use
- # test input == target
- layer = L1LossMasked(seq_len_norm=False)
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.ones(4, 8, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 0.0
-
- # test input != target
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.zeros(4, 8, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 1.0, "1.0 vs {}".format(output.item())
-
- # test if padded values of input makes any difference
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.zeros(4, 8, 128).float()
- dummy_length = (T.arange(5, 9)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert output.item() == 1.0, "1.0 vs {}".format(output.item())
-
- dummy_input = T.rand(4, 8, 128).float()
- dummy_target = dummy_input.detach()
- dummy_length = (T.arange(5, 9)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert output.item() == 0, "0 vs {}".format(output.item())
-
- # seq_len_norm = True
- # test input == target
- layer = L1LossMasked(seq_len_norm=True)
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.ones(4, 8, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 0.0
-
- # test input != target
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.zeros(4, 8, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 1.0, "1.0 vs {}".format(output.item())
-
- # test if padded values of input makes any difference
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.zeros(4, 8, 128).float()
- dummy_length = (T.arange(5, 9)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert abs(output.item() - 1.0) < 1e-5, "1.0 vs {}".format(output.item())
-
- dummy_input = T.rand(4, 8, 128).float()
- dummy_target = dummy_input.detach()
- dummy_length = (T.arange(5, 9)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert output.item() == 0, "0 vs {}".format(output.item())
-
-
-class MSELossMaskedTests(unittest.TestCase):
- def test_in_out(self): # pylint: disable=no-self-use
- # test input == target
- layer = MSELossMasked(seq_len_norm=False)
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.ones(4, 8, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 0.0
-
- # test input != target
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.zeros(4, 8, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 1.0, "1.0 vs {}".format(output.item())
-
- # test if padded values of input makes any difference
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.zeros(4, 8, 128).float()
- dummy_length = (T.arange(5, 9)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert output.item() == 1.0, "1.0 vs {}".format(output.item())
-
- dummy_input = T.rand(4, 8, 128).float()
- dummy_target = dummy_input.detach()
- dummy_length = (T.arange(5, 9)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert output.item() == 0, "0 vs {}".format(output.item())
-
- # seq_len_norm = True
- # test input == target
- layer = MSELossMasked(seq_len_norm=True)
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.ones(4, 8, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 0.0
-
- # test input != target
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.zeros(4, 8, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 1.0, "1.0 vs {}".format(output.item())
-
- # test if padded values of input makes any difference
- dummy_input = T.ones(4, 8, 128).float()
- dummy_target = T.zeros(4, 8, 128).float()
- dummy_length = (T.arange(5, 9)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert abs(output.item() - 1.0) < 1e-5, "1.0 vs {}".format(output.item())
-
- dummy_input = T.rand(4, 8, 128).float()
- dummy_target = dummy_input.detach()
- dummy_length = (T.arange(5, 9)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert output.item() == 0, "0 vs {}".format(output.item())
-
-
-class SSIMLossTests(unittest.TestCase):
- def test_in_out(self): # pylint: disable=no-self-use
- # test input == target
- layer = SSIMLoss()
- dummy_input = T.ones(4, 57, 128).float()
- dummy_target = T.ones(4, 57, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 0.0
-
- # test input != target
- dummy_input = T.arange(0, 4 * 57 * 128)
- dummy_input = dummy_input.reshape(4, 57, 128).float()
- dummy_target = T.arange(-4 * 57 * 128, 0)
- dummy_target = dummy_target.reshape(4, 57, 128).float()
- dummy_target = -dummy_target
-
- dummy_length = (T.ones(4) * 58).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() >= 1.0, "0 vs {}".format(output.item())
-
- # test if padded values of input makes any difference
- dummy_input = T.ones(4, 57, 128).float()
- dummy_target = T.zeros(4, 57, 128).float()
- dummy_length = (T.arange(54, 58)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert output.item() == 0.0
-
- dummy_input = T.rand(4, 57, 128).float()
- dummy_target = dummy_input.detach()
- dummy_length = (T.arange(54, 58)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert output.item() == 0, "0 vs {}".format(output.item())
-
- # seq_len_norm = True
- # test input == target
- layer = L1LossMasked(seq_len_norm=True)
- dummy_input = T.ones(4, 57, 128).float()
- dummy_target = T.ones(4, 57, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 0.0
-
- # test input != target
- dummy_input = T.ones(4, 57, 128).float()
- dummy_target = T.zeros(4, 57, 128).float()
- dummy_length = (T.ones(4) * 8).long()
- output = layer(dummy_input, dummy_target, dummy_length)
- assert output.item() == 1.0, "1.0 vs {}".format(output.item())
-
- # test if padded values of input makes any difference
- dummy_input = T.ones(4, 57, 128).float()
- dummy_target = T.zeros(4, 57, 128).float()
- dummy_length = (T.arange(54, 58)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert abs(output.item() - 1.0) < 1e-5, "1.0 vs {}".format(output.item())
-
- dummy_input = T.rand(4, 57, 128).float()
- dummy_target = dummy_input.detach()
- dummy_length = (T.arange(54, 58)).long()
- mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
- output = layer(dummy_input + mask, dummy_target, dummy_length)
- assert output.item() == 0, "0 vs {}".format(output.item())
-
-
-class BCELossTest(unittest.TestCase):
- def test_in_out(self): # pylint: disable=no-self-use
- layer = BCELossMasked(pos_weight=5.0)
-
- length = T.tensor([95])
- target = (
- 1.0 - sequence_mask(length - 1, 100).float()
- ) # [0, 0, .... 1, 1] where the first 1 is the last mel frame
- true_x = target * 200 - 100 # creates logits of [-100, -100, ... 100, 100] corresponding to target
- zero_x = T.zeros(target.shape) - 100.0 # simulate logits if it never stops decoding
- early_x = -200.0 * sequence_mask(length - 3, 100).float() + 100.0 # simulate logits on early stopping
- late_x = -200.0 * sequence_mask(length + 1, 100).float() + 100.0 # simulate logits on late stopping
-
- loss = layer(true_x, target, length)
- self.assertEqual(loss.item(), 0.0)
-
- loss = layer(early_x, target, length)
- self.assertAlmostEqual(loss.item(), 2.1053, places=4)
-
- loss = layer(late_x, target, length)
- self.assertAlmostEqual(loss.item(), 5.2632, places=4)
-
- loss = layer(zero_x, target, length)
- self.assertAlmostEqual(loss.item(), 5.2632, places=4)
-
- # pos_weight should be < 1 to penalize early stopping
- layer = BCELossMasked(pos_weight=0.2)
- loss = layer(true_x, target, length)
- self.assertEqual(loss.item(), 0.0)
-
- # when pos_weight < 1 overweight the early stopping loss
-
- loss_early = layer(early_x, target, length)
- loss_late = layer(late_x, target, length)
- self.assertGreater(loss_early.item(), loss_late.item())
diff --git a/TTS/tests/tts_tests/test_neuralhmm_tts_train.py b/TTS/tests/tts_tests/test_neuralhmm_tts_train.py
deleted file mode 100644
index 25d9aa8148aff95a75aad823eb8d7bff13f09e12..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_neuralhmm_tts_train.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-import torch
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.neuralhmm_tts_config import NeuralhmmTTSConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-parameter_path = os.path.join(get_tests_output_path(), "lj_parameters.pt")
-
-torch.save({"mean": -5.5138, "std": 2.0636, "init_transition_prob": 0.3212}, parameter_path)
-
-config = NeuralhmmTTSConfig(
- batch_size=3,
- eval_batch_size=3,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
- run_eval=True,
- test_delay_epochs=-1,
- mel_statistics_parameter_path=parameter_path,
- epochs=1,
- print_step=1,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- print_eval=True,
- max_sampling_time=50,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-
-# train the model for one epoch when mel parameters exists
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.test_delay_epochs 0 "
-)
-run_cli(command_train)
-
-
-# train the model for one epoch when mel parameters have to be computed from the dataset
-if os.path.exists(parameter_path):
- os.remove(parameter_path)
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.test_delay_epochs 0 "
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_overflow.py b/TTS/tests/tts_tests/test_overflow.py
deleted file mode 100644
index 01c447198f1bc0ebd4f330299d3387c9911fd344..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_overflow.py
+++ /dev/null
@@ -1,399 +0,0 @@
-import os
-import random
-import unittest
-from copy import deepcopy
-
-import torch
-
-from tests import get_tests_output_path
-from TTS.tts.configs.overflow_config import OverflowConfig
-from TTS.tts.layers.overflow.common_layers import Encoder, Outputnet, OverflowUtils
-from TTS.tts.layers.overflow.decoder import Decoder
-from TTS.tts.layers.overflow.neural_hmm import EmissionModel, NeuralHMM, TransitionModel
-from TTS.tts.models.overflow import Overflow
-from TTS.tts.utils.helpers import sequence_mask
-from TTS.utils.audio import AudioProcessor
-
-# pylint: disable=unused-variable
-
-torch.manual_seed(1)
-use_cuda = torch.cuda.is_available()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-config_global = OverflowConfig(num_chars=24)
-ap = AudioProcessor.init_from_config(config_global)
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-parameter_path = os.path.join(get_tests_output_path(), "lj_parameters.pt")
-
-torch.save({"mean": -5.5138, "std": 2.0636, "init_transition_prob": 0.3212}, parameter_path)
-
-
-def _create_inputs(batch_size=8):
- max_len_t, max_len_m = random.randint(25, 50), random.randint(50, 80)
- input_dummy = torch.randint(0, 24, (batch_size, max_len_t)).long().to(device)
- input_lengths = torch.randint(20, max_len_t, (batch_size,)).long().to(device).sort(descending=True)[0]
- input_lengths[0] = max_len_t
- input_dummy = input_dummy * sequence_mask(input_lengths)
- mel_spec = torch.randn(batch_size, max_len_m, config_global.audio["num_mels"]).to(device)
- mel_lengths = torch.randint(40, max_len_m, (batch_size,)).long().to(device).sort(descending=True)[0]
- mel_lengths[0] = max_len_m
- mel_spec = mel_spec * sequence_mask(mel_lengths).unsqueeze(2)
- return input_dummy, input_lengths, mel_spec, mel_lengths
-
-
-def get_model(config=None):
- if config is None:
- config = config_global
- config.mel_statistics_parameter_path = parameter_path
- model = Overflow(config)
- model = model.to(device)
- return model
-
-
-def reset_all_weights(model):
- """
- refs:
- - https://discuss.pytorch.org/t/how-to-re-set-alll-parameters-in-a-network/20819/6
- - https://stackoverflow.com/questions/63627997/reset-parameters-of-a-neural-network-in-pytorch
- - https://pytorch.org/docs/stable/generated/torch.nn.Module.html
- """
-
- @torch.no_grad()
- def weight_reset(m):
- # - check if the current module has reset_parameters & if it's callabed called it on m
- reset_parameters = getattr(m, "reset_parameters", None)
- if callable(reset_parameters):
- m.reset_parameters()
-
- # Applies fn recursively to every submodule see: https://pytorch.org/docs/stable/generated/torch.nn.Module.html
- model.apply(fn=weight_reset)
-
-
-class TestOverflow(unittest.TestCase):
- def test_forward(self):
- model = get_model()
- input_dummy, input_lengths, mel_spec, mel_lengths = _create_inputs()
- outputs = model(input_dummy, input_lengths, mel_spec, mel_lengths)
- self.assertEqual(outputs["log_probs"].shape, (input_dummy.shape[0],))
- self.assertEqual(model.state_per_phone * max(input_lengths), outputs["alignments"].shape[2])
-
- def test_inference(self):
- model = get_model()
- input_dummy, input_lengths, mel_spec, mel_lengths = _create_inputs()
- output_dict = model.inference(input_dummy)
- self.assertEqual(output_dict["model_outputs"].shape[2], config_global.out_channels)
-
- def test_init_from_config(self):
- config = deepcopy(config_global)
- config.mel_statistics_parameter_path = parameter_path
- config.prenet_dim = 256
- model = Overflow.init_from_config(config_global)
- self.assertEqual(model.prenet_dim, config.prenet_dim)
-
-
-class TestOverflowEncoder(unittest.TestCase):
- @staticmethod
- def get_encoder(state_per_phone):
- config = deepcopy(config_global)
- config.state_per_phone = state_per_phone
- config.num_chars = 24
- return Encoder(config.num_chars, config.state_per_phone, config.prenet_dim, config.encoder_n_convolutions).to(
- device
- )
-
- def test_forward_with_state_per_phone_multiplication(self):
- for s_p_p in [1, 2, 3]:
- input_dummy, input_lengths, _, _ = _create_inputs()
- model = self.get_encoder(s_p_p)
- x, x_len = model(input_dummy, input_lengths)
- self.assertEqual(x.shape[1], input_dummy.shape[1] * s_p_p)
-
- def test_inference_with_state_per_phone_multiplication(self):
- for s_p_p in [1, 2, 3]:
- input_dummy, input_lengths, _, _ = _create_inputs()
- model = self.get_encoder(s_p_p)
- x, x_len = model.inference(input_dummy, input_lengths)
- self.assertEqual(x.shape[1], input_dummy.shape[1] * s_p_p)
-
-
-class TestOverflowUtils(unittest.TestCase):
- def test_logsumexp(self):
- a = torch.randn(10) # random numbers
- self.assertTrue(torch.eq(torch.logsumexp(a, dim=0), OverflowUtils.logsumexp(a, dim=0)).all())
-
- a = torch.zeros(10) # all zeros
- self.assertTrue(torch.eq(torch.logsumexp(a, dim=0), OverflowUtils.logsumexp(a, dim=0)).all())
-
- a = torch.ones(10) # all ones
- self.assertTrue(torch.eq(torch.logsumexp(a, dim=0), OverflowUtils.logsumexp(a, dim=0)).all())
-
-
-class TestOverflowDecoder(unittest.TestCase):
- @staticmethod
- def _get_decoder(num_flow_blocks_dec=None, hidden_channels_dec=None, reset_weights=True):
- config = deepcopy(config_global)
- config.num_flow_blocks_dec = (
- num_flow_blocks_dec if num_flow_blocks_dec is not None else config.num_flow_blocks_dec
- )
- config.hidden_channels_dec = (
- hidden_channels_dec if hidden_channels_dec is not None else config.hidden_channels_dec
- )
- config.dropout_p_dec = 0.0 # turn off dropout to check invertibility
- decoder = Decoder(
- config.out_channels,
- config.hidden_channels_dec,
- config.kernel_size_dec,
- config.dilation_rate,
- config.num_flow_blocks_dec,
- config.num_block_layers,
- config.dropout_p_dec,
- config.num_splits,
- config.num_squeeze,
- config.sigmoid_scale,
- config.c_in_channels,
- ).to(device)
- if reset_weights:
- reset_all_weights(decoder)
- return decoder
-
- def test_decoder_forward_backward(self):
- for num_flow_blocks_dec in [8, None]:
- for hidden_channels_dec in [100, None]:
- decoder = self._get_decoder(num_flow_blocks_dec, hidden_channels_dec)
- _, _, mel_spec, mel_lengths = _create_inputs()
- z, z_len, _ = decoder(mel_spec.transpose(1, 2), mel_lengths)
- mel_spec_, mel_lengths_, _ = decoder(z, z_len, reverse=True)
- mask = sequence_mask(z_len).unsqueeze(1)
- mel_spec = mel_spec[:, : z.shape[2], :].transpose(1, 2) * mask
- z = z * mask
- self.assertTrue(
- torch.isclose(mel_spec, mel_spec_, atol=1e-2).all(),
- f"num_flow_blocks_dec={num_flow_blocks_dec}, hidden_channels_dec={hidden_channels_dec}",
- )
-
-
-class TestNeuralHMM(unittest.TestCase):
- @staticmethod
- def _get_neural_hmm(deterministic_transition=None):
- config = deepcopy(config_global)
- neural_hmm = NeuralHMM(
- config.out_channels,
- config.ar_order,
- config.deterministic_transition if deterministic_transition is None else deterministic_transition,
- config.encoder_in_out_features,
- config.prenet_type,
- config.prenet_dim,
- config.prenet_n_layers,
- config.prenet_dropout,
- config.prenet_dropout_at_inference,
- config.memory_rnn_dim,
- config.outputnet_size,
- config.flat_start_params,
- config.std_floor,
- ).to(device)
- return neural_hmm
-
- @staticmethod
- def _get_emission_model():
- return EmissionModel().to(device)
-
- @staticmethod
- def _get_transition_model():
- return TransitionModel().to(device)
-
- @staticmethod
- def _get_embedded_input():
- input_dummy, input_lengths, mel_spec, mel_lengths = _create_inputs()
- input_dummy = torch.nn.Embedding(config_global.num_chars, config_global.encoder_in_out_features).to(device)(
- input_dummy
- )
- return input_dummy, input_lengths, mel_spec, mel_lengths
-
- def test_neural_hmm_forward(self):
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- neural_hmm = self._get_neural_hmm()
- log_prob, log_alpha_scaled, transition_matrix, means = neural_hmm(
- input_dummy, input_lengths, mel_spec.transpose(1, 2), mel_lengths
- )
- self.assertEqual(log_prob.shape, (input_dummy.shape[0],))
- self.assertEqual(log_alpha_scaled.shape, transition_matrix.shape)
-
- def test_mask_lengths(self):
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- neural_hmm = self._get_neural_hmm()
- log_prob, log_alpha_scaled, transition_matrix, means = neural_hmm(
- input_dummy, input_lengths, mel_spec.transpose(1, 2), mel_lengths
- )
- log_c = torch.randn(mel_spec.shape[0], mel_spec.shape[1], device=device)
- log_c, log_alpha_scaled = neural_hmm._mask_lengths( # pylint: disable=protected-access
- mel_lengths, log_c, log_alpha_scaled
- )
- assertions = []
- for i in range(mel_spec.shape[0]):
- assertions.append(log_c[i, mel_lengths[i] :].sum() == 0.0)
- self.assertTrue(all(assertions), "Incorrect masking")
- assertions = []
- for i in range(mel_spec.shape[0]):
- assertions.append(log_alpha_scaled[i, mel_lengths[i] :, : input_lengths[i]].sum() == 0.0)
- self.assertTrue(all(assertions), "Incorrect masking")
-
- def test_process_ar_timestep(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
-
- h_post_prenet, c_post_prenet = model._init_lstm_states( # pylint: disable=protected-access
- input_dummy.shape[0], config_global.memory_rnn_dim, mel_spec
- )
- h_post_prenet, c_post_prenet = model._process_ar_timestep( # pylint: disable=protected-access
- 1,
- mel_spec,
- h_post_prenet,
- c_post_prenet,
- )
-
- self.assertEqual(h_post_prenet.shape, (input_dummy.shape[0], config_global.memory_rnn_dim))
- self.assertEqual(c_post_prenet.shape, (input_dummy.shape[0], config_global.memory_rnn_dim))
-
- def test_add_go_token(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
-
- out = model._add_go_token(mel_spec) # pylint: disable=protected-access
- self.assertEqual(out.shape, mel_spec.shape)
- self.assertTrue((out[:, 1:] == mel_spec[:, :-1]).all(), "Go token not appended properly")
-
- def test_forward_algorithm_variables(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
-
- (
- log_c,
- log_alpha_scaled,
- transition_matrix,
- _,
- ) = model._initialize_forward_algorithm_variables( # pylint: disable=protected-access
- mel_spec, input_dummy.shape[1] * config_global.state_per_phone
- )
-
- self.assertEqual(log_c.shape, (mel_spec.shape[0], mel_spec.shape[1]))
- self.assertEqual(
- log_alpha_scaled.shape,
- (
- mel_spec.shape[0],
- mel_spec.shape[1],
- input_dummy.shape[1] * config_global.state_per_phone,
- ),
- )
- self.assertEqual(
- transition_matrix.shape,
- (mel_spec.shape[0], mel_spec.shape[1], input_dummy.shape[1] * config_global.state_per_phone),
- )
-
- def test_get_absorption_state_scaling_factor(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- input_lengths = input_lengths * config_global.state_per_phone
- (
- log_c,
- log_alpha_scaled,
- transition_matrix,
- _,
- ) = model._initialize_forward_algorithm_variables( # pylint: disable=protected-access
- mel_spec, input_dummy.shape[1] * config_global.state_per_phone
- )
- log_alpha_scaled = torch.rand_like(log_alpha_scaled).clamp(1e-3)
- transition_matrix = torch.randn_like(transition_matrix).sigmoid().log()
- sum_final_log_c = model.get_absorption_state_scaling_factor(
- mel_lengths, log_alpha_scaled, input_lengths, transition_matrix
- )
-
- text_mask = ~sequence_mask(input_lengths)
- transition_prob_mask = ~model.get_mask_for_last_item(input_lengths, device=input_lengths.device)
-
- outputs = []
-
- for i in range(input_dummy.shape[0]):
- last_log_alpha_scaled = log_alpha_scaled[i, mel_lengths[i] - 1].masked_fill(text_mask[i], -float("inf"))
- log_last_transition_probability = OverflowUtils.log_clamped(
- torch.sigmoid(transition_matrix[i, mel_lengths[i] - 1])
- ).masked_fill(transition_prob_mask[i], -float("inf"))
- outputs.append(last_log_alpha_scaled + log_last_transition_probability)
-
- sum_final_log_c_computed = torch.logsumexp(torch.stack(outputs), dim=1)
-
- self.assertTrue(torch.isclose(sum_final_log_c_computed, sum_final_log_c).all())
-
- def test_inference(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- for temp in [0.334, 0.667, 1.0]:
- outputs = model.inference(
- input_dummy, input_lengths, temp, config_global.max_sampling_time, config_global.duration_threshold
- )
- self.assertEqual(outputs["hmm_outputs"].shape[-1], outputs["input_parameters"][0][0][0].shape[-1])
- self.assertEqual(
- outputs["output_parameters"][0][0][0].shape[-1], outputs["input_parameters"][0][0][0].shape[-1]
- )
- self.assertEqual(len(outputs["alignments"]), input_dummy.shape[0])
-
- def test_emission_model(self):
- model = self._get_emission_model()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- x_t = torch.randn(input_dummy.shape[0], config_global.out_channels).to(device)
- means = torch.randn(input_dummy.shape[0], input_dummy.shape[1], config_global.out_channels).to(device)
- std = torch.rand_like(means).to(device).clamp_(1e-3) # std should be positive
- out = model(x_t, means, std, input_lengths)
- self.assertEqual(out.shape, (input_dummy.shape[0], input_dummy.shape[1]))
-
- # testing sampling
- for temp in [0, 0.334, 0.667]:
- out = model.sample(means, std, 0)
- self.assertEqual(out.shape, means.shape)
- if temp == 0:
- self.assertTrue(torch.isclose(out, means).all())
-
- def test_transition_model(self):
- model = self._get_transition_model()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- prev_t_log_scaled_alph = torch.randn(input_dummy.shape[0], input_lengths.max()).to(device)
- transition_vector = torch.randn(input_lengths.max()).to(device)
- out = model(prev_t_log_scaled_alph, transition_vector, input_lengths)
- self.assertEqual(out.shape, (input_dummy.shape[0], input_lengths.max()))
-
-
-class TestOverflowOutputNet(unittest.TestCase):
- @staticmethod
- def _get_outputnet():
- config = deepcopy(config_global)
- outputnet = Outputnet(
- config.encoder_in_out_features,
- config.memory_rnn_dim,
- config.out_channels,
- config.outputnet_size,
- config.flat_start_params,
- config.std_floor,
- ).to(device)
- return outputnet
-
- @staticmethod
- def _get_embedded_input():
- input_dummy, input_lengths, mel_spec, mel_lengths = _create_inputs()
- input_dummy = torch.nn.Embedding(config_global.num_chars, config_global.encoder_in_out_features).to(device)(
- input_dummy
- )
- one_timestep_frame = torch.randn(input_dummy.shape[0], config_global.memory_rnn_dim).to(device)
- return input_dummy, one_timestep_frame
-
- def test_outputnet_forward_with_flat_start(self):
- model = self._get_outputnet()
- input_dummy, one_timestep_frame = self._get_embedded_input()
- mean, std, transition_vector = model(one_timestep_frame, input_dummy)
- self.assertTrue(torch.isclose(mean, torch.tensor(model.flat_start_params["mean"] * 1.0)).all())
- self.assertTrue(torch.isclose(std, torch.tensor(model.flat_start_params["std"] * 1.0)).all())
- self.assertTrue(
- torch.isclose(
- transition_vector.sigmoid(), torch.tensor(model.flat_start_params["transition_p"] * 1.0)
- ).all()
- )
diff --git a/TTS/tests/tts_tests/test_overflow_train.py b/TTS/tests/tts_tests/test_overflow_train.py
deleted file mode 100644
index 86fa60af72b7cda704aa6e1618793f2d52d463af..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_overflow_train.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-import torch
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.overflow_config import OverflowConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-parameter_path = os.path.join(get_tests_output_path(), "lj_parameters.pt")
-
-torch.save({"mean": -5.5138, "std": 2.0636, "init_transition_prob": 0.3212}, parameter_path)
-
-config = OverflowConfig(
- batch_size=3,
- eval_batch_size=3,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="phoneme_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
- run_eval=True,
- test_delay_epochs=-1,
- mel_statistics_parameter_path=parameter_path,
- epochs=1,
- print_step=1,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- print_eval=True,
- max_sampling_time=50,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-
-# train the model for one epoch when mel parameters exists
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.test_delay_epochs 0 "
-)
-run_cli(command_train)
-
-
-# train the model for one epoch when mel parameters have to be computed from the dataset
-if os.path.exists(parameter_path):
- os.remove(parameter_path)
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.test_delay_epochs 0 "
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_speedy_speech_train.py b/TTS/tests/tts_tests/test_speedy_speech_train.py
deleted file mode 100644
index 530781ef887d17a290b65810d0f3c5760217c920..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_speedy_speech_train.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_speedy_speech_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = SpeedySpeechConfig(
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example for it.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_tacotron2_d-vectors_train.py b/TTS/tests/tts_tests/test_tacotron2_d-vectors_train.py
deleted file mode 100644
index 99ba4349c48fcc10a352459d9d5d5f64c0b7a4de..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_tacotron2_d-vectors_train.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = Tacotron2Config(
- r=5,
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=False,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- use_speaker_embedding=False,
- use_d_vector_file=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- d_vector_file="tests/data/ljspeech/speakers.json",
- d_vector_dim=256,
- max_decoder_steps=50,
-)
-
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech_test "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.test_delay_epochs 0 "
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech-1"
-continue_speakers_path = config.d_vector_file
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_tacotron2_model.py b/TTS/tests/tts_tests/test_tacotron2_model.py
deleted file mode 100644
index b1bdeb9fd16536efe22c64f2309c46b7bae44e22..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_tacotron2_model.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import os
-import unittest
-
-import torch
-from torch import nn, optim
-
-from tests import get_tests_input_path
-from TTS.tts.configs.shared_configs import CapacitronVAEConfig, GSTConfig
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from TTS.tts.layers.losses import MSELossMasked
-from TTS.tts.models.tacotron2 import Tacotron2
-from TTS.utils.audio import AudioProcessor
-
-# pylint: disable=unused-variable
-
-torch.manual_seed(1)
-use_cuda = torch.cuda.is_available()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-config_global = Tacotron2Config(num_chars=32, num_speakers=5, out_channels=80, decoder_output_dim=80)
-
-ap = AudioProcessor(**config_global.audio)
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-
-
-class TacotronTrainTest(unittest.TestCase):
- """Test vanilla Tacotron2 model."""
-
- def test_train_step(self): # pylint: disable=no-self-use
- config = config_global.copy()
- config.use_speaker_embedding = False
- config.num_speakers = 1
-
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 128, (8,)).long().to(device)
- input_lengths = torch.sort(input_lengths, descending=True)[0]
- mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
- mel_lengths[0] = 30
- stop_targets = torch.zeros(8, 30, 1).float().to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- criterion = MSELossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- model = Tacotron2(config).to(device)
- model.train()
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for i in range(5):
- outputs = model.forward(input_dummy, input_lengths, mel_spec, mel_lengths)
- assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
- assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], mel_postnet_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- # if count not in [145, 59]:
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
-
-class MultiSpeakerTacotronTrainTest(unittest.TestCase):
- """Test multi-speaker Tacotron2 with speaker embedding layer"""
-
- @staticmethod
- def test_train_step():
- config = config_global.copy()
- config.use_speaker_embedding = True
- config.num_speakers = 5
-
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 128, (8,)).long().to(device)
- input_lengths = torch.sort(input_lengths, descending=True)[0]
- mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
- mel_lengths[0] = 30
- stop_targets = torch.zeros(8, 30, 1).float().to(device)
- speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- criterion = MSELossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- config.d_vector_dim = 55
- model = Tacotron2(config).to(device)
- model.train()
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for _ in range(5):
- outputs = model.forward(
- input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"speaker_ids": speaker_ids}
- )
- assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
- assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], mel_postnet_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- # if count not in [145, 59]:
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
-
-class TacotronGSTTrainTest(unittest.TestCase):
- """Test multi-speaker Tacotron2 with Global Style Token and Speaker Embedding"""
-
- # pylint: disable=no-self-use
- def test_train_step(self):
- # with random gst mel style
- config = config_global.copy()
- config.use_speaker_embedding = True
- config.num_speakers = 10
- config.use_gst = True
- config.gst = GSTConfig()
-
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 128, (8,)).long().to(device)
- input_lengths = torch.sort(input_lengths, descending=True)[0]
- mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
- mel_lengths[0] = 30
- stop_targets = torch.zeros(8, 30, 1).float().to(device)
- speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- criterion = MSELossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- config.use_gst = True
- config.gst = GSTConfig()
- model = Tacotron2(config).to(device)
- model.train()
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for i in range(10):
- outputs = model.forward(
- input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"speaker_ids": speaker_ids}
- )
- assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
- assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], mel_postnet_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for name_param, param_ref in zip(model.named_parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- # if count not in [145, 59]:
- name, param = name_param
- if name == "gst_layer.encoder.recurrence.weight_hh_l0":
- # print(param.grad)
- continue
- assert (param != param_ref).any(), "param {} {} with shape {} not updated!! \n{}\n{}".format(
- name, count, param.shape, param, param_ref
- )
- count += 1
-
- # with file gst style
- mel_spec = (
- torch.FloatTensor(ap.melspectrogram(ap.load_wav(WAV_FILE)))[:, :30].unsqueeze(0).transpose(1, 2).to(device)
- )
- mel_spec = mel_spec.repeat(8, 1, 1)
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 128, (8,)).long().to(device)
- input_lengths = torch.sort(input_lengths, descending=True)[0]
- mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
- mel_lengths[0] = 30
- stop_targets = torch.zeros(8, 30, 1).float().to(device)
- speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- criterion = MSELossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- model = Tacotron2(config).to(device)
- model.train()
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for i in range(10):
- outputs = model.forward(
- input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"speaker_ids": speaker_ids}
- )
- assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
- assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], mel_postnet_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for name_param, param_ref in zip(model.named_parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- # if count not in [145, 59]:
- name, param = name_param
- if name == "gst_layer.encoder.recurrence.weight_hh_l0":
- # print(param.grad)
- continue
- assert (param != param_ref).any(), "param {} {} with shape {} not updated!! \n{}\n{}".format(
- name, count, param.shape, param, param_ref
- )
- count += 1
-
-
-class TacotronCapacitronTrainTest(unittest.TestCase):
- @staticmethod
- def test_train_step():
- config = Tacotron2Config(
- num_chars=32,
- num_speakers=10,
- use_speaker_embedding=True,
- out_channels=80,
- decoder_output_dim=80,
- use_capacitron_vae=True,
- capacitron_vae=CapacitronVAEConfig(),
- optimizer="CapacitronOptimizer",
- optimizer_params={
- "RAdam": {"betas": [0.9, 0.998], "weight_decay": 1e-6},
- "SGD": {"lr": 1e-5, "momentum": 0.9},
- },
- )
-
- batch = dict({})
- batch["text_input"] = torch.randint(0, 24, (8, 128)).long().to(device)
- batch["text_lengths"] = torch.randint(100, 129, (8,)).long().to(device)
- batch["text_lengths"] = torch.sort(batch["text_lengths"], descending=True)[0]
- batch["text_lengths"][0] = 128
- batch["mel_input"] = torch.rand(8, 120, config.audio["num_mels"]).to(device)
- batch["mel_lengths"] = torch.randint(20, 120, (8,)).long().to(device)
- batch["mel_lengths"] = torch.sort(batch["mel_lengths"], descending=True)[0]
- batch["mel_lengths"][0] = 120
- batch["stop_targets"] = torch.zeros(8, 120, 1).float().to(device)
- batch["stop_target_lengths"] = torch.randint(0, 120, (8,)).to(device)
- batch["speaker_ids"] = torch.randint(0, 5, (8,)).long().to(device)
- batch["d_vectors"] = None
-
- for idx in batch["mel_lengths"]:
- batch["stop_targets"][:, int(idx.item()) :, 0] = 1.0
-
- batch["stop_targets"] = batch["stop_targets"].view(
- batch["text_input"].shape[0], batch["stop_targets"].size(1) // config.r, -1
- )
- batch["stop_targets"] = (batch["stop_targets"].sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- model = Tacotron2(config).to(device)
- criterion = model.get_criterion().to(device)
- optimizer = model.get_optimizer()
-
- model.train()
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- for _ in range(10):
- _, loss_dict = model.train_step(batch, criterion)
- optimizer.zero_grad()
- loss_dict["capacitron_vae_beta_loss"].backward()
- optimizer.first_step()
- loss_dict["loss"].backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
-
-class SCGSTMultiSpeakeTacotronTrainTest(unittest.TestCase):
- """Test multi-speaker Tacotron2 with Global Style Tokens and d-vector inputs."""
-
- @staticmethod
- def test_train_step():
- config = config_global.copy()
- config.use_d_vector_file = True
-
- config.use_gst = True
- config.gst = GSTConfig()
-
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 128, (8,)).long().to(device)
- input_lengths = torch.sort(input_lengths, descending=True)[0]
- mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
- mel_lengths[0] = 30
- stop_targets = torch.zeros(8, 30, 1).float().to(device)
- speaker_embeddings = torch.rand(8, 55).to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
- criterion = MSELossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- config.d_vector_dim = 55
- model = Tacotron2(config).to(device)
- model.train()
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for i in range(5):
- outputs = model.forward(
- input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"d_vectors": speaker_embeddings}
- )
- assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
- assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], mel_postnet_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for name_param, param_ref in zip(model.named_parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- # if count not in [145, 59]:
- name, param = name_param
- if name == "gst_layer.encoder.recurrence.weight_hh_l0":
- continue
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
diff --git a/TTS/tests/tts_tests/test_tacotron2_speaker_emb_train.py b/TTS/tests/tts_tests/test_tacotron2_speaker_emb_train.py
deleted file mode 100644
index 5f1bc3fd50bc3dfeabf4c4834d7492b11717dc43..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_tacotron2_speaker_emb_train.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = Tacotron2Config(
- r=5,
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=False,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- use_speaker_embedding=True,
- num_speakers=4,
- max_decoder_steps=50,
-)
-
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech_test "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.test_delay_epochs 0 "
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech-1"
-continue_speakers_path = os.path.join(continue_path, "speakers.json")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_tacotron2_train.py b/TTS/tests/tts_tests/test_tacotron2_train.py
deleted file mode 100644
index 40107070e1f19fbb8cf4e259b6a232c6d892357e..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_tacotron2_train.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.tacotron2_config import Tacotron2Config
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = Tacotron2Config(
- r=5,
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=False,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- print_eval=True,
- max_decoder_steps=50,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.test_delay_epochs 0 "
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_tacotron_layers.py b/TTS/tests/tts_tests/test_tacotron_layers.py
deleted file mode 100644
index 43e72417c200493c6392b3acb131a43738dad2bd..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_tacotron_layers.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import unittest
-
-import torch as T
-
-from TTS.tts.layers.tacotron.tacotron import CBHG, Decoder, Encoder, Prenet
-
-# pylint: disable=unused-variable
-
-
-class PrenetTests(unittest.TestCase):
- def test_in_out(self): # pylint: disable=no-self-use
- layer = Prenet(128, out_features=[256, 128])
- dummy_input = T.rand(4, 128)
-
- print(layer)
- output = layer(dummy_input)
- assert output.shape[0] == 4
- assert output.shape[1] == 128
-
-
-class CBHGTests(unittest.TestCase):
- def test_in_out(self):
- # pylint: disable=attribute-defined-outside-init
- layer = self.cbhg = CBHG(
- 128,
- K=8,
- conv_bank_features=80,
- conv_projections=[160, 128],
- highway_features=80,
- gru_features=80,
- num_highways=4,
- )
- # B x D x T
- dummy_input = T.rand(4, 128, 8)
-
- print(layer)
- output = layer(dummy_input)
- assert output.shape[0] == 4
- assert output.shape[1] == 8
- assert output.shape[2] == 160
-
-
-class DecoderTests(unittest.TestCase):
- @staticmethod
- def test_in_out():
- layer = Decoder(
- in_channels=256,
- frame_channels=80,
- r=2,
- memory_size=4,
- attn_windowing=False,
- attn_norm="sigmoid",
- attn_K=5,
- attn_type="original",
- prenet_type="original",
- prenet_dropout=True,
- forward_attn=True,
- trans_agent=True,
- forward_attn_mask=True,
- location_attn=True,
- separate_stopnet=True,
- max_decoder_steps=50,
- )
- dummy_input = T.rand(4, 8, 256)
- dummy_memory = T.rand(4, 2, 80)
-
- output, alignment, stop_tokens = layer(dummy_input, dummy_memory, mask=None)
-
- assert output.shape[0] == 4
- assert output.shape[1] == 80, "size not {}".format(output.shape[1])
- assert output.shape[2] == 2, "size not {}".format(output.shape[2])
- assert stop_tokens.shape[0] == 4
-
-
-class EncoderTests(unittest.TestCase):
- def test_in_out(self): # pylint: disable=no-self-use
- layer = Encoder(128)
- dummy_input = T.rand(4, 8, 128)
-
- print(layer)
- output = layer(dummy_input)
- print(output.shape)
- assert output.shape[0] == 4
- assert output.shape[1] == 8
- assert output.shape[2] == 256 # 128 * 2 BiRNN
diff --git a/TTS/tests/tts_tests/test_tacotron_model.py b/TTS/tests/tts_tests/test_tacotron_model.py
deleted file mode 100644
index 906ec3d09f47ec4221aebc9f29eec41c3ecd6971..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_tacotron_model.py
+++ /dev/null
@@ -1,376 +0,0 @@
-import copy
-import os
-import unittest
-
-import torch
-from torch import nn, optim
-
-from tests import get_tests_input_path
-from TTS.tts.configs.shared_configs import CapacitronVAEConfig, GSTConfig
-from TTS.tts.configs.tacotron_config import TacotronConfig
-from TTS.tts.layers.losses import L1LossMasked
-from TTS.tts.models.tacotron import Tacotron
-from TTS.utils.audio import AudioProcessor
-
-# pylint: disable=unused-variable
-
-torch.manual_seed(1)
-use_cuda = torch.cuda.is_available()
-device = torch.device("cuda" if use_cuda else "cpu")
-
-config_global = TacotronConfig(num_chars=32, num_speakers=5, out_channels=513, decoder_output_dim=80)
-
-ap = AudioProcessor(**config_global.audio)
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-
-
-def count_parameters(model):
- r"""Count number of trainable parameters in a network"""
- return sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-
-class TacotronTrainTest(unittest.TestCase):
- @staticmethod
- def test_train_step():
- config = config_global.copy()
- config.use_speaker_embedding = False
- config.num_speakers = 1
-
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 129, (8,)).long().to(device)
- input_lengths[-1] = 128
- mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- linear_spec = torch.rand(8, 30, config.audio["fft_size"] // 2 + 1).to(device)
- mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
- mel_lengths[-1] = mel_spec.size(1)
- stop_targets = torch.zeros(8, 30, 1).float().to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- criterion = L1LossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- model = Tacotron(config).to(device) # FIXME: missing num_speakers parameter to Tacotron ctor
- model.train()
- print(" > Num parameters for Tacotron model:%s" % (count_parameters(model)))
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for _ in range(5):
- outputs = model.forward(input_dummy, input_lengths, mel_spec, mel_lengths)
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], linear_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- # if count not in [145, 59]:
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
-
-class MultiSpeakeTacotronTrainTest(unittest.TestCase):
- @staticmethod
- def test_train_step():
- config = config_global.copy()
- config.use_speaker_embedding = True
- config.num_speakers = 5
-
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 129, (8,)).long().to(device)
- input_lengths[-1] = 128
- mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- linear_spec = torch.rand(8, 30, config.audio["fft_size"] // 2 + 1).to(device)
- mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
- mel_lengths[-1] = mel_spec.size(1)
- stop_targets = torch.zeros(8, 30, 1).float().to(device)
- speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- criterion = L1LossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- config.d_vector_dim = 55
- model = Tacotron(config).to(device) # FIXME: missing num_speakers parameter to Tacotron ctor
- model.train()
- print(" > Num parameters for Tacotron model:%s" % (count_parameters(model)))
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for _ in range(5):
- outputs = model.forward(
- input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"speaker_ids": speaker_ids}
- )
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], linear_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- # if count not in [145, 59]:
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
-
-class TacotronGSTTrainTest(unittest.TestCase):
- @staticmethod
- def test_train_step():
- config = config_global.copy()
- config.use_speaker_embedding = True
- config.num_speakers = 10
- config.use_gst = True
- config.gst = GSTConfig()
- # with random gst mel style
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 129, (8,)).long().to(device)
- input_lengths[-1] = 128
- mel_spec = torch.rand(8, 120, config.audio["num_mels"]).to(device)
- linear_spec = torch.rand(8, 120, config.audio["fft_size"] // 2 + 1).to(device)
- mel_lengths = torch.randint(20, 120, (8,)).long().to(device)
- mel_lengths[-1] = 120
- stop_targets = torch.zeros(8, 120, 1).float().to(device)
- speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- criterion = L1LossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- config.use_gst = True
- config.gst = GSTConfig()
- model = Tacotron(config).to(device) # FIXME: missing num_speakers parameter to Tacotron ctor
- model.train()
- # print(model)
- print(" > Num parameters for Tacotron GST model:%s" % (count_parameters(model)))
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for _ in range(10):
- outputs = model.forward(
- input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"speaker_ids": speaker_ids}
- )
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], linear_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
- # with file gst style
- mel_spec = (
- torch.FloatTensor(ap.melspectrogram(ap.load_wav(WAV_FILE)))[:, :120].unsqueeze(0).transpose(1, 2).to(device)
- )
- mel_spec = mel_spec.repeat(8, 1, 1)
-
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 129, (8,)).long().to(device)
- input_lengths[-1] = 128
- linear_spec = torch.rand(8, mel_spec.size(1), config.audio["fft_size"] // 2 + 1).to(device)
- mel_lengths = torch.randint(20, mel_spec.size(1), (8,)).long().to(device)
- mel_lengths[-1] = mel_spec.size(1)
- stop_targets = torch.zeros(8, mel_spec.size(1), 1).float().to(device)
- speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- criterion = L1LossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- model = Tacotron(config).to(device) # FIXME: missing num_speakers parameter to Tacotron ctor
- model.train()
- # print(model)
- print(" > Num parameters for Tacotron GST model:%s" % (count_parameters(model)))
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for _ in range(10):
- outputs = model.forward(
- input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"speaker_ids": speaker_ids}
- )
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], linear_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
-
-class TacotronCapacitronTrainTest(unittest.TestCase):
- @staticmethod
- def test_train_step():
- config = TacotronConfig(
- num_chars=32,
- num_speakers=10,
- use_speaker_embedding=True,
- out_channels=513,
- decoder_output_dim=80,
- use_capacitron_vae=True,
- capacitron_vae=CapacitronVAEConfig(),
- optimizer="CapacitronOptimizer",
- optimizer_params={
- "RAdam": {"betas": [0.9, 0.998], "weight_decay": 1e-6},
- "SGD": {"lr": 1e-5, "momentum": 0.9},
- },
- )
-
- batch = dict({})
- batch["text_input"] = torch.randint(0, 24, (8, 128)).long().to(device)
- batch["text_lengths"] = torch.randint(100, 129, (8,)).long().to(device)
- batch["text_lengths"] = torch.sort(batch["text_lengths"], descending=True)[0]
- batch["text_lengths"][0] = 128
- batch["linear_input"] = torch.rand(8, 120, config.audio["fft_size"] // 2 + 1).to(device)
- batch["mel_input"] = torch.rand(8, 120, config.audio["num_mels"]).to(device)
- batch["mel_lengths"] = torch.randint(20, 120, (8,)).long().to(device)
- batch["mel_lengths"] = torch.sort(batch["mel_lengths"], descending=True)[0]
- batch["mel_lengths"][0] = 120
- batch["stop_targets"] = torch.zeros(8, 120, 1).float().to(device)
- batch["stop_target_lengths"] = torch.randint(0, 120, (8,)).to(device)
- batch["speaker_ids"] = torch.randint(0, 5, (8,)).long().to(device)
- batch["d_vectors"] = None
-
- for idx in batch["mel_lengths"]:
- batch["stop_targets"][:, int(idx.item()) :, 0] = 1.0
-
- batch["stop_targets"] = batch["stop_targets"].view(
- batch["text_input"].shape[0], batch["stop_targets"].size(1) // config.r, -1
- )
- batch["stop_targets"] = (batch["stop_targets"].sum(2) > 0.0).unsqueeze(2).float().squeeze()
- model = Tacotron(config).to(device)
- criterion = model.get_criterion()
- optimizer = model.get_optimizer()
- model.train()
- print(" > Num parameters for Tacotron with Capacitron VAE model:%s" % (count_parameters(model)))
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- for _ in range(10):
- _, loss_dict = model.train_step(batch, criterion)
- optimizer.zero_grad()
- loss_dict["capacitron_vae_beta_loss"].backward()
- optimizer.first_step()
- loss_dict["loss"].backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
-
-class SCGSTMultiSpeakeTacotronTrainTest(unittest.TestCase):
- @staticmethod
- def test_train_step():
- config = config_global.copy()
- config.use_d_vector_file = True
-
- config.use_gst = True
- config.gst = GSTConfig()
-
- input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
- input_lengths = torch.randint(100, 129, (8,)).long().to(device)
- input_lengths[-1] = 128
- mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
- linear_spec = torch.rand(8, 30, config.audio["fft_size"] // 2 + 1).to(device)
- mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
- mel_lengths[-1] = mel_spec.size(1)
- stop_targets = torch.zeros(8, 30, 1).float().to(device)
- speaker_embeddings = torch.rand(8, 55).to(device)
-
- for idx in mel_lengths:
- stop_targets[:, int(idx.item()) :, 0] = 1.0
-
- stop_targets = stop_targets.view(input_dummy.shape[0], stop_targets.size(1) // config.r, -1)
- stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
- criterion = L1LossMasked(seq_len_norm=False).to(device)
- criterion_st = nn.BCEWithLogitsLoss().to(device)
- config.d_vector_dim = 55
- model = Tacotron(config).to(device) # FIXME: missing num_speakers parameter to Tacotron ctor
- model.train()
- print(" > Num parameters for Tacotron model:%s" % (count_parameters(model)))
- model_ref = copy.deepcopy(model)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=config.lr)
- for _ in range(5):
- outputs = model.forward(
- input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"d_vectors": speaker_embeddings}
- )
- optimizer.zero_grad()
- loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
- stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
- loss = loss + criterion(outputs["model_outputs"], linear_spec, mel_lengths) + stop_loss
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for name_param, param_ref in zip(model.named_parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- # if count not in [145, 59]:
- name, param = name_param
- if name == "gst_layer.encoder.recurrence.weight_hh_l0":
- continue
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
diff --git a/TTS/tests/tts_tests/test_tacotron_train.py b/TTS/tests/tts_tests/test_tacotron_train.py
deleted file mode 100644
index f7751931ae77cedd2ed38f12fcfb7b6ed92f9aa2..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_tacotron_train.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import glob
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.tacotron_config import TacotronConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = TacotronConfig(
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=False,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- print_eval=True,
- r=5,
- max_decoder_steps=50,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_vits.py b/TTS/tests/tts_tests/test_vits.py
deleted file mode 100644
index fca99556199efb79a9c65378c40faebdb2cf51b6..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_vits.py
+++ /dev/null
@@ -1,595 +0,0 @@
-import copy
-import os
-import unittest
-
-import torch
-from trainer.logging.tensorboard_logger import TensorboardLogger
-
-from tests import assertHasAttr, assertHasNotAttr, get_tests_data_path, get_tests_input_path, get_tests_output_path
-from TTS.config import load_config
-from TTS.encoder.utils.generic_utils import setup_encoder_model
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.models.vits import (
- Vits,
- VitsArgs,
- VitsAudioConfig,
- amp_to_db,
- db_to_amp,
- load_audio,
- spec_to_mel,
- wav_to_mel,
- wav_to_spec,
-)
-from TTS.tts.utils.speakers import SpeakerManager
-
-LANG_FILE = os.path.join(get_tests_input_path(), "language_ids.json")
-SPEAKER_ENCODER_CONFIG = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-
-
-torch.manual_seed(1)
-use_cuda = torch.cuda.is_available()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-# pylint: disable=no-self-use
-class TestVits(unittest.TestCase):
- def test_load_audio(self):
- wav, sr = load_audio(WAV_FILE)
- self.assertEqual(wav.shape, (1, 41885))
- self.assertEqual(sr, 22050)
-
- spec = wav_to_spec(wav, n_fft=1024, hop_length=512, win_length=1024, center=False)
- mel = wav_to_mel(
- wav,
- n_fft=1024,
- num_mels=80,
- sample_rate=sr,
- hop_length=512,
- win_length=1024,
- fmin=0,
- fmax=8000,
- center=False,
- )
- mel2 = spec_to_mel(spec, n_fft=1024, num_mels=80, sample_rate=sr, fmin=0, fmax=8000)
-
- self.assertEqual((mel - mel2).abs().max(), 0)
- self.assertEqual(spec.shape[0], mel.shape[0])
- self.assertEqual(spec.shape[2], mel.shape[2])
-
- spec_db = amp_to_db(spec)
- spec_amp = db_to_amp(spec_db)
-
- self.assertAlmostEqual((spec - spec_amp).abs().max(), 0, delta=1e-4)
-
- def test_dataset(self):
- """TODO:"""
- ...
-
- def test_init_multispeaker(self):
- num_speakers = 10
- args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
- model = Vits(args)
- assertHasAttr(self, model, "emb_g")
-
- args = VitsArgs(num_speakers=0, use_speaker_embedding=True)
- model = Vits(args)
- assertHasNotAttr(self, model, "emb_g")
-
- args = VitsArgs(num_speakers=10, use_speaker_embedding=False)
- model = Vits(args)
- assertHasNotAttr(self, model, "emb_g")
-
- args = VitsArgs(d_vector_dim=101, use_d_vector_file=True)
- model = Vits(args)
- self.assertEqual(model.embedded_speaker_dim, 101)
-
- def test_init_multilingual(self):
- args = VitsArgs(language_ids_file=None, use_language_embedding=False)
- model = Vits(args)
- self.assertEqual(model.language_manager, None)
- self.assertEqual(model.embedded_language_dim, 0)
- assertHasNotAttr(self, model, "emb_l")
-
- args = VitsArgs(language_ids_file=LANG_FILE)
- model = Vits(args)
- self.assertNotEqual(model.language_manager, None)
- self.assertEqual(model.embedded_language_dim, 0)
- assertHasNotAttr(self, model, "emb_l")
-
- args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True)
- model = Vits(args)
- self.assertNotEqual(model.language_manager, None)
- self.assertEqual(model.embedded_language_dim, args.embedded_language_dim)
- assertHasAttr(self, model, "emb_l")
-
- args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, embedded_language_dim=102)
- model = Vits(args)
- self.assertNotEqual(model.language_manager, None)
- self.assertEqual(model.embedded_language_dim, args.embedded_language_dim)
- assertHasAttr(self, model, "emb_l")
-
- def test_get_aux_input(self):
- aux_input = {"speaker_ids": None, "style_wav": None, "d_vectors": None, "language_ids": None}
- args = VitsArgs()
- model = Vits(args)
- aux_out = model.get_aux_input(aux_input)
-
- speaker_id = torch.randint(10, (1,))
- language_id = torch.randint(10, (1,))
- d_vector = torch.rand(1, 128)
- aux_input = {"speaker_ids": speaker_id, "style_wav": None, "d_vectors": d_vector, "language_ids": language_id}
- aux_out = model.get_aux_input(aux_input)
- self.assertEqual(aux_out["speaker_ids"].shape, speaker_id.shape)
- self.assertEqual(aux_out["language_ids"].shape, language_id.shape)
- self.assertEqual(aux_out["d_vectors"].shape, d_vector.unsqueeze(0).transpose(2, 1).shape)
-
- def test_voice_conversion(self):
- num_speakers = 10
- spec_len = 101
- spec_effective_len = 50
-
- args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
- model = Vits(args)
-
- ref_inp = torch.randn(1, 513, spec_len)
- ref_inp_len = torch.randint(1, spec_effective_len, (1,))
- ref_spk_id = torch.randint(1, num_speakers, (1,)).item()
- tgt_spk_id = torch.randint(1, num_speakers, (1,)).item()
- o_hat, y_mask, (z, z_p, z_hat) = model.voice_conversion(ref_inp, ref_inp_len, ref_spk_id, tgt_spk_id)
-
- self.assertEqual(o_hat.shape, (1, 1, spec_len * 256))
- self.assertEqual(y_mask.shape, (1, 1, spec_len))
- self.assertEqual(y_mask.sum(), ref_inp_len[0])
- self.assertEqual(z.shape, (1, args.hidden_channels, spec_len))
- self.assertEqual(z_p.shape, (1, args.hidden_channels, spec_len))
- self.assertEqual(z_hat.shape, (1, args.hidden_channels, spec_len))
-
- def _create_inputs(self, config, batch_size=2):
- input_dummy = torch.randint(0, 24, (batch_size, 128)).long().to(device)
- input_lengths = torch.randint(100, 129, (batch_size,)).long().to(device)
- input_lengths[-1] = 128
- spec = torch.rand(batch_size, config.audio["fft_size"] // 2 + 1, 30).to(device)
- mel = torch.rand(batch_size, config.audio["num_mels"], 30).to(device)
- spec_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
- spec_lengths[-1] = spec.size(2)
- waveform = torch.rand(batch_size, 1, spec.size(2) * config.audio["hop_length"]).to(device)
- return input_dummy, input_lengths, mel, spec, spec_lengths, waveform
-
- def _check_forward_outputs(self, config, output_dict, encoder_config=None, batch_size=2):
- self.assertEqual(
- output_dict["model_outputs"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
- )
- self.assertEqual(output_dict["alignments"].shape, (batch_size, 128, 30))
- self.assertEqual(output_dict["alignments"].max(), 1)
- self.assertEqual(output_dict["alignments"].min(), 0)
- self.assertEqual(output_dict["z"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["z_p"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["m_p"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["logs_p"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["m_q"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["logs_q"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(
- output_dict["waveform_seg"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
- )
- if encoder_config:
- self.assertEqual(output_dict["gt_spk_emb"].shape, (batch_size, encoder_config.model_params["proj_dim"]))
- self.assertEqual(output_dict["syn_spk_emb"].shape, (batch_size, encoder_config.model_params["proj_dim"]))
- else:
- self.assertEqual(output_dict["gt_spk_emb"], None)
- self.assertEqual(output_dict["syn_spk_emb"], None)
-
- def test_forward(self):
- num_speakers = 0
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
- config.model_args.spec_segment_size = 10
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config)
- model = Vits(config).to(device)
- output_dict = model.forward(input_dummy, input_lengths, spec, spec_lengths, waveform)
- self._check_forward_outputs(config, output_dict)
-
- def test_multispeaker_forward(self):
- num_speakers = 10
-
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
- config.model_args.spec_segment_size = 10
-
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config)
- speaker_ids = torch.randint(0, num_speakers, (8,)).long().to(device)
-
- model = Vits(config).to(device)
- output_dict = model.forward(
- input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids}
- )
- self._check_forward_outputs(config, output_dict)
-
- def test_d_vector_forward(self):
- batch_size = 2
- args = VitsArgs(
- spec_segment_size=10,
- num_chars=32,
- use_d_vector_file=True,
- d_vector_dim=256,
- d_vector_file=[os.path.join(get_tests_data_path(), "dummy_speakers.json")],
- )
- config = VitsConfig(model_args=args)
- model = Vits.init_from_config(config, verbose=False).to(device)
- model.train()
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config, batch_size=batch_size)
- d_vectors = torch.randn(batch_size, 256).to(device)
- output_dict = model.forward(
- input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"d_vectors": d_vectors}
- )
- self._check_forward_outputs(config, output_dict)
-
- def test_multilingual_forward(self):
- num_speakers = 10
- num_langs = 3
- batch_size = 2
-
- args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, spec_segment_size=10)
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
-
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
-
- model = Vits(config).to(device)
- output_dict = model.forward(
- input_dummy,
- input_lengths,
- spec,
- spec_lengths,
- waveform,
- aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
- )
- self._check_forward_outputs(config, output_dict)
-
- def test_secl_forward(self):
- num_speakers = 10
- num_langs = 3
- batch_size = 2
-
- speaker_encoder_config = load_config(SPEAKER_ENCODER_CONFIG)
- speaker_encoder_config.model_params["use_torch_spec"] = True
- speaker_encoder = setup_encoder_model(speaker_encoder_config).to(device)
- speaker_manager = SpeakerManager()
- speaker_manager.encoder = speaker_encoder
-
- args = VitsArgs(
- language_ids_file=LANG_FILE,
- use_language_embedding=True,
- spec_segment_size=10,
- use_speaker_encoder_as_loss=True,
- )
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
- config.audio.sample_rate = 16000
-
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
-
- model = Vits(config, speaker_manager=speaker_manager).to(device)
- output_dict = model.forward(
- input_dummy,
- input_lengths,
- spec,
- spec_lengths,
- waveform,
- aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
- )
- self._check_forward_outputs(config, output_dict, speaker_encoder_config)
-
- def _check_inference_outputs(self, config, outputs, input_dummy, batch_size=1):
- feat_len = outputs["z"].shape[2]
- self.assertEqual(outputs["model_outputs"].shape[:2], (batch_size, 1)) # we don't know the channel dimension
- self.assertEqual(outputs["alignments"].shape, (batch_size, input_dummy.shape[1], feat_len))
- self.assertEqual(outputs["z"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
- self.assertEqual(outputs["z_p"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
- self.assertEqual(outputs["m_p"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
- self.assertEqual(outputs["logs_p"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
-
- def test_inference(self):
- num_speakers = 0
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
- model = Vits(config).to(device)
-
- batch_size = 1
- input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
- outputs = model.inference(input_dummy)
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- batch_size = 2
- input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
- outputs = model.inference(input_dummy, aux_input={"x_lengths": input_lengths})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- def test_multispeaker_inference(self):
- num_speakers = 10
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
- model = Vits(config).to(device)
-
- batch_size = 1
- input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- batch_size = 2
- input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- outputs = model.inference(input_dummy, {"x_lengths": input_lengths, "speaker_ids": speaker_ids})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- def test_multilingual_inference(self):
- num_speakers = 10
- num_langs = 3
- args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, spec_segment_size=10)
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
- model = Vits(config).to(device)
-
- input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
- speaker_ids = torch.randint(0, num_speakers, (1,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (1,)).long().to(device)
- _ = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})
-
- batch_size = 1
- input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
- outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- batch_size = 2
- input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
- outputs = model.inference(
- input_dummy, {"x_lengths": input_lengths, "speaker_ids": speaker_ids, "language_ids": lang_ids}
- )
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- def test_d_vector_inference(self):
- args = VitsArgs(
- spec_segment_size=10,
- num_chars=32,
- use_d_vector_file=True,
- d_vector_dim=256,
- d_vector_file=[os.path.join(get_tests_data_path(), "dummy_speakers.json")],
- )
- config = VitsConfig(model_args=args)
- model = Vits.init_from_config(config, verbose=False).to(device)
- model.eval()
- # batch size = 1
- input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
- d_vectors = torch.randn(1, 256).to(device)
- outputs = model.inference(input_dummy, aux_input={"d_vectors": d_vectors})
- self._check_inference_outputs(config, outputs, input_dummy)
- # batch size = 2
- input_dummy, input_lengths, *_ = self._create_inputs(config)
- d_vectors = torch.randn(2, 256).to(device)
- outputs = model.inference(input_dummy, aux_input={"x_lengths": input_lengths, "d_vectors": d_vectors})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=2)
-
- @staticmethod
- def _check_parameter_changes(model, model_ref):
- count = 0
- for item1, item2 in zip(model.named_parameters(), model_ref.named_parameters()):
- name = item1[0]
- param = item1[1]
- param_ref = item2[1]
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- name, param.shape, param, param_ref
- )
- count = count + 1
-
- def _create_batch(self, config, batch_size):
- input_dummy, input_lengths, mel, spec, mel_lengths, _ = self._create_inputs(config, batch_size)
- batch = {}
- batch["tokens"] = input_dummy
- batch["token_lens"] = input_lengths
- batch["spec_lens"] = mel_lengths
- batch["mel_lens"] = mel_lengths
- batch["spec"] = spec
- batch["mel"] = mel
- batch["waveform"] = torch.rand(batch_size, 1, config.audio["sample_rate"] * 10).to(device)
- batch["d_vectors"] = None
- batch["speaker_ids"] = None
- batch["language_ids"] = None
- return batch
-
- def test_train_step(self):
- # setup the model
- with torch.autograd.set_detect_anomaly(True):
- config = VitsConfig(model_args=VitsArgs(num_chars=32, spec_segment_size=10))
- model = Vits(config).to(device)
- model.train()
- # model to train
- optimizers = model.get_optimizer()
- criterions = model.get_criterion()
- criterions = [criterions[0].to(device), criterions[1].to(device)]
- # reference model to compare model weights
- model_ref = Vits(config).to(device)
- # # pass the state to ref model
- model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count = count + 1
- for _ in range(5):
- batch = self._create_batch(config, 2)
- for idx in [0, 1]:
- outputs, loss_dict = model.train_step(batch, criterions, idx)
- self.assertFalse(not outputs)
- self.assertFalse(not loss_dict)
- loss_dict["loss"].backward()
- optimizers[idx].step()
- optimizers[idx].zero_grad()
-
- # check parameter changes
- self._check_parameter_changes(model, model_ref)
-
- def test_train_step_upsampling(self):
- """Upsampling by the decoder upsampling layers"""
- # setup the model
- with torch.autograd.set_detect_anomaly(True):
- audio_config = VitsAudioConfig(sample_rate=22050)
- model_args = VitsArgs(
- num_chars=32,
- spec_segment_size=10,
- encoder_sample_rate=11025,
- interpolate_z=False,
- upsample_rates_decoder=[8, 8, 4, 2],
- )
- config = VitsConfig(model_args=model_args, audio=audio_config)
- model = Vits(config).to(device)
- model.train()
- # model to train
- optimizers = model.get_optimizer()
- criterions = model.get_criterion()
- criterions = [criterions[0].to(device), criterions[1].to(device)]
- # reference model to compare model weights
- model_ref = Vits(config).to(device)
- # # pass the state to ref model
- model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count = count + 1
- for _ in range(5):
- batch = self._create_batch(config, 2)
- for idx in [0, 1]:
- outputs, loss_dict = model.train_step(batch, criterions, idx)
- self.assertFalse(not outputs)
- self.assertFalse(not loss_dict)
- loss_dict["loss"].backward()
- optimizers[idx].step()
- optimizers[idx].zero_grad()
-
- # check parameter changes
- self._check_parameter_changes(model, model_ref)
-
- def test_train_step_upsampling_interpolation(self):
- """Upsampling by interpolation"""
- # setup the model
- with torch.autograd.set_detect_anomaly(True):
- audio_config = VitsAudioConfig(sample_rate=22050)
- model_args = VitsArgs(
- num_chars=32,
- spec_segment_size=10,
- encoder_sample_rate=11025,
- interpolate_z=True,
- upsample_rates_decoder=[8, 8, 2, 2],
- )
- config = VitsConfig(model_args=model_args, audio=audio_config)
- model = Vits(config).to(device)
- model.train()
- # model to train
- optimizers = model.get_optimizer()
- criterions = model.get_criterion()
- criterions = [criterions[0].to(device), criterions[1].to(device)]
- # reference model to compare model weights
- model_ref = Vits(config).to(device)
- # # pass the state to ref model
- model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count = count + 1
- for _ in range(5):
- batch = self._create_batch(config, 2)
- for idx in [0, 1]:
- outputs, loss_dict = model.train_step(batch, criterions, idx)
- self.assertFalse(not outputs)
- self.assertFalse(not loss_dict)
- loss_dict["loss"].backward()
- optimizers[idx].step()
- optimizers[idx].zero_grad()
-
- # check parameter changes
- self._check_parameter_changes(model, model_ref)
-
- def test_train_eval_log(self):
- batch_size = 2
- config = VitsConfig(model_args=VitsArgs(num_chars=32, spec_segment_size=10))
- model = Vits.init_from_config(config, verbose=False).to(device)
- model.run_data_dep_init = False
- model.train()
- batch = self._create_batch(config, batch_size)
- logger = TensorboardLogger(
- log_dir=os.path.join(get_tests_output_path(), "dummy_vits_logs"), model_name="vits_test_train_log"
- )
- criterion = model.get_criterion()
- criterion = [criterion[0].to(device), criterion[1].to(device)]
- outputs = [None] * 2
- outputs[0], _ = model.train_step(batch, criterion, 0)
- outputs[1], _ = model.train_step(batch, criterion, 1)
- model.train_log(batch, outputs, logger, None, 1)
-
- model.eval_log(batch, outputs, logger, None, 1)
- logger.finish()
-
- def test_test_run(self):
- config = VitsConfig(model_args=VitsArgs(num_chars=32))
- model = Vits.init_from_config(config, verbose=False).to(device)
- model.run_data_dep_init = False
- model.eval()
- test_figures, test_audios = model.test_run(None)
- self.assertTrue(test_figures is not None)
- self.assertTrue(test_audios is not None)
-
- def test_load_checkpoint(self):
- chkp_path = os.path.join(get_tests_output_path(), "dummy_glow_tts_checkpoint.pth")
- config = VitsConfig(VitsArgs(num_chars=32))
- model = Vits.init_from_config(config, verbose=False).to(device)
- chkp = {}
- chkp["model"] = model.state_dict()
- torch.save(chkp, chkp_path)
- model.load_checkpoint(config, chkp_path)
- self.assertTrue(model.training)
- model.load_checkpoint(config, chkp_path, eval=True)
- self.assertFalse(model.training)
-
- def test_get_criterion(self):
- config = VitsConfig(VitsArgs(num_chars=32))
- model = Vits.init_from_config(config, verbose=False).to(device)
- criterion = model.get_criterion()
- self.assertTrue(criterion is not None)
-
- def test_init_from_config(self):
- config = VitsConfig(model_args=VitsArgs(num_chars=32))
- model = Vits.init_from_config(config, verbose=False).to(device)
-
- config = VitsConfig(model_args=VitsArgs(num_chars=32, num_speakers=2))
- model = Vits.init_from_config(config, verbose=False).to(device)
- self.assertTrue(not hasattr(model, "emb_g"))
-
- config = VitsConfig(model_args=VitsArgs(num_chars=32, num_speakers=2, use_speaker_embedding=True))
- model = Vits.init_from_config(config, verbose=False).to(device)
- self.assertEqual(model.num_speakers, 2)
- self.assertTrue(hasattr(model, "emb_g"))
-
- config = VitsConfig(
- model_args=VitsArgs(
- num_chars=32,
- num_speakers=2,
- use_speaker_embedding=True,
- speakers_file=os.path.join(get_tests_data_path(), "ljspeech", "speakers.json"),
- )
- )
- model = Vits.init_from_config(config, verbose=False).to(device)
- self.assertEqual(model.num_speakers, 10)
- self.assertTrue(hasattr(model, "emb_g"))
-
- config = VitsConfig(
- model_args=VitsArgs(
- num_chars=32,
- use_d_vector_file=True,
- d_vector_dim=256,
- d_vector_file=[os.path.join(get_tests_data_path(), "dummy_speakers.json")],
- )
- )
- model = Vits.init_from_config(config, verbose=False).to(device)
- self.assertTrue(model.num_speakers == 1)
- self.assertTrue(not hasattr(model, "emb_g"))
- self.assertTrue(model.embedded_speaker_dim == config.d_vector_dim)
diff --git a/TTS/tests/tts_tests/test_vits_d-vectors_train.py b/TTS/tests/tts_tests/test_vits_d-vectors_train.py
deleted file mode 100644
index 741bda91e91c9c98ead928589fd3030ed9bb030d..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_vits_d-vectors_train.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.vits_config import VitsConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = VitsConfig(
- batch_size=2,
- eval_batch_size=2,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- ["Be a voice, not an echo.", "ljspeech-0"],
- ],
-)
-# set audio config
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-
-# active multispeaker d-vec mode
-config.model_args.use_d_vector_file = True
-config.model_args.d_vector_file = ["tests/data/ljspeech/speakers.json"]
-config.model_args.d_vector_dim = 256
-
-
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_vits_multilingual_speaker_emb_train.py b/TTS/tests/tts_tests/test_vits_multilingual_speaker_emb_train.py
deleted file mode 100644
index 71597ef32fef6aa3ef5b3877ee2065aed6cf95cc..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_vits_multilingual_speaker_emb_train.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-dataset_config_en = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- meta_file_val="metadata.csv",
- path="tests/data/ljspeech",
- language="en",
-)
-
-dataset_config_pt = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- meta_file_val="metadata.csv",
- path="tests/data/ljspeech",
- language="pt-br",
-)
-
-config = VitsConfig(
- batch_size=2,
- eval_batch_size=2,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- ["Be a voice, not an echo.", "ljspeech", None, "en"],
- ["Be a voice, not an echo.", "ljspeech", None, "pt-br"],
- ],
- datasets=[dataset_config_en, dataset_config_pt],
-)
-# set audio config
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-
-# active multilingual mode
-config.model_args.use_language_embedding = True
-config.use_language_embedding = True
-# active multispeaker mode
-config.model_args.use_speaker_embedding = True
-config.use_speaker_embedding = True
-
-# deactivate multispeaker d-vec mode
-config.model_args.use_d_vector_file = False
-config.use_d_vector_file = False
-
-# duration predictor
-config.model_args.use_sdp = False
-config.use_sdp = False
-
-# active language sampler
-config.use_language_weighted_sampler = True
-
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech"
-languae_id = "en"
-continue_speakers_path = os.path.join(continue_path, "speakers.json")
-continue_languages_path = os.path.join(continue_path, "language_ids.json")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --language_ids_file_path {continue_languages_path} --language_idx {languae_id} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_vits_multilingual_train-d_vectors.py b/TTS/tests/tts_tests/test_vits_multilingual_train-d_vectors.py
deleted file mode 100644
index fd58db534af914849f30ca821436f3aaabceabb8..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_vits_multilingual_train-d_vectors.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-dataset_config_en = BaseDatasetConfig(
- formatter="ljspeech_test",
- meta_file_train="metadata.csv",
- meta_file_val="metadata.csv",
- path="tests/data/ljspeech",
- language="en",
-)
-
-dataset_config_pt = BaseDatasetConfig(
- formatter="ljspeech_test",
- meta_file_train="metadata.csv",
- meta_file_val="metadata.csv",
- path="tests/data/ljspeech",
- language="pt-br",
-)
-
-config = VitsConfig(
- batch_size=2,
- eval_batch_size=2,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="multilingual_cleaners",
- use_phonemes=False,
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- ["Be a voice, not an echo.", "ljspeech-0", None, "en"],
- ["Be a voice, not an echo.", "ljspeech-1", None, "pt-br"],
- ],
- datasets=[dataset_config_en, dataset_config_en, dataset_config_en, dataset_config_pt],
-)
-# set audio config
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-
-# active multilingual mode
-config.model_args.use_language_embedding = True
-config.use_language_embedding = True
-
-# deactivate multispeaker mode
-config.model_args.use_speaker_embedding = False
-config.use_speaker_embedding = False
-
-# active multispeaker d-vec mode
-config.model_args.use_d_vector_file = True
-config.use_d_vector_file = True
-config.model_args.d_vector_file = ["tests/data/ljspeech/speakers.json"]
-config.d_vector_file = ["tests/data/ljspeech/speakers.json"]
-config.model_args.d_vector_dim = 256
-config.d_vector_dim = 256
-
-# duration predictor
-config.model_args.use_sdp = True
-config.use_sdp = True
-
-# activate language and speaker samplers
-config.use_language_weighted_sampler = True
-config.language_weighted_sampler_alpha = 10
-config.use_speaker_weighted_sampler = True
-config.speaker_weighted_sampler_alpha = 5
-
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech-1"
-languae_id = "en"
-continue_speakers_path = config.d_vector_file
-continue_languages_path = os.path.join(continue_path, "language_ids.json")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --language_ids_file_path {continue_languages_path} --language_idx {languae_id} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_vits_speaker_emb_train.py b/TTS/tests/tts_tests/test_vits_speaker_emb_train.py
deleted file mode 100644
index b7fe197cfef7f2154cd2563e096d9be4f531524d..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_vits_speaker_emb_train.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.vits_config import VitsConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = VitsConfig(
- batch_size=2,
- eval_batch_size=2,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- ["Be a voice, not an echo.", "ljspeech-1"],
- ],
-)
-# set audio config
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-
-# active multispeaker d-vec mode
-config.model_args.use_speaker_embedding = True
-config.model_args.use_d_vector_file = False
-config.model_args.d_vector_file = None
-config.model_args.d_vector_dim = 256
-
-
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech_test "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech-1"
-continue_speakers_path = os.path.join(continue_path, "speakers.json")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests/test_vits_train.py b/TTS/tests/tts_tests/test_vits_train.py
deleted file mode 100644
index ea5dc02405ab1450d905f95a39ebea65dd72c4a4..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests/test_vits_train.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.vits_config import VitsConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = VitsConfig(
- batch_size=2,
- eval_batch_size=2,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- ["Be a voice, not an echo."],
- ],
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests2/__init__.py b/TTS/tests/tts_tests2/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/tts_tests2/test_align_tts_train.py b/TTS/tests/tts_tests2/test_align_tts_train.py
deleted file mode 100644
index 9b0b730df4995072239077a2dfd76ca2b5094497..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_align_tts_train.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.align_tts_config import AlignTTSConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = AlignTTSConfig(
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=False,
- phoneme_language="en-us",
- phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
-)
-
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.test_delay_epochs 0 "
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests2/test_delightful_tts_d-vectors_train.py b/TTS/tests/tts_tests2/test_delightful_tts_d-vectors_train.py
deleted file mode 100644
index 8fc4ea7e9b518cd6754ec70c59bc0ed7a6503908..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_delightful_tts_d-vectors_train.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.delightful_tts_config import DelightfulTtsAudioConfig, DelightfulTTSConfig
-from TTS.tts.models.delightful_tts import DelightfulTtsArgs, VocoderConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-audio_config = DelightfulTtsAudioConfig()
-model_args = DelightfulTtsArgs(
- use_speaker_embedding=False, d_vector_dim=256, use_d_vector_file=True, speaker_embedding_channels=256
-)
-
-vocoder_config = VocoderConfig()
-
-config = DelightfulTTSConfig(
- model_args=model_args,
- audio=audio_config,
- vocoder=vocoder_config,
- batch_size=2,
- eval_batch_size=8,
- compute_f0=True,
- run_eval=True,
- test_delay_epochs=-1,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- f0_cache_path="tests/data/ljspeech/f0_cache_delightful/", ## delightful f0 cache is incompatible with other models
- epochs=1,
- print_step=1,
- print_eval=True,
- binary_align_loss_alpha=0.0,
- use_attn_priors=False,
- test_sentences=[
- ["Be a voice, not an echo.", "ljspeech-0"],
- ],
- output_path=output_path,
- use_speaker_embedding=False,
- use_d_vector_file=True,
- d_vector_file="tests/data/ljspeech/speakers.json",
- d_vector_dim=256,
- speaker_embedding_channels=256,
-)
-
-# active multispeaker d-vec mode
-config.model_args.use_speaker_embedding = False
-config.model_args.use_d_vector_file = True
-config.model_args.d_vector_file = "tests/data/ljspeech/speakers.json"
-config.model_args.d_vector_dim = 256
-
-
-config.save_json(config_path)
-
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-speaker_id = "ljspeech-1"
-continue_speakers_path = config.d_vector_file
-
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --config_path {continue_config_path} --speakers_file_path {continue_speakers_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
-shutil.rmtree("tests/data/ljspeech/f0_cache_delightful/")
diff --git a/TTS/tests/tts_tests2/test_delightful_tts_emb_spk.py b/TTS/tests/tts_tests2/test_delightful_tts_emb_spk.py
deleted file mode 100644
index 6fb70c5f613fe28c693121383b90867a4ae10069..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_delightful_tts_emb_spk.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.delightful_tts_config import DelightfulTtsAudioConfig, DelightfulTTSConfig
-from TTS.tts.models.delightful_tts import DelightfulTtsArgs, VocoderConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-audio_config = DelightfulTtsAudioConfig()
-model_args = DelightfulTtsArgs(use_speaker_embedding=False)
-
-vocoder_config = VocoderConfig()
-
-config = DelightfulTTSConfig(
- model_args=model_args,
- audio=audio_config,
- vocoder=vocoder_config,
- batch_size=2,
- eval_batch_size=8,
- compute_f0=True,
- run_eval=True,
- test_delay_epochs=-1,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- f0_cache_path="tests/data/ljspeech/f0_cache_delightful/", ## delightful f0 cache is incompatible with other models
- epochs=1,
- print_step=1,
- print_eval=True,
- binary_align_loss_alpha=0.0,
- use_attn_priors=False,
- test_sentences=[
- ["Be a voice, not an echo.", "ljspeech"],
- ],
- output_path=output_path,
- num_speakers=4,
- use_speaker_embedding=True,
-)
-
-# active multispeaker d-vec mode
-config.model_args.use_speaker_embedding = True
-config.model_args.use_d_vector_file = False
-config.model_args.d_vector_file = None
-config.model_args.d_vector_dim = 256
-
-
-config.save_json(config_path)
-
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.dataset_name ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech"
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
-shutil.rmtree("tests/data/ljspeech/f0_cache_delightful/")
diff --git a/TTS/tests/tts_tests2/test_delightful_tts_layers.py b/TTS/tests/tts_tests2/test_delightful_tts_layers.py
deleted file mode 100644
index 073bb1eb5a1f1f82eeb6afaad2ef3069e0d728ca..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_delightful_tts_layers.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import torch
-
-from TTS.tts.configs.delightful_tts_config import DelightfulTTSConfig
-from TTS.tts.layers.delightful_tts.acoustic_model import AcousticModel
-from TTS.tts.models.delightful_tts import DelightfulTtsArgs, VocoderConfig
-from TTS.tts.utils.helpers import rand_segments
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.vocoder.models.hifigan_generator import HifiganGenerator
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-args = DelightfulTtsArgs()
-v_args = VocoderConfig()
-
-
-config = DelightfulTTSConfig(
- model_args=args,
- # compute_f0=True,
- # f0_cache_path=os.path.join(output_path, "f0_cache"),
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- # phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
-)
-
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-
-def test_acoustic_model():
- dummy_tokens = torch.rand((1, 41)).long().to(device)
- dummy_text_lens = torch.tensor([41]).to(device)
- dummy_spec = torch.rand((1, 100, 207)).to(device)
- dummy_spec_lens = torch.tensor([207]).to(device)
- dummy_pitch = torch.rand((1, 1, 207)).long().to(device)
- dummy_energy = torch.rand((1, 1, 207)).long().to(device)
-
- args.out_channels = 100
- args.num_mels = 100
-
- acoustic_model = AcousticModel(args=args, tokenizer=tokenizer, speaker_manager=None).to(device)
-
- output = acoustic_model(
- tokens=dummy_tokens,
- src_lens=dummy_text_lens,
- mel_lens=dummy_spec_lens,
- mels=dummy_spec,
- pitches=dummy_pitch,
- energies=dummy_energy,
- attn_priors=None,
- d_vectors=None,
- speaker_idx=None,
- )
- assert list(output["model_outputs"].shape) == [1, 207, 100]
- output["model_outputs"].sum().backward()
-
-
-def test_hifi_decoder():
- dummy_input = torch.rand((1, 207, 100)).to(device)
- dummy_text_lens = torch.tensor([41]).to(device)
- dummy_spec = torch.rand((1, 100, 207)).to(device)
- dummy_spec_lens = torch.tensor([207]).to(device)
- dummy_pitch = torch.rand((1, 1, 207)).long().to(device)
- dummy_energy = torch.rand((1, 1, 207)).long().to(device)
-
- waveform_decoder = HifiganGenerator(
- 100,
- 1,
- v_args.resblock_type_decoder,
- v_args.resblock_dilation_sizes_decoder,
- v_args.resblock_kernel_sizes_decoder,
- v_args.upsample_kernel_sizes_decoder,
- v_args.upsample_initial_channel_decoder,
- v_args.upsample_rates_decoder,
- inference_padding=0,
- cond_channels=0,
- conv_pre_weight_norm=False,
- conv_post_weight_norm=False,
- conv_post_bias=False,
- ).to(device)
-
- vocoder_input_slices, slice_ids = rand_segments( # pylint: disable=unused-variable
- x=dummy_input.transpose(1, 2),
- x_lengths=dummy_spec_lens,
- segment_size=32,
- let_short_samples=True,
- pad_short=True,
- )
-
- outputs = waveform_decoder(x=vocoder_input_slices.detach())
- assert list(outputs.shape) == [1, 1, 8192]
- outputs.sum().backward()
diff --git a/TTS/tests/tts_tests2/test_delightful_tts_train.py b/TTS/tests/tts_tests2/test_delightful_tts_train.py
deleted file mode 100644
index a917d776570c5d2077890a7bfaf624f69f6d48f6..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_delightful_tts_train.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.delightful_tts_config import DelightfulTTSConfig
-from TTS.tts.models.delightful_tts import DelightfulTtsArgs, DelightfulTtsAudioConfig, VocoderConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-audio_config = DelightfulTtsAudioConfig()
-model_args = DelightfulTtsArgs()
-
-vocoder_config = VocoderConfig()
-
-
-config = DelightfulTTSConfig(
- audio=audio_config,
- batch_size=2,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- f0_cache_path="tests/data/ljspeech/f0_cache_delightful/", ## delightful f0 cache is incompatible with other models
- run_eval=True,
- test_delay_epochs=-1,
- binary_align_loss_alpha=0.0,
- epochs=1,
- print_step=1,
- use_attn_priors=False,
- print_eval=True,
- test_sentences=[
- ["Be a voice, not an echo."],
- ],
- use_speaker_embedding=False,
-)
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{'cpu'}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs -1"
-)
-
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == -1
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
-shutil.rmtree("tests/data/ljspeech/f0_cache_delightful/")
diff --git a/TTS/tests/tts_tests2/test_fast_pitch_speaker_emb_train.py b/TTS/tests/tts_tests2/test_fast_pitch_speaker_emb_train.py
deleted file mode 100644
index 7f79bfcab29531fa7b3f158a42f5b2f1975469c3..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_fast_pitch_speaker_emb_train.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.fast_pitch_config import FastPitchConfig
-
-config_path = os.path.join(get_tests_output_path(), "fast_pitch_speaker_emb_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = FastPitchConfig(
- audio=audio_config,
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- f0_cache_path="tests/data/ljspeech/f0_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- use_speaker_embedding=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
-)
-config.audio.do_trim_silence = True
-config.use_speaker_embedding = True
-config.model_args.use_speaker_embedding = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech_test "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech-1"
-continue_speakers_path = os.path.join(continue_path, "speakers.json")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests2/test_fast_pitch_train.py b/TTS/tests/tts_tests2/test_fast_pitch_train.py
deleted file mode 100644
index a525715b53826157a96d45dd4b204644836e0114..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_fast_pitch_train.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.fast_pitch_config import FastPitchConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = FastPitchConfig(
- audio=audio_config,
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- f0_cache_path="tests/data/ljspeech/f0_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- use_speaker_embedding=False,
-)
-config.audio.do_trim_silence = True
-config.use_speaker_embedding = False
-config.model_args.use_speaker_embedding = False
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests2/test_fastspeech_2_speaker_emb_train.py b/TTS/tests/tts_tests2/test_fastspeech_2_speaker_emb_train.py
deleted file mode 100644
index 35bda597d532455bc21422d35bce9732f47de0c1..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_fastspeech_2_speaker_emb_train.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.fastspeech2_config import Fastspeech2Config
-
-config_path = os.path.join(get_tests_output_path(), "fast_pitch_speaker_emb_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = Fastspeech2Config(
- audio=audio_config,
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- f0_cache_path="tests/data/ljspeech/f0_cache/",
- compute_f0=True,
- compute_energy=True,
- energy_cache_path="tests/data/ljspeech/energy_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- use_speaker_embedding=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
-)
-config.audio.do_trim_silence = True
-config.use_speaker_embedding = True
-config.model_args.use_speaker_embedding = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech_test "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech-1"
-continue_speakers_path = os.path.join(continue_path, "speakers.json")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests2/test_fastspeech_2_train.py b/TTS/tests/tts_tests2/test_fastspeech_2_train.py
deleted file mode 100644
index dd4b07d240939413edbb6c4f6d818a856628c8a8..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_fastspeech_2_train.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.fastspeech2_config import Fastspeech2Config
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-audio_config = BaseAudioConfig(
- sample_rate=22050,
- do_trim_silence=True,
- trim_db=60.0,
- signal_norm=False,
- mel_fmin=0.0,
- mel_fmax=8000,
- spec_gain=1.0,
- log_func="np.log",
- ref_level_db=20,
- preemphasis=0.0,
-)
-
-config = Fastspeech2Config(
- audio=audio_config,
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- f0_cache_path="tests/data/ljspeech/f0_cache/",
- compute_f0=True,
- compute_energy=True,
- energy_cache_path="tests/data/ljspeech/energy_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- use_speaker_embedding=False,
-)
-config.audio.do_trim_silence = True
-config.use_speaker_embedding = False
-config.model_args.use_speaker_embedding = False
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests2/test_feed_forward_layers.py b/TTS/tests/tts_tests2/test_feed_forward_layers.py
deleted file mode 100644
index 6b26b88f382a1876fd197b632c9bd2b4aca1e06f..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_feed_forward_layers.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import torch
-
-from TTS.tts.layers.feed_forward.decoder import Decoder
-from TTS.tts.layers.feed_forward.encoder import Encoder
-from TTS.tts.utils.helpers import sequence_mask
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-def test_encoder():
- input_dummy = torch.rand(8, 14, 37).to(device)
- input_lengths = torch.randint(31, 37, (8,)).long().to(device)
- input_lengths[-1] = 37
- input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
- # relative positional transformer encoder
- layer = Encoder(
- out_channels=11,
- in_hidden_channels=14,
- encoder_type="relative_position_transformer",
- encoder_params={
- "hidden_channels_ffn": 768,
- "num_heads": 2,
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 6,
- "rel_attn_window_size": 4,
- "input_length": None,
- },
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
- # residual conv bn encoder
- layer = Encoder(
- out_channels=11,
- in_hidden_channels=14,
- encoder_type="residual_conv_bn",
- encoder_params={"kernel_size": 4, "dilations": 4 * [1, 2, 4] + [1], "num_conv_blocks": 2, "num_res_blocks": 13},
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
- # FFTransformer encoder
- layer = Encoder(
- out_channels=14,
- in_hidden_channels=14,
- encoder_type="fftransformer",
- encoder_params={"hidden_channels_ffn": 31, "num_heads": 2, "num_layers": 2, "dropout_p": 0.1},
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 14, 37]
-
-
-def test_decoder():
- input_dummy = torch.rand(8, 128, 37).to(device)
- input_lengths = torch.randint(31, 37, (8,)).long().to(device)
- input_lengths[-1] = 37
-
- input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
- # residual bn conv decoder
- layer = Decoder(out_channels=11, in_hidden_channels=128).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
- # transformer decoder
- layer = Decoder(
- out_channels=11,
- in_hidden_channels=128,
- decoder_type="relative_position_transformer",
- decoder_params={
- "hidden_channels_ffn": 128,
- "num_heads": 2,
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 8,
- "rel_attn_window_size": 4,
- "input_length": None,
- },
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
- # wavenet decoder
- layer = Decoder(
- out_channels=11,
- in_hidden_channels=128,
- decoder_type="wavenet",
- decoder_params={
- "num_blocks": 12,
- "hidden_channels": 192,
- "kernel_size": 5,
- "dilation_rate": 1,
- "num_layers": 4,
- "dropout_p": 0.05,
- },
- ).to(device)
- output = layer(input_dummy, input_mask)
- # FFTransformer decoder
- layer = Decoder(
- out_channels=11,
- in_hidden_channels=128,
- decoder_type="fftransformer",
- decoder_params={
- "hidden_channels_ffn": 31,
- "num_heads": 2,
- "dropout_p": 0.1,
- "num_layers": 2,
- },
- ).to(device)
- output = layer(input_dummy, input_mask)
- assert list(output.shape) == [8, 11, 37]
diff --git a/TTS/tests/tts_tests2/test_forward_tts.py b/TTS/tests/tts_tests2/test_forward_tts.py
deleted file mode 100644
index cec0f211c85c70b17f289e37368638911b911742..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_forward_tts.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import torch as T
-
-from TTS.tts.models.forward_tts import ForwardTTS, ForwardTTSArgs
-from TTS.tts.utils.helpers import sequence_mask
-
-# pylint: disable=unused-variable
-
-
-def expand_encoder_outputs_test():
- model = ForwardTTS(ForwardTTSArgs(num_chars=10))
-
- inputs = T.rand(2, 5, 57)
- durations = T.randint(1, 4, (2, 57))
-
- x_mask = T.ones(2, 1, 57)
- y_mask = T.ones(2, 1, durations.sum(1).max())
-
- expanded, _ = model.expand_encoder_outputs(inputs, durations, x_mask, y_mask)
-
- for b in range(durations.shape[0]):
- index = 0
- for idx, dur in enumerate(durations[b]):
- diff = (
- expanded[b, :, index : index + dur.item()]
- - inputs[b, :, idx].repeat(dur.item()).view(expanded[b, :, index : index + dur.item()].shape)
- ).sum()
- assert abs(diff) < 1e-6, diff
- index += dur
-
-
-def model_input_output_test():
- """Assert the output shapes of the model in different modes"""
-
- # VANILLA MODEL
- model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=False))
-
- x = T.randint(0, 10, (2, 21))
- x_lengths = T.randint(10, 22, (2,))
- x_lengths[-1] = 21
- x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
- durations = T.randint(1, 4, (2, 21))
- durations = durations * x_mask.squeeze(1)
- y_lengths = durations.sum(1)
- y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
-
- outputs = model.forward(x, x_lengths, y_lengths, dr=durations)
-
- assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
- assert outputs["durations_log"].shape == (2, 21)
- assert outputs["durations"].shape == (2, 21)
- assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
- assert (outputs["x_mask"] - x_mask).sum() == 0.0
- assert (outputs["y_mask"] - y_mask).sum() == 0.0
-
- assert outputs["alignment_soft"] is None
- assert outputs["alignment_mas"] is None
- assert outputs["alignment_logprob"] is None
- assert outputs["o_alignment_dur"] is None
- assert outputs["pitch_avg"] is None
- assert outputs["pitch_avg_gt"] is None
-
- # USE PITCH
- model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=False))
-
- x = T.randint(0, 10, (2, 21))
- x_lengths = T.randint(10, 22, (2,))
- x_lengths[-1] = 21
- x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
- durations = T.randint(1, 4, (2, 21))
- durations = durations * x_mask.squeeze(1)
- y_lengths = durations.sum(1)
- y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
- pitch = T.rand(2, 1, y_lengths.max())
-
- outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch)
-
- assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
- assert outputs["durations_log"].shape == (2, 21)
- assert outputs["durations"].shape == (2, 21)
- assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
- assert (outputs["x_mask"] - x_mask).sum() == 0.0
- assert (outputs["y_mask"] - y_mask).sum() == 0.0
- assert outputs["pitch_avg"].shape == (2, 1, 21)
- assert outputs["pitch_avg_gt"].shape == (2, 1, 21)
-
- assert outputs["alignment_soft"] is None
- assert outputs["alignment_mas"] is None
- assert outputs["alignment_logprob"] is None
- assert outputs["o_alignment_dur"] is None
-
- # USE ALIGNER NETWORK
- model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=True))
-
- x = T.randint(0, 10, (2, 21))
- x_lengths = T.randint(10, 22, (2,))
- x_lengths[-1] = 21
- x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
- durations = T.randint(1, 4, (2, 21))
- durations = durations * x_mask.squeeze(1)
- y_lengths = durations.sum(1)
- y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
- y = T.rand(2, y_lengths.max(), 80)
-
- outputs = model.forward(x, x_lengths, y_lengths, dr=durations, y=y)
-
- assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
- assert outputs["durations_log"].shape == (2, 21)
- assert outputs["durations"].shape == (2, 21)
- assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
- assert (outputs["x_mask"] - x_mask).sum() == 0.0
- assert (outputs["y_mask"] - y_mask).sum() == 0.0
- assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21)
- assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21)
- assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21)
- assert outputs["o_alignment_dur"].shape == (2, 21)
-
- assert outputs["pitch_avg"] is None
- assert outputs["pitch_avg_gt"] is None
-
- # USE ALIGNER NETWORK AND PITCH
- model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=True))
-
- x = T.randint(0, 10, (2, 21))
- x_lengths = T.randint(10, 22, (2,))
- x_lengths[-1] = 21
- x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
- durations = T.randint(1, 4, (2, 21))
- durations = durations * x_mask.squeeze(1)
- y_lengths = durations.sum(1)
- y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
- y = T.rand(2, y_lengths.max(), 80)
- pitch = T.rand(2, 1, y_lengths.max())
-
- outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch, y=y)
-
- assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
- assert outputs["durations_log"].shape == (2, 21)
- assert outputs["durations"].shape == (2, 21)
- assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
- assert (outputs["x_mask"] - x_mask).sum() == 0.0
- assert (outputs["y_mask"] - y_mask).sum() == 0.0
- assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21)
- assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21)
- assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21)
- assert outputs["o_alignment_dur"].shape == (2, 21)
- assert outputs["pitch_avg"].shape == (2, 1, 21)
- assert outputs["pitch_avg_gt"].shape == (2, 1, 21)
diff --git a/TTS/tests/tts_tests2/test_glow_tts.py b/TTS/tests/tts_tests2/test_glow_tts.py
deleted file mode 100644
index 2a723f105f56e25fee096831719f78155180ee89..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_glow_tts.py
+++ /dev/null
@@ -1,378 +0,0 @@
-import copy
-import os
-import unittest
-
-import torch
-from torch import optim
-from trainer.logging.tensorboard_logger import TensorboardLogger
-
-from tests import get_tests_data_path, get_tests_input_path, get_tests_output_path
-from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-from TTS.tts.layers.losses import GlowTTSLoss
-from TTS.tts.models.glow_tts import GlowTTS
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.utils.audio import AudioProcessor
-
-# pylint: disable=unused-variable
-
-torch.manual_seed(1)
-use_cuda = torch.cuda.is_available()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-c = GlowTTSConfig()
-
-ap = AudioProcessor(**c.audio)
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-BATCH_SIZE = 3
-
-
-def count_parameters(model):
- r"""Count number of trainable parameters in a network"""
- return sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-
-class TestGlowTTS(unittest.TestCase):
- @staticmethod
- def _create_inputs(batch_size=8):
- input_dummy = torch.randint(0, 24, (batch_size, 128)).long().to(device)
- input_lengths = torch.randint(100, 129, (batch_size,)).long().to(device)
- input_lengths[-1] = 128
- mel_spec = torch.rand(batch_size, 30, c.audio["num_mels"]).to(device)
- mel_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
- speaker_ids = torch.randint(0, 5, (batch_size,)).long().to(device)
- return input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids
-
- @staticmethod
- def _check_parameter_changes(model, model_ref):
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
- def test_init_multispeaker(self):
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS(config)
- # speaker embedding with default speaker_embedding_dim
- config.use_speaker_embedding = True
- config.num_speakers = 5
- config.d_vector_dim = None
- model.init_multispeaker(config)
- self.assertEqual(model.c_in_channels, model.hidden_channels_enc)
- # use external speaker embeddings with speaker_embedding_dim = 301
- config = GlowTTSConfig(num_chars=32)
- config.use_d_vector_file = True
- config.d_vector_dim = 301
- model = GlowTTS(config)
- model.init_multispeaker(config)
- self.assertEqual(model.c_in_channels, 301)
- # use speaker embedddings by the provided speaker_manager
- config = GlowTTSConfig(num_chars=32)
- config.use_speaker_embedding = True
- config.speakers_file = os.path.join(get_tests_data_path(), "ljspeech", "speakers.json")
- speaker_manager = SpeakerManager.init_from_config(config)
- model = GlowTTS(config)
- model.speaker_manager = speaker_manager
- model.init_multispeaker(config)
- self.assertEqual(model.c_in_channels, model.hidden_channels_enc)
- self.assertEqual(model.num_speakers, speaker_manager.num_speakers)
- # use external speaker embeddings by the provided speaker_manager
- config = GlowTTSConfig(num_chars=32)
- config.use_d_vector_file = True
- config.d_vector_dim = 256
- config.d_vector_file = os.path.join(get_tests_data_path(), "dummy_speakers.json")
- speaker_manager = SpeakerManager.init_from_config(config)
- model = GlowTTS(config)
- model.speaker_manager = speaker_manager
- model.init_multispeaker(config)
- self.assertEqual(model.c_in_channels, speaker_manager.embedding_dim)
- self.assertEqual(model.num_speakers, speaker_manager.num_speakers)
-
- def test_unlock_act_norm_layers(self):
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS(config).to(device)
- model.unlock_act_norm_layers()
- for f in model.decoder.flows:
- if getattr(f, "set_ddi", False):
- self.assertFalse(f.initialized)
-
- def test_lock_act_norm_layers(self):
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS(config).to(device)
- model.lock_act_norm_layers()
- for f in model.decoder.flows:
- if getattr(f, "set_ddi", False):
- self.assertTrue(f.initialized)
-
- def _test_forward(self, batch_size):
- input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(batch_size)
- # create model
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS(config).to(device)
- model.train()
- print(" > Num parameters for GlowTTS model:%s" % (count_parameters(model)))
- # inference encoder and decoder with MAS
- y = model.forward(input_dummy, input_lengths, mel_spec, mel_lengths)
- self.assertEqual(y["z"].shape, mel_spec.shape)
- self.assertEqual(y["logdet"].shape, torch.Size([batch_size]))
- self.assertEqual(y["y_mean"].shape, mel_spec.shape)
- self.assertEqual(y["y_log_scale"].shape, mel_spec.shape)
- self.assertEqual(y["alignments"].shape, mel_spec.shape[:2] + (input_dummy.shape[1],))
- self.assertEqual(y["durations_log"].shape, input_dummy.shape + (1,))
- self.assertEqual(y["total_durations_log"].shape, input_dummy.shape + (1,))
-
- def test_forward(self):
- self._test_forward(1)
- self._test_forward(3)
-
- def _test_forward_with_d_vector(self, batch_size):
- input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(batch_size)
- d_vector = torch.rand(batch_size, 256).to(device)
- # create model
- config = GlowTTSConfig(
- num_chars=32,
- use_d_vector_file=True,
- d_vector_dim=256,
- d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"),
- )
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- model.train()
- print(" > Num parameters for GlowTTS model:%s" % (count_parameters(model)))
- # inference encoder and decoder with MAS
- y = model.forward(input_dummy, input_lengths, mel_spec, mel_lengths, {"d_vectors": d_vector})
- self.assertEqual(y["z"].shape, mel_spec.shape)
- self.assertEqual(y["logdet"].shape, torch.Size([batch_size]))
- self.assertEqual(y["y_mean"].shape, mel_spec.shape)
- self.assertEqual(y["y_log_scale"].shape, mel_spec.shape)
- self.assertEqual(y["alignments"].shape, mel_spec.shape[:2] + (input_dummy.shape[1],))
- self.assertEqual(y["durations_log"].shape, input_dummy.shape + (1,))
- self.assertEqual(y["total_durations_log"].shape, input_dummy.shape + (1,))
-
- def test_forward_with_d_vector(self):
- self._test_forward_with_d_vector(1)
- self._test_forward_with_d_vector(3)
-
- def _test_forward_with_speaker_id(self, batch_size):
- input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(batch_size)
- speaker_ids = torch.randint(0, 24, (batch_size,)).long().to(device)
- # create model
- config = GlowTTSConfig(
- num_chars=32,
- use_speaker_embedding=True,
- num_speakers=24,
- )
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- model.train()
- print(" > Num parameters for GlowTTS model:%s" % (count_parameters(model)))
- # inference encoder and decoder with MAS
- y = model.forward(input_dummy, input_lengths, mel_spec, mel_lengths, {"speaker_ids": speaker_ids})
- self.assertEqual(y["z"].shape, mel_spec.shape)
- self.assertEqual(y["logdet"].shape, torch.Size([batch_size]))
- self.assertEqual(y["y_mean"].shape, mel_spec.shape)
- self.assertEqual(y["y_log_scale"].shape, mel_spec.shape)
- self.assertEqual(y["alignments"].shape, mel_spec.shape[:2] + (input_dummy.shape[1],))
- self.assertEqual(y["durations_log"].shape, input_dummy.shape + (1,))
- self.assertEqual(y["total_durations_log"].shape, input_dummy.shape + (1,))
-
- def test_forward_with_speaker_id(self):
- self._test_forward_with_speaker_id(1)
- self._test_forward_with_speaker_id(3)
-
- def _assert_inference_outputs(self, outputs, input_dummy, mel_spec):
- output_shape = outputs["model_outputs"].shape
- self.assertEqual(outputs["model_outputs"].shape[::2], mel_spec.shape[::2])
- self.assertEqual(outputs["logdet"], None)
- self.assertEqual(outputs["y_mean"].shape, output_shape)
- self.assertEqual(outputs["y_log_scale"].shape, output_shape)
- self.assertEqual(outputs["alignments"].shape, output_shape[:2] + (input_dummy.shape[1],))
- self.assertEqual(outputs["durations_log"].shape, input_dummy.shape + (1,))
- self.assertEqual(outputs["total_durations_log"].shape, input_dummy.shape + (1,))
-
- def _test_inference(self, batch_size):
- input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(batch_size)
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS(config).to(device)
- model.eval()
- outputs = model.inference(input_dummy, {"x_lengths": input_lengths})
- self._assert_inference_outputs(outputs, input_dummy, mel_spec)
-
- def test_inference(self):
- self._test_inference(1)
- self._test_inference(3)
-
- def _test_inference_with_d_vector(self, batch_size):
- input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(batch_size)
- d_vector = torch.rand(batch_size, 256).to(device)
- config = GlowTTSConfig(
- num_chars=32,
- use_d_vector_file=True,
- d_vector_dim=256,
- d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"),
- )
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- model.eval()
- outputs = model.inference(input_dummy, {"x_lengths": input_lengths, "d_vectors": d_vector})
- self._assert_inference_outputs(outputs, input_dummy, mel_spec)
-
- def test_inference_with_d_vector(self):
- self._test_inference_with_d_vector(1)
- self._test_inference_with_d_vector(3)
-
- def _test_inference_with_speaker_ids(self, batch_size):
- input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(batch_size)
- speaker_ids = torch.randint(0, 24, (batch_size,)).long().to(device)
- # create model
- config = GlowTTSConfig(
- num_chars=32,
- use_speaker_embedding=True,
- num_speakers=24,
- )
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- outputs = model.inference(input_dummy, {"x_lengths": input_lengths, "speaker_ids": speaker_ids})
- self._assert_inference_outputs(outputs, input_dummy, mel_spec)
-
- def test_inference_with_speaker_ids(self):
- self._test_inference_with_speaker_ids(1)
- self._test_inference_with_speaker_ids(3)
-
- def _test_inference_with_MAS(self, batch_size):
- input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(batch_size)
- # create model
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS(config).to(device)
- model.eval()
- # inference encoder and decoder with MAS
- y = model.inference_with_MAS(input_dummy, input_lengths, mel_spec, mel_lengths)
- y2 = model.decoder_inference(mel_spec, mel_lengths)
- assert (
- y2["model_outputs"].shape == y["model_outputs"].shape
- ), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
- y["model_outputs"].shape, y2["model_outputs"].shape
- )
-
- def test_inference_with_MAS(self):
- self._test_inference_with_MAS(1)
- self._test_inference_with_MAS(3)
-
- def test_train_step(self):
- batch_size = BATCH_SIZE
- input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(batch_size)
- criterion = GlowTTSLoss()
- # model to train
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS(config).to(device)
- # reference model to compare model weights
- model_ref = GlowTTS(config).to(device)
- model.train()
- print(" > Num parameters for GlowTTS model:%s" % (count_parameters(model)))
- # pass the state to ref model
- model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=0.001)
- for _ in range(5):
- optimizer.zero_grad()
- outputs = model.forward(input_dummy, input_lengths, mel_spec, mel_lengths, None)
- loss_dict = criterion(
- outputs["z"],
- outputs["y_mean"],
- outputs["y_log_scale"],
- outputs["logdet"],
- mel_lengths,
- outputs["durations_log"],
- outputs["total_durations_log"],
- input_lengths,
- )
- loss = loss_dict["loss"]
- loss.backward()
- optimizer.step()
- # check parameter changes
- self._check_parameter_changes(model, model_ref)
-
- def test_train_eval_log(self):
- batch_size = BATCH_SIZE
- input_dummy, input_lengths, mel_spec, mel_lengths, _ = self._create_inputs(batch_size)
- batch = {}
- batch["text_input"] = input_dummy
- batch["text_lengths"] = input_lengths
- batch["mel_lengths"] = mel_lengths
- batch["mel_input"] = mel_spec
- batch["d_vectors"] = None
- batch["speaker_ids"] = None
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- model.run_data_dep_init = False
- model.train()
- logger = TensorboardLogger(
- log_dir=os.path.join(get_tests_output_path(), "dummy_glow_tts_logs"), model_name="glow_tts_test_train_log"
- )
- criterion = model.get_criterion()
- outputs, _ = model.train_step(batch, criterion)
- model.train_log(batch, outputs, logger, None, 1)
- model.eval_log(batch, outputs, logger, None, 1)
- logger.finish()
-
- def test_test_run(self):
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- model.run_data_dep_init = False
- model.eval()
- test_figures, test_audios = model.test_run(None)
- self.assertTrue(test_figures is not None)
- self.assertTrue(test_audios is not None)
-
- def test_load_checkpoint(self):
- chkp_path = os.path.join(get_tests_output_path(), "dummy_glow_tts_checkpoint.pth")
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- chkp = {}
- chkp["model"] = model.state_dict()
- torch.save(chkp, chkp_path)
- model.load_checkpoint(config, chkp_path)
- self.assertTrue(model.training)
- model.load_checkpoint(config, chkp_path, eval=True)
- self.assertFalse(model.training)
-
- def test_get_criterion(self):
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- criterion = model.get_criterion()
- self.assertTrue(criterion is not None)
-
- def test_init_from_config(self):
- config = GlowTTSConfig(num_chars=32)
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
-
- config = GlowTTSConfig(num_chars=32, num_speakers=2)
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- self.assertTrue(model.num_speakers == 2)
- self.assertTrue(not hasattr(model, "emb_g"))
-
- config = GlowTTSConfig(num_chars=32, num_speakers=2, use_speaker_embedding=True)
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- self.assertTrue(model.num_speakers == 2)
- self.assertTrue(hasattr(model, "emb_g"))
-
- config = GlowTTSConfig(
- num_chars=32,
- num_speakers=2,
- use_speaker_embedding=True,
- speakers_file=os.path.join(get_tests_data_path(), "ljspeech", "speakers.json"),
- )
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- self.assertTrue(model.num_speakers == 10)
- self.assertTrue(hasattr(model, "emb_g"))
-
- config = GlowTTSConfig(
- num_chars=32,
- use_d_vector_file=True,
- d_vector_dim=256,
- d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"),
- )
- model = GlowTTS.init_from_config(config, verbose=False).to(device)
- self.assertTrue(model.num_speakers == 1)
- self.assertTrue(not hasattr(model, "emb_g"))
- self.assertTrue(model.c_in_channels == config.d_vector_dim)
diff --git a/TTS/tests/tts_tests2/test_glow_tts_d-vectors_train.py b/TTS/tests/tts_tests2/test_glow_tts_d-vectors_train.py
deleted file mode 100644
index f1cfd4368f9a0658e6b94ad9fc9697ba75f30fed..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_glow_tts_d-vectors_train.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = GlowTTSConfig(
- batch_size=2,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- data_dep_init_steps=1.0,
- use_speaker_embedding=False,
- use_d_vector_file=True,
- d_vector_file="tests/data/ljspeech/speakers.json",
- d_vector_dim=256,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech_test "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech-1"
-continue_speakers_path = config.d_vector_file
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests2/test_glow_tts_speaker_emb_train.py b/TTS/tests/tts_tests2/test_glow_tts_speaker_emb_train.py
deleted file mode 100644
index b1eb6237a48ccec10eea03c6e81773f34af7275d..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_glow_tts_speaker_emb_train.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = GlowTTSConfig(
- batch_size=2,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- data_dep_init_steps=1.0,
- use_speaker_embedding=True,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech_test "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-speaker_id = "ljspeech-1"
-continue_speakers_path = os.path.join(continue_path, "speakers.json")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/tts_tests2/test_glow_tts_train.py b/TTS/tests/tts_tests2/test_glow_tts_train.py
deleted file mode 100644
index 0a8e226b65edf1da6ed477422d579c420ecdf74d..0000000000000000000000000000000000000000
--- a/TTS/tests/tts_tests2/test_glow_tts_train.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import glob
-import json
-import os
-import shutil
-
-from trainer import get_last_checkpoint
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = GlowTTSConfig(
- batch_size=2,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- test_sentences=[
- "Be a voice, not an echo.",
- ],
- data_dep_init_steps=1.0,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
- "--coqpit.test_delay_epochs 0"
-)
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# Inference using TTS API
-continue_config_path = os.path.join(continue_path, "config.json")
-continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
-
-# Check integrity of the config
-with open(continue_config_path, "r", encoding="utf-8") as f:
- config_loaded = json.load(f)
-assert config_loaded["characters"] is not None
-assert config_loaded["output_path"] in continue_path
-assert config_loaded["test_delay_epochs"] == 0
-
-# Load the model and run inference
-inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
-run_cli(inference_command)
-
-# restore the model and continue training for one more epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/vc_tests/__init__.py b/TTS/tests/vc_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/vc_tests/test_freevc.py b/TTS/tests/vc_tests/test_freevc.py
deleted file mode 100644
index a4a4f72679843a3340518c66077afbdb41ae1f28..0000000000000000000000000000000000000000
--- a/TTS/tests/vc_tests/test_freevc.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import os
-import unittest
-
-import torch
-
-from tests import get_tests_input_path
-from TTS.vc.configs.freevc_config import FreeVCConfig
-from TTS.vc.models.freevc import FreeVC
-
-# pylint: disable=unused-variable
-# pylint: disable=no-self-use
-
-torch.manual_seed(1)
-use_cuda = torch.cuda.is_available()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-c = FreeVCConfig()
-
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-BATCH_SIZE = 3
-
-
-def count_parameters(model):
- r"""Count number of trainable parameters in a network"""
- return sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-
-class TestFreeVC(unittest.TestCase):
- def _create_inputs(self, config, batch_size=2):
- input_dummy = torch.rand(batch_size, 30 * config.audio["hop_length"]).to(device)
- input_lengths = torch.randint(100, 30 * config.audio["hop_length"], (batch_size,)).long().to(device)
- input_lengths[-1] = 30 * config.audio["hop_length"]
- spec = torch.rand(batch_size, 30, config.audio["filter_length"] // 2 + 1).to(device)
- mel = torch.rand(batch_size, 30, config.audio["n_mel_channels"]).to(device)
- spec_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
- spec_lengths[-1] = spec.size(2)
- waveform = torch.rand(batch_size, spec.size(2) * config.audio["hop_length"]).to(device)
- return input_dummy, input_lengths, mel, spec, spec_lengths, waveform
-
- @staticmethod
- def _create_inputs_inference():
- source_wav = torch.rand(16000)
- target_wav = torch.rand(16000)
- return source_wav, target_wav
-
- @staticmethod
- def _check_parameter_changes(model, model_ref):
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
-
- def test_methods(self):
- config = FreeVCConfig()
- model = FreeVC(config).to(device)
- model.load_pretrained_speaker_encoder()
- model.init_multispeaker(config)
- wavlm_feats = model.extract_wavlm_features(torch.rand(1, 16000))
- assert wavlm_feats.shape == (1, 1024, 49), wavlm_feats.shape
-
- def test_load_audio(self):
- config = FreeVCConfig()
- model = FreeVC(config).to(device)
- wav = model.load_audio(WAV_FILE)
- wav2 = model.load_audio(wav)
- assert all(torch.isclose(wav, wav2))
-
- def _test_forward(self, batch_size):
- # create model
- config = FreeVCConfig()
- model = FreeVC(config).to(device)
- model.train()
- print(" > Num parameters for FreeVC model:%s" % (count_parameters(model)))
-
- _, _, mel, spec, spec_lengths, waveform = self._create_inputs(config, batch_size)
-
- wavlm_vec = model.extract_wavlm_features(waveform)
- wavlm_vec_lengths = torch.ones(batch_size, dtype=torch.long)
-
- y = model.forward(wavlm_vec, spec, None, mel, spec_lengths, wavlm_vec_lengths)
- # TODO: assert with training implementation
-
- def test_forward(self):
- self._test_forward(1)
- self._test_forward(3)
-
- def _test_inference(self, batch_size):
- config = FreeVCConfig()
- model = FreeVC(config).to(device)
- model.eval()
-
- _, _, mel, _, _, waveform = self._create_inputs(config, batch_size)
-
- wavlm_vec = model.extract_wavlm_features(waveform)
- wavlm_vec_lengths = torch.ones(batch_size, dtype=torch.long)
-
- output_wav = model.inference(wavlm_vec, None, mel, wavlm_vec_lengths)
- assert (
- output_wav.shape[-1] // config.audio.hop_length == wavlm_vec.shape[-1]
- ), f"{output_wav.shape[-1] // config.audio.hop_length} != {wavlm_vec.shape}"
-
- def test_inference(self):
- self._test_inference(1)
- self._test_inference(3)
-
- def test_voice_conversion(self):
- config = FreeVCConfig()
- model = FreeVC(config).to(device)
- model.eval()
-
- source_wav, target_wav = self._create_inputs_inference()
- output_wav = model.voice_conversion(source_wav, target_wav)
- assert (
- output_wav.shape[0] + config.audio.hop_length == source_wav.shape[0]
- ), f"{output_wav.shape} != {source_wav.shape}"
-
- def test_train_step(self):
- ...
-
- def test_train_eval_log(self):
- ...
-
- def test_test_run(self):
- ...
-
- def test_load_checkpoint(self):
- ...
-
- def test_get_criterion(self):
- ...
-
- def test_init_from_config(self):
- ...
diff --git a/TTS/tests/vocoder_tests/__init__.py b/TTS/tests/vocoder_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/vocoder_tests/test_fullband_melgan_train.py b/TTS/tests/vocoder_tests/test_fullband_melgan_train.py
deleted file mode 100644
index 9d4e193382eb5b1638e70a53fa17a33796870339..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_fullband_melgan_train.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.vocoder.configs import FullbandMelganConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = FullbandMelganConfig(
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- seq_len=8192,
- eval_split_size=1,
- print_step=1,
- print_eval=True,
- data_path="tests/data/ljspeech",
- discriminator_model_params={"base_channels": 16, "max_channels": 64, "downsample_factors": [4, 4, 4]},
- output_path=output_path,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/vocoder_tests/test_hifigan_train.py b/TTS/tests/vocoder_tests/test_hifigan_train.py
deleted file mode 100644
index c506fb48dca4dd71eb439489e0af5275b565a8a1..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_hifigan_train.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.vocoder.configs import HifiganConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = HifiganConfig(
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- seq_len=1024,
- eval_split_size=1,
- print_step=1,
- print_eval=True,
- data_path="tests/data/ljspeech",
- output_path=output_path,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/vocoder_tests/test_melgan_train.py b/TTS/tests/vocoder_tests/test_melgan_train.py
deleted file mode 100644
index 6ef9cd495b022f8d01d4c2ed6cd2667e1b1894ce..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_melgan_train.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.vocoder.configs import MelganConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = MelganConfig(
- batch_size=4,
- eval_batch_size=4,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- seq_len=2048,
- eval_split_size=1,
- print_step=1,
- discriminator_model_params={"base_channels": 16, "max_channels": 64, "downsample_factors": [4, 4, 4]},
- print_eval=True,
- data_path="tests/data/ljspeech",
- output_path=output_path,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/vocoder_tests/test_multiband_melgan_train.py b/TTS/tests/vocoder_tests/test_multiband_melgan_train.py
deleted file mode 100644
index 8002760706d1687fb7cb5e33107cc84add71a51a..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_multiband_melgan_train.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.vocoder.configs import MultibandMelganConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = MultibandMelganConfig(
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- seq_len=8192,
- eval_split_size=1,
- print_step=1,
- print_eval=True,
- steps_to_start_discriminator=1,
- data_path="tests/data/ljspeech",
- discriminator_model_params={"base_channels": 16, "max_channels": 64, "downsample_factors": [4, 4, 4]},
- output_path=output_path,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/vocoder_tests/test_parallel_wavegan_train.py b/TTS/tests/vocoder_tests/test_parallel_wavegan_train.py
deleted file mode 100644
index a126befe2e24cb67500bc6ee5b7450acfee5369b..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_parallel_wavegan_train.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.vocoder.configs import ParallelWaveganConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = ParallelWaveganConfig(
- batch_size=4,
- eval_batch_size=4,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- seq_len=2048,
- eval_split_size=1,
- print_step=1,
- print_eval=True,
- data_path="tests/data/ljspeech",
- output_path=output_path,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/vocoder_tests/test_vocoder_gan_datasets.py b/TTS/tests/vocoder_tests/test_vocoder_gan_datasets.py
deleted file mode 100644
index c39d70e94c5b9f55f6261c3987db38df65ea136f..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_gan_datasets.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import os
-
-import numpy as np
-from torch.utils.data import DataLoader
-
-from tests import get_tests_output_path, get_tests_path
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.configs import BaseGANVocoderConfig
-from TTS.vocoder.datasets.gan_dataset import GANDataset
-from TTS.vocoder.datasets.preprocess import load_wav_data
-
-file_path = os.path.dirname(os.path.realpath(__file__))
-OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
-os.makedirs(OUTPATH, exist_ok=True)
-
-C = BaseGANVocoderConfig()
-
-test_data_path = os.path.join(get_tests_path(), "data/ljspeech/")
-ok_ljspeech = os.path.exists(test_data_path)
-
-
-def gan_dataset_case(
- batch_size, seq_len, hop_len, conv_pad, return_pairs, return_segments, use_noise_augment, use_cache, num_workers
-):
- """Run dataloader with given parameters and check conditions"""
- ap = AudioProcessor(**C.audio)
- _, train_items = load_wav_data(test_data_path, 10)
- dataset = GANDataset(
- ap,
- train_items,
- seq_len=seq_len,
- hop_len=hop_len,
- pad_short=2000,
- conv_pad=conv_pad,
- return_pairs=return_pairs,
- return_segments=return_segments,
- use_noise_augment=use_noise_augment,
- use_cache=use_cache,
- )
- loader = DataLoader(
- dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True, drop_last=True
- )
-
- max_iter = 10
- count_iter = 0
-
- def check_item(feat, wav):
- """Pass a single pair of features and waveform"""
- feat = feat.numpy()
- wav = wav.numpy()
- expected_feat_shape = (batch_size, ap.num_mels, seq_len // hop_len + conv_pad * 2)
-
- # check shapes
- assert np.all(feat.shape == expected_feat_shape), f" [!] {feat.shape} vs {expected_feat_shape}"
- assert (feat.shape[2] - conv_pad * 2) * hop_len == wav.shape[2]
-
- # check feature vs audio match
- if not use_noise_augment:
- for idx in range(batch_size):
- audio = wav[idx].squeeze()
- feat = feat[idx]
- mel = ap.melspectrogram(audio)
- # the first 2 and the last 2 frames are skipped due to the padding
- # differences in stft
- max_diff = abs((feat - mel[:, : feat.shape[-1]])[:, 2:-2]).max()
- assert max_diff <= 1e-6, f" [!] {max_diff}"
-
- # return random segments or return the whole audio
- if return_segments:
- if return_pairs:
- for item1, item2 in loader:
- feat1, wav1 = item1
- feat2, wav2 = item2
- check_item(feat1, wav1)
- check_item(feat2, wav2)
- count_iter += 1
- else:
- for item1 in loader:
- feat1, wav1 = item1
- check_item(feat1, wav1)
- count_iter += 1
- else:
- for item in loader:
- feat, wav = item
- expected_feat_shape = (batch_size, ap.num_mels, (wav.shape[-1] // hop_len) + (conv_pad * 2))
- assert np.all(feat.shape == expected_feat_shape), f" [!] {feat.shape} vs {expected_feat_shape}"
- assert (feat.shape[2] - conv_pad * 2) * hop_len == wav.shape[2]
- count_iter += 1
- if count_iter == max_iter:
- break
-
-
-def test_parametrized_gan_dataset():
- """test dataloader with different parameters"""
- params = [
- [32, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, False, True, 0],
- [32, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, False, True, 4],
- [1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, True, True, 0],
- [1, C.audio["hop_length"], C.audio["hop_length"], 0, True, True, True, True, 0],
- [1, C.audio["hop_length"] * 10, C.audio["hop_length"], 2, True, True, True, True, 0],
- [1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, False, True, True, 0],
- [1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, False, True, 0],
- [1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, False, True, True, False, 0],
- [1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, False, False, False, 0],
- [1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, False, False, False, 0],
- ]
- for param in params:
- print(param)
- gan_dataset_case(*param)
diff --git a/TTS/tests/vocoder_tests/test_vocoder_losses.py b/TTS/tests/vocoder_tests/test_vocoder_losses.py
deleted file mode 100644
index 2a35aa2e3717ee7332e1a3926736971c3c97a090..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_losses.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import os
-
-import torch
-
-from tests import get_tests_input_path, get_tests_output_path, get_tests_path
-from TTS.config import BaseAudioConfig
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.layers.losses import MelganFeatureLoss, MultiScaleSTFTLoss, STFTLoss, TorchSTFT
-
-TESTS_PATH = get_tests_path()
-
-OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests")
-os.makedirs(OUT_PATH, exist_ok=True)
-
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-
-ap = AudioProcessor(**BaseAudioConfig().to_dict())
-
-
-def test_torch_stft():
- torch_stft = TorchSTFT(ap.fft_size, ap.hop_length, ap.win_length)
- # librosa stft
- wav = ap.load_wav(WAV_FILE)
- M_librosa = abs(ap._stft(wav)) # pylint: disable=protected-access
- # torch stft
- wav = torch.from_numpy(wav[None, :]).float()
- M_torch = torch_stft(wav)
- # check the difference b/w librosa and torch outputs
- assert (M_librosa - M_torch[0].data.numpy()).max() < 1e-5
-
-
-def test_stft_loss():
- stft_loss = STFTLoss(ap.fft_size, ap.hop_length, ap.win_length)
- wav = ap.load_wav(WAV_FILE)
- wav = torch.from_numpy(wav[None, :]).float()
- loss_m, loss_sc = stft_loss(wav, wav)
- assert loss_m + loss_sc == 0
- loss_m, loss_sc = stft_loss(wav, torch.rand_like(wav))
- assert loss_sc < 1.0
- assert loss_m + loss_sc > 0
-
-
-def test_multiscale_stft_loss():
- stft_loss = MultiScaleSTFTLoss(
- [ap.fft_size // 2, ap.fft_size, ap.fft_size * 2],
- [ap.hop_length // 2, ap.hop_length, ap.hop_length * 2],
- [ap.win_length // 2, ap.win_length, ap.win_length * 2],
- )
- wav = ap.load_wav(WAV_FILE)
- wav = torch.from_numpy(wav[None, :]).float()
- loss_m, loss_sc = stft_loss(wav, wav)
- assert loss_m + loss_sc == 0
- loss_m, loss_sc = stft_loss(wav, torch.rand_like(wav))
- assert loss_sc < 1.0
- assert loss_m + loss_sc > 0
-
-
-def test_melgan_feature_loss():
- feats_real = []
- feats_fake = []
-
- # if all the features are different.
- for _ in range(5): # different scales
- scale_feats_real = []
- scale_feats_fake = []
- for _ in range(4): # different layers
- scale_feats_real.append(torch.rand([3, 5, 7]))
- scale_feats_fake.append(torch.rand([3, 5, 7]))
- feats_real.append(scale_feats_real)
- feats_fake.append(scale_feats_fake)
-
- loss_func = MelganFeatureLoss()
- loss = loss_func(feats_fake, feats_real)
- assert loss.item() <= 1.0
-
- feats_real = []
- feats_fake = []
-
- # if all the features are the same
- for _ in range(5): # different scales
- scale_feats_real = []
- scale_feats_fake = []
- for _ in range(4): # different layers
- tensor = torch.rand([3, 5, 7])
- scale_feats_real.append(tensor)
- scale_feats_fake.append(tensor)
- feats_real.append(scale_feats_real)
- feats_fake.append(scale_feats_fake)
-
- loss_func = MelganFeatureLoss()
- loss = loss_func(feats_fake, feats_real)
- assert loss.item() == 0
diff --git a/TTS/tests/vocoder_tests/test_vocoder_melgan_discriminator.py b/TTS/tests/vocoder_tests/test_vocoder_melgan_discriminator.py
deleted file mode 100644
index a4564b5654255ff9cab6ee082b9c74e38d20b2c3..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_melgan_discriminator.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-import torch
-
-from TTS.vocoder.models.melgan_discriminator import MelganDiscriminator
-from TTS.vocoder.models.melgan_multiscale_discriminator import MelganMultiscaleDiscriminator
-
-
-def test_melgan_discriminator():
- model = MelganDiscriminator()
- print(model)
- dummy_input = torch.rand((4, 1, 256 * 10))
- output, _ = model(dummy_input)
- assert np.all(output.shape == (4, 1, 10))
-
-
-def test_melgan_multi_scale_discriminator():
- model = MelganMultiscaleDiscriminator()
- print(model)
- dummy_input = torch.rand((4, 1, 256 * 16))
- scores, feats = model(dummy_input)
- assert len(scores) == 3
- assert len(scores) == len(feats)
- assert np.all(scores[0].shape == (4, 1, 64))
- assert np.all(feats[0][0].shape == (4, 16, 4096))
- assert np.all(feats[0][1].shape == (4, 64, 1024))
- assert np.all(feats[0][2].shape == (4, 256, 256))
diff --git a/TTS/tests/vocoder_tests/test_vocoder_melgan_generator.py b/TTS/tests/vocoder_tests/test_vocoder_melgan_generator.py
deleted file mode 100644
index f4958de427ece20296adbcec54441455de997518..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_melgan_generator.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import numpy as np
-import torch
-
-from TTS.vocoder.models.melgan_generator import MelganGenerator
-
-
-def test_melgan_generator():
- model = MelganGenerator()
- print(model)
- dummy_input = torch.rand((4, 80, 64))
- output = model(dummy_input)
- assert np.all(output.shape == (4, 1, 64 * 256))
- output = model.inference(dummy_input)
- assert np.all(output.shape == (4, 1, (64 + 4) * 256))
diff --git a/TTS/tests/vocoder_tests/test_vocoder_parallel_wavegan_discriminator.py b/TTS/tests/vocoder_tests/test_vocoder_parallel_wavegan_discriminator.py
deleted file mode 100644
index d4eca0d1374fb5cabf111cb52cf249969392bad4..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_parallel_wavegan_discriminator.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import numpy as np
-import torch
-
-from TTS.vocoder.models.parallel_wavegan_discriminator import (
- ParallelWaveganDiscriminator,
- ResidualParallelWaveganDiscriminator,
-)
-
-
-def test_pwgan_disciminator():
- model = ParallelWaveganDiscriminator(
- in_channels=1,
- out_channels=1,
- kernel_size=3,
- num_layers=10,
- conv_channels=64,
- dilation_factor=1,
- nonlinear_activation="LeakyReLU",
- nonlinear_activation_params={"negative_slope": 0.2},
- bias=True,
- )
- dummy_x = torch.rand((4, 1, 64 * 256))
- output = model(dummy_x)
- assert np.all(output.shape == (4, 1, 64 * 256))
- model.remove_weight_norm()
-
-
-def test_redisual_pwgan_disciminator():
- model = ResidualParallelWaveganDiscriminator(
- in_channels=1,
- out_channels=1,
- kernel_size=3,
- num_layers=30,
- stacks=3,
- res_channels=64,
- gate_channels=128,
- skip_channels=64,
- dropout=0.0,
- bias=True,
- nonlinear_activation="LeakyReLU",
- nonlinear_activation_params={"negative_slope": 0.2},
- )
- dummy_x = torch.rand((4, 1, 64 * 256))
- output = model(dummy_x)
- assert np.all(output.shape == (4, 1, 64 * 256))
- model.remove_weight_norm()
diff --git a/TTS/tests/vocoder_tests/test_vocoder_parallel_wavegan_generator.py b/TTS/tests/vocoder_tests/test_vocoder_parallel_wavegan_generator.py
deleted file mode 100644
index 21f6f08fd6b10e5ad9fe36e452f46d488cad3503..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_parallel_wavegan_generator.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import numpy as np
-import torch
-
-from TTS.vocoder.models.parallel_wavegan_generator import ParallelWaveganGenerator
-
-
-def test_pwgan_generator():
- model = ParallelWaveganGenerator(
- in_channels=1,
- out_channels=1,
- kernel_size=3,
- num_res_blocks=30,
- stacks=3,
- res_channels=64,
- gate_channels=128,
- skip_channels=64,
- aux_channels=80,
- dropout=0.0,
- bias=True,
- use_weight_norm=True,
- upsample_factors=[4, 4, 4, 4],
- )
- dummy_c = torch.rand((2, 80, 5))
- output = model(dummy_c)
- assert np.all(output.shape == (2, 1, 5 * 256)), output.shape
- model.remove_weight_norm()
- output = model.inference(dummy_c)
- assert np.all(output.shape == (2, 1, (5 + 4) * 256))
diff --git a/TTS/tests/vocoder_tests/test_vocoder_pqmf.py b/TTS/tests/vocoder_tests/test_vocoder_pqmf.py
deleted file mode 100644
index afe8d1dc8f8bf462cb3f030d3d8f113ed547c7d9..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_pqmf.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-
-import soundfile as sf
-import torch
-from librosa.core import load
-
-from tests import get_tests_input_path, get_tests_output_path, get_tests_path
-from TTS.vocoder.layers.pqmf import PQMF
-
-TESTS_PATH = get_tests_path()
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-
-
-def test_pqmf():
- w, sr = load(WAV_FILE)
-
- layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
- w, sr = load(WAV_FILE)
- w2 = torch.from_numpy(w[None, None, :])
- b2 = layer.analysis(w2)
- w2_ = layer.synthesis(b2)
-
- print(w2_.max())
- print(w2_.min())
- print(w2_.mean())
- sf.write(os.path.join(get_tests_output_path(), "pqmf_output.wav"), w2_.flatten().detach(), sr)
diff --git a/TTS/tests/vocoder_tests/test_vocoder_rwd.py b/TTS/tests/vocoder_tests/test_vocoder_rwd.py
deleted file mode 100644
index 371ad9e41e584c41564dbcd7b9ff9548c61aac75..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_rwd.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-
-from TTS.vocoder.models.random_window_discriminator import RandomWindowDiscriminator
-
-
-def test_rwd():
- layer = RandomWindowDiscriminator(
- cond_channels=80,
- window_sizes=(512, 1024, 2048, 4096, 8192),
- cond_disc_downsample_factors=[(8, 4, 2, 2, 2), (8, 4, 2, 2), (8, 4, 2), (8, 4), (4, 2, 2)],
- hop_length=256,
- )
- x = torch.rand([4, 1, 22050])
- c = torch.rand([4, 80, 22050 // 256])
-
- scores, _ = layer(x, c)
- assert len(scores) == 10
- assert np.all(scores[0].shape == (4, 1, 1))
diff --git a/TTS/tests/vocoder_tests/test_vocoder_wavernn.py b/TTS/tests/vocoder_tests/test_vocoder_wavernn.py
deleted file mode 100644
index 966ea3dd00c1f745afbde4f26e9097f355e651a2..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_wavernn.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import random
-
-import numpy as np
-import torch
-
-from TTS.vocoder.configs import WavernnConfig
-from TTS.vocoder.models.wavernn import Wavernn, WavernnArgs
-
-
-def test_wavernn():
- config = WavernnConfig()
- config.model_args = WavernnArgs(
- rnn_dims=512,
- fc_dims=512,
- mode="mold",
- mulaw=False,
- pad=2,
- use_aux_net=True,
- use_upsample_net=True,
- upsample_factors=[4, 8, 8],
- feat_dims=80,
- compute_dims=128,
- res_out_dims=128,
- num_res_blocks=10,
- )
- config.audio.hop_length = 256
- config.audio.sample_rate = 2048
-
- dummy_x = torch.rand((2, 1280))
- dummy_m = torch.rand((2, 80, 9))
- y_size = random.randrange(20, 60)
- dummy_y = torch.rand((80, y_size))
-
- # mode: mold
- model = Wavernn(config)
- output = model(dummy_x, dummy_m)
- assert np.all(output.shape == (2, 1280, 30)), output.shape
-
- # mode: gauss
- config.model_args.mode = "gauss"
- model = Wavernn(config)
- output = model(dummy_x, dummy_m)
- assert np.all(output.shape == (2, 1280, 2)), output.shape
-
- # mode: quantized
- config.model_args.mode = 4
- model = Wavernn(config)
- output = model(dummy_x, dummy_m)
- assert np.all(output.shape == (2, 1280, 2**4)), output.shape
- output = model.inference(dummy_y, True, 5500, 550)
- assert np.all(output.shape == (256 * (y_size - 1),))
diff --git a/TTS/tests/vocoder_tests/test_vocoder_wavernn_datasets.py b/TTS/tests/vocoder_tests/test_vocoder_wavernn_datasets.py
deleted file mode 100644
index 503b4e2483b447a01b0cb4abb02bc6cf34c80b90..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_vocoder_wavernn_datasets.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-import shutil
-
-import numpy as np
-from torch.utils.data import DataLoader
-
-from tests import get_tests_output_path, get_tests_path
-from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.configs import WavernnConfig
-from TTS.vocoder.datasets.preprocess import load_wav_feat_data, preprocess_wav_files
-from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset
-
-file_path = os.path.dirname(os.path.realpath(__file__))
-OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
-os.makedirs(OUTPATH, exist_ok=True)
-
-C = WavernnConfig()
-
-test_data_path = os.path.join(get_tests_path(), "data/ljspeech/")
-test_mel_feat_path = os.path.join(test_data_path, "mel")
-test_quant_feat_path = os.path.join(test_data_path, "quant")
-ok_ljspeech = os.path.exists(test_data_path)
-
-
-def wavernn_dataset_case(batch_size, seq_len, hop_len, pad, mode, mulaw, num_workers):
- """run dataloader with given parameters and check conditions"""
- ap = AudioProcessor(**C.audio)
-
- C.batch_size = batch_size
- C.mode = mode
- C.seq_len = seq_len
- C.data_path = test_data_path
-
- preprocess_wav_files(test_data_path, C, ap)
- _, train_items = load_wav_feat_data(test_data_path, test_mel_feat_path, 5)
-
- dataset = WaveRNNDataset(
- ap=ap, items=train_items, seq_len=seq_len, hop_len=hop_len, pad=pad, mode=mode, mulaw=mulaw
- )
- # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
- loader = DataLoader(
- dataset,
- shuffle=True,
- collate_fn=dataset.collate,
- batch_size=batch_size,
- num_workers=num_workers,
- pin_memory=True,
- )
-
- max_iter = 10
- count_iter = 0
-
- try:
- for data in loader:
- x_input, mels, _ = data
- expected_feat_shape = (ap.num_mels, (x_input.shape[-1] // hop_len) + (pad * 2))
- assert np.all(mels.shape[1:] == expected_feat_shape), f" [!] {mels.shape} vs {expected_feat_shape}"
-
- assert (mels.shape[2] - pad * 2) * hop_len == x_input.shape[1]
- count_iter += 1
- if count_iter == max_iter:
- break
- # except AssertionError:
- # shutil.rmtree(test_mel_feat_path)
- # shutil.rmtree(test_quant_feat_path)
- finally:
- shutil.rmtree(test_mel_feat_path)
- shutil.rmtree(test_quant_feat_path)
-
-
-def test_parametrized_wavernn_dataset():
- """test dataloader with different parameters"""
- params = [
- [16, C.audio["hop_length"] * 10, C.audio["hop_length"], 2, 10, True, 0],
- [16, C.audio["hop_length"] * 10, C.audio["hop_length"], 2, "mold", False, 4],
- [1, C.audio["hop_length"] * 10, C.audio["hop_length"], 2, 9, False, 0],
- [1, C.audio["hop_length"], C.audio["hop_length"], 2, 10, True, 0],
- [1, C.audio["hop_length"], C.audio["hop_length"], 2, "mold", False, 0],
- [1, C.audio["hop_length"] * 5, C.audio["hop_length"], 4, 10, False, 2],
- [1, C.audio["hop_length"] * 5, C.audio["hop_length"], 2, "mold", False, 0],
- ]
- for param in params:
- print(param)
- wavernn_dataset_case(*param)
diff --git a/TTS/tests/vocoder_tests/test_wavegrad.py b/TTS/tests/vocoder_tests/test_wavegrad.py
deleted file mode 100644
index 43b5f08042f1139e536aae2d57cd85675dce49e7..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_wavegrad.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import unittest
-
-import numpy as np
-import torch
-from torch import optim
-
-from TTS.vocoder.configs import WavegradConfig
-from TTS.vocoder.models.wavegrad import Wavegrad, WavegradArgs
-
-# pylint: disable=unused-variable
-
-torch.manual_seed(1)
-use_cuda = torch.cuda.is_available()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-class WavegradTrainTest(unittest.TestCase):
- def test_train_step(self): # pylint: disable=no-self-use
- """Test if all layers are updated in a basic training cycle"""
- input_dummy = torch.rand(8, 1, 20 * 300).to(device)
- mel_spec = torch.rand(8, 80, 20).to(device)
-
- criterion = torch.nn.L1Loss().to(device)
- args = WavegradArgs(
- in_channels=80,
- out_channels=1,
- upsample_factors=[5, 5, 3, 2, 2],
- upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 4, 8], [1, 2, 4, 8], [1, 2, 4, 8]],
- )
- config = WavegradConfig(model_params=args)
- model = Wavegrad(config)
-
- model_ref = Wavegrad(config)
- model.train()
- model.to(device)
- betas = np.linspace(1e-6, 1e-2, 1000)
- model.compute_noise_level(betas)
- model_ref.load_state_dict(model.state_dict())
- model_ref.to(device)
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count += 1
- optimizer = optim.Adam(model.parameters(), lr=0.001)
- for i in range(5):
- y_hat = model.forward(input_dummy, mel_spec, torch.rand(8).to(device))
- optimizer.zero_grad()
- loss = criterion(y_hat, input_dummy)
- loss.backward()
- optimizer.step()
- # check parameter changes
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- # ignore pre-higway layer since it works conditional
- # if count not in [145, 59]:
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- count, param.shape, param, param_ref
- )
- count += 1
diff --git a/TTS/tests/vocoder_tests/test_wavegrad_layers.py b/TTS/tests/vocoder_tests/test_wavegrad_layers.py
deleted file mode 100644
index a0b021dcf649bddd9aad940cb399cac1ca884e58..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_wavegrad_layers.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import torch
-
-from TTS.vocoder.configs import WavegradConfig
-from TTS.vocoder.layers.wavegrad import DBlock, FiLM, PositionalEncoding, UBlock
-from TTS.vocoder.models.wavegrad import Wavegrad, WavegradArgs
-
-
-def test_positional_encoding():
- layer = PositionalEncoding(50)
- inp = torch.rand(32, 50, 100)
- nl = torch.rand(32)
- o = layer(inp, nl)
-
- assert o.shape[0] == 32
- assert o.shape[1] == 50
- assert o.shape[2] == 100
- assert isinstance(o, torch.FloatTensor)
-
-
-def test_film():
- layer = FiLM(50, 76)
- inp = torch.rand(32, 50, 100)
- nl = torch.rand(32)
- shift, scale = layer(inp, nl)
-
- assert shift.shape[0] == 32
- assert shift.shape[1] == 76
- assert shift.shape[2] == 100
- assert isinstance(shift, torch.FloatTensor)
-
- assert scale.shape[0] == 32
- assert scale.shape[1] == 76
- assert scale.shape[2] == 100
- assert isinstance(scale, torch.FloatTensor)
-
- layer.apply_weight_norm()
- layer.remove_weight_norm()
-
-
-def test_ublock():
- inp1 = torch.rand(32, 50, 100)
- inp2 = torch.rand(32, 50, 50)
- nl = torch.rand(32)
-
- layer_film = FiLM(50, 100)
- layer = UBlock(50, 100, 2, [1, 2, 4, 8])
-
- scale, shift = layer_film(inp1, nl)
- o = layer(inp2, shift, scale)
-
- assert o.shape[0] == 32
- assert o.shape[1] == 100
- assert o.shape[2] == 100
- assert isinstance(o, torch.FloatTensor)
-
- layer.apply_weight_norm()
- layer.remove_weight_norm()
-
-
-def test_dblock():
- inp = torch.rand(32, 50, 130)
- layer = DBlock(50, 100, 2)
- o = layer(inp)
-
- assert o.shape[0] == 32
- assert o.shape[1] == 100
- assert o.shape[2] == 65
- assert isinstance(o, torch.FloatTensor)
-
- layer.apply_weight_norm()
- layer.remove_weight_norm()
-
-
-def test_wavegrad_forward():
- x = torch.rand(32, 1, 20 * 300)
- c = torch.rand(32, 80, 20)
- noise_scale = torch.rand(32)
-
- args = WavegradArgs(
- in_channels=80,
- out_channels=1,
- upsample_factors=[5, 5, 3, 2, 2],
- upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 4, 8], [1, 2, 4, 8], [1, 2, 4, 8]],
- )
- config = WavegradConfig(model_params=args)
- model = Wavegrad(config)
- o = model.forward(x, c, noise_scale)
-
- assert o.shape[0] == 32
- assert o.shape[1] == 1
- assert o.shape[2] == 20 * 300
- assert isinstance(o, torch.FloatTensor)
-
- model.apply_weight_norm()
- model.remove_weight_norm()
diff --git a/TTS/tests/vocoder_tests/test_wavegrad_train.py b/TTS/tests/vocoder_tests/test_wavegrad_train.py
deleted file mode 100644
index fe56ee783f36b89879af78e58316b19ff0e23f54..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_wavegrad_train.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.vocoder.configs import WavegradConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = WavegradConfig(
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- seq_len=8192,
- eval_split_size=1,
- print_step=1,
- print_eval=True,
- data_path="tests/data/ljspeech",
- output_path=output_path,
- test_noise_schedule={"min_val": 1e-6, "max_val": 1e-2, "num_steps": 2},
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/vocoder_tests/test_wavernn_train.py b/TTS/tests/vocoder_tests/test_wavernn_train.py
deleted file mode 100644
index 337e24259f0ffa39d4d77b57749988b64763c2f1..0000000000000000000000000000000000000000
--- a/TTS/tests/vocoder_tests/test_wavernn_train.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.vocoder.configs import WavernnConfig
-from TTS.vocoder.models.wavernn import WavernnArgs
-
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = WavernnConfig(
- model_args=WavernnArgs(),
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- seq_len=256, # for shorter test time
- eval_split_size=1,
- print_step=1,
- print_eval=True,
- data_path="tests/data/ljspeech",
- output_path=output_path,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/TTS/tests/zoo_tests/__init__.py b/TTS/tests/zoo_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/zoo_tests/test_models.py b/TTS/tests/zoo_tests/test_models.py
deleted file mode 100644
index d3a83980c0628deef0bfeacd8547ed65231f8fd2..0000000000000000000000000000000000000000
--- a/TTS/tests/zoo_tests/test_models.py
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/usr/bin/env python3`
-import glob
-import os
-import shutil
-
-from tests import get_tests_data_path, get_tests_output_path, run_cli
-from TTS.tts.utils.languages import LanguageManager
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.utils.generic_utils import get_user_data_dir
-from TTS.utils.manage import ModelManager
-
-
-def run_models(offset=0, step=1):
- """Check if all the models are downloadable and tts models run correctly."""
- print(" > Run synthesizer with all the models.")
- output_path = os.path.join(get_tests_output_path(), "output.wav")
- manager = ModelManager(output_prefix=get_tests_output_path(), progress_bar=False)
- model_names = [name for name in manager.list_models() if "bark" not in name]
- for model_name in model_names[offset::step]:
- print(f"\n > Run - {model_name}")
- model_path, _, _ = manager.download_model(model_name)
- if "tts_models" in model_name:
- local_download_dir = os.path.dirname(model_path)
- # download and run the model
- speaker_files = glob.glob(local_download_dir + "/speaker*")
- language_files = glob.glob(local_download_dir + "/language*")
- language_id = ""
- if len(speaker_files) > 0:
- # multi-speaker model
- if "speaker_ids" in speaker_files[0]:
- speaker_manager = SpeakerManager(speaker_id_file_path=speaker_files[0])
- elif "speakers" in speaker_files[0]:
- speaker_manager = SpeakerManager(d_vectors_file_path=speaker_files[0])
-
- # multi-lingual model - Assuming multi-lingual models are also multi-speaker
- if len(language_files) > 0 and "language_ids" in language_files[0]:
- language_manager = LanguageManager(language_ids_file_path=language_files[0])
- language_id = language_manager.language_names[0]
-
- speaker_id = list(speaker_manager.name_to_id.keys())[0]
- run_cli(
- f"tts --model_name {model_name} "
- f'--text "This is an example." --out_path "{output_path}" --speaker_idx "{speaker_id}" --language_idx "{language_id}" --progress_bar False'
- )
- else:
- # single-speaker model
- run_cli(
- f"tts --model_name {model_name} "
- f'--text "This is an example." --out_path "{output_path}" --progress_bar False'
- )
- # remove downloaded models
- shutil.rmtree(local_download_dir)
- shutil.rmtree(get_user_data_dir("tts"))
- elif "voice_conversion_models" in model_name:
- speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")
- reference_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0032.wav")
- run_cli(
- f"tts --model_name {model_name} "
- f'--out_path "{output_path}" --source_wav "{speaker_wav}" --target_wav "{reference_wav}" --progress_bar False'
- )
- else:
- # only download the model
- manager.download_model(model_name)
- print(f" | > OK: {model_name}")
-
- # folders = glob.glob(os.path.join(manager.output_prefix, "*"))
- # assert len(folders) == len(model_names) // step
-
-
-def test_models_offset_0_step_3():
- run_models(offset=0, step=3)
-
-
-def test_models_offset_1_step_3():
- run_models(offset=1, step=3)
-
-
-def test_models_offset_2_step_3():
- run_models(offset=2, step=3)
-
-
-def test_bark():
- """Bark is too big to run on github actions. We need to test it locally"""
- output_path = os.path.join(get_tests_output_path(), "output.wav")
- run_cli(
- f" tts --model_name tts_models/multilingual/multi-dataset/bark "
- f'--text "This is an example." --out_path "{output_path}" --progress_bar False'
- )
-
-
-def test_voice_conversion():
- print(" > Run voice conversion inference using YourTTS model.")
- model_name = "tts_models/multilingual/multi-dataset/your_tts"
- language_id = "en"
- speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")
- reference_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0032.wav")
- output_path = os.path.join(get_tests_output_path(), "output.wav")
- run_cli(
- f"tts --model_name {model_name}"
- f" --out_path {output_path} --speaker_wav {speaker_wav} --reference_wav {reference_wav} --language_idx {language_id} --progress_bar False"
- )
diff --git a/TTS/tts/__init__.py b/TTS/tts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tts/configs/__init__.py b/TTS/tts/configs/__init__.py
deleted file mode 100644
index 3146ac1c116cb807a81889b7a9ab223b9a051036..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import importlib
-import os
-from inspect import isclass
-
-# import all files under configs/
-# configs_dir = os.path.dirname(__file__)
-# for file in os.listdir(configs_dir):
-# path = os.path.join(configs_dir, file)
-# if not file.startswith("_") and not file.startswith(".") and (file.endswith(".py") or os.path.isdir(path)):
-# config_name = file[: file.find(".py")] if file.endswith(".py") else file
-# module = importlib.import_module("TTS.tts.configs." + config_name)
-# for attribute_name in dir(module):
-# attribute = getattr(module, attribute_name)
-
-# if isclass(attribute):
-# # Add the class to this package's variables
-# globals()[attribute_name] = attribute
diff --git a/TTS/tts/configs/align_tts_config.py b/TTS/tts/configs/align_tts_config.py
deleted file mode 100644
index 317a01af53ce26914d83610a913eb44b5836dac2..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/align_tts_config.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.align_tts import AlignTTSArgs
-
-
-@dataclass
-class AlignTTSConfig(BaseTTSConfig):
- """Defines parameters for AlignTTS model.
- Example:
-
- >>> from TTS.tts.configs.align_tts_config import AlignTTSConfig
- >>> config = AlignTTSConfig()
-
- Args:
- model(str):
- Model name used for selecting the right model at initialization. Defaults to `align_tts`.
- positional_encoding (bool):
- enable / disable positional encoding applied to the encoder output. Defaults to True.
- hidden_channels (int):
- Base number of hidden channels. Defines all the layers expect ones defined by the specific encoder or decoder
- parameters. Defaults to 256.
- hidden_channels_dp (int):
- Number of hidden channels of the duration predictor's layers. Defaults to 256.
- encoder_type (str):
- Type of the encoder used by the model. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
- Defaults to `fftransformer`.
- encoder_params (dict):
- Parameters used to define the encoder network. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
- Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
- decoder_type (str):
- Type of the decoder used by the model. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
- Defaults to `fftransformer`.
- decoder_params (dict):
- Parameters used to define the decoder network. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
- Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
- phase_start_steps (List[int]):
- A list of number of steps required to start the next training phase. AlignTTS has 4 different training
- phases. Thus you need to define 4 different values to enable phase based training. If None, it
- trains the whole model together. Defaults to None.
- ssim_alpha (float):
- Weight for the SSIM loss. If set <= 0, disables the SSIM loss. Defaults to 1.0.
- duration_loss_alpha (float):
- Weight for the duration predictor's loss. Defaults to 1.0.
- mdn_alpha (float):
- Weight for the MDN loss. Defaults to 1.0.
- spec_loss_alpha (float):
- Weight for the MSE spectrogram loss. If set <= 0, disables the L1 loss. Defaults to 1.0.
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
- noam_schedule (bool):
- enable / disable the use of Noam LR scheduler. Defaults to False.
- warmup_steps (int):
- Number of warm-up steps for the Noam scheduler. Defaults 4000.
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
- min_seq_len (int):
- Minimum input sequence length to be used at training.
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage."""
-
- model: str = "align_tts"
- # model specific params
- model_args: AlignTTSArgs = field(default_factory=AlignTTSArgs)
- phase_start_steps: List[int] = None
-
- ssim_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- dur_loss_alpha: float = 1.0
- mdn_alpha: float = 1.0
-
- # multi-speaker settings
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = None
- lr_scheduler_params: dict = None
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
diff --git a/TTS/tts/configs/bark_config.py b/TTS/tts/configs/bark_config.py
deleted file mode 100644
index 4d1cd1374afe8d5f0b9e87ed81db25d7e4032af9..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/bark_config.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import os
-from dataclasses import dataclass, field
-from typing import Dict
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.layers.bark.model import GPTConfig
-from TTS.tts.layers.bark.model_fine import FineGPTConfig
-from TTS.tts.models.bark import BarkAudioConfig
-from TTS.utils.generic_utils import get_user_data_dir
-
-
-@dataclass
-class BarkConfig(BaseTTSConfig):
- """Bark TTS configuration
-
- Args:
- model (str): model name that registers the model.
- audio (BarkAudioConfig): audio configuration. Defaults to BarkAudioConfig().
- num_chars (int): number of characters in the alphabet. Defaults to 0.
- semantic_config (GPTConfig): semantic configuration. Defaults to GPTConfig().
- fine_config (FineGPTConfig): fine configuration. Defaults to FineGPTConfig().
- coarse_config (GPTConfig): coarse configuration. Defaults to GPTConfig().
- CONTEXT_WINDOW_SIZE (int): GPT context window size. Defaults to 1024.
- SEMANTIC_RATE_HZ (float): semantic tokens rate in Hz. Defaults to 49.9.
- SEMANTIC_VOCAB_SIZE (int): semantic vocabulary size. Defaults to 10_000.
- CODEBOOK_SIZE (int): encodec codebook size. Defaults to 1024.
- N_COARSE_CODEBOOKS (int): number of coarse codebooks. Defaults to 2.
- N_FINE_CODEBOOKS (int): number of fine codebooks. Defaults to 8.
- COARSE_RATE_HZ (int): coarse tokens rate in Hz. Defaults to 75.
- SAMPLE_RATE (int): sample rate. Defaults to 24_000.
- USE_SMALLER_MODELS (bool): use smaller models. Defaults to False.
- TEXT_ENCODING_OFFSET (int): text encoding offset. Defaults to 10_048.
- SEMANTIC_PAD_TOKEN (int): semantic pad token. Defaults to 10_000.
- TEXT_PAD_TOKEN ([type]): text pad token. Defaults to 10_048.
- TEXT_EOS_TOKEN ([type]): text end of sentence token. Defaults to 10_049.
- TEXT_SOS_TOKEN ([type]): text start of sentence token. Defaults to 10_050.
- SEMANTIC_INFER_TOKEN (int): semantic infer token. Defaults to 10_051.
- COARSE_SEMANTIC_PAD_TOKEN (int): coarse semantic pad token. Defaults to 12_048.
- COARSE_INFER_TOKEN (int): coarse infer token. Defaults to 12_050.
- REMOTE_BASE_URL ([type]): remote base url. Defaults to "https://huggingface.co/erogol/bark/tree".
- REMOTE_MODEL_PATHS (Dict): remote model paths. Defaults to None.
- LOCAL_MODEL_PATHS (Dict): local model paths. Defaults to None.
- SMALL_REMOTE_MODEL_PATHS (Dict): small remote model paths. Defaults to None.
- CACHE_DIR (str): local cache directory. Defaults to get_user_data_dir().
- DEF_SPEAKER_DIR (str): default speaker directory to stoke speaker values for voice cloning. Defaults to get_user_data_dir().
- """
-
- model: str = "bark"
- audio: BarkAudioConfig = field(default_factory=BarkAudioConfig)
- num_chars: int = 0
- semantic_config: GPTConfig = field(default_factory=GPTConfig)
- fine_config: FineGPTConfig = field(default_factory=FineGPTConfig)
- coarse_config: GPTConfig = field(default_factory=GPTConfig)
- CONTEXT_WINDOW_SIZE: int = 1024
- SEMANTIC_RATE_HZ: float = 49.9
- SEMANTIC_VOCAB_SIZE: int = 10_000
- CODEBOOK_SIZE: int = 1024
- N_COARSE_CODEBOOKS: int = 2
- N_FINE_CODEBOOKS: int = 8
- COARSE_RATE_HZ: int = 75
- SAMPLE_RATE: int = 24_000
- USE_SMALLER_MODELS: bool = False
-
- TEXT_ENCODING_OFFSET: int = 10_048
- SEMANTIC_PAD_TOKEN: int = 10_000
- TEXT_PAD_TOKEN: int = 129_595
- SEMANTIC_INFER_TOKEN: int = 129_599
- COARSE_SEMANTIC_PAD_TOKEN: int = 12_048
- COARSE_INFER_TOKEN: int = 12_050
-
- REMOTE_BASE_URL = "https://huggingface.co/erogol/bark/tree/main/"
- REMOTE_MODEL_PATHS: Dict = None
- LOCAL_MODEL_PATHS: Dict = None
- SMALL_REMOTE_MODEL_PATHS: Dict = None
- CACHE_DIR: str = str(get_user_data_dir("tts/suno/bark_v0"))
- DEF_SPEAKER_DIR: str = str(get_user_data_dir("tts/bark_v0/speakers"))
-
- def __post_init__(self):
- self.REMOTE_MODEL_PATHS = {
- "text": {
- "path": os.path.join(self.REMOTE_BASE_URL, "text_2.pt"),
- "checksum": "54afa89d65e318d4f5f80e8e8799026a",
- },
- "coarse": {
- "path": os.path.join(self.REMOTE_BASE_URL, "coarse_2.pt"),
- "checksum": "8a98094e5e3a255a5c9c0ab7efe8fd28",
- },
- "fine": {
- "path": os.path.join(self.REMOTE_BASE_URL, "fine_2.pt"),
- "checksum": "59d184ed44e3650774a2f0503a48a97b",
- },
- }
- self.LOCAL_MODEL_PATHS = {
- "text": os.path.join(self.CACHE_DIR, "text_2.pt"),
- "coarse": os.path.join(self.CACHE_DIR, "coarse_2.pt"),
- "fine": os.path.join(self.CACHE_DIR, "fine_2.pt"),
- "hubert_tokenizer": os.path.join(self.CACHE_DIR, "tokenizer.pth"),
- "hubert": os.path.join(self.CACHE_DIR, "hubert.pt"),
- }
- self.SMALL_REMOTE_MODEL_PATHS = {
- "text": {"path": os.path.join(self.REMOTE_BASE_URL, "text.pt")},
- "coarse": {"path": os.path.join(self.REMOTE_BASE_URL, "coarse.pt")},
- "fine": {"path": os.path.join(self.REMOTE_BASE_URL, "fine.pt")},
- }
- self.sample_rate = self.SAMPLE_RATE # pylint: disable=attribute-defined-outside-init
diff --git a/TTS/tts/configs/delightful_tts_config.py b/TTS/tts/configs/delightful_tts_config.py
deleted file mode 100644
index 805d995369e29fce7d6aa87750356b21458cd64a..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/delightful_tts_config.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.delightful_tts import DelightfulTtsArgs, DelightfulTtsAudioConfig, VocoderConfig
-
-
-@dataclass
-class DelightfulTTSConfig(BaseTTSConfig):
- """
- Configuration class for the DelightfulTTS model.
-
- Attributes:
- model (str): Name of the model ("delightful_tts").
- audio (DelightfulTtsAudioConfig): Configuration for audio settings.
- model_args (DelightfulTtsArgs): Configuration for model arguments.
- use_attn_priors (bool): Whether to use attention priors.
- vocoder (VocoderConfig): Configuration for the vocoder.
- init_discriminator (bool): Whether to initialize the discriminator.
- steps_to_start_discriminator (int): Number of steps to start the discriminator.
- grad_clip (List[float]): Gradient clipping values.
- lr_gen (float): Learning rate for the gan generator.
- lr_disc (float): Learning rate for the gan discriminator.
- lr_scheduler_gen (str): Name of the learning rate scheduler for the generator.
- lr_scheduler_gen_params (dict): Parameters for the learning rate scheduler for the generator.
- lr_scheduler_disc (str): Name of the learning rate scheduler for the discriminator.
- lr_scheduler_disc_params (dict): Parameters for the learning rate scheduler for the discriminator.
- scheduler_after_epoch (bool): Whether to schedule after each epoch.
- optimizer (str): Name of the optimizer.
- optimizer_params (dict): Parameters for the optimizer.
- ssim_loss_alpha (float): Alpha value for the SSIM loss.
- mel_loss_alpha (float): Alpha value for the mel loss.
- aligner_loss_alpha (float): Alpha value for the aligner loss.
- pitch_loss_alpha (float): Alpha value for the pitch loss.
- energy_loss_alpha (float): Alpha value for the energy loss.
- u_prosody_loss_alpha (float): Alpha value for the utterance prosody loss.
- p_prosody_loss_alpha (float): Alpha value for the phoneme prosody loss.
- dur_loss_alpha (float): Alpha value for the duration loss.
- char_dur_loss_alpha (float): Alpha value for the character duration loss.
- binary_align_loss_alpha (float): Alpha value for the binary alignment loss.
- binary_loss_warmup_epochs (int): Number of warm-up epochs for the binary loss.
- disc_loss_alpha (float): Alpha value for the discriminator loss.
- gen_loss_alpha (float): Alpha value for the generator loss.
- feat_loss_alpha (float): Alpha value for the feature loss.
- vocoder_mel_loss_alpha (float): Alpha value for the vocoder mel loss.
- multi_scale_stft_loss_alpha (float): Alpha value for the multi-scale STFT loss.
- multi_scale_stft_loss_params (dict): Parameters for the multi-scale STFT loss.
- return_wav (bool): Whether to return audio waveforms.
- use_weighted_sampler (bool): Whether to use a weighted sampler.
- weighted_sampler_attrs (dict): Attributes for the weighted sampler.
- weighted_sampler_multipliers (dict): Multipliers for the weighted sampler.
- r (int): Value for the `r` override.
- compute_f0 (bool): Whether to compute F0 values.
- f0_cache_path (str): Path to the F0 cache.
- attn_prior_cache_path (str): Path to the attention prior cache.
- num_speakers (int): Number of speakers.
- use_speaker_embedding (bool): Whether to use speaker embedding.
- speakers_file (str): Path to the speaker file.
- speaker_embedding_channels (int): Number of channels for the speaker embedding.
- language_ids_file (str): Path to the language IDs file.
- """
-
- model: str = "delightful_tts"
-
- # model specific params
- audio: DelightfulTtsAudioConfig = field(default_factory=DelightfulTtsAudioConfig)
- model_args: DelightfulTtsArgs = field(default_factory=DelightfulTtsArgs)
- use_attn_priors: bool = True
-
- # vocoder
- vocoder: VocoderConfig = field(default_factory=VocoderConfig)
- init_discriminator: bool = True
-
- # optimizer
- steps_to_start_discriminator: int = 200000
- grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])
- lr_gen: float = 0.0002
- lr_disc: float = 0.0002
- lr_scheduler_gen: str = "ExponentialLR"
- lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
- lr_scheduler_disc: str = "ExponentialLR"
- lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
- scheduler_after_epoch: bool = True
- optimizer: str = "AdamW"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01})
-
- # acoustic model loss params
- ssim_loss_alpha: float = 1.0
- mel_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 1.0
- energy_loss_alpha: float = 1.0
- u_prosody_loss_alpha: float = 0.5
- p_prosody_loss_alpha: float = 0.5
- dur_loss_alpha: float = 1.0
- char_dur_loss_alpha: float = 0.01
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 10
-
- # vocoder loss params
- disc_loss_alpha: float = 1.0
- gen_loss_alpha: float = 1.0
- feat_loss_alpha: float = 1.0
- vocoder_mel_loss_alpha: float = 10.0
- multi_scale_stft_loss_alpha: float = 2.5
- multi_scale_stft_loss_params: dict = field(
- default_factory=lambda: {
- "n_ffts": [1024, 2048, 512],
- "hop_lengths": [120, 240, 50],
- "win_lengths": [600, 1200, 240],
- }
- )
-
- # data loader params
- return_wav: bool = True
- use_weighted_sampler: bool = False
- weighted_sampler_attrs: dict = field(default_factory=lambda: {})
- weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
-
- # overrides
- r: int = 1
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
- attn_prior_cache_path: str = None
-
- # multi-speaker settings
- # use speaker embedding layer
- num_speakers: int = 0
- use_speaker_embedding: bool = False
- speakers_file: str = None
- speaker_embedding_channels: int = 256
- language_ids_file: str = None
- use_language_embedding: bool = False
-
- # use d-vectors
- use_d_vector_file: bool = False
- d_vector_file: str = None
- d_vector_dim: int = None
-
- # testing
- test_sentences: List[List[str]] = field(
- default_factory=lambda: [
- ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."],
- ["Be a voice, not an echo."],
- ["I'm sorry Dave. I'm afraid I can't do that."],
- ["This cake is great. It's so delicious and moist."],
- ["Prior to November 22, 1963."],
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/tts/configs/fast_pitch_config.py b/TTS/tts/configs/fast_pitch_config.py
deleted file mode 100644
index d086d26564450c60fa04a7f3a068506f4147d3be..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/fast_pitch_config.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class FastPitchConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as FastPitch model.
-
- Example:
-
- >>> from TTS.tts.configs.fast_pitch_config import FastPitchConfig
- >>> config = FastPitchConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- binary_align_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
-
- # dataset configs
- compute_f0(bool):
- Compute pitch. defaults to True
-
- f0_cache_path(str):
- pith cache path. defaults to None
- """
-
- model: str = "fast_pitch"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=ForwardTTSArgs)
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.1
- dur_loss_alpha: float = 0.1
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/tts/configs/fast_speech_config.py b/TTS/tts/configs/fast_speech_config.py
deleted file mode 100644
index af6c2db6faf55ee2b15047fff86281d42dab1b87..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/fast_speech_config.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class FastSpeechConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as FastSpeech model.
-
- Example:
-
- >>> from TTS.tts.configs.fast_speech_config import FastSpeechConfig
- >>> config = FastSpeechConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastSpeechArgs` for more details. Defaults to `FastSpeechArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- binary_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "fast_speech"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=lambda: ForwardTTSArgs(use_pitch=False))
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- dur_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.0
- aligner_loss_alpha: float = 1.0
- binary_align_loss_alpha: float = 1.0
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = False
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/tts/configs/fastspeech2_config.py b/TTS/tts/configs/fastspeech2_config.py
deleted file mode 100644
index d179617fb034fff269355ce7e3d78b67db90aacd..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/fastspeech2_config.py
+++ /dev/null
@@ -1,198 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class Fastspeech2Config(BaseTTSConfig):
- """Configure `ForwardTTS` as FastPitch model.
-
- Example:
-
- >>> from TTS.tts.configs.fastspeech2_config import FastSpeech2Config
- >>> config = FastSpeech2Config()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- energy_loss_alpha (float):
- Weight for the energy predictor's loss. If set 0, disables the energy predictor. Defaults to 1.0.
-
- binary_align_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
-
- # dataset configs
- compute_f0(bool):
- Compute pitch. defaults to True
-
- f0_cache_path(str):
- pith cache path. defaults to None
-
- # dataset configs
- compute_energy(bool):
- Compute energy. defaults to True
-
- energy_cache_path(str):
- energy cache path. defaults to None
- """
-
- model: str = "fastspeech2"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=lambda: ForwardTTSArgs(use_pitch=True, use_energy=True))
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.1
- energy_loss_alpha: float = 0.1
- dur_loss_alpha: float = 0.1
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
-
- # dataset configs
- compute_energy: bool = True
- energy_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/tts/configs/glow_tts_config.py b/TTS/tts/configs/glow_tts_config.py
deleted file mode 100644
index f42f3e5a510bacf1b2312ccea7d46201bbcb774f..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/glow_tts_config.py
+++ /dev/null
@@ -1,182 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-
-
-@dataclass
-class GlowTTSConfig(BaseTTSConfig):
- """Defines parameters for GlowTTS model.
-
- Example:
-
- >>> from TTS.tts.configs.glow_tts_config import GlowTTSConfig
- >>> config = GlowTTSConfig()
-
- Args:
- model(str):
- Model name used for selecting the right model at initialization. Defaults to `glow_tts`.
- encoder_type (str):
- Type of the encoder used by the model. Look at `TTS.tts.layers.glow_tts.encoder` for more details.
- Defaults to `rel_pos_transformers`.
- encoder_params (dict):
- Parameters used to define the encoder network. Look at `TTS.tts.layers.glow_tts.encoder` for more details.
- Defaults to `{"kernel_size": 3, "dropout_p": 0.1, "num_layers": 6, "num_heads": 2, "hidden_channels_ffn": 768}`
- use_encoder_prenet (bool):
- enable / disable the use of a prenet for the encoder. Defaults to True.
- hidden_channels_enc (int):
- Number of base hidden channels used by the encoder network. It defines the input and the output channel sizes,
- and for some encoder types internal hidden channels sizes too. Defaults to 192.
- hidden_channels_dec (int):
- Number of base hidden channels used by the decoder WaveNet network. Defaults to 192 as in the original work.
- hidden_channels_dp (int):
- Number of layer channels of the duration predictor network. Defaults to 256 as in the original work.
- mean_only (bool):
- If true predict only the mean values by the decoder flow. Defaults to True.
- out_channels (int):
- Number of channels of the model output tensor. Defaults to 80.
- num_flow_blocks_dec (int):
- Number of decoder blocks. Defaults to 12.
- inference_noise_scale (float):
- Noise scale used at inference. Defaults to 0.33.
- kernel_size_dec (int):
- Decoder kernel size. Defaults to 5
- dilation_rate (int):
- Rate to increase dilation by each layer in a decoder block. Defaults to 1.
- num_block_layers (int):
- Number of decoder layers in each decoder block. Defaults to 4.
- dropout_p_dec (float):
- Dropout rate for decoder. Defaults to 0.1.
- num_speaker (int):
- Number of speaker to define the size of speaker embedding layer. Defaults to 0.
- c_in_channels (int):
- Number of speaker embedding channels. It is set to 512 if embeddings are learned. Defaults to 0.
- num_splits (int):
- Number of split levels in inversible conv1x1 operation. Defaults to 4.
- num_squeeze (int):
- Number of squeeze levels. When squeezing channels increases and time steps reduces by the factor
- 'num_squeeze'. Defaults to 2.
- sigmoid_scale (bool):
- enable/disable sigmoid scaling in decoder. Defaults to False.
- mean_only (bool):
- If True, encoder only computes mean value and uses constant variance for each time step. Defaults to true.
- encoder_type (str):
- Encoder module type. Possible values are`["rel_pos_transformer", "gated_conv", "residual_conv_bn", "time_depth_separable"]`
- Check `TTS.tts.layers.glow_tts.encoder` for more details. Defaults to `rel_pos_transformers` as in the original paper.
- encoder_params (dict):
- Encoder module parameters. Defaults to None.
- d_vector_dim (int):
- Channels of external speaker embedding vectors. Defaults to 0.
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
- style_wav_for_test (str):
- Path to the wav file used for changing the style of the speech. Defaults to None.
- inference_noise_scale (float):
- Variance used for sampling the random noise added to the decoder's input at inference. Defaults to 0.0.
- length_scale (float):
- Multiply the predicted durations with this value to change the speech speed. Defaults to 1.
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
- noam_schedule (bool):
- enable / disable the use of Noam LR scheduler. Defaults to False.
- warmup_steps (int):
- Number of warm-up steps for the Noam scheduler. Defaults 4000.
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
- min_seq_len (int):
- Minimum input sequence length to be used at training.
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "glow_tts"
-
- # model params
- num_chars: int = None
- encoder_type: str = "rel_pos_transformer"
- encoder_params: dict = field(
- default_factory=lambda: {
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 6,
- "num_heads": 2,
- "hidden_channels_ffn": 768,
- }
- )
- use_encoder_prenet: bool = True
- hidden_channels_enc: int = 192
- hidden_channels_dec: int = 192
- hidden_channels_dp: int = 256
- dropout_p_dp: float = 0.1
- dropout_p_dec: float = 0.05
- mean_only: bool = True
- out_channels: int = 80
- num_flow_blocks_dec: int = 12
- inference_noise_scale: float = 0.33
- kernel_size_dec: int = 5
- dilation_rate: int = 1
- num_block_layers: int = 4
- num_speakers: int = 0
- c_in_channels: int = 0
- num_splits: int = 4
- num_squeeze: int = 2
- sigmoid_scale: bool = False
- encoder_type: str = "rel_pos_transformer"
- encoder_params: dict = field(
- default_factory=lambda: {
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 6,
- "num_heads": 2,
- "hidden_channels_ffn": 768,
- "input_length": None,
- }
- )
- d_vector_dim: int = 0
-
- # training params
- data_dep_init_steps: int = 10
-
- # inference params
- style_wav_for_test: str = None
- inference_noise_scale: float = 0.0
- length_scale: float = 1.0
-
- # multi-speaker settings
- use_speaker_embedding: bool = False
- speakers_file: str = None
- use_d_vector_file: bool = False
- d_vector_file: str = False
-
- # optimizer parameters
- optimizer: str = "RAdam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- grad_clip: float = 5.0
- lr: float = 1e-3
-
- # overrides
- min_seq_len: int = 3
- max_seq_len: int = 500
- r: int = 1 # DO NOT CHANGE - TODO: make this immutable once coqpit implements it.
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
diff --git a/TTS/tts/configs/neuralhmm_tts_config.py b/TTS/tts/configs/neuralhmm_tts_config.py
deleted file mode 100644
index 50f72847ed3e1c7089915ef8fd77ae5775c5b260..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/neuralhmm_tts_config.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-
-
-@dataclass
-class NeuralhmmTTSConfig(BaseTTSConfig):
- """
- Define parameters for Neural HMM TTS model.
-
- Example:
-
- >>> from TTS.tts.configs.overflow_config import OverflowConfig
- >>> config = OverflowConfig()
-
- Args:
- model (str):
- Model name used to select the right model class to initilize. Defaults to `Overflow`.
- run_eval_steps (int):
- Run evalulation epoch after N steps. If None, waits until training epoch is completed. Defaults to None.
- save_step (int):
- Save local checkpoint every save_step steps. Defaults to 500.
- plot_step (int):
- Plot training stats on the logger every plot_step steps. Defaults to 1.
- model_param_stats (bool):
- Log model parameters stats on the logger dashboard. Defaults to False.
- force_generate_statistics (bool):
- Force generate mel normalization statistics. Defaults to False.
- mel_statistics_parameter_path (str):
- Path to the mel normalization statistics.If the model doesn't finds a file there it will generate statistics.
- Defaults to None.
- num_chars (int):
- Number of characters used by the model. It must be defined before initializing the model. Defaults to None.
- state_per_phone (int):
- Generates N states per phone. Similar, to `add_blank` parameter in GlowTTS but in Overflow it is upsampled by model's encoder. Defaults to 2.
- encoder_in_out_features (int):
- Channels of encoder input and character embedding tensors. Defaults to 512.
- encoder_n_convolutions (int):
- Number of convolution layers in the encoder. Defaults to 3.
- out_channels (int):
- Channels of the final model output. It must match the spectragram size. Defaults to 80.
- ar_order (int):
- Autoregressive order of the model. Defaults to 1. In ablations of Neural HMM it was found that more autoregression while giving more variation hurts naturalness of the synthesised audio.
- sampling_temp (float):
- Variation added to the sample from the latent space of neural HMM. Defaults to 0.334.
- deterministic_transition (bool):
- deterministic duration generation based on duration quantiles as defiend in "S. Ronanki, O. Watts, S. King, and G. E. Henter, “Medianbased generation of synthetic speech durations using a nonparametric approach,” in Proc. SLT, 2016.". Defaults to True.
- duration_threshold (float):
- Threshold for duration quantiles. Defaults to 0.55. Tune this to change the speaking rate of the synthesis, where lower values defines a slower speaking rate and higher values defines a faster speaking rate.
- use_grad_checkpointing (bool):
- Use gradient checkpointing to save memory. In a multi-GPU setting currently pytorch does not supports gradient checkpoint inside a loop so we will have to turn it off then.Adjust depending on whatever get more batch size either by using a single GPU or multi-GPU. Defaults to True.
- max_sampling_time (int):
- Maximum sampling time while synthesising latents from neural HMM. Defaults to 1000.
- prenet_type (str):
- `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the
- Prenet. Defaults to `original`.
- prenet_dim (int):
- Dimension of the Prenet. Defaults to 256.
- prenet_n_layers (int):
- Number of layers in the Prenet. Defaults to 2.
- prenet_dropout (float):
- Dropout rate of the Prenet. Defaults to 0.5.
- prenet_dropout_at_inference (bool):
- Use dropout at inference time. Defaults to False.
- memory_rnn_dim (int):
- Dimension of the memory LSTM to process the prenet output. Defaults to 1024.
- outputnet_size (list[int]):
- Size of the output network inside the neural HMM. Defaults to [1024].
- flat_start_params (dict):
- Parameters for the flat start initialization of the neural HMM. Defaults to `{"mean": 0.0, "std": 1.0, "transition_p": 0.14}`.
- It will be recomputed when you pass the dataset.
- std_floor (float):
- Floor value for the standard deviation of the neural HMM. Prevents model cheating by putting point mass and getting infinite likelihood at any datapoint. Defaults to 0.01.
- It is called `variance flooring` in standard HMM literature.
- optimizer (str):
- Optimizer to use for training. Defaults to `adam`.
- optimizer_params (dict):
- Parameters for the optimizer. Defaults to `{"weight_decay": 1e-6}`.
- grad_clip (float):
- Gradient clipping threshold. Defaults to 40_000.
- lr (float):
- Learning rate. Defaults to 1e-3.
- lr_scheduler (str):
- Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
- `TTS.utils.training`. Defaults to `None`.
- min_seq_len (int):
- Minimum input sequence length to be used at training.
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "NeuralHMM_TTS"
-
- # Training and Checkpoint configs
- run_eval_steps: int = 100
- save_step: int = 500
- plot_step: int = 1
- model_param_stats: bool = False
-
- # data parameters
- force_generate_statistics: bool = False
- mel_statistics_parameter_path: str = None
-
- # Encoder parameters
- num_chars: int = None
- state_per_phone: int = 2
- encoder_in_out_features: int = 512
- encoder_n_convolutions: int = 3
-
- # HMM parameters
- out_channels: int = 80
- ar_order: int = 1
- sampling_temp: float = 0
- deterministic_transition: bool = True
- duration_threshold: float = 0.43
- use_grad_checkpointing: bool = True
- max_sampling_time: int = 1000
-
- ## Prenet parameters
- prenet_type: str = "original"
- prenet_dim: int = 256
- prenet_n_layers: int = 2
- prenet_dropout: float = 0.5
- prenet_dropout_at_inference: bool = True
- memory_rnn_dim: int = 1024
-
- ## Outputnet parameters
- outputnet_size: List[int] = field(default_factory=lambda: [1024])
- flat_start_params: dict = field(default_factory=lambda: {"mean": 0.0, "std": 1.0, "transition_p": 0.14})
- std_floor: float = 0.001
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"weight_decay": 1e-6})
- grad_clip: float = 40000.0
- lr: float = 1e-3
- lr_scheduler: str = None
-
- # overrides
- min_text_len: int = 10
- max_text_len: int = 500
- min_audio_len: int = 512
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "Be a voice, not an echo.",
- ]
- )
-
- # Extra needed config
- r: int = 1
- use_d_vector_file: bool = False
- use_speaker_embedding: bool = False
-
- def check_values(self):
- """Validate the hyperparameters.
-
- Raises:
- AssertionError: when the parameters network is not defined
- AssertionError: transition probability is not between 0 and 1
- """
- assert self.ar_order > 0, "AR order must be greater than 0 it is an autoregressive model."
- assert (
- len(self.outputnet_size) >= 1
- ), f"Parameter Network must have atleast one layer check the config file for parameter network. Provided: {self.parameternetwork}"
- assert (
- 0 < self.flat_start_params["transition_p"] < 1
- ), f"Transition probability must be between 0 and 1. Provided: {self.flat_start_params['transition_p']}"
diff --git a/TTS/tts/configs/overflow_config.py b/TTS/tts/configs/overflow_config.py
deleted file mode 100644
index dc3e5548b8f62f76c88acca85d19e2cee8687ebd..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/overflow_config.py
+++ /dev/null
@@ -1,201 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-
-
-@dataclass
-class OverflowConfig(BaseTTSConfig): # The classname has to be camel case
- """
- Define parameters for OverFlow model.
-
- Example:
-
- >>> from TTS.tts.configs.overflow_config import OverflowConfig
- >>> config = OverflowConfig()
-
- Args:
- model (str):
- Model name used to select the right model class to initilize. Defaults to `Overflow`.
- run_eval_steps (int):
- Run evalulation epoch after N steps. If None, waits until training epoch is completed. Defaults to None.
- save_step (int):
- Save local checkpoint every save_step steps. Defaults to 500.
- plot_step (int):
- Plot training stats on the logger every plot_step steps. Defaults to 1.
- model_param_stats (bool):
- Log model parameters stats on the logger dashboard. Defaults to False.
- force_generate_statistics (bool):
- Force generate mel normalization statistics. Defaults to False.
- mel_statistics_parameter_path (str):
- Path to the mel normalization statistics.If the model doesn't finds a file there it will generate statistics.
- Defaults to None.
- num_chars (int):
- Number of characters used by the model. It must be defined before initializing the model. Defaults to None.
- state_per_phone (int):
- Generates N states per phone. Similar, to `add_blank` parameter in GlowTTS but in Overflow it is upsampled by model's encoder. Defaults to 2.
- encoder_in_out_features (int):
- Channels of encoder input and character embedding tensors. Defaults to 512.
- encoder_n_convolutions (int):
- Number of convolution layers in the encoder. Defaults to 3.
- out_channels (int):
- Channels of the final model output. It must match the spectragram size. Defaults to 80.
- ar_order (int):
- Autoregressive order of the model. Defaults to 1. In ablations of Neural HMM it was found that more autoregression while giving more variation hurts naturalness of the synthesised audio.
- sampling_temp (float):
- Variation added to the sample from the latent space of neural HMM. Defaults to 0.334.
- deterministic_transition (bool):
- deterministic duration generation based on duration quantiles as defiend in "S. Ronanki, O. Watts, S. King, and G. E. Henter, “Medianbased generation of synthetic speech durations using a nonparametric approach,” in Proc. SLT, 2016.". Defaults to True.
- duration_threshold (float):
- Threshold for duration quantiles. Defaults to 0.55. Tune this to change the speaking rate of the synthesis, where lower values defines a slower speaking rate and higher values defines a faster speaking rate.
- use_grad_checkpointing (bool):
- Use gradient checkpointing to save memory. In a multi-GPU setting currently pytorch does not supports gradient checkpoint inside a loop so we will have to turn it off then.Adjust depending on whatever get more batch size either by using a single GPU or multi-GPU. Defaults to True.
- max_sampling_time (int):
- Maximum sampling time while synthesising latents from neural HMM. Defaults to 1000.
- prenet_type (str):
- `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the
- Prenet. Defaults to `original`.
- prenet_dim (int):
- Dimension of the Prenet. Defaults to 256.
- prenet_n_layers (int):
- Number of layers in the Prenet. Defaults to 2.
- prenet_dropout (float):
- Dropout rate of the Prenet. Defaults to 0.5.
- prenet_dropout_at_inference (bool):
- Use dropout at inference time. Defaults to False.
- memory_rnn_dim (int):
- Dimension of the memory LSTM to process the prenet output. Defaults to 1024.
- outputnet_size (list[int]):
- Size of the output network inside the neural HMM. Defaults to [1024].
- flat_start_params (dict):
- Parameters for the flat start initialization of the neural HMM. Defaults to `{"mean": 0.0, "std": 1.0, "transition_p": 0.14}`.
- It will be recomputed when you pass the dataset.
- std_floor (float):
- Floor value for the standard deviation of the neural HMM. Prevents model cheating by putting point mass and getting infinite likelihood at any datapoint. Defaults to 0.01.
- It is called `variance flooring` in standard HMM literature.
- hidden_channels_dec (int):
- Number of base hidden channels used by the decoder WaveNet network. Defaults to 150.
- kernel_size_dec (int):
- Decoder kernel size. Defaults to 5
- dilation_rate (int):
- Rate to increase dilation by each layer in a decoder block. Defaults to 1.
- num_flow_blocks_dec (int):
- Number of decoder layers in each decoder block. Defaults to 4.
- dropout_p_dec (float):
- Dropout rate of the decoder. Defaults to 0.05.
- num_splits (int):
- Number of split levels in inversible conv1x1 operation. Defaults to 4.
- num_squeeze (int):
- Number of squeeze levels. When squeezing channels increases and time steps reduces by the factor
- 'num_squeeze'. Defaults to 2.
- sigmoid_scale (bool):
- enable/disable sigmoid scaling in decoder. Defaults to False.
- c_in_channels (int):
- Unused parameter from GlowTTS's decoder. Defaults to 0.
- optimizer (str):
- Optimizer to use for training. Defaults to `adam`.
- optimizer_params (dict):
- Parameters for the optimizer. Defaults to `{"weight_decay": 1e-6}`.
- grad_clip (float):
- Gradient clipping threshold. Defaults to 40_000.
- lr (float):
- Learning rate. Defaults to 1e-3.
- lr_scheduler (str):
- Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
- `TTS.utils.training`. Defaults to `None`.
- min_seq_len (int):
- Minimum input sequence length to be used at training.
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "Overflow"
-
- # Training and Checkpoint configs
- run_eval_steps: int = 100
- save_step: int = 500
- plot_step: int = 1
- model_param_stats: bool = False
-
- # data parameters
- force_generate_statistics: bool = False
- mel_statistics_parameter_path: str = None
-
- # Encoder parameters
- num_chars: int = None
- state_per_phone: int = 2
- encoder_in_out_features: int = 512
- encoder_n_convolutions: int = 3
-
- # HMM parameters
- out_channels: int = 80
- ar_order: int = 1
- sampling_temp: float = 0.334
- deterministic_transition: bool = True
- duration_threshold: float = 0.55
- use_grad_checkpointing: bool = True
- max_sampling_time: int = 1000
-
- ## Prenet parameters
- prenet_type: str = "original"
- prenet_dim: int = 256
- prenet_n_layers: int = 2
- prenet_dropout: float = 0.5
- prenet_dropout_at_inference: bool = False
- memory_rnn_dim: int = 1024
-
- ## Outputnet parameters
- outputnet_size: List[int] = field(default_factory=lambda: [1024])
- flat_start_params: dict = field(default_factory=lambda: {"mean": 0.0, "std": 1.0, "transition_p": 0.14})
- std_floor: float = 0.01
-
- # Decoder parameters
- hidden_channels_dec: int = 150
- kernel_size_dec: int = 5
- dilation_rate: int = 1
- num_flow_blocks_dec: int = 12
- num_block_layers: int = 4
- dropout_p_dec: float = 0.05
- num_splits: int = 4
- num_squeeze: int = 2
- sigmoid_scale: bool = False
- c_in_channels: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"weight_decay": 1e-6})
- grad_clip: float = 40000.0
- lr: float = 1e-3
- lr_scheduler: str = None
-
- # overrides
- min_text_len: int = 10
- max_text_len: int = 500
- min_audio_len: int = 512
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "Be a voice, not an echo.",
- ]
- )
-
- # Extra needed config
- r: int = 1
- use_d_vector_file: bool = False
- use_speaker_embedding: bool = False
-
- def check_values(self):
- """Validate the hyperparameters.
-
- Raises:
- AssertionError: when the parameters network is not defined
- AssertionError: transition probability is not between 0 and 1
- """
- assert self.ar_order > 0, "AR order must be greater than 0 it is an autoregressive model."
- assert (
- len(self.outputnet_size) >= 1
- ), f"Parameter Network must have atleast one layer check the config file for parameter network. Provided: {self.parameternetwork}"
- assert (
- 0 < self.flat_start_params["transition_p"] < 1
- ), f"Transition probability must be between 0 and 1. Provided: {self.flat_start_params['transition_p']}"
diff --git a/TTS/tts/configs/shared_configs.py b/TTS/tts/configs/shared_configs.py
deleted file mode 100644
index bf17322c190bb234d4e27c6196e53b276fb5f09d..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/shared_configs.py
+++ /dev/null
@@ -1,344 +0,0 @@
-from dataclasses import asdict, dataclass, field
-from typing import Dict, List
-
-from coqpit import Coqpit, check_argument
-
-from TTS.config import BaseAudioConfig, BaseDatasetConfig, BaseTrainingConfig
-
-
-@dataclass
-class GSTConfig(Coqpit):
- """Defines the Global Style Token Module
-
- Args:
- gst_style_input_wav (str):
- Path to the wav file used to define the style of the output speech at inference. Defaults to None.
-
- gst_style_input_weights (dict):
- Defines the weights for each style token used at inference. Defaults to None.
-
- gst_embedding_dim (int):
- Defines the size of the GST embedding vector dimensions. Defaults to 256.
-
- gst_num_heads (int):
- Number of attention heads used by the multi-head attention. Defaults to 4.
-
- gst_num_style_tokens (int):
- Number of style token vectors. Defaults to 10.
- """
-
- gst_style_input_wav: str = None
- gst_style_input_weights: dict = None
- gst_embedding_dim: int = 256
- gst_use_speaker_embedding: bool = False
- gst_num_heads: int = 4
- gst_num_style_tokens: int = 10
-
- def check_values(
- self,
- ):
- """Check config fields"""
- c = asdict(self)
- super().check_values()
- check_argument("gst_style_input_weights", c, restricted=False)
- check_argument("gst_style_input_wav", c, restricted=False)
- check_argument("gst_embedding_dim", c, restricted=True, min_val=0, max_val=1000)
- check_argument("gst_use_speaker_embedding", c, restricted=False)
- check_argument("gst_num_heads", c, restricted=True, min_val=2, max_val=10)
- check_argument("gst_num_style_tokens", c, restricted=True, min_val=1, max_val=1000)
-
-
-@dataclass
-class CapacitronVAEConfig(Coqpit):
- """Defines the capacitron VAE Module
- Args:
- capacitron_capacity (int):
- Defines the variational capacity limit of the prosody embeddings. Defaults to 150.
- capacitron_VAE_embedding_dim (int):
- Defines the size of the Capacitron embedding vector dimension. Defaults to 128.
- capacitron_use_text_summary_embeddings (bool):
- If True, use a text summary embedding in Capacitron. Defaults to True.
- capacitron_text_summary_embedding_dim (int):
- Defines the size of the capacitron text embedding vector dimension. Defaults to 128.
- capacitron_use_speaker_embedding (bool):
- if True use speaker embeddings in Capacitron. Defaults to False.
- capacitron_VAE_loss_alpha (float):
- Weight for the VAE loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- capacitron_grad_clip (float):
- Gradient clipping value for all gradients except beta. Defaults to 5.0
- """
-
- capacitron_loss_alpha: int = 1
- capacitron_capacity: int = 150
- capacitron_VAE_embedding_dim: int = 128
- capacitron_use_text_summary_embeddings: bool = True
- capacitron_text_summary_embedding_dim: int = 128
- capacitron_use_speaker_embedding: bool = False
- capacitron_VAE_loss_alpha: float = 0.25
- capacitron_grad_clip: float = 5.0
-
- def check_values(
- self,
- ):
- """Check config fields"""
- c = asdict(self)
- super().check_values()
- check_argument("capacitron_capacity", c, restricted=True, min_val=10, max_val=500)
- check_argument("capacitron_VAE_embedding_dim", c, restricted=True, min_val=16, max_val=1024)
- check_argument("capacitron_use_speaker_embedding", c, restricted=False)
- check_argument("capacitron_text_summary_embedding_dim", c, restricted=False, min_val=16, max_val=512)
- check_argument("capacitron_VAE_loss_alpha", c, restricted=False)
- check_argument("capacitron_grad_clip", c, restricted=False)
-
-
-@dataclass
-class CharactersConfig(Coqpit):
- """Defines arguments for the `BaseCharacters` or `BaseVocabulary` and their subclasses.
-
- Args:
- characters_class (str):
- Defines the class of the characters used. If None, we pick ```Phonemes``` or ```Graphemes``` based on
- the configuration. Defaults to None.
-
- vocab_dict (dict):
- Defines the vocabulary dictionary used to encode the characters. Defaults to None.
-
- pad (str):
- characters in place of empty padding. Defaults to None.
-
- eos (str):
- characters showing the end of a sentence. Defaults to None.
-
- bos (str):
- characters showing the beginning of a sentence. Defaults to None.
-
- blank (str):
- Optional character used between characters by some models for better prosody. Defaults to `_blank`.
-
- characters (str):
- character set used by the model. Characters not in this list are ignored when converting input text to
- a list of sequence IDs. Defaults to None.
-
- punctuations (str):
- characters considered as punctuation as parsing the input sentence. Defaults to None.
-
- phonemes (str):
- characters considered as parsing phonemes. This is only for backwards compat. Use `characters` for new
- models. Defaults to None.
-
- is_unique (bool):
- remove any duplicate characters in the character lists. It is a bandaid for compatibility with the old
- models trained with character lists with duplicates. Defaults to True.
-
- is_sorted (bool):
- Sort the characters in alphabetical order. Defaults to True.
- """
-
- characters_class: str = None
-
- # using BaseVocabulary
- vocab_dict: Dict = None
-
- # using on BaseCharacters
- pad: str = None
- eos: str = None
- bos: str = None
- blank: str = None
- characters: str = None
- punctuations: str = None
- phonemes: str = None
- is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates
- is_sorted: bool = True
-
-
-@dataclass
-class BaseTTSConfig(BaseTrainingConfig):
- """Shared parameters among all the tts models.
-
- Args:
-
- audio (BaseAudioConfig):
- Audio processor config object instance.
-
- use_phonemes (bool):
- enable / disable phoneme use.
-
- phonemizer (str):
- Name of the phonemizer to use. If set None, the phonemizer will be selected by `phoneme_language`.
- Defaults to None.
-
- phoneme_language (str):
- Language code for the phonemizer. You can check the list of supported languages by running
- `python TTS/tts/utils/text/phonemizers/__init__.py`. Defaults to None.
-
- compute_input_seq_cache (bool):
- enable / disable precomputation of the phoneme sequences. At the expense of some delay at the beginning of
- the training, It allows faster data loader time and precise limitation with `max_seq_len` and
- `min_seq_len`.
-
- text_cleaner (str):
- Name of the text cleaner used for cleaning and formatting transcripts.
-
- enable_eos_bos_chars (bool):
- enable / disable the use of eos and bos characters.
-
- test_senteces_file (str):
- Path to a txt file that has sentences used at test time. The file must have a sentence per line.
-
- phoneme_cache_path (str):
- Path to the output folder caching the computed phonemes for each sample.
-
- characters (CharactersConfig):
- Instance of a CharactersConfig class.
-
- batch_group_size (int):
- Size of the batch groups used for bucketing. By default, the dataloader orders samples by the sequence
- length for a more efficient and stable training. If `batch_group_size > 1` then it performs bucketing to
- prevent using the same batches for each epoch.
-
- loss_masking (bool):
- enable / disable masking loss values against padded segments of samples in a batch.
-
- min_text_len (int):
- Minimum length of input text to be used. All shorter samples will be ignored. Defaults to 0.
-
- max_text_len (int):
- Maximum length of input text to be used. All longer samples will be ignored. Defaults to float("inf").
-
- min_audio_len (int):
- Minimum length of input audio to be used. All shorter samples will be ignored. Defaults to 0.
-
- max_audio_len (int):
- Maximum length of input audio to be used. All longer samples will be ignored. The maximum length in the
- dataset defines the VRAM used in the training. Hence, pay attention to this value if you encounter an
- OOM error in training. Defaults to float("inf").
-
- compute_f0 (int):
- (Not in use yet).
-
- compute_energy (int):
- (Not in use yet).
-
- compute_linear_spec (bool):
- If True data loader computes and returns linear spectrograms alongside the other data.
-
- precompute_num_workers (int):
- Number of workers to precompute features. Defaults to 0.
-
- use_noise_augment (bool):
- Augment the input audio with random noise.
-
- start_by_longest (bool):
- If True, the data loader will start loading the longest batch first. It is useful for checking OOM issues.
- Defaults to False.
-
- shuffle (bool):
- If True, the data loader will shuffle the dataset when there is not sampler defined. Defaults to True.
-
- drop_last (bool):
- If True, the data loader will drop the last batch if it is not complete. It helps to prevent
- issues that emerge from the partial batch statistics. Defaults to True.
-
- add_blank (bool):
- Add blank characters between each other two characters. It improves performance for some models at expense
- of slower run-time due to the longer input sequence.
-
- datasets (List[BaseDatasetConfig]):
- List of datasets used for training. If multiple datasets are provided, they are merged and used together
- for training.
-
- optimizer (str):
- Optimizer used for the training. Set one from `torch.optim.Optimizer` or `TTS.utils.training`.
- Defaults to ``.
-
- optimizer_params (dict):
- Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}`
-
- lr_scheduler (str):
- Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
- `TTS.utils.training`. Defaults to ``.
-
- lr_scheduler_params (dict):
- Parameters for the generator learning rate scheduler. Defaults to `{"warmup": 4000}`.
-
- test_sentences (List[str]):
- List of sentences to be used at testing. Defaults to '[]'
-
- eval_split_max_size (int):
- Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
-
- eval_split_size (float):
- If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
- If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
-
- use_speaker_weighted_sampler (bool):
- Enable / Disable the batch balancer by speaker. Defaults to ```False```.
-
- speaker_weighted_sampler_alpha (float):
- Number that control the influence of the speaker sampler weights. Defaults to ```1.0```.
-
- use_language_weighted_sampler (bool):
- Enable / Disable the batch balancer by language. Defaults to ```False```.
-
- language_weighted_sampler_alpha (float):
- Number that control the influence of the language sampler weights. Defaults to ```1.0```.
-
- use_length_weighted_sampler (bool):
- Enable / Disable the batch balancer by audio length. If enabled the dataset will be divided
- into 10 buckets considering the min and max audio of the dataset. The sampler weights will be
- computed forcing to have the same quantity of data for each bucket in each training batch. Defaults to ```False```.
-
- length_weighted_sampler_alpha (float):
- Number that control the influence of the length sampler weights. Defaults to ```1.0```.
- """
-
- audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
- # phoneme settings
- use_phonemes: bool = False
- phonemizer: str = None
- phoneme_language: str = None
- compute_input_seq_cache: bool = False
- text_cleaner: str = None
- enable_eos_bos_chars: bool = False
- test_sentences_file: str = ""
- phoneme_cache_path: str = None
- # vocabulary parameters
- characters: CharactersConfig = None
- add_blank: bool = False
- # training params
- batch_group_size: int = 0
- loss_masking: bool = None
- # dataloading
- min_audio_len: int = 1
- max_audio_len: int = float("inf")
- min_text_len: int = 1
- max_text_len: int = float("inf")
- compute_f0: bool = False
- compute_energy: bool = False
- compute_linear_spec: bool = False
- precompute_num_workers: int = 0
- use_noise_augment: bool = False
- start_by_longest: bool = False
- shuffle: bool = False
- drop_last: bool = False
- # dataset
- datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
- # optimizer
- optimizer: str = "radam"
- optimizer_params: dict = None
- # scheduler
- lr_scheduler: str = None
- lr_scheduler_params: dict = field(default_factory=lambda: {})
- # testing
- test_sentences: List[str] = field(default_factory=lambda: [])
- # evaluation
- eval_split_max_size: int = None
- eval_split_size: float = 0.01
- # weighted samplers
- use_speaker_weighted_sampler: bool = False
- speaker_weighted_sampler_alpha: float = 1.0
- use_language_weighted_sampler: bool = False
- language_weighted_sampler_alpha: float = 1.0
- use_length_weighted_sampler: bool = False
- length_weighted_sampler_alpha: float = 1.0
diff --git a/TTS/tts/configs/speedy_speech_config.py b/TTS/tts/configs/speedy_speech_config.py
deleted file mode 100644
index bf8517dfc478a135978df19f3126313a616c14c2..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/speedy_speech_config.py
+++ /dev/null
@@ -1,194 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class SpeedySpeechConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as SpeedySpeech model.
-
- Example:
-
- >>> from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
- >>> config = SpeedySpeechConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `speedy_speech`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `RAdam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `l1`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `huber`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- binary_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
- """
-
- model: str = "speedy_speech"
- base_model: str = "forward_tts"
-
- # set model args as SpeedySpeech
- model_args: ForwardTTSArgs = field(
- default_factory=lambda: ForwardTTSArgs(
- use_pitch=False,
- encoder_type="residual_conv_bn",
- encoder_params={
- "kernel_size": 4,
- "dilations": 4 * [1, 2, 4] + [1],
- "num_conv_blocks": 2,
- "num_res_blocks": 13,
- },
- decoder_type="residual_conv_bn",
- decoder_params={
- "kernel_size": 4,
- "dilations": 4 * [1, 2, 4, 8] + [1],
- "num_conv_blocks": 2,
- "num_res_blocks": 17,
- },
- out_channels=80,
- hidden_channels=128,
- positional_encoding=True,
- detach_duration_predictor=True,
- )
- )
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "l1"
- duration_loss_type: str = "huber"
- use_ssim_loss: bool = False
- ssim_loss_alpha: float = 1.0
- dur_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- binary_align_loss_alpha: float = 0.3
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = False
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/tts/configs/tacotron2_config.py b/TTS/tts/configs/tacotron2_config.py
deleted file mode 100644
index 95b65202218cf3aa0dd70c8d8cd55a3f913ed308..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/tacotron2_config.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from dataclasses import dataclass
-
-from TTS.tts.configs.tacotron_config import TacotronConfig
-
-
-@dataclass
-class Tacotron2Config(TacotronConfig):
- """Defines parameters for Tacotron2 based models.
-
- Example:
-
- >>> from TTS.tts.configs.tacotron2_config import Tacotron2Config
- >>> config = Tacotron2Config()
-
- Check `TacotronConfig` for argument descriptions.
- """
-
- model: str = "tacotron2"
- out_channels: int = 80
- encoder_in_features: int = 512
- decoder_in_features: int = 512
diff --git a/TTS/tts/configs/tacotron_config.py b/TTS/tts/configs/tacotron_config.py
deleted file mode 100644
index 350b5ea99633569d6977851875d5d8d83175ac36..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/tacotron_config.py
+++ /dev/null
@@ -1,235 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig, CapacitronVAEConfig, GSTConfig
-
-
-@dataclass
-class TacotronConfig(BaseTTSConfig):
- """Defines parameters for Tacotron based models.
-
- Example:
-
- >>> from TTS.tts.configs.tacotron_config import TacotronConfig
- >>> config = TacotronConfig()
-
- Args:
- model (str):
- Model name used to select the right model class to initilize. Defaults to `Tacotron`.
- use_gst (bool):
- enable / disable the use of Global Style Token modules. Defaults to False.
- gst (GSTConfig):
- Instance of `GSTConfig` class.
- gst_style_input (str):
- Path to the wav file used at inference to set the speech style through GST. If `GST` is enabled and
- this is not defined, the model uses a zero vector as an input. Defaults to None.
- use_capacitron_vae (bool):
- enable / disable the use of Capacitron modules. Defaults to False.
- capacitron_vae (CapacitronConfig):
- Instance of `CapacitronConfig` class.
- num_chars (int):
- Number of characters used by the model. It must be defined before initializing the model. Defaults to None.
- num_speakers (int):
- Number of speakers for multi-speaker models. Defaults to 1.
- r (int):
- Initial number of output frames that the decoder computed per iteration. Larger values makes training and inference
- faster but reduces the quality of the output frames. This must be equal to the largest `r` value used in
- `gradual_training` schedule. Defaults to 1.
- gradual_training (List[List]):
- Parameters for the gradual training schedule. It is in the form `[[a, b, c], [d ,e ,f] ..]` where `a` is
- the step number to start using the rest of the values, `b` is the `r` value and `c` is the batch size.
- If sets None, no gradual training is used. Defaults to None.
- memory_size (int):
- Defines the number of previous frames used by the Prenet. If set to < 0, then it uses only the last frame.
- Defaults to -1.
- prenet_type (str):
- `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the
- Prenet. Defaults to `original`.
- prenet_dropout (bool):
- enables / disables the use of dropout in the Prenet. Defaults to True.
- prenet_dropout_at_inference (bool):
- enable / disable the use of dropout in the Prenet at the inference time. Defaults to False.
- stopnet (bool):
- enable /disable the Stopnet that predicts the end of the decoder sequence. Defaults to True.
- stopnet_pos_weight (float):
- Weight that is applied to over-weight positive instances in the Stopnet loss. Use larger values with
- datasets with longer sentences. Defaults to 0.2.
- max_decoder_steps (int):
- Max number of steps allowed for the decoder. Defaults to 50.
- encoder_in_features (int):
- Channels of encoder input and character embedding tensors. Defaults to 256.
- decoder_in_features (int):
- Channels of decoder input and encoder output tensors. Defaults to 256.
- out_channels (int):
- Channels of the final model output. It must match the spectragram size. Defaults to 80.
- separate_stopnet (bool):
- Use a distinct Stopnet which is trained separately from the rest of the model. Defaults to True.
- attention_type (str):
- attention type. Check ```TTS.tts.layers.attentions.init_attn```. Defaults to 'original'.
- attention_heads (int):
- Number of attention heads for GMM attention. Defaults to 5.
- windowing (bool):
- It especially useful at inference to keep attention alignment diagonal. Defaults to False.
- use_forward_attn (bool):
- It is only valid if ```attn_type``` is ```original```. Defaults to False.
- forward_attn_mask (bool):
- enable/disable extra masking over forward attention. It is useful at inference to prevent
- possible attention failures. Defaults to False.
- transition_agent (bool):
- enable/disable transition agent in forward attention. Defaults to False.
- location_attn (bool):
- enable/disable location sensitive attention as in the original Tacotron2 paper.
- It is only valid if ```attn_type``` is ```original```. Defaults to True.
- bidirectional_decoder (bool):
- enable/disable bidirectional decoding. Defaults to False.
- double_decoder_consistency (bool):
- enable/disable double decoder consistency. Defaults to False.
- ddc_r (int):
- reduction rate used by the coarse decoder when `double_decoder_consistency` is in use. Set this
- as a multiple of the `r` value. Defaults to 6.
- speakers_file (str):
- Path to the speaker mapping file for the Speaker Manager. Defaults to None.
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
- optimizer (str):
- Optimizer used for the training. Set one from `torch.optim.Optimizer` or `TTS.utils.training`.
- Defaults to `RAdam`.
- optimizer_params (dict):
- Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}`
- lr_scheduler (str):
- Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
- `TTS.utils.training`. Defaults to `NoamLR`.
- lr_scheduler_params (dict):
- Parameters for the generator learning rate scheduler. Defaults to `{"warmup": 4000}`.
- lr (float):
- Initial learning rate. Defaults to `1e-4`.
- wd (float):
- Weight decay coefficient. Defaults to `1e-6`.
- grad_clip (float):
- Gradient clipping threshold. Defaults to `5`.
- seq_len_norm (bool):
- enable / disable the sequnce length normalization in the loss functions. If set True, loss of a sample
- is divided by the sequence length. Defaults to False.
- loss_masking (bool):
- enable / disable masking the paddings of the samples in loss computation. Defaults to True.
- decoder_loss_alpha (float):
- Weight for the decoder loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- postnet_loss_alpha (float):
- Weight for the postnet loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- postnet_diff_spec_alpha (float):
- Weight for the postnet differential loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- decoder_diff_spec_alpha (float):
-
- Weight for the decoder differential loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- decoder_ssim_alpha (float):
- Weight for the decoder SSIM loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- postnet_ssim_alpha (float):
- Weight for the postnet SSIM loss of the Tacotron model. If set less than or equal to zero, it disables the
- corresponding loss function. Defaults to 0.25
- ga_alpha (float):
- Weight for the guided attention loss. If set less than or equal to zero, it disables the corresponding loss
- function. Defaults to 5.
- """
-
- model: str = "tacotron"
- # model_params: TacotronArgs = field(default_factory=lambda: TacotronArgs())
- use_gst: bool = False
- gst: GSTConfig = None
- gst_style_input: str = None
-
- use_capacitron_vae: bool = False
- capacitron_vae: CapacitronVAEConfig = None
-
- # model specific params
- num_speakers: int = 1
- num_chars: int = 0
- r: int = 2
- gradual_training: List[List[int]] = None
- memory_size: int = -1
- prenet_type: str = "original"
- prenet_dropout: bool = True
- prenet_dropout_at_inference: bool = False
- stopnet: bool = True
- separate_stopnet: bool = True
- stopnet_pos_weight: float = 0.2
- max_decoder_steps: int = 10000
- encoder_in_features: int = 256
- decoder_in_features: int = 256
- decoder_output_dim: int = 80
- out_channels: int = 513
-
- # attention layers
- attention_type: str = "original"
- attention_heads: int = None
- attention_norm: str = "sigmoid"
- attention_win: bool = False
- windowing: bool = False
- use_forward_attn: bool = False
- forward_attn_mask: bool = False
- transition_agent: bool = False
- location_attn: bool = True
-
- # advance methods
- bidirectional_decoder: bool = False
- double_decoder_consistency: bool = False
- ddc_r: int = 6
-
- # multi-speaker settings
- speakers_file: str = None
- use_speaker_embedding: bool = False
- speaker_embedding_dim: int = 512
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = None
-
- # optimizer parameters
- optimizer: str = "RAdam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
- seq_len_norm: bool = False
- loss_masking: bool = True
-
- # loss params
- decoder_loss_alpha: float = 0.25
- postnet_loss_alpha: float = 0.25
- postnet_diff_spec_alpha: float = 0.25
- decoder_diff_spec_alpha: float = 0.25
- decoder_ssim_alpha: float = 0.25
- postnet_ssim_alpha: float = 0.25
- ga_alpha: float = 5.0
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def check_values(self):
- if self.gradual_training:
- assert (
- self.gradual_training[0][1] == self.r
- ), f"[!] the first scheduled gradual training `r` must be equal to the model's `r` value. {self.gradual_training[0][1]} vs {self.r}"
- if self.model == "tacotron" and self.audio is not None:
- assert self.out_channels == (
- self.audio.fft_size // 2 + 1
- ), f"{self.out_channels} vs {self.audio.fft_size // 2 + 1}"
- if self.model == "tacotron2" and self.audio is not None:
- assert self.out_channels == self.audio.num_mels
diff --git a/TTS/tts/configs/tortoise_config.py b/TTS/tts/configs/tortoise_config.py
deleted file mode 100644
index d60e43d71280bfa085988e31a52acfeef015c5f0..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/tortoise_config.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from dataclasses import dataclass, field
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.tortoise import TortoiseArgs, TortoiseAudioConfig
-
-
-@dataclass
-class TortoiseConfig(BaseTTSConfig):
- """Defines parameters for Tortoise TTS model.
-
- Args:
- model (str):
- Model name. Do not change unless you know what you are doing.
-
- model_args (TortoiseArgs):
- Model architecture arguments. Defaults to `TortoiseArgs()`.
-
- audio (TortoiseAudioConfig):
- Audio processing configuration. Defaults to `TortoiseAudioConfig()`.
-
- model_dir (str):
- Path to the folder that has all the Tortoise models. Defaults to None.
-
- temperature (float):
- Temperature for the autoregressive model inference. Larger values makes predictions more creative sacrificing stability. Defaults to `0.2`.
-
- length_penalty (float):
- Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to the sequence length,
- which in turn is used to divide the score of the sequence. Since the score is the log likelihood of the sequence (i.e. negative),
- length_penalty > 0.0 promotes longer sequences, while length_penalty < 0.0 encourages shorter sequences.
-
- reperation_penalty (float):
- The parameter for repetition penalty. 1.0 means no penalty. Defaults to `2.0`.
-
- top_p (float):
- If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
- Defaults to `0.8`.
-
- cond_free_k (float):
- Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
- As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
- Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k. Defaults to `2.0`.
-
- diffusion_temperature (float):
- Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
- are the "mean" prediction of the diffusion network and will sound bland and smeared.
- Defaults to `1.0`.
-
- num_autoregressive_samples (int):
- Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
- As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
- Defaults to `16`.
-
- diffusion_iterations (int):
- Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
- the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
- however. Defaults to `30`.
-
- sampler (str):
- Diffusion sampler to be used. `ddim` or `dpm++2m`. Defaults to `ddim`.
- Note:
- Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
-
- Example:
-
- >>> from TTS.tts.configs.tortoise_config import TortoiseConfig
- >>> config = TortoiseConfig()
- """
-
- model: str = "tortoise"
- # model specific params
- model_args: TortoiseArgs = field(default_factory=TortoiseArgs)
- audio: TortoiseAudioConfig = field(default_factory=TortoiseAudioConfig)
- model_dir: str = None
-
- # settings
- temperature: float = 0.2
- length_penalty: float = 1.0
- repetition_penalty: float = 2.0
- top_p: float = 0.8
- cond_free_k: float = 2.0
- diffusion_temperature: float = 1.0
-
- # inference params
- num_autoregressive_samples: int = 16
- diffusion_iterations: int = 30
- sampler: str = "ddim"
diff --git a/TTS/tts/configs/vits_config.py b/TTS/tts/configs/vits_config.py
deleted file mode 100644
index 2d0242bf131a25d6b2cef7a297a3c32b283f908a..0000000000000000000000000000000000000000
--- a/TTS/tts/configs/vits_config.py
+++ /dev/null
@@ -1,176 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.vits import VitsArgs, VitsAudioConfig
-
-
-@dataclass
-class VitsConfig(BaseTTSConfig):
- """Defines parameters for VITS End2End TTS model.
-
- Args:
- model (str):
- Model name. Do not change unless you know what you are doing.
-
- model_args (VitsArgs):
- Model architecture arguments. Defaults to `VitsArgs()`.
-
- audio (VitsAudioConfig):
- Audio processing configuration. Defaults to `VitsAudioConfig()`.
-
- grad_clip (List):
- Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
-
- lr_gen (float):
- Initial learning rate for the generator. Defaults to 0.0002.
-
- lr_disc (float):
- Initial learning rate for the discriminator. Defaults to 0.0002.
-
- lr_scheduler_gen (str):
- Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to
- `ExponentialLR`.
-
- lr_scheduler_gen_params (dict):
- Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
-
- lr_scheduler_disc (str):
- Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to
- `ExponentialLR`.
-
- lr_scheduler_disc_params (dict):
- Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
-
- scheduler_after_epoch (bool):
- If true, step the schedulers after each epoch else after each step. Defaults to `False`.
-
- optimizer (str):
- Name of the optimizer to use with both the generator and the discriminator networks. One of the
- `torch.optim.*`. Defaults to `AdamW`.
-
- kl_loss_alpha (float):
- Loss weight for KL loss. Defaults to 1.0.
-
- disc_loss_alpha (float):
- Loss weight for the discriminator loss. Defaults to 1.0.
-
- gen_loss_alpha (float):
- Loss weight for the generator loss. Defaults to 1.0.
-
- feat_loss_alpha (float):
- Loss weight for the feature matching loss. Defaults to 1.0.
-
- mel_loss_alpha (float):
- Loss weight for the mel loss. Defaults to 45.0.
-
- return_wav (bool):
- If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`.
-
- compute_linear_spec (bool):
- If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`.
-
- use_weighted_sampler (bool):
- If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`.
-
- weighted_sampler_attrs (dict):
- Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities
- by overweighting `root_path` by 2.0. Defaults to `{}`.
-
- weighted_sampler_multipliers (dict):
- Weight each unique value of a key returned by the formatter for weighted sampling.
- For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`.
- It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`.
-
- r (int):
- Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`.
-
- add_blank (bool):
- If true, a blank token is added in between every character. Defaults to `True`.
-
- test_sentences (List[List]):
- List of sentences with speaker and language information to be used for testing.
-
- language_ids_file (str):
- Path to the language ids file.
-
- use_language_embedding (bool):
- If true, language embedding is used. Defaults to `False`.
-
- Note:
- Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
-
- Example:
-
- >>> from TTS.tts.configs.vits_config import VitsConfig
- >>> config = VitsConfig()
- """
-
- model: str = "vits"
- # model specific params
- model_args: VitsArgs = field(default_factory=VitsArgs)
- audio: VitsAudioConfig = field(default_factory=VitsAudioConfig)
-
- # optimizer
- grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])
- lr_gen: float = 0.0002
- lr_disc: float = 0.0002
- lr_scheduler_gen: str = "ExponentialLR"
- lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
- lr_scheduler_disc: str = "ExponentialLR"
- lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
- scheduler_after_epoch: bool = True
- optimizer: str = "AdamW"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01})
-
- # loss params
- kl_loss_alpha: float = 1.0
- disc_loss_alpha: float = 1.0
- gen_loss_alpha: float = 1.0
- feat_loss_alpha: float = 1.0
- mel_loss_alpha: float = 45.0
- dur_loss_alpha: float = 1.0
- speaker_encoder_loss_alpha: float = 1.0
-
- # data loader params
- return_wav: bool = True
- compute_linear_spec: bool = True
-
- # sampler params
- use_weighted_sampler: bool = False # TODO: move it to the base config
- weighted_sampler_attrs: dict = field(default_factory=lambda: {})
- weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
-
- # overrides
- r: int = 1 # DO NOT CHANGE
- add_blank: bool = True
-
- # testing
- test_sentences: List[List] = field(
- default_factory=lambda: [
- ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."],
- ["Be a voice, not an echo."],
- ["I'm sorry Dave. I'm afraid I can't do that."],
- ["This cake is great. It's so delicious and moist."],
- ["Prior to November 22, 1963."],
- ]
- )
-
- # multi-speaker settings
- # use speaker embedding layer
- num_speakers: int = 0
- use_speaker_embedding: bool = False
- speakers_file: str = None
- speaker_embedding_channels: int = 256
- language_ids_file: str = None
- use_language_embedding: bool = False
-
- # use d-vectors
- use_d_vector_file: bool = False
- d_vector_file: List[str] = None
- d_vector_dim: int = None
-
- def __post_init__(self):
- for key, val in self.model_args.items():
- if hasattr(self, key):
- self[key] = val
diff --git a/TTS/tts/datasets/__init__.py b/TTS/tts/datasets/__init__.py
deleted file mode 100644
index 192138561fdb4e85978fe8beb52eae2edf73888e..0000000000000000000000000000000000000000
--- a/TTS/tts/datasets/__init__.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import os
-import sys
-from collections import Counter
-from pathlib import Path
-from typing import Callable, Dict, List, Tuple, Union
-
-import numpy as np
-
-from TTS.tts.datasets.dataset import *
-from TTS.tts.datasets.formatters import *
-
-
-def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
- """Split a dataset into train and eval. Consider speaker distribution in multi-speaker training.
-
- Args:
- items (List[List]):
- A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`.
-
- eval_split_max_size (int):
- Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
-
- eval_split_size (float):
- If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
- If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
- """
- speakers = [item["speaker_name"] for item in items]
- is_multi_speaker = len(set(speakers)) > 1
- if eval_split_size > 1:
- eval_split_size = int(eval_split_size)
- else:
- if eval_split_max_size:
- eval_split_size = min(eval_split_max_size, int(len(items) * eval_split_size))
- else:
- eval_split_size = int(len(items) * eval_split_size)
-
- assert (
- eval_split_size > 0
- ), " [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}".format(
- 1 / len(items)
- )
- np.random.seed(0)
- np.random.shuffle(items)
- if is_multi_speaker:
- items_eval = []
- speakers = [item["speaker_name"] for item in items]
- speaker_counter = Counter(speakers)
- while len(items_eval) < eval_split_size:
- item_idx = np.random.randint(0, len(items))
- speaker_to_be_removed = items[item_idx]["speaker_name"]
- if speaker_counter[speaker_to_be_removed] > 1:
- items_eval.append(items[item_idx])
- speaker_counter[speaker_to_be_removed] -= 1
- del items[item_idx]
- return items_eval, items
- return items[:eval_split_size], items[eval_split_size:]
-
-
-def add_extra_keys(metadata, language, dataset_name):
- for item in metadata:
- # add language name
- item["language"] = language
- # add unique audio name
- relfilepath = os.path.splitext(os.path.relpath(item["audio_file"], item["root_path"]))[0]
- audio_unique_name = f"{dataset_name}#{relfilepath}"
- item["audio_unique_name"] = audio_unique_name
- return metadata
-
-
-def load_tts_samples(
- datasets: Union[List[Dict], Dict],
- eval_split=True,
- formatter: Callable = None,
- eval_split_max_size=None,
- eval_split_size=0.01,
-) -> Tuple[List[List], List[List]]:
- """Parse the dataset from the datasets config, load the samples as a List and load the attention alignments if provided.
- If `formatter` is not None, apply the formatter to the samples else pick the formatter from the available ones based
- on the dataset name.
-
- Args:
- datasets (List[Dict], Dict): A list of datasets or a single dataset dictionary. If multiple datasets are
- in the list, they are all merged.
-
- eval_split (bool, optional): If true, create a evaluation split. If an eval split provided explicitly, generate
- an eval split automatically. Defaults to True.
-
- formatter (Callable, optional): The preprocessing function to be applied to create the list of samples. It
- must take the root_path and the meta_file name and return a list of samples in the format of
- `[[text, audio_path, speaker_id], ...]]`. See the available formatters in `TTS.tts.dataset.formatter` as
- example. Defaults to None.
-
- eval_split_max_size (int):
- Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
-
- eval_split_size (float):
- If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
- If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
-
- Returns:
- Tuple[List[List], List[List]: training and evaluation splits of the dataset.
- """
- meta_data_train_all = []
- meta_data_eval_all = [] if eval_split else None
- if not isinstance(datasets, list):
- datasets = [datasets]
- for dataset in datasets:
- formatter_name = dataset["formatter"]
- dataset_name = dataset["dataset_name"]
- root_path = dataset["path"]
- meta_file_train = dataset["meta_file_train"]
- meta_file_val = dataset["meta_file_val"]
- ignored_speakers = dataset["ignored_speakers"]
- language = dataset["language"]
-
- # setup the right data processor
- if formatter is None:
- formatter = _get_formatter_by_name(formatter_name)
- # load train set
- meta_data_train = formatter(root_path, meta_file_train, ignored_speakers=ignored_speakers)
- assert len(meta_data_train) > 0, f" [!] No training samples found in {root_path}/{meta_file_train}"
-
- meta_data_train = add_extra_keys(meta_data_train, language, dataset_name)
-
- print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
- # load evaluation split if set
- if eval_split:
- if meta_file_val:
- meta_data_eval = formatter(root_path, meta_file_val, ignored_speakers=ignored_speakers)
- meta_data_eval = add_extra_keys(meta_data_eval, language, dataset_name)
- else:
- eval_size_per_dataset = eval_split_max_size // len(datasets) if eval_split_max_size else None
- meta_data_eval, meta_data_train = split_dataset(meta_data_train, eval_size_per_dataset, eval_split_size)
- meta_data_eval_all += meta_data_eval
- meta_data_train_all += meta_data_train
- # load attention masks for the duration predictor training
- if dataset.meta_file_attn_mask:
- meta_data = dict(load_attention_mask_meta_data(dataset["meta_file_attn_mask"]))
- for idx, ins in enumerate(meta_data_train_all):
- attn_file = meta_data[ins["audio_file"]].strip()
- meta_data_train_all[idx].update({"alignment_file": attn_file})
- if meta_data_eval_all:
- for idx, ins in enumerate(meta_data_eval_all):
- attn_file = meta_data[ins["audio_file"]].strip()
- meta_data_eval_all[idx].update({"alignment_file": attn_file})
- # set none for the next iter
- formatter = None
- return meta_data_train_all, meta_data_eval_all
-
-
-def load_attention_mask_meta_data(metafile_path):
- """Load meta data file created by compute_attention_masks.py"""
- with open(metafile_path, "r", encoding="utf-8") as f:
- lines = f.readlines()
-
- meta_data = []
- for line in lines:
- wav_file, attn_file = line.split("|")
- meta_data.append([wav_file, attn_file])
- return meta_data
-
-
-def _get_formatter_by_name(name):
- """Returns the respective preprocessing function."""
- thismodule = sys.modules[__name__]
- return getattr(thismodule, name.lower())
-
-
-def find_unique_chars(data_samples, verbose=True):
- texts = "".join(item[0] for item in data_samples)
- chars = set(texts)
- lower_chars = filter(lambda c: c.islower(), chars)
- chars_force_lower = [c.lower() for c in chars]
- chars_force_lower = set(chars_force_lower)
-
- if verbose:
- print(f" > Number of unique characters: {len(chars)}")
- print(f" > Unique characters: {''.join(sorted(chars))}")
- print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
- print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
- return chars_force_lower
diff --git a/TTS/tts/datasets/dataset.py b/TTS/tts/datasets/dataset.py
deleted file mode 100644
index c673c963b65398b624688b630962efa0baf71732..0000000000000000000000000000000000000000
--- a/TTS/tts/datasets/dataset.py
+++ /dev/null
@@ -1,962 +0,0 @@
-import base64
-import collections
-import os
-import random
-from typing import Dict, List, Union
-
-import numpy as np
-import torch
-import tqdm
-from torch.utils.data import Dataset
-
-from TTS.tts.utils.data import prepare_data, prepare_stop_target, prepare_tensor
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.audio.numpy_transforms import compute_energy as calculate_energy
-
-# to prevent too many open files error as suggested here
-# https://github.com/pytorch/pytorch/issues/11201#issuecomment-421146936
-torch.multiprocessing.set_sharing_strategy("file_system")
-
-
-def _parse_sample(item):
- language_name = None
- attn_file = None
- if len(item) == 5:
- text, wav_file, speaker_name, language_name, attn_file = item
- elif len(item) == 4:
- text, wav_file, speaker_name, language_name = item
- elif len(item) == 3:
- text, wav_file, speaker_name = item
- else:
- raise ValueError(" [!] Dataset cannot parse the sample.")
- return text, wav_file, speaker_name, language_name, attn_file
-
-
-def noise_augment_audio(wav):
- return wav + (1.0 / 32768.0) * np.random.rand(*wav.shape)
-
-
-def string2filename(string):
- # generate a safe and reversible filename based on a string
- filename = base64.urlsafe_b64encode(string.encode("utf-8")).decode("utf-8", "ignore")
- return filename
-
-
-class TTSDataset(Dataset):
- def __init__(
- self,
- outputs_per_step: int = 1,
- compute_linear_spec: bool = False,
- ap: AudioProcessor = None,
- samples: List[Dict] = None,
- tokenizer: "TTSTokenizer" = None,
- compute_f0: bool = False,
- compute_energy: bool = False,
- f0_cache_path: str = None,
- energy_cache_path: str = None,
- return_wav: bool = False,
- batch_group_size: int = 0,
- min_text_len: int = 0,
- max_text_len: int = float("inf"),
- min_audio_len: int = 0,
- max_audio_len: int = float("inf"),
- phoneme_cache_path: str = None,
- precompute_num_workers: int = 0,
- speaker_id_mapping: Dict = None,
- d_vector_mapping: Dict = None,
- language_id_mapping: Dict = None,
- use_noise_augment: bool = False,
- start_by_longest: bool = False,
- verbose: bool = False,
- ):
- """Generic 📂 data loader for `tts` models. It is configurable for different outputs and needs.
-
- If you need something different, you can subclass and override.
-
- Args:
- outputs_per_step (int): Number of time frames predicted per step.
-
- compute_linear_spec (bool): compute linear spectrogram if True.
-
- ap (TTS.tts.utils.AudioProcessor): Audio processor object.
-
- samples (list): List of dataset samples.
-
- tokenizer (TTSTokenizer): tokenizer to convert text to sequence IDs. If None init internally else
- use the given. Defaults to None.
-
- compute_f0 (bool): compute f0 if True. Defaults to False.
-
- compute_energy (bool): compute energy if True. Defaults to False.
-
- f0_cache_path (str): Path to store f0 cache. Defaults to None.
-
- energy_cache_path (str): Path to store energy cache. Defaults to None.
-
- return_wav (bool): Return the waveform of the sample. Defaults to False.
-
- batch_group_size (int): Range of batch randomization after sorting
- sequences by length. It shuffles each batch with bucketing to gather similar lenght sequences in a
- batch. Set 0 to disable. Defaults to 0.
-
- min_text_len (int): Minimum length of input text to be used. All shorter samples will be ignored.
- Defaults to 0.
-
- max_text_len (int): Maximum length of input text to be used. All longer samples will be ignored.
- Defaults to float("inf").
-
- min_audio_len (int): Minimum length of input audio to be used. All shorter samples will be ignored.
- Defaults to 0.
-
- max_audio_len (int): Maximum length of input audio to be used. All longer samples will be ignored.
- The maximum length in the dataset defines the VRAM used in the training. Hence, pay attention to
- this value if you encounter an OOM error in training. Defaults to float("inf").
-
- phoneme_cache_path (str): Path to cache computed phonemes. It writes phonemes of each sample to a
- separate file. Defaults to None.
-
- precompute_num_workers (int): Number of workers to precompute features. Defaults to 0.
-
- speaker_id_mapping (dict): Mapping of speaker names to IDs used to compute embedding vectors by the
- embedding layer. Defaults to None.
-
- d_vector_mapping (dict): Mapping of wav files to computed d-vectors. Defaults to None.
-
- use_noise_augment (bool): Enable adding random noise to wav for augmentation. Defaults to False.
-
- start_by_longest (bool): Start by longest sequence. It is especially useful to check OOM. Defaults to False.
-
- verbose (bool): Print diagnostic information. Defaults to false.
- """
- super().__init__()
- self.batch_group_size = batch_group_size
- self._samples = samples
- self.outputs_per_step = outputs_per_step
- self.compute_linear_spec = compute_linear_spec
- self.return_wav = return_wav
- self.compute_f0 = compute_f0
- self.compute_energy = compute_energy
- self.f0_cache_path = f0_cache_path
- self.energy_cache_path = energy_cache_path
- self.min_audio_len = min_audio_len
- self.max_audio_len = max_audio_len
- self.min_text_len = min_text_len
- self.max_text_len = max_text_len
- self.ap = ap
- self.phoneme_cache_path = phoneme_cache_path
- self.speaker_id_mapping = speaker_id_mapping
- self.d_vector_mapping = d_vector_mapping
- self.language_id_mapping = language_id_mapping
- self.use_noise_augment = use_noise_augment
- self.start_by_longest = start_by_longest
-
- self.verbose = verbose
- self.rescue_item_idx = 1
- self.pitch_computed = False
- self.tokenizer = tokenizer
-
- if self.tokenizer.use_phonemes:
- self.phoneme_dataset = PhonemeDataset(
- self.samples, self.tokenizer, phoneme_cache_path, precompute_num_workers=precompute_num_workers
- )
-
- if compute_f0:
- self.f0_dataset = F0Dataset(
- self.samples, self.ap, cache_path=f0_cache_path, precompute_num_workers=precompute_num_workers
- )
- if compute_energy:
- self.energy_dataset = EnergyDataset(
- self.samples, self.ap, cache_path=energy_cache_path, precompute_num_workers=precompute_num_workers
- )
- if self.verbose:
- self.print_logs()
-
- @property
- def lengths(self):
- lens = []
- for item in self.samples:
- _, wav_file, *_ = _parse_sample(item)
- audio_len = os.path.getsize(wav_file) / 16 * 8 # assuming 16bit audio
- lens.append(audio_len)
- return lens
-
- @property
- def samples(self):
- return self._samples
-
- @samples.setter
- def samples(self, new_samples):
- self._samples = new_samples
- if hasattr(self, "f0_dataset"):
- self.f0_dataset.samples = new_samples
- if hasattr(self, "energy_dataset"):
- self.energy_dataset.samples = new_samples
- if hasattr(self, "phoneme_dataset"):
- self.phoneme_dataset.samples = new_samples
-
- def __len__(self):
- return len(self.samples)
-
- def __getitem__(self, idx):
- return self.load_data(idx)
-
- def print_logs(self, level: int = 0) -> None:
- indent = "\t" * level
- print("\n")
- print(f"{indent}> DataLoader initialization")
- print(f"{indent}| > Tokenizer:")
- self.tokenizer.print_logs(level + 1)
- print(f"{indent}| > Number of instances : {len(self.samples)}")
-
- def load_wav(self, filename):
- waveform = self.ap.load_wav(filename)
- assert waveform.size > 0
- return waveform
-
- def get_phonemes(self, idx, text):
- out_dict = self.phoneme_dataset[idx]
- assert text == out_dict["text"], f"{text} != {out_dict['text']}"
- assert len(out_dict["token_ids"]) > 0
- return out_dict
-
- def get_f0(self, idx):
- out_dict = self.f0_dataset[idx]
- item = self.samples[idx]
- assert item["audio_unique_name"] == out_dict["audio_unique_name"]
- return out_dict
-
- def get_energy(self, idx):
- out_dict = self.energy_dataset[idx]
- item = self.samples[idx]
- assert item["audio_unique_name"] == out_dict["audio_unique_name"]
- return out_dict
-
- @staticmethod
- def get_attn_mask(attn_file):
- return np.load(attn_file)
-
- def get_token_ids(self, idx, text):
- if self.tokenizer.use_phonemes:
- token_ids = self.get_phonemes(idx, text)["token_ids"]
- else:
- token_ids = self.tokenizer.text_to_ids(text)
- return np.array(token_ids, dtype=np.int32)
-
- def load_data(self, idx):
- item = self.samples[idx]
-
- raw_text = item["text"]
-
- wav = np.asarray(self.load_wav(item["audio_file"]), dtype=np.float32)
-
- # apply noise for augmentation
- if self.use_noise_augment:
- wav = noise_augment_audio(wav)
-
- # get token ids
- token_ids = self.get_token_ids(idx, item["text"])
-
- # get pre-computed attention maps
- attn = None
- if "alignment_file" in item:
- attn = self.get_attn_mask(item["alignment_file"])
-
- # after phonemization the text length may change
- # this is a shareful 🤭 hack to prevent longer phonemes
- # TODO: find a better fix
- if len(token_ids) > self.max_text_len or len(wav) < self.min_audio_len:
- self.rescue_item_idx += 1
- return self.load_data(self.rescue_item_idx)
-
- # get f0 values
- f0 = None
- if self.compute_f0:
- f0 = self.get_f0(idx)["f0"]
- energy = None
- if self.compute_energy:
- energy = self.get_energy(idx)["energy"]
-
- sample = {
- "raw_text": raw_text,
- "token_ids": token_ids,
- "wav": wav,
- "pitch": f0,
- "energy": energy,
- "attn": attn,
- "item_idx": item["audio_file"],
- "speaker_name": item["speaker_name"],
- "language_name": item["language"],
- "wav_file_name": os.path.basename(item["audio_file"]),
- "audio_unique_name": item["audio_unique_name"],
- }
- return sample
-
- @staticmethod
- def _compute_lengths(samples):
- new_samples = []
- for item in samples:
- audio_length = os.path.getsize(item["audio_file"]) / 16 * 8 # assuming 16bit audio
- text_lenght = len(item["text"])
- item["audio_length"] = audio_length
- item["text_length"] = text_lenght
- new_samples += [item]
- return new_samples
-
- @staticmethod
- def filter_by_length(lengths: List[int], min_len: int, max_len: int):
- idxs = np.argsort(lengths) # ascending order
- ignore_idx = []
- keep_idx = []
- for idx in idxs:
- length = lengths[idx]
- if length < min_len or length > max_len:
- ignore_idx.append(idx)
- else:
- keep_idx.append(idx)
- return ignore_idx, keep_idx
-
- @staticmethod
- def sort_by_length(samples: List[List]):
- audio_lengths = [s["audio_length"] for s in samples]
- idxs = np.argsort(audio_lengths) # ascending order
- return idxs
-
- @staticmethod
- def create_buckets(samples, batch_group_size: int):
- assert batch_group_size > 0
- for i in range(len(samples) // batch_group_size):
- offset = i * batch_group_size
- end_offset = offset + batch_group_size
- temp_items = samples[offset:end_offset]
- random.shuffle(temp_items)
- samples[offset:end_offset] = temp_items
- return samples
-
- @staticmethod
- def _select_samples_by_idx(idxs, samples):
- samples_new = []
- for idx in idxs:
- samples_new.append(samples[idx])
- return samples_new
-
- def preprocess_samples(self):
- r"""Sort `items` based on text length or audio length in ascending order. Filter out samples out or the length
- range.
- """
- samples = self._compute_lengths(self.samples)
-
- # sort items based on the sequence length in ascending order
- text_lengths = [i["text_length"] for i in samples]
- audio_lengths = [i["audio_length"] for i in samples]
- text_ignore_idx, text_keep_idx = self.filter_by_length(text_lengths, self.min_text_len, self.max_text_len)
- audio_ignore_idx, audio_keep_idx = self.filter_by_length(audio_lengths, self.min_audio_len, self.max_audio_len)
- keep_idx = list(set(audio_keep_idx) & set(text_keep_idx))
- ignore_idx = list(set(audio_ignore_idx) | set(text_ignore_idx))
-
- samples = self._select_samples_by_idx(keep_idx, samples)
-
- sorted_idxs = self.sort_by_length(samples)
-
- if self.start_by_longest:
- longest_idxs = sorted_idxs[-1]
- sorted_idxs[-1] = sorted_idxs[0]
- sorted_idxs[0] = longest_idxs
-
- samples = self._select_samples_by_idx(sorted_idxs, samples)
-
- if len(samples) == 0:
- raise RuntimeError(" [!] No samples left")
-
- # shuffle batch groups
- # create batches with similar length items
- # the larger the `batch_group_size`, the higher the length variety in a batch.
- if self.batch_group_size > 0:
- samples = self.create_buckets(samples, self.batch_group_size)
-
- # update items to the new sorted items
- audio_lengths = [s["audio_length"] for s in samples]
- text_lengths = [s["text_length"] for s in samples]
- self.samples = samples
-
- if self.verbose:
- print(" | > Preprocessing samples")
- print(" | > Max text length: {}".format(np.max(text_lengths)))
- print(" | > Min text length: {}".format(np.min(text_lengths)))
- print(" | > Avg text length: {}".format(np.mean(text_lengths)))
- print(" | ")
- print(" | > Max audio length: {}".format(np.max(audio_lengths)))
- print(" | > Min audio length: {}".format(np.min(audio_lengths)))
- print(" | > Avg audio length: {}".format(np.mean(audio_lengths)))
- print(f" | > Num. instances discarded samples: {len(ignore_idx)}")
- print(" | > Batch group size: {}.".format(self.batch_group_size))
-
- @staticmethod
- def _sort_batch(batch, text_lengths):
- """Sort the batch by the input text length for RNN efficiency.
-
- Args:
- batch (Dict): Batch returned by `__getitem__`.
- text_lengths (List[int]): Lengths of the input character sequences.
- """
- text_lengths, ids_sorted_decreasing = torch.sort(torch.LongTensor(text_lengths), dim=0, descending=True)
- batch = [batch[idx] for idx in ids_sorted_decreasing]
- return batch, text_lengths, ids_sorted_decreasing
-
- def collate_fn(self, batch):
- r"""
- Perform preprocessing and create a final data batch:
- 1. Sort batch instances by text-length
- 2. Convert Audio signal to features.
- 3. PAD sequences wrt r.
- 4. Load to Torch.
- """
-
- # Puts each data field into a tensor with outer dimension batch size
- if isinstance(batch[0], collections.abc.Mapping):
- token_ids_lengths = np.array([len(d["token_ids"]) for d in batch])
-
- # sort items with text input length for RNN efficiency
- batch, token_ids_lengths, ids_sorted_decreasing = self._sort_batch(batch, token_ids_lengths)
-
- # convert list of dicts to dict of lists
- batch = {k: [dic[k] for dic in batch] for k in batch[0]}
-
- # get language ids from language names
- if self.language_id_mapping is not None:
- language_ids = [self.language_id_mapping[ln] for ln in batch["language_name"]]
- else:
- language_ids = None
- # get pre-computed d-vectors
- if self.d_vector_mapping is not None:
- embedding_keys = list(batch["audio_unique_name"])
- d_vectors = [self.d_vector_mapping[w]["embedding"] for w in embedding_keys]
- else:
- d_vectors = None
-
- # get numerical speaker ids from speaker names
- if self.speaker_id_mapping:
- speaker_ids = [self.speaker_id_mapping[sn] for sn in batch["speaker_name"]]
- else:
- speaker_ids = None
- # compute features
- mel = [self.ap.melspectrogram(w).astype("float32") for w in batch["wav"]]
-
- mel_lengths = [m.shape[1] for m in mel]
-
- # lengths adjusted by the reduction factor
- mel_lengths_adjusted = [
- m.shape[1] + (self.outputs_per_step - (m.shape[1] % self.outputs_per_step))
- if m.shape[1] % self.outputs_per_step
- else m.shape[1]
- for m in mel
- ]
-
- # compute 'stop token' targets
- stop_targets = [np.array([0.0] * (mel_len - 1) + [1.0]) for mel_len in mel_lengths]
-
- # PAD stop targets
- stop_targets = prepare_stop_target(stop_targets, self.outputs_per_step)
-
- # PAD sequences with longest instance in the batch
- token_ids = prepare_data(batch["token_ids"]).astype(np.int32)
-
- # PAD features with longest instance
- mel = prepare_tensor(mel, self.outputs_per_step)
-
- # B x D x T --> B x T x D
- mel = mel.transpose(0, 2, 1)
-
- # convert things to pytorch
- token_ids_lengths = torch.LongTensor(token_ids_lengths)
- token_ids = torch.LongTensor(token_ids)
- mel = torch.FloatTensor(mel).contiguous()
- mel_lengths = torch.LongTensor(mel_lengths)
- stop_targets = torch.FloatTensor(stop_targets)
-
- # speaker vectors
- if d_vectors is not None:
- d_vectors = torch.FloatTensor(d_vectors)
-
- if speaker_ids is not None:
- speaker_ids = torch.LongTensor(speaker_ids)
-
- if language_ids is not None:
- language_ids = torch.LongTensor(language_ids)
-
- # compute linear spectrogram
- linear = None
- if self.compute_linear_spec:
- linear = [self.ap.spectrogram(w).astype("float32") for w in batch["wav"]]
- linear = prepare_tensor(linear, self.outputs_per_step)
- linear = linear.transpose(0, 2, 1)
- assert mel.shape[1] == linear.shape[1]
- linear = torch.FloatTensor(linear).contiguous()
-
- # format waveforms
- wav_padded = None
- if self.return_wav:
- wav_lengths = [w.shape[0] for w in batch["wav"]]
- max_wav_len = max(mel_lengths_adjusted) * self.ap.hop_length
- wav_lengths = torch.LongTensor(wav_lengths)
- wav_padded = torch.zeros(len(batch["wav"]), 1, max_wav_len)
- for i, w in enumerate(batch["wav"]):
- mel_length = mel_lengths_adjusted[i]
- w = np.pad(w, (0, self.ap.hop_length * self.outputs_per_step), mode="edge")
- w = w[: mel_length * self.ap.hop_length]
- wav_padded[i, :, : w.shape[0]] = torch.from_numpy(w)
- wav_padded.transpose_(1, 2)
-
- # format F0
- if self.compute_f0:
- pitch = prepare_data(batch["pitch"])
- assert mel.shape[1] == pitch.shape[1], f"[!] {mel.shape} vs {pitch.shape}"
- pitch = torch.FloatTensor(pitch)[:, None, :].contiguous() # B x 1 xT
- else:
- pitch = None
- # format energy
- if self.compute_energy:
- energy = prepare_data(batch["energy"])
- assert mel.shape[1] == energy.shape[1], f"[!] {mel.shape} vs {energy.shape}"
- energy = torch.FloatTensor(energy)[:, None, :].contiguous() # B x 1 xT
- else:
- energy = None
- # format attention masks
- attns = None
- if batch["attn"][0] is not None:
- attns = [batch["attn"][idx].T for idx in ids_sorted_decreasing]
- for idx, attn in enumerate(attns):
- pad2 = mel.shape[1] - attn.shape[1]
- pad1 = token_ids.shape[1] - attn.shape[0]
- assert pad1 >= 0 and pad2 >= 0, f"[!] Negative padding - {pad1} and {pad2}"
- attn = np.pad(attn, [[0, pad1], [0, pad2]])
- attns[idx] = attn
- attns = prepare_tensor(attns, self.outputs_per_step)
- attns = torch.FloatTensor(attns).unsqueeze(1)
-
- return {
- "token_id": token_ids,
- "token_id_lengths": token_ids_lengths,
- "speaker_names": batch["speaker_name"],
- "linear": linear,
- "mel": mel,
- "mel_lengths": mel_lengths,
- "stop_targets": stop_targets,
- "item_idxs": batch["item_idx"],
- "d_vectors": d_vectors,
- "speaker_ids": speaker_ids,
- "attns": attns,
- "waveform": wav_padded,
- "raw_text": batch["raw_text"],
- "pitch": pitch,
- "energy": energy,
- "language_ids": language_ids,
- "audio_unique_names": batch["audio_unique_name"],
- }
-
- raise TypeError(
- (
- "batch must contain tensors, numbers, dicts or lists;\
- found {}".format(
- type(batch[0])
- )
- )
- )
-
-
-class PhonemeDataset(Dataset):
- """Phoneme Dataset for converting input text to phonemes and then token IDs
-
- At initialization, it pre-computes the phonemes under `cache_path` and loads them in training to reduce data
- loading latency. If `cache_path` is already present, it skips the pre-computation.
-
- Args:
- samples (Union[List[List], List[Dict]]):
- List of samples. Each sample is a list or a dict.
-
- tokenizer (TTSTokenizer):
- Tokenizer to convert input text to phonemes.
-
- cache_path (str):
- Path to cache phonemes. If `cache_path` is already present or None, it skips the pre-computation.
-
- precompute_num_workers (int):
- Number of workers used for pre-computing the phonemes. Defaults to 0.
- """
-
- def __init__(
- self,
- samples: Union[List[Dict], List[List]],
- tokenizer: "TTSTokenizer",
- cache_path: str,
- precompute_num_workers=0,
- ):
- self.samples = samples
- self.tokenizer = tokenizer
- self.cache_path = cache_path
- if cache_path is not None and not os.path.exists(cache_path):
- os.makedirs(cache_path)
- self.precompute(precompute_num_workers)
-
- def __getitem__(self, index):
- item = self.samples[index]
- ids = self.compute_or_load(string2filename(item["audio_unique_name"]), item["text"], item["language"])
- ph_hat = self.tokenizer.ids_to_text(ids)
- return {"text": item["text"], "ph_hat": ph_hat, "token_ids": ids, "token_ids_len": len(ids)}
-
- def __len__(self):
- return len(self.samples)
-
- def compute_or_load(self, file_name, text, language):
- """Compute phonemes for the given text.
-
- If the phonemes are already cached, load them from cache.
- """
- file_ext = "_phoneme.npy"
- cache_path = os.path.join(self.cache_path, file_name + file_ext)
- try:
- ids = np.load(cache_path)
- except FileNotFoundError:
- ids = self.tokenizer.text_to_ids(text, language=language)
- np.save(cache_path, ids)
- return ids
-
- def get_pad_id(self):
- """Get pad token ID for sequence padding"""
- return self.tokenizer.pad_id
-
- def precompute(self, num_workers=1):
- """Precompute phonemes for all samples.
-
- We use pytorch dataloader because we are lazy.
- """
- print("[*] Pre-computing phonemes...")
- with tqdm.tqdm(total=len(self)) as pbar:
- batch_size = num_workers if num_workers > 0 else 1
- dataloder = torch.utils.data.DataLoader(
- batch_size=batch_size, dataset=self, shuffle=False, num_workers=num_workers, collate_fn=self.collate_fn
- )
- for _ in dataloder:
- pbar.update(batch_size)
-
- def collate_fn(self, batch):
- ids = [item["token_ids"] for item in batch]
- ids_lens = [item["token_ids_len"] for item in batch]
- texts = [item["text"] for item in batch]
- texts_hat = [item["ph_hat"] for item in batch]
- ids_lens_max = max(ids_lens)
- ids_torch = torch.LongTensor(len(ids), ids_lens_max).fill_(self.get_pad_id())
- for i, ids_len in enumerate(ids_lens):
- ids_torch[i, :ids_len] = torch.LongTensor(ids[i])
- return {"text": texts, "ph_hat": texts_hat, "token_ids": ids_torch}
-
- def print_logs(self, level: int = 0) -> None:
- indent = "\t" * level
- print("\n")
- print(f"{indent}> PhonemeDataset ")
- print(f"{indent}| > Tokenizer:")
- self.tokenizer.print_logs(level + 1)
- print(f"{indent}| > Number of instances : {len(self.samples)}")
-
-
-class F0Dataset:
- """F0 Dataset for computing F0 from wav files in CPU
-
- Pre-compute F0 values for all the samples at initialization if `cache_path` is not None or already present. It
- also computes the mean and std of F0 values if `normalize_f0` is True.
-
- Args:
- samples (Union[List[List], List[Dict]]):
- List of samples. Each sample is a list or a dict.
-
- ap (AudioProcessor):
- AudioProcessor to compute F0 from wav files.
-
- cache_path (str):
- Path to cache F0 values. If `cache_path` is already present or None, it skips the pre-computation.
- Defaults to None.
-
- precompute_num_workers (int):
- Number of workers used for pre-computing the F0 values. Defaults to 0.
-
- normalize_f0 (bool):
- Whether to normalize F0 values by mean and std. Defaults to True.
- """
-
- def __init__(
- self,
- samples: Union[List[List], List[Dict]],
- ap: "AudioProcessor",
- audio_config=None, # pylint: disable=unused-argument
- verbose=False,
- cache_path: str = None,
- precompute_num_workers=0,
- normalize_f0=True,
- ):
- self.samples = samples
- self.ap = ap
- self.verbose = verbose
- self.cache_path = cache_path
- self.normalize_f0 = normalize_f0
- self.pad_id = 0.0
- self.mean = None
- self.std = None
- if cache_path is not None and not os.path.exists(cache_path):
- os.makedirs(cache_path)
- self.precompute(precompute_num_workers)
- if normalize_f0:
- self.load_stats(cache_path)
-
- def __getitem__(self, idx):
- item = self.samples[idx]
- f0 = self.compute_or_load(item["audio_file"], string2filename(item["audio_unique_name"]))
- if self.normalize_f0:
- assert self.mean is not None and self.std is not None, " [!] Mean and STD is not available"
- f0 = self.normalize(f0)
- return {"audio_unique_name": item["audio_unique_name"], "f0": f0}
-
- def __len__(self):
- return len(self.samples)
-
- def precompute(self, num_workers=0):
- print("[*] Pre-computing F0s...")
- with tqdm.tqdm(total=len(self)) as pbar:
- batch_size = num_workers if num_workers > 0 else 1
- # we do not normalize at preproessing
- normalize_f0 = self.normalize_f0
- self.normalize_f0 = False
- dataloder = torch.utils.data.DataLoader(
- batch_size=batch_size, dataset=self, shuffle=False, num_workers=num_workers, collate_fn=self.collate_fn
- )
- computed_data = []
- for batch in dataloder:
- f0 = batch["f0"]
- computed_data.append(f for f in f0)
- pbar.update(batch_size)
- self.normalize_f0 = normalize_f0
-
- if self.normalize_f0:
- computed_data = [tensor for batch in computed_data for tensor in batch] # flatten
- pitch_mean, pitch_std = self.compute_pitch_stats(computed_data)
- pitch_stats = {"mean": pitch_mean, "std": pitch_std}
- np.save(os.path.join(self.cache_path, "pitch_stats"), pitch_stats, allow_pickle=True)
-
- def get_pad_id(self):
- return self.pad_id
-
- @staticmethod
- def create_pitch_file_path(file_name, cache_path):
- pitch_file = os.path.join(cache_path, file_name + "_pitch.npy")
- return pitch_file
-
- @staticmethod
- def _compute_and_save_pitch(ap, wav_file, pitch_file=None):
- wav = ap.load_wav(wav_file)
- pitch = ap.compute_f0(wav)
- if pitch_file:
- np.save(pitch_file, pitch)
- return pitch
-
- @staticmethod
- def compute_pitch_stats(pitch_vecs):
- nonzeros = np.concatenate([v[np.where(v != 0.0)[0]] for v in pitch_vecs])
- mean, std = np.mean(nonzeros), np.std(nonzeros)
- return mean, std
-
- def load_stats(self, cache_path):
- stats_path = os.path.join(cache_path, "pitch_stats.npy")
- stats = np.load(stats_path, allow_pickle=True).item()
- self.mean = stats["mean"].astype(np.float32)
- self.std = stats["std"].astype(np.float32)
-
- def normalize(self, pitch):
- zero_idxs = np.where(pitch == 0.0)[0]
- pitch = pitch - self.mean
- pitch = pitch / self.std
- pitch[zero_idxs] = 0.0
- return pitch
-
- def denormalize(self, pitch):
- zero_idxs = np.where(pitch == 0.0)[0]
- pitch *= self.std
- pitch += self.mean
- pitch[zero_idxs] = 0.0
- return pitch
-
- def compute_or_load(self, wav_file, audio_unique_name):
- """
- compute pitch and return a numpy array of pitch values
- """
- pitch_file = self.create_pitch_file_path(audio_unique_name, self.cache_path)
- if not os.path.exists(pitch_file):
- pitch = self._compute_and_save_pitch(self.ap, wav_file, pitch_file)
- else:
- pitch = np.load(pitch_file)
- return pitch.astype(np.float32)
-
- def collate_fn(self, batch):
- audio_unique_name = [item["audio_unique_name"] for item in batch]
- f0s = [item["f0"] for item in batch]
- f0_lens = [len(item["f0"]) for item in batch]
- f0_lens_max = max(f0_lens)
- f0s_torch = torch.LongTensor(len(f0s), f0_lens_max).fill_(self.get_pad_id())
- for i, f0_len in enumerate(f0_lens):
- f0s_torch[i, :f0_len] = torch.LongTensor(f0s[i])
- return {"audio_unique_name": audio_unique_name, "f0": f0s_torch, "f0_lens": f0_lens}
-
- def print_logs(self, level: int = 0) -> None:
- indent = "\t" * level
- print("\n")
- print(f"{indent}> F0Dataset ")
- print(f"{indent}| > Number of instances : {len(self.samples)}")
-
-
-class EnergyDataset:
- """Energy Dataset for computing Energy from wav files in CPU
-
- Pre-compute Energy values for all the samples at initialization if `cache_path` is not None or already present. It
- also computes the mean and std of Energy values if `normalize_Energy` is True.
-
- Args:
- samples (Union[List[List], List[Dict]]):
- List of samples. Each sample is a list or a dict.
-
- ap (AudioProcessor):
- AudioProcessor to compute Energy from wav files.
-
- cache_path (str):
- Path to cache Energy values. If `cache_path` is already present or None, it skips the pre-computation.
- Defaults to None.
-
- precompute_num_workers (int):
- Number of workers used for pre-computing the Energy values. Defaults to 0.
-
- normalize_Energy (bool):
- Whether to normalize Energy values by mean and std. Defaults to True.
- """
-
- def __init__(
- self,
- samples: Union[List[List], List[Dict]],
- ap: "AudioProcessor",
- verbose=False,
- cache_path: str = None,
- precompute_num_workers=0,
- normalize_energy=True,
- ):
- self.samples = samples
- self.ap = ap
- self.verbose = verbose
- self.cache_path = cache_path
- self.normalize_energy = normalize_energy
- self.pad_id = 0.0
- self.mean = None
- self.std = None
- if cache_path is not None and not os.path.exists(cache_path):
- os.makedirs(cache_path)
- self.precompute(precompute_num_workers)
- if normalize_energy:
- self.load_stats(cache_path)
-
- def __getitem__(self, idx):
- item = self.samples[idx]
- energy = self.compute_or_load(item["audio_file"], string2filename(item["audio_unique_name"]))
- if self.normalize_energy:
- assert self.mean is not None and self.std is not None, " [!] Mean and STD is not available"
- energy = self.normalize(energy)
- return {"audio_unique_name": item["audio_unique_name"], "energy": energy}
-
- def __len__(self):
- return len(self.samples)
-
- def precompute(self, num_workers=0):
- print("[*] Pre-computing energys...")
- with tqdm.tqdm(total=len(self)) as pbar:
- batch_size = num_workers if num_workers > 0 else 1
- # we do not normalize at preproessing
- normalize_energy = self.normalize_energy
- self.normalize_energy = False
- dataloder = torch.utils.data.DataLoader(
- batch_size=batch_size, dataset=self, shuffle=False, num_workers=num_workers, collate_fn=self.collate_fn
- )
- computed_data = []
- for batch in dataloder:
- energy = batch["energy"]
- computed_data.append(e for e in energy)
- pbar.update(batch_size)
- self.normalize_energy = normalize_energy
-
- if self.normalize_energy:
- computed_data = [tensor for batch in computed_data for tensor in batch] # flatten
- energy_mean, energy_std = self.compute_energy_stats(computed_data)
- energy_stats = {"mean": energy_mean, "std": energy_std}
- np.save(os.path.join(self.cache_path, "energy_stats"), energy_stats, allow_pickle=True)
-
- def get_pad_id(self):
- return self.pad_id
-
- @staticmethod
- def create_energy_file_path(wav_file, cache_path):
- file_name = os.path.splitext(os.path.basename(wav_file))[0]
- energy_file = os.path.join(cache_path, file_name + "_energy.npy")
- return energy_file
-
- @staticmethod
- def _compute_and_save_energy(ap, wav_file, energy_file=None):
- wav = ap.load_wav(wav_file)
- energy = calculate_energy(wav, fft_size=ap.fft_size, hop_length=ap.hop_length, win_length=ap.win_length)
- if energy_file:
- np.save(energy_file, energy)
- return energy
-
- @staticmethod
- def compute_energy_stats(energy_vecs):
- nonzeros = np.concatenate([v[np.where(v != 0.0)[0]] for v in energy_vecs])
- mean, std = np.mean(nonzeros), np.std(nonzeros)
- return mean, std
-
- def load_stats(self, cache_path):
- stats_path = os.path.join(cache_path, "energy_stats.npy")
- stats = np.load(stats_path, allow_pickle=True).item()
- self.mean = stats["mean"].astype(np.float32)
- self.std = stats["std"].astype(np.float32)
-
- def normalize(self, energy):
- zero_idxs = np.where(energy == 0.0)[0]
- energy = energy - self.mean
- energy = energy / self.std
- energy[zero_idxs] = 0.0
- return energy
-
- def denormalize(self, energy):
- zero_idxs = np.where(energy == 0.0)[0]
- energy *= self.std
- energy += self.mean
- energy[zero_idxs] = 0.0
- return energy
-
- def compute_or_load(self, wav_file, audio_unique_name):
- """
- compute energy and return a numpy array of energy values
- """
- energy_file = self.create_energy_file_path(audio_unique_name, self.cache_path)
- if not os.path.exists(energy_file):
- energy = self._compute_and_save_energy(self.ap, wav_file, energy_file)
- else:
- energy = np.load(energy_file)
- return energy.astype(np.float32)
-
- def collate_fn(self, batch):
- audio_unique_name = [item["audio_unique_name"] for item in batch]
- energys = [item["energy"] for item in batch]
- energy_lens = [len(item["energy"]) for item in batch]
- energy_lens_max = max(energy_lens)
- energys_torch = torch.LongTensor(len(energys), energy_lens_max).fill_(self.get_pad_id())
- for i, energy_len in enumerate(energy_lens):
- energys_torch[i, :energy_len] = torch.LongTensor(energys[i])
- return {"audio_unique_name": audio_unique_name, "energy": energys_torch, "energy_lens": energy_lens}
-
- def print_logs(self, level: int = 0) -> None:
- indent = "\t" * level
- print("\n")
- print(f"{indent}> energyDataset ")
- print(f"{indent}| > Number of instances : {len(self.samples)}")
diff --git a/TTS/tts/datasets/formatters.py b/TTS/tts/datasets/formatters.py
deleted file mode 100644
index 0eac29c8f7b6af90b5bfcff6c59a7f4597709633..0000000000000000000000000000000000000000
--- a/TTS/tts/datasets/formatters.py
+++ /dev/null
@@ -1,616 +0,0 @@
-import os
-import re
-import xml.etree.ElementTree as ET
-from glob import glob
-from pathlib import Path
-from typing import List
-
-import pandas as pd
-from tqdm import tqdm
-
-########################
-# DATASETS
-########################
-
-
-def coqui(root_path, meta_file, ignored_speakers=None):
- """Interal dataset formatter."""
- filepath = os.path.join(root_path, meta_file)
- # ensure there are 4 columns for every line
- with open(filepath, "r", encoding="utf8") as f:
- lines = f.readlines()
- num_cols = len(lines[0].split("|")) # take the first row as reference
- for idx, line in enumerate(lines[1:]):
- if len(line.split("|")) != num_cols:
- print(f" > Missing column in line {idx + 1} -> {line.strip()}")
- # load metadata
- metadata = pd.read_csv(os.path.join(root_path, meta_file), sep="|")
- assert all(x in metadata.columns for x in ["audio_file", "text"])
- speaker_name = None if "speaker_name" in metadata.columns else "coqui"
- emotion_name = None if "emotion_name" in metadata.columns else "neutral"
- items = []
- not_found_counter = 0
- for row in metadata.itertuples():
- if speaker_name is None and ignored_speakers is not None and row.speaker_name in ignored_speakers:
- continue
- audio_path = os.path.join(root_path, row.audio_file)
- if not os.path.exists(audio_path):
- not_found_counter += 1
- continue
- items.append(
- {
- "text": row.text,
- "audio_file": audio_path,
- "speaker_name": speaker_name if speaker_name is not None else row.speaker_name,
- "emotion_name": emotion_name if emotion_name is not None else row.emotion_name,
- "root_path": root_path,
- }
- )
- if not_found_counter > 0:
- print(f" | > [!] {not_found_counter} files not found")
- return items
-
-
-def tweb(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalize TWEB dataset.
- https://www.kaggle.com/bryanpark/the-world-english-bible-speech-dataset
- """
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "tweb"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("\t")
- wav_file = os.path.join(root_path, cols[0] + ".wav")
- text = cols[1]
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def mozilla(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes Mozilla meta data files to TTS format"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "mozilla"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = cols[1].strip()
- text = cols[0].strip()
- wav_file = os.path.join(root_path, "wavs", wav_file)
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def mozilla_de(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes Mozilla meta data files to TTS format"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "mozilla"
- with open(txt_file, "r", encoding="ISO 8859-1") as ttf:
- for line in ttf:
- cols = line.strip().split("|")
- wav_file = cols[0].strip()
- text = cols[1].strip()
- folder_name = f"BATCH_{wav_file.split('_')[0]}_FINAL"
- wav_file = os.path.join(root_path, folder_name, wav_file)
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def mailabs(root_path, meta_files=None, ignored_speakers=None):
- """Normalizes M-AI-Labs meta data files to TTS format
-
- Args:
- root_path (str): root folder of the MAILAB language folder.
- meta_files (str): list of meta files to be used in the training. If None, finds all the csv files
- recursively. Defaults to None
- """
- speaker_regex = re.compile(f"by_book{os.sep}(male|female){os.sep}(?P[^{os.sep}]+){os.sep}")
- if not meta_files:
- csv_files = glob(root_path + f"{os.sep}**{os.sep}metadata.csv", recursive=True)
- else:
- csv_files = meta_files
-
- # meta_files = [f.strip() for f in meta_files.split(",")]
- items = []
- for csv_file in csv_files:
- if os.path.isfile(csv_file):
- txt_file = csv_file
- else:
- txt_file = os.path.join(root_path, csv_file)
-
- folder = os.path.dirname(txt_file)
- # determine speaker based on folder structure...
- speaker_name_match = speaker_regex.search(txt_file)
- if speaker_name_match is None:
- continue
- speaker_name = speaker_name_match.group("speaker_name")
- # ignore speakers
- if isinstance(ignored_speakers, list):
- if speaker_name in ignored_speakers:
- continue
- print(" | > {}".format(csv_file))
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- if not meta_files:
- wav_file = os.path.join(folder, "wavs", cols[0] + ".wav")
- else:
- wav_file = os.path.join(root_path, folder.replace("metadata.csv", ""), "wavs", cols[0] + ".wav")
- if os.path.isfile(wav_file):
- text = cols[1].strip()
- items.append(
- {"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path}
- )
- else:
- # M-AI-Labs have some missing samples, so just print the warning
- print("> File %s does not exist!" % (wav_file))
- return items
-
-
-def ljspeech(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes the LJSpeech meta data file to TTS format
- https://keithito.com/LJ-Speech-Dataset/"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "ljspeech"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
- text = cols[2]
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def ljspeech_test(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes the LJSpeech meta data file for TTS testing
- https://keithito.com/LJ-Speech-Dataset/"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- with open(txt_file, "r", encoding="utf-8") as ttf:
- speaker_id = 0
- for idx, line in enumerate(ttf):
- # 2 samples per speaker to avoid eval split issues
- if idx % 2 == 0:
- speaker_id += 1
- cols = line.split("|")
- wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
- text = cols[2]
- items.append(
- {"text": text, "audio_file": wav_file, "speaker_name": f"ljspeech-{speaker_id}", "root_path": root_path}
- )
- return items
-
-
-def thorsten(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes the thorsten meta data file to TTS format
- https://github.com/thorstenMueller/deep-learning-german-tts/"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "thorsten"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
- text = cols[1]
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def sam_accenture(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes the sam-accenture meta data file to TTS format
- https://github.com/Sam-Accenture-Non-Binary-Voice/non-binary-voice-files"""
- xml_file = os.path.join(root_path, "voice_over_recordings", meta_file)
- xml_root = ET.parse(xml_file).getroot()
- items = []
- speaker_name = "sam_accenture"
- for item in xml_root.findall("./fileid"):
- text = item.text
- wav_file = os.path.join(root_path, "vo_voice_quality_transformation", item.get("id") + ".wav")
- if not os.path.exists(wav_file):
- print(f" [!] {wav_file} in metafile does not exist. Skipping...")
- continue
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def ruslan(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes the RUSLAN meta data file to TTS format
- https://ruslan-corpus.github.io/"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "ruslan"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = os.path.join(root_path, "RUSLAN", cols[0] + ".wav")
- text = cols[1]
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def css10(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes the CSS10 dataset file to TTS format"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "css10"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = os.path.join(root_path, cols[0])
- text = cols[1]
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
- return items
-
-
-def nancy(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Normalizes the Nancy meta data file to TTS format"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "nancy"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- utt_id = line.split()[1]
- text = line[line.find('"') + 1 : line.rfind('"') - 1]
- wav_file = os.path.join(root_path, "wavn", utt_id + ".wav")
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
- return items
-
-
-def common_voice(root_path, meta_file, ignored_speakers=None):
- """Normalize the common voice meta data file to TTS format."""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- if line.startswith("client_id"):
- continue
- cols = line.split("\t")
- text = cols[2]
- speaker_name = cols[0]
- # ignore speakers
- if isinstance(ignored_speakers, list):
- if speaker_name in ignored_speakers:
- continue
- wav_file = os.path.join(root_path, "clips", cols[1].replace(".mp3", ".wav"))
- items.append(
- {"text": text, "audio_file": wav_file, "speaker_name": "MCV_" + speaker_name, "root_path": root_path}
- )
- return items
-
-
-def libri_tts(root_path, meta_files=None, ignored_speakers=None):
- """https://ai.google/tools/datasets/libri-tts/"""
- items = []
- if not meta_files:
- meta_files = glob(f"{root_path}/**/*trans.tsv", recursive=True)
- else:
- if isinstance(meta_files, str):
- meta_files = [os.path.join(root_path, meta_files)]
-
- for meta_file in meta_files:
- _meta_file = os.path.basename(meta_file).split(".")[0]
- with open(meta_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("\t")
- file_name = cols[0]
- speaker_name, chapter_id, *_ = cols[0].split("_")
- _root_path = os.path.join(root_path, f"{speaker_name}/{chapter_id}")
- wav_file = os.path.join(_root_path, file_name + ".wav")
- text = cols[2]
- # ignore speakers
- if isinstance(ignored_speakers, list):
- if speaker_name in ignored_speakers:
- continue
- items.append(
- {
- "text": text,
- "audio_file": wav_file,
- "speaker_name": f"LTTS_{speaker_name}",
- "root_path": root_path,
- }
- )
- for item in items:
- assert os.path.exists(item["audio_file"]), f" [!] wav files don't exist - {item['audio_file']}"
- return items
-
-
-def custom_turkish(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "turkish-female"
- skipped_files = []
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = os.path.join(root_path, "wavs", cols[0].strip() + ".wav")
- if not os.path.exists(wav_file):
- skipped_files.append(wav_file)
- continue
- text = cols[1].strip()
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- print(f" [!] {len(skipped_files)} files skipped. They don't exist...")
- return items
-
-
-# ToDo: add the dataset link when the dataset is released publicly
-def brspeech(root_path, meta_file, ignored_speakers=None):
- """BRSpeech 3.0 beta"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- if line.startswith("wav_filename"):
- continue
- cols = line.split("|")
- wav_file = os.path.join(root_path, cols[0])
- text = cols[2]
- speaker_id = cols[3]
- # ignore speakers
- if isinstance(ignored_speakers, list):
- if speaker_id in ignored_speakers:
- continue
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_id, "root_path": root_path})
- return items
-
-
-def vctk(root_path, meta_files=None, wavs_path="wav48_silence_trimmed", mic="mic1", ignored_speakers=None):
- """VCTK dataset v0.92.
-
- URL:
- https://datashare.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip
-
- This dataset has 2 recordings per speaker that are annotated with ```mic1``` and ```mic2```.
- It is believed that (😄 ) ```mic1``` files are the same as the previous version of the dataset.
-
- mic1:
- Audio recorded using an omni-directional microphone (DPA 4035).
- Contains very low frequency noises.
- This is the same audio released in previous versions of VCTK:
- https://doi.org/10.7488/ds/1994
-
- mic2:
- Audio recorded using a small diaphragm condenser microphone with
- very wide bandwidth (Sennheiser MKH 800).
- Two speakers, p280 and p315 had technical issues of the audio
- recordings using MKH 800.
- """
- file_ext = "flac"
- items = []
- meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
- for meta_file in meta_files:
- _, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
- file_id = txt_file.split(".")[0]
- # ignore speakers
- if isinstance(ignored_speakers, list):
- if speaker_id in ignored_speakers:
- continue
- with open(meta_file, "r", encoding="utf-8") as file_text:
- text = file_text.readlines()[0]
- # p280 has no mic2 recordings
- if speaker_id == "p280":
- wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + f"_mic1.{file_ext}")
- else:
- wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + f"_{mic}.{file_ext}")
- if os.path.exists(wav_file):
- items.append(
- {"text": text, "audio_file": wav_file, "speaker_name": "VCTK_" + speaker_id, "root_path": root_path}
- )
- else:
- print(f" [!] wav files don't exist - {wav_file}")
- return items
-
-
-def vctk_old(root_path, meta_files=None, wavs_path="wav48", ignored_speakers=None):
- """homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
- items = []
- meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
- for meta_file in meta_files:
- _, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
- file_id = txt_file.split(".")[0]
- # ignore speakers
- if isinstance(ignored_speakers, list):
- if speaker_id in ignored_speakers:
- continue
- with open(meta_file, "r", encoding="utf-8") as file_text:
- text = file_text.readlines()[0]
- wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + ".wav")
- items.append(
- {"text": text, "audio_file": wav_file, "speaker_name": "VCTK_old_" + speaker_id, "root_path": root_path}
- )
- return items
-
-
-def synpaflex(root_path, metafiles=None, **kwargs): # pylint: disable=unused-argument
- items = []
- speaker_name = "synpaflex"
- root_path = os.path.join(root_path, "")
- wav_files = glob(f"{root_path}**/*.wav", recursive=True)
- for wav_file in wav_files:
- if os.sep + "wav" + os.sep in wav_file:
- txt_file = wav_file.replace("wav", "txt")
- else:
- txt_file = os.path.join(
- os.path.dirname(wav_file), "txt", os.path.basename(wav_file).replace(".wav", ".txt")
- )
- if os.path.exists(txt_file) and os.path.exists(wav_file):
- with open(txt_file, "r", encoding="utf-8") as file_text:
- text = file_text.readlines()[0]
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def open_bible(root_path, meta_files="train", ignore_digits_sentences=True, ignored_speakers=None):
- """ToDo: Refer the paper when available"""
- items = []
- split_dir = meta_files
- meta_files = glob(f"{os.path.join(root_path, split_dir)}/**/*.txt", recursive=True)
- for meta_file in meta_files:
- _, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
- file_id = txt_file.split(".")[0]
- # ignore speakers
- if isinstance(ignored_speakers, list):
- if speaker_id in ignored_speakers:
- continue
- with open(meta_file, "r", encoding="utf-8") as file_text:
- text = file_text.readline().replace("\n", "")
- # ignore sentences that contains digits
- if ignore_digits_sentences and any(map(str.isdigit, text)):
- continue
- wav_file = os.path.join(root_path, split_dir, speaker_id, file_id + ".flac")
- items.append({"text": text, "audio_file": wav_file, "speaker_name": "OB_" + speaker_id, "root_path": root_path})
- return items
-
-
-def mls(root_path, meta_files=None, ignored_speakers=None):
- """http://www.openslr.org/94/"""
- items = []
- with open(os.path.join(root_path, meta_files), "r", encoding="utf-8") as meta:
- for line in meta:
- file, text = line.split("\t")
- text = text[:-1]
- speaker, book, *_ = file.split("_")
- wav_file = os.path.join(root_path, os.path.dirname(meta_files), "audio", speaker, book, file + ".wav")
- # ignore speakers
- if isinstance(ignored_speakers, list):
- if speaker in ignored_speakers:
- continue
- items.append(
- {"text": text, "audio_file": wav_file, "speaker_name": "MLS_" + speaker, "root_path": root_path}
- )
- return items
-
-
-# ======================================== VOX CELEB ===========================================
-def voxceleb2(root_path, meta_file=None, **kwargs): # pylint: disable=unused-argument
- """
- :param meta_file Used only for consistency with load_tts_samples api
- """
- return _voxcel_x(root_path, meta_file, voxcel_idx="2")
-
-
-def voxceleb1(root_path, meta_file=None, **kwargs): # pylint: disable=unused-argument
- """
- :param meta_file Used only for consistency with load_tts_samples api
- """
- return _voxcel_x(root_path, meta_file, voxcel_idx="1")
-
-
-def _voxcel_x(root_path, meta_file, voxcel_idx):
- assert voxcel_idx in ["1", "2"]
- expected_count = 148_000 if voxcel_idx == "1" else 1_000_000
- voxceleb_path = Path(root_path)
- cache_to = voxceleb_path / f"metafile_voxceleb{voxcel_idx}.csv"
- cache_to.parent.mkdir(exist_ok=True)
-
- # if not exists meta file, crawl recursively for 'wav' files
- if meta_file is not None:
- with open(str(meta_file), "r", encoding="utf-8") as f:
- return [x.strip().split("|") for x in f.readlines()]
-
- elif not cache_to.exists():
- cnt = 0
- meta_data = []
- wav_files = voxceleb_path.rglob("**/*.wav")
- for path in tqdm(
- wav_files,
- desc=f"Building VoxCeleb {voxcel_idx} Meta file ... this needs to be done only once.",
- total=expected_count,
- ):
- speaker_id = str(Path(path).parent.parent.stem)
- assert speaker_id.startswith("id")
- text = None # VoxCel does not provide transciptions, and they are not needed for training the SE
- meta_data.append(f"{text}|{path}|voxcel{voxcel_idx}_{speaker_id}\n")
- cnt += 1
- with open(str(cache_to), "w", encoding="utf-8") as f:
- f.write("".join(meta_data))
- if cnt < expected_count:
- raise ValueError(f"Found too few instances for Voxceleb. Should be around {expected_count}, is: {cnt}")
-
- with open(str(cache_to), "r", encoding="utf-8") as f:
- return [x.strip().split("|") for x in f.readlines()]
-
-
-def emotion(root_path, meta_file, ignored_speakers=None):
- """Generic emotion dataset"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- if line.startswith("file_path"):
- continue
- cols = line.split(",")
- wav_file = os.path.join(root_path, cols[0])
- speaker_id = cols[1]
- emotion_id = cols[2].replace("\n", "")
- # ignore speakers
- if isinstance(ignored_speakers, list):
- if speaker_id in ignored_speakers:
- continue
- items.append(
- {"audio_file": wav_file, "speaker_name": speaker_id, "emotion_name": emotion_id, "root_path": root_path}
- )
- return items
-
-
-def baker(root_path: str, meta_file: str, **kwargs) -> List[List[str]]: # pylint: disable=unused-argument
- """Normalizes the Baker meta data file to TTS format
-
- Args:
- root_path (str): path to the baker dataset
- meta_file (str): name of the meta dataset containing names of wav to select and the transcript of the sentence
- Returns:
- List[List[str]]: List of (text, wav_path, speaker_name) associated with each sentences
- """
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "baker"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- wav_name, text = line.rstrip("\n").split("|")
- wav_path = os.path.join(root_path, "clips_22", wav_name)
- items.append({"text": text, "audio_file": wav_path, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def kokoro(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Japanese single-speaker dataset from https://github.com/kaiidams/Kokoro-Speech-Dataset"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "kokoro"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
- text = cols[2].replace(" ", "")
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def kss(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- """Korean single-speaker dataset from https://www.kaggle.com/datasets/bryanpark/korean-single-speaker-speech-dataset"""
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "kss"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = os.path.join(root_path, cols[0])
- text = cols[2] # cols[1] => 6월, cols[2] => 유월
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
-
-
-def bel_tts_formatter(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
- txt_file = os.path.join(root_path, meta_file)
- items = []
- speaker_name = "bel_tts"
- with open(txt_file, "r", encoding="utf-8") as ttf:
- for line in ttf:
- cols = line.split("|")
- wav_file = os.path.join(root_path, cols[0])
- text = cols[1]
- items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
- return items
diff --git a/TTS/tts/layers/__init__.py b/TTS/tts/layers/__init__.py
deleted file mode 100644
index f93efdb7fc41109ec3497d8e5e37ba05b0a4315e..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from TTS.tts.layers.losses import *
diff --git a/TTS/tts/layers/align_tts/__init__.py b/TTS/tts/layers/align_tts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tts/layers/align_tts/duration_predictor.py b/TTS/tts/layers/align_tts/duration_predictor.py
deleted file mode 100644
index b2b83894cc3f87575a89ea8fd7bf4a584ca22c28..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/align_tts/duration_predictor.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from torch import nn
-
-from TTS.tts.layers.generic.pos_encoding import PositionalEncoding
-from TTS.tts.layers.generic.transformer import FFTransformerBlock
-
-
-class DurationPredictor(nn.Module):
- def __init__(self, num_chars, hidden_channels, hidden_channels_ffn, num_heads):
- super().__init__()
- self.embed = nn.Embedding(num_chars, hidden_channels)
- self.pos_enc = PositionalEncoding(hidden_channels, dropout_p=0.1)
- self.FFT = FFTransformerBlock(hidden_channels, num_heads, hidden_channels_ffn, 2, 0.1)
- self.out_layer = nn.Conv1d(hidden_channels, 1, 1)
-
- def forward(self, text, text_lengths):
- # B, L -> B, L
- emb = self.embed(text)
- emb = self.pos_enc(emb.transpose(1, 2))
- x = self.FFT(emb, text_lengths)
- x = self.out_layer(x).squeeze(-1)
- return x
diff --git a/TTS/tts/layers/align_tts/mdn.py b/TTS/tts/layers/align_tts/mdn.py
deleted file mode 100644
index cdb332524bf7a5fec6a23da9e7977de6325a0324..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/align_tts/mdn.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from torch import nn
-
-
-class MDNBlock(nn.Module):
- """Mixture of Density Network implementation
- https://arxiv.org/pdf/2003.01950.pdf
- """
-
- def __init__(self, in_channels, out_channels):
- super().__init__()
- self.out_channels = out_channels
- self.conv1 = nn.Conv1d(in_channels, in_channels, 1)
- self.norm = nn.LayerNorm(in_channels)
- self.relu = nn.ReLU()
- self.dropout = nn.Dropout(0.1)
- self.conv2 = nn.Conv1d(in_channels, out_channels, 1)
-
- def forward(self, x):
- o = self.conv1(x)
- o = o.transpose(1, 2)
- o = self.norm(o)
- o = o.transpose(1, 2)
- o = self.relu(o)
- o = self.dropout(o)
- mu_sigma = self.conv2(o)
- # TODO: check this sigmoid
- # mu = torch.sigmoid(mu_sigma[:, :self.out_channels//2, :])
- mu = mu_sigma[:, : self.out_channels // 2, :]
- log_sigma = mu_sigma[:, self.out_channels // 2 :, :]
- return mu, log_sigma
diff --git a/TTS/tts/layers/bark/__init__.py b/TTS/tts/layers/bark/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tts/layers/bark/hubert/__init__.py b/TTS/tts/layers/bark/hubert/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tts/layers/bark/hubert/hubert_manager.py b/TTS/tts/layers/bark/hubert/hubert_manager.py
deleted file mode 100644
index 4bc199294164da0e8c480e292dd5a478e72f4daf..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/bark/hubert/hubert_manager.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer
-
-import os.path
-import shutil
-import urllib.request
-
-import huggingface_hub
-
-
-class HubertManager:
- @staticmethod
- def make_sure_hubert_installed(
- download_url: str = "https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt", model_path: str = ""
- ):
- if not os.path.isfile(model_path):
- print("Downloading HuBERT base model")
- urllib.request.urlretrieve(download_url, model_path)
- print("Downloaded HuBERT")
- return model_path
- return None
-
- @staticmethod
- def make_sure_tokenizer_installed(
- model: str = "quantifier_hubert_base_ls960_14.pth",
- repo: str = "GitMylo/bark-voice-cloning",
- model_path: str = "",
- ):
- model_dir = os.path.dirname(model_path)
- if not os.path.isfile(model_path):
- print("Downloading HuBERT custom tokenizer")
- huggingface_hub.hf_hub_download(repo, model, local_dir=model_dir, local_dir_use_symlinks=False)
- shutil.move(os.path.join(model_dir, model), model_path)
- print("Downloaded tokenizer")
- return model_path
- return None
diff --git a/TTS/tts/layers/bark/hubert/kmeans_hubert.py b/TTS/tts/layers/bark/hubert/kmeans_hubert.py
deleted file mode 100644
index a6a3b9aeb1111ca0abeccb6142007ecc5b39d78d..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/bark/hubert/kmeans_hubert.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""
-Modified HuBERT model without kmeans.
-Original author: https://github.com/lucidrains/
-Modified by: https://www.github.com/gitmylo/
-License: MIT
-"""
-
-# Modified code from https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch/hubert_kmeans.py
-
-import logging
-from pathlib import Path
-
-import torch
-from einops import pack, unpack
-from torch import nn
-from torchaudio.functional import resample
-from transformers import HubertModel
-
-
-def round_down_nearest_multiple(num, divisor):
- return num // divisor * divisor
-
-
-def curtail_to_multiple(t, mult, from_left=False):
- data_len = t.shape[-1]
- rounded_seq_len = round_down_nearest_multiple(data_len, mult)
- seq_slice = slice(None, rounded_seq_len) if not from_left else slice(-rounded_seq_len, None)
- return t[..., seq_slice]
-
-
-def exists(val):
- return val is not None
-
-
-def default(val, d):
- return val if exists(val) else d
-
-
-class CustomHubert(nn.Module):
- """
- checkpoint and kmeans can be downloaded at https://github.com/facebookresearch/fairseq/tree/main/examples/hubert
- or you can train your own
- """
-
- def __init__(self, checkpoint_path, target_sample_hz=16000, seq_len_multiple_of=None, output_layer=9, device=None):
- super().__init__()
- self.target_sample_hz = target_sample_hz
- self.seq_len_multiple_of = seq_len_multiple_of
- self.output_layer = output_layer
- if device is not None:
- self.to(device)
- self.model = HubertModel.from_pretrained("facebook/hubert-base-ls960")
- if device is not None:
- self.model.to(device)
- self.model.eval()
-
- @property
- def groups(self):
- return 1
-
- @torch.no_grad()
- def forward(self, wav_input, flatten=True, input_sample_hz=None):
- device = wav_input.device
-
- if exists(input_sample_hz):
- wav_input = resample(wav_input, input_sample_hz, self.target_sample_hz)
-
- if exists(self.seq_len_multiple_of):
- wav_input = curtail_to_multiple(wav_input, self.seq_len_multiple_of)
-
- outputs = self.model.forward(
- wav_input,
- output_hidden_states=True,
- )
- embed = outputs["hidden_states"][self.output_layer]
- embed, packed_shape = pack([embed], "* d")
- codebook_indices = torch.from_numpy(embed.cpu().detach().numpy()).to(device)
- if flatten:
- return codebook_indices
-
- (codebook_indices,) = unpack(codebook_indices, packed_shape, "*")
- return codebook_indices
diff --git a/TTS/tts/layers/bark/hubert/tokenizer.py b/TTS/tts/layers/bark/hubert/tokenizer.py
deleted file mode 100644
index 3070241f1cc1ac95867f2d4173495b9a7047a15e..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/bark/hubert/tokenizer.py
+++ /dev/null
@@ -1,195 +0,0 @@
-"""
-Custom tokenizer model.
-Author: https://www.github.com/gitmylo/
-License: MIT
-"""
-
-import json
-import os.path
-from zipfile import ZipFile
-
-import numpy
-import torch
-from torch import nn, optim
-
-
-class HubertTokenizer(nn.Module):
- def __init__(self, hidden_size=1024, input_size=768, output_size=10000, version=0):
- super().__init__()
- next_size = input_size
- if version == 0:
- self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
- next_size = hidden_size
- if version == 1:
- self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True)
- self.intermediate = nn.Linear(hidden_size, 4096)
- next_size = 4096
-
- self.fc = nn.Linear(next_size, output_size)
- self.softmax = nn.LogSoftmax(dim=1)
- self.optimizer: optim.Optimizer = None
- self.lossfunc = nn.CrossEntropyLoss()
- self.input_size = input_size
- self.hidden_size = hidden_size
- self.output_size = output_size
- self.version = version
-
- def forward(self, x):
- x, _ = self.lstm(x)
- if self.version == 1:
- x = self.intermediate(x)
- x = self.fc(x)
- x = self.softmax(x)
- return x
-
- @torch.no_grad()
- def get_token(self, x):
- """
- Used to get the token for the first
- :param x: An array with shape (N, input_size) where N is a whole number greater or equal to 1, and input_size is the input size used when creating the model.
- :return: An array with shape (N,) where N is the same as N from the input. Every number in the array is a whole number in range 0...output_size - 1 where output_size is the output size used when creating the model.
- """
- return torch.argmax(self(x), dim=1)
-
- def prepare_training(self):
- self.optimizer = optim.Adam(self.parameters(), 0.001)
-
- def train_step(self, x_train, y_train, log_loss=False):
- # y_train = y_train[:-1]
- # y_train = y_train[1:]
-
- optimizer = self.optimizer
- lossfunc = self.lossfunc
- # Zero the gradients
- self.zero_grad()
-
- # Forward pass
- y_pred = self(x_train)
-
- y_train_len = len(y_train)
- y_pred_len = y_pred.shape[0]
-
- if y_train_len > y_pred_len:
- diff = y_train_len - y_pred_len
- y_train = y_train[diff:]
- elif y_train_len < y_pred_len:
- diff = y_pred_len - y_train_len
- y_pred = y_pred[:-diff, :]
-
- y_train_hot = torch.zeros(len(y_train), self.output_size)
- y_train_hot[range(len(y_train)), y_train] = 1
- y_train_hot = y_train_hot.to("cuda")
-
- # Calculate the loss
- loss = lossfunc(y_pred, y_train_hot)
-
- # Print loss
- if log_loss:
- print("Loss", loss.item())
-
- # Backward pass
- loss.backward()
-
- # Update the weights
- optimizer.step()
-
- def save(self, path):
- info_path = ".".join(os.path.basename(path).split(".")[:-1]) + "/.info"
- torch.save(self.state_dict(), path)
- data_from_model = Data(self.input_size, self.hidden_size, self.output_size, self.version)
- with ZipFile(path, "a") as model_zip:
- model_zip.writestr(info_path, data_from_model.save())
- model_zip.close()
-
- @staticmethod
- def load_from_checkpoint(path, map_location=None):
- old = True
- with ZipFile(path) as model_zip:
- filesMatch = [file for file in model_zip.namelist() if file.endswith("/.info")]
- file = filesMatch[0] if filesMatch else None
- if file:
- old = False
- data_from_model = Data.load(model_zip.read(file).decode("utf-8"))
- model_zip.close()
- if old:
- model = HubertTokenizer()
- else:
- model = HubertTokenizer(
- data_from_model.hidden_size,
- data_from_model.input_size,
- data_from_model.output_size,
- data_from_model.version,
- )
- model.load_state_dict(torch.load(path, map_location=map_location))
- if map_location:
- model = model.to(map_location)
- return model
-
-
-class Data:
- input_size: int
- hidden_size: int
- output_size: int
- version: int
-
- def __init__(self, input_size=768, hidden_size=1024, output_size=10000, version=0):
- self.input_size = input_size
- self.hidden_size = hidden_size
- self.output_size = output_size
- self.version = version
-
- @staticmethod
- def load(string):
- data = json.loads(string)
- return Data(data["input_size"], data["hidden_size"], data["output_size"], data["version"])
-
- def save(self):
- data = {
- "input_size": self.input_size,
- "hidden_size": self.hidden_size,
- "output_size": self.output_size,
- "version": self.version,
- }
- return json.dumps(data)
-
-
-def auto_train(data_path, save_path="model.pth", load_model: str = None, save_epochs=1):
- data_x, data_y = [], []
-
- if load_model and os.path.isfile(load_model):
- print("Loading model from", load_model)
- model_training = HubertTokenizer.load_from_checkpoint(load_model, "cuda")
- else:
- print("Creating new model.")
- model_training = HubertTokenizer(version=1).to("cuda") # Settings for the model to run without lstm
- save_path = os.path.join(data_path, save_path)
- base_save_path = ".".join(save_path.split(".")[:-1])
-
- sem_string = "_semantic.npy"
- feat_string = "_semantic_features.npy"
-
- ready = os.path.join(data_path, "ready")
- for input_file in os.listdir(ready):
- full_path = os.path.join(ready, input_file)
- if input_file.endswith(sem_string):
- data_y.append(numpy.load(full_path))
- elif input_file.endswith(feat_string):
- data_x.append(numpy.load(full_path))
- model_training.prepare_training()
-
- epoch = 1
-
- while 1:
- for _ in range(save_epochs):
- j = 0
- for x, y in zip(data_x, data_y):
- model_training.train_step(
- torch.tensor(x).to("cuda"), torch.tensor(y).to("cuda"), j % 50 == 0
- ) # Print loss every 50 steps
- j += 1
- save_p = save_path
- save_p_2 = f"{base_save_path}_epoch_{epoch}.pth"
- model_training.save(save_p)
- model_training.save(save_p_2)
- print(f"Epoch {epoch} completed")
- epoch += 1
diff --git a/TTS/tts/layers/bark/inference_funcs.py b/TTS/tts/layers/bark/inference_funcs.py
deleted file mode 100644
index f3d3fee9371fae0cd06187c967a5b0028940138e..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/bark/inference_funcs.py
+++ /dev/null
@@ -1,606 +0,0 @@
-import logging
-import os
-import re
-from glob import glob
-from typing import Dict, List
-
-import librosa
-import numpy as np
-import torch
-import torchaudio
-import tqdm
-from encodec.utils import convert_audio
-from scipy.special import softmax
-from torch.nn import functional as F
-
-from TTS.tts.layers.bark.hubert.hubert_manager import HubertManager
-from TTS.tts.layers.bark.hubert.kmeans_hubert import CustomHubert
-from TTS.tts.layers.bark.hubert.tokenizer import HubertTokenizer
-from TTS.tts.layers.bark.load_model import clear_cuda_cache, inference_mode
-
-logger = logging.getLogger(__name__)
-
-
-def _tokenize(tokenizer, text):
- return tokenizer.encode(text, add_special_tokens=False)
-
-
-def _detokenize(tokenizer, enc_text):
- return tokenizer.decode(enc_text)
-
-
-def _normalize_whitespace(text):
- return re.sub(r"\s+", " ", text).strip()
-
-
-def get_voices(extra_voice_dirs: List[str] = []): # pylint: disable=dangerous-default-value
- dirs = extra_voice_dirs
- voices: Dict[str, List[str]] = {}
- for d in dirs:
- subs = os.listdir(d)
- for sub in subs:
- subj = os.path.join(d, sub)
- if os.path.isdir(subj):
- voices[sub] = list(glob(f"{subj}/*.npz"))
- # fetch audio files if no npz files are found
- if len(voices[sub]) == 0:
- voices[sub] = list(glob(f"{subj}/*.wav")) + list(glob(f"{subj}/*.mp3"))
- return voices
-
-
-def load_npz(npz_file):
- x_history = np.load(npz_file)
- semantic = x_history["semantic_prompt"]
- coarse = x_history["coarse_prompt"]
- fine = x_history["fine_prompt"]
- return semantic, coarse, fine
-
-
-def load_voice(model, voice: str, extra_voice_dirs: List[str] = []): # pylint: disable=dangerous-default-value
- if voice == "random":
- return None, None, None
-
- voices = get_voices(extra_voice_dirs)
- paths = voices[voice]
-
- # bark only uses a single sample for cloning
- if len(paths) > 1:
- raise ValueError(f"Voice {voice} has multiple paths: {paths}")
-
- try:
- path = voices[voice]
- except KeyError as e:
- raise KeyError(f"Voice {voice} not found in {extra_voice_dirs}") from e
-
- if len(paths) == 1 and paths[0].endswith(".npz"):
- return load_npz(path[0])
-
- audio_path = paths[0]
- # replace the file extension with .npz
- output_path = os.path.splitext(audio_path)[0] + ".npz"
- generate_voice(audio=audio_path, model=model, output_path=output_path)
- return load_voice(model, voice, extra_voice_dirs)
-
-
-def zero_crossing_rate(audio, frame_length=1024, hop_length=512):
- zero_crossings = np.sum(np.abs(np.diff(np.sign(audio))) / 2)
- total_frames = 1 + int((len(audio) - frame_length) / hop_length)
- return zero_crossings / total_frames
-
-
-def compute_spectral_contrast(audio_data, sample_rate, n_bands=6, fmin=200.0):
- spectral_contrast = librosa.feature.spectral_contrast(y=audio_data, sr=sample_rate, n_bands=n_bands, fmin=fmin)
- return np.mean(spectral_contrast)
-
-
-def compute_average_bass_energy(audio_data, sample_rate, max_bass_freq=250):
- stft = librosa.stft(audio_data)
- power_spectrogram = np.abs(stft) ** 2
- frequencies = librosa.fft_frequencies(sr=sample_rate, n_fft=stft.shape[0])
- bass_mask = frequencies <= max_bass_freq
- bass_energy = power_spectrogram[np.ix_(bass_mask, np.arange(power_spectrogram.shape[1]))].mean()
- return bass_energy
-
-
-def generate_voice(
- audio,
- model,
- output_path,
-):
- """Generate a new voice from a given audio and text prompt.
-
- Args:
- audio (np.ndarray): The audio to use as a base for the new voice.
- text (str): Transcription of the audio you are clonning.
- model (BarkModel): The BarkModel to use for generating the new voice.
- output_path (str): The path to save the generated voice to.
- """
- if isinstance(audio, str):
- audio, sr = torchaudio.load(audio)
- audio = convert_audio(audio, sr, model.config.sample_rate, model.encodec.channels)
- audio = audio.unsqueeze(0).to(model.device)
-
- with torch.no_grad():
- encoded_frames = model.encodec.encode(audio)
- codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze() # [n_q, T]
-
- # move codes to cpu
- codes = codes.cpu().numpy()
-
- # generate semantic tokens
- # Load the HuBERT model
- hubert_manager = HubertManager()
- # hubert_manager.make_sure_hubert_installed(model_path=model.config.LOCAL_MODEL_PATHS["hubert"])
- hubert_manager.make_sure_tokenizer_installed(model_path=model.config.LOCAL_MODEL_PATHS["hubert_tokenizer"])
-
- hubert_model = CustomHubert(checkpoint_path=model.config.LOCAL_MODEL_PATHS["hubert"]).to(model.device)
-
- # Load the CustomTokenizer model
- tokenizer = HubertTokenizer.load_from_checkpoint(
- model.config.LOCAL_MODEL_PATHS["hubert_tokenizer"], map_location=model.device
- )
- # semantic_tokens = model.text_to_semantic(
- # text, max_gen_duration_s=seconds, top_k=50, top_p=0.95, temp=0.7
- # ) # not 100%
- semantic_vectors = hubert_model.forward(audio[0], input_sample_hz=model.config.sample_rate)
- semantic_tokens = tokenizer.get_token(semantic_vectors)
- semantic_tokens = semantic_tokens.cpu().numpy()
-
- np.savez(output_path, fine_prompt=codes, coarse_prompt=codes[:2, :], semantic_prompt=semantic_tokens)
-
-
-def generate_text_semantic(
- text,
- model,
- history_prompt=None,
- temp=0.7,
- top_k=None,
- top_p=None,
- silent=False,
- min_eos_p=0.2,
- max_gen_duration_s=None,
- allow_early_stop=True,
- base=None,
- use_kv_caching=True,
- **kwargs, # pylint: disable=unused-argument
-):
- """Generate semantic tokens from text.
-
- Args:
- text (str): The text to generate semantic tokens from.
- model (BarkModel): The BarkModel to use for generating the semantic tokens.
- history_prompt (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a prompt for the generation.
- temp (float): The temperature to use for the generation.
- top_k (int): The number of top tokens to consider for the generation.
- top_p (float): The cumulative probability to consider for the generation.
- silent (bool): Whether to silence the tqdm progress bar.
- min_eos_p (float): The minimum probability to consider for the end of sentence token.
- max_gen_duration_s (float): The maximum duration in seconds to generate for.
- allow_early_stop (bool): Whether to allow the generation to stop early.
- base (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a base for the generation.
- use_kv_caching (bool): Whether to use key-value caching for the generation.
- **kwargs: Additional keyword arguments. They are ignored.
-
- Returns:
- np.ndarray: The generated semantic tokens.
- """
- assert isinstance(text, str)
- text = _normalize_whitespace(text)
- assert len(text.strip()) > 0
- if all(v is not None for v in history_prompt) or base is not None:
- if history_prompt is not None:
- semantic_history = history_prompt[0]
- if base is not None:
- semantic_history = base[0]
- assert (
- isinstance(semantic_history, np.ndarray)
- and len(semantic_history.shape) == 1
- and len(semantic_history) > 0
- and semantic_history.min() >= 0
- and semantic_history.max() <= model.config.SEMANTIC_VOCAB_SIZE - 1
- )
- else:
- semantic_history = None
- encoded_text = np.array(_tokenize(model.tokenizer, text)) + model.config.TEXT_ENCODING_OFFSET
- if len(encoded_text) > 256:
- p = round((len(encoded_text) - 256) / len(encoded_text) * 100, 1)
- logger.warning(f"warning, text too long, lopping of last {p}%")
- encoded_text = encoded_text[:256]
- encoded_text = np.pad(
- encoded_text,
- (0, 256 - len(encoded_text)),
- constant_values=model.config.TEXT_PAD_TOKEN,
- mode="constant",
- )
- if semantic_history is not None:
- semantic_history = semantic_history.astype(np.int64)
- # lop off if history is too long, pad if needed
- semantic_history = semantic_history[-256:]
- semantic_history = np.pad(
- semantic_history,
- (0, 256 - len(semantic_history)),
- constant_values=model.config.SEMANTIC_PAD_TOKEN,
- mode="constant",
- )
- else:
- semantic_history = np.array([model.config.SEMANTIC_PAD_TOKEN] * 256)
- x = torch.from_numpy(
- np.hstack([encoded_text, semantic_history, np.array([model.config.SEMANTIC_INFER_TOKEN])]).astype(np.int64)
- )[None]
- assert x.shape[1] == 256 + 256 + 1
- with inference_mode():
- x = x.to(model.device)
- n_tot_steps = 768
- # custom tqdm updates since we don't know when eos will occur
- pbar = tqdm.tqdm(disable=silent, total=100)
- pbar_state = 0
- tot_generated_duration_s = 0
- kv_cache = None
- for n in range(n_tot_steps):
- if use_kv_caching and kv_cache is not None:
- x_input = x[:, [-1]]
- else:
- x_input = x
- logits, kv_cache = model.semantic_model(
- x_input, merge_context=True, use_cache=use_kv_caching, past_kv=kv_cache
- )
- relevant_logits = logits[0, 0, : model.config.SEMANTIC_VOCAB_SIZE]
- if allow_early_stop:
- relevant_logits = torch.hstack(
- (relevant_logits, logits[0, 0, [model.config.SEMANTIC_PAD_TOKEN]])
- ) # eos
- if top_p is not None:
- # faster to convert to numpy
- logits_device = relevant_logits.device
- logits_dtype = relevant_logits.type()
- relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
- sorted_indices = np.argsort(relevant_logits)[::-1]
- sorted_logits = relevant_logits[sorted_indices]
- cumulative_probs = np.cumsum(softmax(sorted_logits))
- sorted_indices_to_remove = cumulative_probs > top_p
- sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy()
- sorted_indices_to_remove[0] = False
- relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
- relevant_logits = torch.from_numpy(relevant_logits)
- relevant_logits = relevant_logits.to(logits_device).type(logits_dtype)
- if top_k is not None:
- v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
- relevant_logits[relevant_logits < v[-1]] = -float("Inf")
- probs = torch.softmax(relevant_logits / temp, dim=-1)
- item_next = torch.multinomial(probs, num_samples=1)
- if allow_early_stop and (
- item_next == model.config.SEMANTIC_VOCAB_SIZE or (min_eos_p is not None and probs[-1] >= min_eos_p)
- ):
- # eos found, so break
- pbar.update(100 - pbar_state)
- break
- x = torch.cat((x, item_next[None]), dim=1)
- tot_generated_duration_s += 1 / model.config.SEMANTIC_RATE_HZ
- if max_gen_duration_s is not None and tot_generated_duration_s > max_gen_duration_s:
- pbar.update(100 - pbar_state)
- break
- if n == n_tot_steps - 1:
- pbar.update(100 - pbar_state)
- break
- del logits, relevant_logits, probs, item_next
- req_pbar_state = np.min([100, int(round(100 * n / n_tot_steps))])
- if req_pbar_state > pbar_state:
- pbar.update(req_pbar_state - pbar_state)
- pbar_state = req_pbar_state
- pbar.close()
- out = x.detach().cpu().numpy().squeeze()[256 + 256 + 1 :]
- assert all(out >= 0) and all(out < model.config.SEMANTIC_VOCAB_SIZE)
- clear_cuda_cache()
- return out
-
-
-def _flatten_codebooks(arr, offset_size):
- assert len(arr.shape) == 2
- arr = arr.copy()
- if offset_size is not None:
- for n in range(1, arr.shape[0]):
- arr[n, :] += offset_size * n
- flat_arr = arr.ravel("F")
- return flat_arr
-
-
-def generate_coarse(
- x_semantic,
- model,
- history_prompt=None,
- temp=0.7,
- top_k=None,
- top_p=None,
- silent=False,
- max_coarse_history=630, # min 60 (faster), max 630 (more context)
- sliding_window_len=60,
- base=None,
- use_kv_caching=True,
-):
- """Generate coarse audio codes from semantic tokens.
-
- Args:
- x_semantic (np.ndarray): The semantic tokens to generate coarse audio codes from.
- model (BarkModel): The BarkModel to use for generating the coarse audio codes.
- history_prompt (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a prompt for the generation.
- temp (float): The temperature to use for the generation.
- top_k (int): The number of top tokens to consider for the generation.
- top_p (float): The cumulative probability to consider for the generation.
- silent (bool): Whether to silence the tqdm progress bar.
- max_coarse_history (int): The maximum number of coarse audio codes to use as history.
- sliding_window_len (int): The length of the sliding window to use for the generation.
- base (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a base for the generation.
- use_kv_caching (bool): Whether to use key-value caching for the generation.
-
- Returns:
- np.ndarray: The generated coarse audio codes.
- """
- assert (
- isinstance(x_semantic, np.ndarray)
- and len(x_semantic.shape) == 1
- and len(x_semantic) > 0
- and x_semantic.min() >= 0
- and x_semantic.max() <= model.config.SEMANTIC_VOCAB_SIZE - 1
- )
- assert 60 <= max_coarse_history <= 630
- assert max_coarse_history + sliding_window_len <= 1024 - 256
- semantic_to_coarse_ratio = (
- model.config.COARSE_RATE_HZ / model.config.SEMANTIC_RATE_HZ * model.config.N_COARSE_CODEBOOKS
- )
- max_semantic_history = int(np.floor(max_coarse_history / semantic_to_coarse_ratio))
- if all(v is not None for v in history_prompt) or base is not None:
- if history_prompt is not None:
- x_history = history_prompt
- x_semantic_history = x_history[0]
- x_coarse_history = x_history[1]
- if base is not None:
- x_semantic_history = base[0]
- x_coarse_history = base[1]
- assert (
- isinstance(x_semantic_history, np.ndarray)
- and len(x_semantic_history.shape) == 1
- and len(x_semantic_history) > 0
- and x_semantic_history.min() >= 0
- and x_semantic_history.max() <= model.config.SEMANTIC_VOCAB_SIZE - 1
- and isinstance(x_coarse_history, np.ndarray)
- and len(x_coarse_history.shape) == 2
- and x_coarse_history.shape[0] == model.config.N_COARSE_CODEBOOKS
- and x_coarse_history.shape[-1] >= 0
- and x_coarse_history.min() >= 0
- and x_coarse_history.max() <= model.config.CODEBOOK_SIZE - 1
- and (
- round(x_coarse_history.shape[-1] / len(x_semantic_history), 1)
- == round(semantic_to_coarse_ratio / model.config.N_COARSE_CODEBOOKS, 1)
- )
- )
- x_coarse_history = (
- _flatten_codebooks(x_coarse_history, model.config.CODEBOOK_SIZE) + model.config.SEMANTIC_VOCAB_SIZE
- )
- # trim histories correctly
- n_semantic_hist_provided = np.min(
- [
- max_semantic_history,
- len(x_semantic_history) - len(x_semantic_history) % 2,
- int(np.floor(len(x_coarse_history) / semantic_to_coarse_ratio)),
- ]
- )
- n_coarse_hist_provided = int(round(n_semantic_hist_provided * semantic_to_coarse_ratio))
- x_semantic_history = x_semantic_history[-n_semantic_hist_provided:].astype(np.int32)
- x_coarse_history = x_coarse_history[-n_coarse_hist_provided:].astype(np.int32)
- # TODO: bit of a hack for time alignment (sounds better)
- x_coarse_history = x_coarse_history[:-2]
- else:
- x_semantic_history = np.array([], dtype=np.int32)
- x_coarse_history = np.array([], dtype=np.int32)
- # start loop
- n_steps = int(
- round(
- np.floor(len(x_semantic) * semantic_to_coarse_ratio / model.config.N_COARSE_CODEBOOKS)
- * model.config.N_COARSE_CODEBOOKS
- )
- )
- assert n_steps > 0 and n_steps % model.config.N_COARSE_CODEBOOKS == 0
- x_semantic = np.hstack([x_semantic_history, x_semantic]).astype(np.int32)
- x_coarse = x_coarse_history.astype(np.int32)
- base_semantic_idx = len(x_semantic_history)
- with inference_mode():
- x_semantic_in = torch.from_numpy(x_semantic)[None].to(model.device)
- x_coarse_in = torch.from_numpy(x_coarse)[None].to(model.device)
- n_window_steps = int(np.ceil(n_steps / sliding_window_len))
- n_step = 0
- for _ in tqdm.tqdm(range(n_window_steps), total=n_window_steps, disable=silent):
- semantic_idx = base_semantic_idx + int(round(n_step / semantic_to_coarse_ratio))
- # pad from right side
- x_in = x_semantic_in[:, np.max([0, semantic_idx - max_semantic_history]) :]
- x_in = x_in[:, :256]
- x_in = F.pad(
- x_in,
- (0, 256 - x_in.shape[-1]),
- "constant",
- model.config.COARSE_SEMANTIC_PAD_TOKEN,
- )
- x_in = torch.hstack(
- [
- x_in,
- torch.tensor([model.config.COARSE_INFER_TOKEN])[None].to(model.device),
- x_coarse_in[:, -max_coarse_history:],
- ]
- )
- kv_cache = None
- for _ in range(sliding_window_len):
- if n_step >= n_steps:
- continue
- is_major_step = n_step % model.config.N_COARSE_CODEBOOKS == 0
-
- if use_kv_caching and kv_cache is not None:
- x_input = x_in[:, [-1]]
- else:
- x_input = x_in
-
- logits, kv_cache = model.coarse_model(x_input, use_cache=use_kv_caching, past_kv=kv_cache)
- logit_start_idx = (
- model.config.SEMANTIC_VOCAB_SIZE + (1 - int(is_major_step)) * model.config.CODEBOOK_SIZE
- )
- logit_end_idx = model.config.SEMANTIC_VOCAB_SIZE + (2 - int(is_major_step)) * model.config.CODEBOOK_SIZE
- relevant_logits = logits[0, 0, logit_start_idx:logit_end_idx]
- if top_p is not None:
- # faster to convert to numpy
- logits_device = relevant_logits.device
- logits_dtype = relevant_logits.type()
- relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
- sorted_indices = np.argsort(relevant_logits)[::-1]
- sorted_logits = relevant_logits[sorted_indices]
- cumulative_probs = np.cumsum(torch.nn.functional.softmax(sorted_logits))
- sorted_indices_to_remove = cumulative_probs > top_p
- sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy()
- sorted_indices_to_remove[0] = False
- relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
- relevant_logits = torch.from_numpy(relevant_logits)
- relevant_logits = relevant_logits.to(logits_device).type(logits_dtype)
- if top_k is not None:
- v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
- relevant_logits[relevant_logits < v[-1]] = -float("Inf")
- probs = torch.nn.functional.softmax(relevant_logits / temp, dim=-1)
- item_next = torch.multinomial(probs, num_samples=1)
- item_next += logit_start_idx
- x_coarse_in = torch.cat((x_coarse_in, item_next[None]), dim=1)
- x_in = torch.cat((x_in, item_next[None]), dim=1)
- del logits, relevant_logits, probs, item_next
- n_step += 1
- del x_in
- del x_semantic_in
- gen_coarse_arr = x_coarse_in.detach().cpu().numpy().squeeze()[len(x_coarse_history) :]
- del x_coarse_in
- assert len(gen_coarse_arr) == n_steps
- gen_coarse_audio_arr = (
- gen_coarse_arr.reshape(-1, model.config.N_COARSE_CODEBOOKS).T - model.config.SEMANTIC_VOCAB_SIZE
- )
- for n in range(1, model.config.N_COARSE_CODEBOOKS):
- gen_coarse_audio_arr[n, :] -= n * model.config.CODEBOOK_SIZE
- clear_cuda_cache()
- return gen_coarse_audio_arr
-
-
-def generate_fine(
- x_coarse_gen,
- model,
- history_prompt=None,
- temp=0.5,
- silent=True,
- base=None,
-):
- """Generate full audio codes from coarse audio codes.
-
- Args:
- x_coarse_gen (np.ndarray): The coarse audio codes to generate full audio codes from.
- model (BarkModel): The BarkModel to use for generating the full audio codes.
- history_prompt (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a prompt for the generation.
- temp (float): The temperature to use for the generation.
- silent (bool): Whether to silence the tqdm progress bar.
- base (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a base for the generation.
-
- Returns:
- np.ndarray: The generated full audio codes.
- """
- assert (
- isinstance(x_coarse_gen, np.ndarray)
- and len(x_coarse_gen.shape) == 2
- and 1 <= x_coarse_gen.shape[0] <= model.config.N_FINE_CODEBOOKS - 1
- and x_coarse_gen.shape[1] > 0
- and x_coarse_gen.min() >= 0
- and x_coarse_gen.max() <= model.config.CODEBOOK_SIZE - 1
- )
- if all(v is not None for v in history_prompt) or base is not None:
- if history_prompt is not None:
- x_fine_history = history_prompt[2]
- if base is not None:
- x_fine_history = base[2]
- assert (
- isinstance(x_fine_history, np.ndarray)
- and len(x_fine_history.shape) == 2
- and x_fine_history.shape[0] == model.config.N_FINE_CODEBOOKS
- and x_fine_history.shape[1] >= 0
- and x_fine_history.min() >= 0
- and x_fine_history.max() <= model.config.CODEBOOK_SIZE - 1
- )
- else:
- x_fine_history = None
- n_coarse = x_coarse_gen.shape[0]
- # make input arr
- in_arr = np.vstack(
- [
- x_coarse_gen,
- np.zeros((model.config.N_FINE_CODEBOOKS - n_coarse, x_coarse_gen.shape[1]))
- + model.config.CODEBOOK_SIZE, # padding
- ]
- ).astype(np.int32)
- # prepend history if available (max 512)
- if x_fine_history is not None:
- x_fine_history = x_fine_history.astype(np.int32)
- in_arr = np.hstack(
- [
- x_fine_history[:, -512:].astype(np.int32),
- in_arr,
- ]
- )
- n_history = x_fine_history[:, -512:].shape[1]
- else:
- n_history = 0
- n_remove_from_end = 0
- # need to pad if too short (since non-causal model)
- if in_arr.shape[1] < 1024:
- n_remove_from_end = 1024 - in_arr.shape[1]
- in_arr = np.hstack(
- [
- in_arr,
- np.zeros((model.config.N_FINE_CODEBOOKS, n_remove_from_end), dtype=np.int32)
- + model.config.CODEBOOK_SIZE,
- ]
- )
- # we can be lazy about fractional loop and just keep overwriting codebooks
- n_loops = np.max([0, int(np.ceil((x_coarse_gen.shape[1] - (1024 - n_history)) / 512))]) + 1
- with inference_mode():
- in_arr = torch.tensor(in_arr.T).to(model.device)
- for n in tqdm.tqdm(range(n_loops), disable=silent):
- start_idx = np.min([n * 512, in_arr.shape[0] - 1024])
- start_fill_idx = np.min([n_history + n * 512, in_arr.shape[0] - 512])
- rel_start_fill_idx = start_fill_idx - start_idx
- in_buffer = in_arr[start_idx : start_idx + 1024, :][None]
- for nn in range(n_coarse, model.config.N_FINE_CODEBOOKS):
- logits = model.fine_model(nn, in_buffer)
- if temp is None:
- relevant_logits = logits[0, rel_start_fill_idx:, : model.config.CODEBOOK_SIZE]
- codebook_preds = torch.argmax(relevant_logits, -1)
- else:
- relevant_logits = logits[0, :, : model.config.CODEBOOK_SIZE] / temp
- probs = F.softmax(relevant_logits, dim=-1)
- codebook_preds = torch.hstack(
- [torch.multinomial(probs[n], num_samples=1) for n in range(rel_start_fill_idx, 1024)]
- )
- in_buffer[0, rel_start_fill_idx:, nn] = codebook_preds
- del logits, codebook_preds
- # transfer over info into model_in and convert to numpy
- for nn in range(n_coarse, model.config.N_FINE_CODEBOOKS):
- in_arr[start_fill_idx : start_fill_idx + (1024 - rel_start_fill_idx), nn] = in_buffer[
- 0, rel_start_fill_idx:, nn
- ]
- del in_buffer
- gen_fine_arr = in_arr.detach().cpu().numpy().squeeze().T
- del in_arr
- gen_fine_arr = gen_fine_arr[:, n_history:]
- if n_remove_from_end > 0:
- gen_fine_arr = gen_fine_arr[:, :-n_remove_from_end]
- assert gen_fine_arr.shape[-1] == x_coarse_gen.shape[-1]
- clear_cuda_cache()
- return gen_fine_arr
-
-
-def codec_decode(fine_tokens, model):
- """Turn quantized audio codes into audio array using encodec."""
- arr = torch.from_numpy(fine_tokens)[None]
- arr = arr.to(model.device)
- arr = arr.transpose(0, 1)
- emb = model.encodec.quantizer.decode(arr)
- out = model.encodec.decoder(emb)
- audio_arr = out.detach().cpu().numpy().squeeze()
- return audio_arr
diff --git a/TTS/tts/layers/bark/load_model.py b/TTS/tts/layers/bark/load_model.py
deleted file mode 100644
index ce6b757f054ce98b91601b494854ef8e7b56b131..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/bark/load_model.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import contextlib
-import functools
-import hashlib
-import logging
-import os
-
-import requests
-import torch
-import tqdm
-
-from TTS.tts.layers.bark.model import GPT, GPTConfig
-from TTS.tts.layers.bark.model_fine import FineGPT, FineGPTConfig
-
-if (
- torch.cuda.is_available()
- and hasattr(torch.cuda, "amp")
- and hasattr(torch.cuda.amp, "autocast")
- and torch.cuda.is_bf16_supported()
-):
- autocast = functools.partial(torch.cuda.amp.autocast, dtype=torch.bfloat16)
-else:
-
- @contextlib.contextmanager
- def autocast():
- yield
-
-
-# hold models in global scope to lazy load
-
-logger = logging.getLogger(__name__)
-
-
-if not hasattr(torch.nn.functional, "scaled_dot_product_attention"):
- logger.warning(
- "torch version does not support flash attention. You will get significantly faster"
- + " inference speed by upgrade torch to newest version / nightly."
- )
-
-
-def _md5(fname):
- hash_md5 = hashlib.md5()
- with open(fname, "rb") as f:
- for chunk in iter(lambda: f.read(4096), b""):
- hash_md5.update(chunk)
- return hash_md5.hexdigest()
-
-
-def _download(from_s3_path, to_local_path, CACHE_DIR):
- os.makedirs(CACHE_DIR, exist_ok=True)
- response = requests.get(from_s3_path, stream=True)
- total_size_in_bytes = int(response.headers.get("content-length", 0))
- block_size = 1024 # 1 Kibibyte
- progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
- with open(to_local_path, "wb") as file:
- for data in response.iter_content(block_size):
- progress_bar.update(len(data))
- file.write(data)
- progress_bar.close()
- if total_size_in_bytes not in [0, progress_bar.n]:
- raise ValueError("ERROR, something went wrong")
-
-
-class InferenceContext:
- def __init__(self, benchmark=False):
- # we can't expect inputs to be the same length, so disable benchmarking by default
- self._chosen_cudnn_benchmark = benchmark
- self._cudnn_benchmark = None
-
- def __enter__(self):
- self._cudnn_benchmark = torch.backends.cudnn.benchmark
- torch.backends.cudnn.benchmark = self._chosen_cudnn_benchmark
-
- def __exit__(self, exc_type, exc_value, exc_traceback):
- torch.backends.cudnn.benchmark = self._cudnn_benchmark
-
-
-if torch.cuda.is_available():
- torch.backends.cuda.matmul.allow_tf32 = True
- torch.backends.cudnn.allow_tf32 = True
-
-
-@contextlib.contextmanager
-def inference_mode():
- with InferenceContext(), torch.inference_mode(), torch.no_grad(), autocast():
- yield
-
-
-def clear_cuda_cache():
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- torch.cuda.synchronize()
-
-
-def load_model(ckpt_path, device, config, model_type="text"):
- logger.info(f"loading {model_type} model from {ckpt_path}...")
-
- if device == "cpu":
- logger.warning("No GPU being used. Careful, Inference might be extremely slow!")
- if model_type == "text":
- ConfigClass = GPTConfig
- ModelClass = GPT
- elif model_type == "coarse":
- ConfigClass = GPTConfig
- ModelClass = GPT
- elif model_type == "fine":
- ConfigClass = FineGPTConfig
- ModelClass = FineGPT
- else:
- raise NotImplementedError()
- if (
- not config.USE_SMALLER_MODELS
- and os.path.exists(ckpt_path)
- and _md5(ckpt_path) != config.REMOTE_MODEL_PATHS[model_type]["checksum"]
- ):
- logger.warning(f"found outdated {model_type} model, removing...")
- os.remove(ckpt_path)
- if not os.path.exists(ckpt_path):
- logger.info(f"{model_type} model not found, downloading...")
- _download(config.REMOTE_MODEL_PATHS[model_type]["path"], ckpt_path, config.CACHE_DIR)
-
- checkpoint = torch.load(ckpt_path, map_location=device)
- # this is a hack
- model_args = checkpoint["model_args"]
- if "input_vocab_size" not in model_args:
- model_args["input_vocab_size"] = model_args["vocab_size"]
- model_args["output_vocab_size"] = model_args["vocab_size"]
- del model_args["vocab_size"]
-
- gptconf = ConfigClass(**checkpoint["model_args"])
- if model_type == "text":
- config.semantic_config = gptconf
- elif model_type == "coarse":
- config.coarse_config = gptconf
- elif model_type == "fine":
- config.fine_config = gptconf
-
- model = ModelClass(gptconf)
- state_dict = checkpoint["model"]
- # fixup checkpoint
- unwanted_prefix = "_orig_mod."
- for k, _ in list(state_dict.items()):
- if k.startswith(unwanted_prefix):
- state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k)
- extra_keys = set(state_dict.keys()) - set(model.state_dict().keys())
- extra_keys = set(k for k in extra_keys if not k.endswith(".attn.bias"))
- missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
- missing_keys = set(k for k in missing_keys if not k.endswith(".attn.bias"))
- if len(extra_keys) != 0:
- raise ValueError(f"extra keys found: {extra_keys}")
- if len(missing_keys) != 0:
- raise ValueError(f"missing keys: {missing_keys}")
- model.load_state_dict(state_dict, strict=False)
- n_params = model.get_num_params()
- val_loss = checkpoint["best_val_loss"].item()
- logger.info(f"model loaded: {round(n_params/1e6,1)}M params, {round(val_loss,3)} loss")
- model.eval()
- model.to(device)
- del checkpoint, state_dict
- clear_cuda_cache()
- return model, config
diff --git a/TTS/tts/layers/bark/model.py b/TTS/tts/layers/bark/model.py
deleted file mode 100644
index c84022bd08bcdd2f3f9f3caadfc15a7bf80ddaf3..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/bark/model.py
+++ /dev/null
@@ -1,233 +0,0 @@
-"""
-Much of this code is adapted from Andrej Karpathy's NanoGPT
-(https://github.com/karpathy/nanoGPT)
-"""
-import math
-from dataclasses import dataclass
-
-import torch
-from coqpit import Coqpit
-from torch import nn
-from torch.nn import functional as F
-
-
-class LayerNorm(nn.Module):
- """LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False"""
-
- def __init__(self, ndim, bias):
- super().__init__()
- self.weight = nn.Parameter(torch.ones(ndim))
- self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
-
- def forward(self, x):
- return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)
-
-
-class CausalSelfAttention(nn.Module):
- def __init__(self, config):
- super().__init__()
- assert config.n_embd % config.n_head == 0
- # key, query, value projections for all heads, but in a batch
- self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
- # output projection
- self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
- # regularization
- self.attn_dropout = nn.Dropout(config.dropout)
- self.resid_dropout = nn.Dropout(config.dropout)
- self.n_head = config.n_head
- self.n_embd = config.n_embd
- self.dropout = config.dropout
- # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
- self.flash = hasattr(torch.nn.functional, "scaled_dot_product_attention")
- if not self.flash:
- # print("WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0")
- # causal mask to ensure that attention is only applied to the left in the input sequence
- self.register_buffer(
- "bias",
- torch.tril(torch.ones(config.block_size, config.block_size)).view(
- 1, 1, config.block_size, config.block_size
- ),
- )
-
- def forward(self, x, past_kv=None, use_cache=False):
- B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
-
- # calculate query, key, values for all heads in batch and move head forward to be the batch dim
- q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
- k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
- q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
- v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-
- if past_kv is not None:
- past_key = past_kv[0]
- past_value = past_kv[1]
- k = torch.cat((past_key, k), dim=-2)
- v = torch.cat((past_value, v), dim=-2)
-
- FULL_T = k.shape[-2]
-
- if use_cache is True:
- present = (k, v)
- else:
- present = None
-
- # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
- if self.flash:
- # efficient attention using Flash Attention CUDA kernels
- if past_kv is not None:
- # When `past_kv` is provided, we're doing incremental decoding and `q.shape[2] == 1`: q only contains
- # the query for the last token. scaled_dot_product_attention interprets this as the first token in the
- # sequence, so if is_causal=True it will mask out all attention from it. This is not what we want, so
- # to work around this we set is_causal=False.
- is_causal = False
- else:
- is_causal = True
-
- # efficient attention using Flash Attention CUDA kernels
- y = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout, is_causal=is_causal)
- else:
- # manual implementation of attention
- att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
- att = att.masked_fill(self.bias[:, :, FULL_T - T : FULL_T, :FULL_T] == 0, float("-inf"))
- att = F.softmax(att, dim=-1)
- att = self.attn_dropout(att)
- y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
- y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
-
- # output projection
- y = self.resid_dropout(self.c_proj(y))
- return (y, present)
-
-
-class MLP(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
- self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
- self.dropout = nn.Dropout(config.dropout)
- self.gelu = nn.GELU()
-
- def forward(self, x):
- x = self.c_fc(x)
- x = self.gelu(x)
- x = self.c_proj(x)
- x = self.dropout(x)
- return x
-
-
-class Block(nn.Module):
- def __init__(self, config, layer_idx):
- super().__init__()
- self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
- self.attn = CausalSelfAttention(config)
- self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
- self.mlp = MLP(config)
- self.layer_idx = layer_idx
-
- def forward(self, x, past_kv=None, use_cache=False):
- attn_output, prev_kvs = self.attn(self.ln_1(x), past_kv=past_kv, use_cache=use_cache)
- x = x + attn_output
- x = x + self.mlp(self.ln_2(x))
- return (x, prev_kvs)
-
-
-@dataclass
-class GPTConfig(Coqpit):
- block_size: int = 1024
- input_vocab_size: int = 10_048
- output_vocab_size: int = 10_048
- n_layer: int = 12
- n_head: int = 12
- n_embd: int = 768
- dropout: float = 0.0
- bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
-
-
-class GPT(nn.Module):
- def __init__(self, config):
- super().__init__()
- assert config.input_vocab_size is not None
- assert config.output_vocab_size is not None
- assert config.block_size is not None
- self.config = config
-
- self.transformer = nn.ModuleDict(
- dict(
- wte=nn.Embedding(config.input_vocab_size, config.n_embd),
- wpe=nn.Embedding(config.block_size, config.n_embd),
- drop=nn.Dropout(config.dropout),
- h=nn.ModuleList([Block(config, idx) for idx in range(config.n_layer)]),
- ln_f=LayerNorm(config.n_embd, bias=config.bias),
- )
- )
- self.lm_head = nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
-
- def get_num_params(self, non_embedding=True):
- """
- Return the number of parameters in the model.
- For non-embedding count (default), the position embeddings get subtracted.
- The token embeddings would too, except due to the parameter sharing these
- params are actually used as weights in the final layer, so we include them.
- """
- n_params = sum(p.numel() for p in self.parameters())
- if non_embedding:
- n_params -= self.transformer.wte.weight.numel()
- n_params -= self.transformer.wpe.weight.numel()
- return n_params
-
- def forward(self, idx, merge_context=False, past_kv=None, position_ids=None, use_cache=False):
- device = idx.device
- _, t = idx.size()
- if past_kv is not None:
- assert t == 1
- tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
- else:
- if merge_context:
- assert idx.shape[1] >= 256 + 256 + 1
- t = idx.shape[1] - 256
- else:
- assert (
- t <= self.config.block_size
- ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
-
- # forward the GPT model itself
- if merge_context:
- tok_emb = torch.cat(
- [
- self.transformer.wte(idx[:, :256]) + self.transformer.wte(idx[:, 256 : 256 + 256]),
- self.transformer.wte(idx[:, 256 + 256 :]),
- ],
- dim=1,
- )
- else:
- tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
-
- if past_kv is None:
- past_length = 0
- past_kv = tuple([None] * len(self.transformer.h))
- else:
- past_length = past_kv[0][0].size(-2)
-
- if position_ids is None:
- position_ids = torch.arange(past_length, t + past_length, dtype=torch.long, device=device)
- position_ids = position_ids.unsqueeze(0) # shape (1, t)
- assert position_ids.shape == (1, t)
-
- pos_emb = self.transformer.wpe(position_ids) # position embeddings of shape (1, t, n_embd)
-
- x = self.transformer.drop(tok_emb + pos_emb)
-
- new_kv = () if use_cache else None
-
- for _, (block, past_layer_kv) in enumerate(zip(self.transformer.h, past_kv)):
- x, kv = block(x, past_kv=past_layer_kv, use_cache=use_cache)
-
- if use_cache:
- new_kv = new_kv + (kv,)
-
- x = self.transformer.ln_f(x)
-
- # inference-time mini-optimization: only forward the lm_head on the very last position
- logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
-
- return (logits, new_kv)
diff --git a/TTS/tts/layers/bark/model_fine.py b/TTS/tts/layers/bark/model_fine.py
deleted file mode 100644
index 09e5f4765dce8743db2a3ed879e7811d2b9d23d6..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/bark/model_fine.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""
-Much of this code is adapted from Andrej Karpathy's NanoGPT
-(https://github.com/karpathy/nanoGPT)
-"""
-import math
-from dataclasses import dataclass
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .model import GPT, MLP, GPTConfig
-
-
-class NonCausalSelfAttention(nn.Module):
- def __init__(self, config):
- super().__init__()
- assert config.n_embd % config.n_head == 0
- # key, query, value projections for all heads, but in a batch
- self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
- # output projection
- self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
- # regularization
- self.attn_dropout = nn.Dropout(config.dropout)
- self.resid_dropout = nn.Dropout(config.dropout)
- self.n_head = config.n_head
- self.n_embd = config.n_embd
- self.dropout = config.dropout
- # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
- self.flash = hasattr(torch.nn.functional, "scaled_dot_product_attention") and self.dropout == 0.0
-
- def forward(self, x):
- B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
-
- # calculate query, key, values for all heads in batch and move head forward to be the batch dim
- q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
- k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
- q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
- v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-
- # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
- if self.flash:
- # efficient attention using Flash Attention CUDA kernels
- y = torch.nn.functional.scaled_dot_product_attention(
- q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=False
- )
- else:
- # manual implementation of attention
- att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
- att = F.softmax(att, dim=-1)
- att = self.attn_dropout(att)
- y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
- y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
-
- # output projection
- y = self.resid_dropout(self.c_proj(y))
- return y
-
-
-class FineBlock(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.ln_1 = nn.LayerNorm(config.n_embd)
- self.attn = NonCausalSelfAttention(config)
- self.ln_2 = nn.LayerNorm(config.n_embd)
- self.mlp = MLP(config)
-
- def forward(self, x):
- x = x + self.attn(self.ln_1(x))
- x = x + self.mlp(self.ln_2(x))
- return x
-
-
-class FineGPT(GPT):
- def __init__(self, config):
- super().__init__(config)
- del self.lm_head
- self.config = config
- self.n_codes_total = config.n_codes_total
- self.transformer = nn.ModuleDict(
- dict(
- wtes=nn.ModuleList(
- [nn.Embedding(config.input_vocab_size, config.n_embd) for _ in range(config.n_codes_total)]
- ),
- wpe=nn.Embedding(config.block_size, config.n_embd),
- drop=nn.Dropout(config.dropout),
- h=nn.ModuleList([FineBlock(config) for _ in range(config.n_layer)]),
- ln_f=nn.LayerNorm(config.n_embd),
- )
- )
- self.lm_heads = nn.ModuleList(
- [
- nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
- for _ in range(config.n_codes_given, self.n_codes_total)
- ]
- )
- for i in range(self.n_codes_total - config.n_codes_given):
- self.transformer.wtes[i + 1].weight = self.lm_heads[i].weight
-
- def forward(self, pred_idx, idx):
- device = idx.device
- b, t, codes = idx.size()
- assert (
- t <= self.config.block_size
- ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
- assert pred_idx > 0, "cannot predict 0th codebook"
- assert codes == self.n_codes_total, (b, t, codes)
- pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0) # shape (1, t)
-
- # forward the GPT model itself
- tok_embs = [
- wte(idx[:, :, i]).unsqueeze(-1) for i, wte in enumerate(self.transformer.wtes)
- ] # token embeddings of shape (b, t, n_embd)
- tok_emb = torch.cat(tok_embs, dim=-1)
- pos_emb = self.transformer.wpe(pos) # position embeddings of shape (1, t, n_embd)
- x = tok_emb[:, :, :, : pred_idx + 1].sum(dim=-1)
- x = self.transformer.drop(x + pos_emb)
- for block in self.transformer.h:
- x = block(x)
- x = self.transformer.ln_f(x)
- logits = self.lm_heads[pred_idx - self.config.n_codes_given](x)
- return logits
-
- def get_num_params(self, non_embedding=True):
- """
- Return the number of parameters in the model.
- For non-embedding count (default), the position embeddings get subtracted.
- The token embeddings would too, except due to the parameter sharing these
- params are actually used as weights in the final layer, so we include them.
- """
- n_params = sum(p.numel() for p in self.parameters())
- if non_embedding:
- for wte in self.transformer.wtes:
- n_params -= wte.weight.numel()
- n_params -= self.transformer.wpe.weight.numel()
- return n_params
-
-
-@dataclass
-class FineGPTConfig(GPTConfig):
- n_codes_total: int = 8
- n_codes_given: int = 1
diff --git a/TTS/tts/layers/delightful_tts/__init__.py b/TTS/tts/layers/delightful_tts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tts/layers/delightful_tts/acoustic_model.py b/TTS/tts/layers/delightful_tts/acoustic_model.py
deleted file mode 100644
index c906b882e567fade64139a8b932c71d554117547..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/acoustic_model.py
+++ /dev/null
@@ -1,563 +0,0 @@
-### credit: https://github.com/dunky11/voicesmith
-from typing import Callable, Dict, Tuple
-
-import torch
-import torch.nn.functional as F
-from coqpit import Coqpit
-from torch import nn
-
-from TTS.tts.layers.delightful_tts.conformer import Conformer
-from TTS.tts.layers.delightful_tts.encoders import (
- PhonemeLevelProsodyEncoder,
- UtteranceLevelProsodyEncoder,
- get_mask_from_lengths,
-)
-from TTS.tts.layers.delightful_tts.energy_adaptor import EnergyAdaptor
-from TTS.tts.layers.delightful_tts.networks import EmbeddingPadded, positional_encoding
-from TTS.tts.layers.delightful_tts.phoneme_prosody_predictor import PhonemeProsodyPredictor
-from TTS.tts.layers.delightful_tts.pitch_adaptor import PitchAdaptor
-from TTS.tts.layers.delightful_tts.variance_predictor import VariancePredictor
-from TTS.tts.layers.generic.aligner import AlignmentNetwork
-from TTS.tts.utils.helpers import generate_path, maximum_path, sequence_mask
-
-
-class AcousticModel(torch.nn.Module):
- def __init__(
- self,
- args: "ModelArgs",
- tokenizer: "TTSTokenizer" = None,
- speaker_manager: "SpeakerManager" = None,
- ):
- super().__init__()
- self.args = args
- self.tokenizer = tokenizer
- self.speaker_manager = speaker_manager
-
- self.init_multispeaker(args)
- # self.set_embedding_dims()
-
- self.length_scale = (
- float(self.args.length_scale) if isinstance(self.args.length_scale, int) else self.args.length_scale
- )
-
- self.emb_dim = args.n_hidden_conformer_encoder
- self.encoder = Conformer(
- dim=self.args.n_hidden_conformer_encoder,
- n_layers=self.args.n_layers_conformer_encoder,
- n_heads=self.args.n_heads_conformer_encoder,
- speaker_embedding_dim=self.embedded_speaker_dim,
- p_dropout=self.args.dropout_conformer_encoder,
- kernel_size_conv_mod=self.args.kernel_size_conv_mod_conformer_encoder,
- lrelu_slope=self.args.lrelu_slope,
- )
- self.pitch_adaptor = PitchAdaptor(
- n_input=self.args.n_hidden_conformer_encoder,
- n_hidden=self.args.n_hidden_variance_adaptor,
- n_out=1,
- kernel_size=self.args.kernel_size_variance_adaptor,
- emb_kernel_size=self.args.emb_kernel_size_variance_adaptor,
- p_dropout=self.args.dropout_variance_adaptor,
- lrelu_slope=self.args.lrelu_slope,
- )
- self.energy_adaptor = EnergyAdaptor(
- channels_in=self.args.n_hidden_conformer_encoder,
- channels_hidden=self.args.n_hidden_variance_adaptor,
- channels_out=1,
- kernel_size=self.args.kernel_size_variance_adaptor,
- emb_kernel_size=self.args.emb_kernel_size_variance_adaptor,
- dropout=self.args.dropout_variance_adaptor,
- lrelu_slope=self.args.lrelu_slope,
- )
-
- self.aligner = AlignmentNetwork(
- in_query_channels=self.args.out_channels,
- in_key_channels=self.args.n_hidden_conformer_encoder,
- )
-
- self.duration_predictor = VariancePredictor(
- channels_in=self.args.n_hidden_conformer_encoder,
- channels=self.args.n_hidden_variance_adaptor,
- channels_out=1,
- kernel_size=self.args.kernel_size_variance_adaptor,
- p_dropout=self.args.dropout_variance_adaptor,
- lrelu_slope=self.args.lrelu_slope,
- )
-
- self.utterance_prosody_encoder = UtteranceLevelProsodyEncoder(
- num_mels=self.args.num_mels,
- ref_enc_filters=self.args.ref_enc_filters_reference_encoder,
- ref_enc_size=self.args.ref_enc_size_reference_encoder,
- ref_enc_gru_size=self.args.ref_enc_gru_size_reference_encoder,
- ref_enc_strides=self.args.ref_enc_strides_reference_encoder,
- n_hidden=self.args.n_hidden_conformer_encoder,
- dropout=self.args.dropout_conformer_encoder,
- bottleneck_size_u=self.args.bottleneck_size_u_reference_encoder,
- token_num=self.args.token_num_reference_encoder,
- )
-
- self.utterance_prosody_predictor = PhonemeProsodyPredictor(
- hidden_size=self.args.n_hidden_conformer_encoder,
- kernel_size=self.args.predictor_kernel_size_reference_encoder,
- dropout=self.args.dropout_conformer_encoder,
- bottleneck_size=self.args.bottleneck_size_u_reference_encoder,
- lrelu_slope=self.args.lrelu_slope,
- )
-
- self.phoneme_prosody_encoder = PhonemeLevelProsodyEncoder(
- num_mels=self.args.num_mels,
- ref_enc_filters=self.args.ref_enc_filters_reference_encoder,
- ref_enc_size=self.args.ref_enc_size_reference_encoder,
- ref_enc_gru_size=self.args.ref_enc_gru_size_reference_encoder,
- ref_enc_strides=self.args.ref_enc_strides_reference_encoder,
- n_hidden=self.args.n_hidden_conformer_encoder,
- dropout=self.args.dropout_conformer_encoder,
- bottleneck_size_p=self.args.bottleneck_size_p_reference_encoder,
- n_heads=self.args.n_heads_conformer_encoder,
- )
-
- self.phoneme_prosody_predictor = PhonemeProsodyPredictor(
- hidden_size=self.args.n_hidden_conformer_encoder,
- kernel_size=self.args.predictor_kernel_size_reference_encoder,
- dropout=self.args.dropout_conformer_encoder,
- bottleneck_size=self.args.bottleneck_size_p_reference_encoder,
- lrelu_slope=self.args.lrelu_slope,
- )
-
- self.u_bottle_out = nn.Linear(
- self.args.bottleneck_size_u_reference_encoder,
- self.args.n_hidden_conformer_encoder,
- )
-
- self.u_norm = nn.InstanceNorm1d(self.args.bottleneck_size_u_reference_encoder)
- self.p_bottle_out = nn.Linear(
- self.args.bottleneck_size_p_reference_encoder,
- self.args.n_hidden_conformer_encoder,
- )
- self.p_norm = nn.InstanceNorm1d(
- self.args.bottleneck_size_p_reference_encoder,
- )
- self.decoder = Conformer(
- dim=self.args.n_hidden_conformer_decoder,
- n_layers=self.args.n_layers_conformer_decoder,
- n_heads=self.args.n_heads_conformer_decoder,
- speaker_embedding_dim=self.embedded_speaker_dim,
- p_dropout=self.args.dropout_conformer_decoder,
- kernel_size_conv_mod=self.args.kernel_size_conv_mod_conformer_decoder,
- lrelu_slope=self.args.lrelu_slope,
- )
-
- padding_idx = self.tokenizer.characters.pad_id
- self.src_word_emb = EmbeddingPadded(
- self.args.num_chars, self.args.n_hidden_conformer_encoder, padding_idx=padding_idx
- )
- self.to_mel = nn.Linear(
- self.args.n_hidden_conformer_decoder,
- self.args.num_mels,
- )
-
- self.energy_scaler = torch.nn.BatchNorm1d(1, affine=False, track_running_stats=True, momentum=None)
- self.energy_scaler.requires_grad_(False)
-
- def init_multispeaker(self, args: Coqpit): # pylint: disable=unused-argument
- """Init for multi-speaker training."""
- self.embedded_speaker_dim = 0
- self.num_speakers = self.args.num_speakers
- self.audio_transform = None
-
- if self.speaker_manager:
- self.num_speakers = self.speaker_manager.num_speakers
-
- if self.args.use_speaker_embedding:
- self._init_speaker_embedding()
-
- if self.args.use_d_vector_file:
- self._init_d_vector()
-
- @staticmethod
- def _set_cond_input(aux_input: Dict):
- """Set the speaker conditioning input based on the multi-speaker mode."""
- sid, g, lid, durations = None, None, None, None
- if "speaker_ids" in aux_input and aux_input["speaker_ids"] is not None:
- sid = aux_input["speaker_ids"]
- if sid.ndim == 0:
- sid = sid.unsqueeze_(0)
- if "d_vectors" in aux_input and aux_input["d_vectors"] is not None:
- g = F.normalize(aux_input["d_vectors"]) # .unsqueeze_(-1)
- if g.ndim == 2:
- g = g # .unsqueeze_(0) # pylint: disable=self-assigning-variable
-
- if "durations" in aux_input and aux_input["durations"] is not None:
- durations = aux_input["durations"]
-
- return sid, g, lid, durations
-
- def get_aux_input(self, aux_input: Dict):
- sid, g, lid, _ = self._set_cond_input(aux_input)
- return {"speaker_ids": sid, "style_wav": None, "d_vectors": g, "language_ids": lid}
-
- def _set_speaker_input(self, aux_input: Dict):
- d_vectors = aux_input.get("d_vectors", None)
- speaker_ids = aux_input.get("speaker_ids", None)
-
- if d_vectors is not None and speaker_ids is not None:
- raise ValueError("[!] Cannot use d-vectors and speaker-ids together.")
-
- if speaker_ids is not None and not hasattr(self, "emb_g"):
- raise ValueError("[!] Cannot use speaker-ids without enabling speaker embedding.")
-
- g = speaker_ids if speaker_ids is not None else d_vectors
- return g
-
- # def set_embedding_dims(self):
- # if self.embedded_speaker_dim > 0:
- # self.embedding_dims = self.embedded_speaker_dim
- # else:
- # self.embedding_dims = 0
-
- def _init_speaker_embedding(self):
- # pylint: disable=attribute-defined-outside-init
- if self.num_speakers > 0:
- print(" > initialization of speaker-embedding layers.")
- self.embedded_speaker_dim = self.args.speaker_embedding_channels
- self.emb_g = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
-
- def _init_d_vector(self):
- # pylint: disable=attribute-defined-outside-init
- if hasattr(self, "emb_g"):
- raise ValueError("[!] Speaker embedding layer already initialized before d_vector settings.")
- self.embedded_speaker_dim = self.args.d_vector_dim
-
- @staticmethod
- def generate_attn(dr, x_mask, y_mask=None):
- """Generate an attention mask from the linear scale durations.
-
- Args:
- dr (Tensor): Linear scale durations.
- x_mask (Tensor): Mask for the input (character) sequence.
- y_mask (Tensor): Mask for the output (spectrogram) sequence. Compute it from the predicted durations
- if None. Defaults to None.
-
- Shapes
- - dr: :math:`(B, T_{en})`
- - x_mask: :math:`(B, T_{en})`
- - y_mask: :math:`(B, T_{de})`
- """
- # compute decode mask from the durations
- if y_mask is None:
- y_lengths = dr.sum(1).long()
- y_lengths[y_lengths < 1] = 1
- y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(dr.dtype)
- attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
- attn = generate_path(dr, attn_mask.squeeze(1)).to(dr.dtype)
- return attn
-
- def _expand_encoder_with_durations(
- self,
- o_en: torch.FloatTensor,
- dr: torch.IntTensor,
- x_mask: torch.IntTensor,
- y_lengths: torch.IntTensor,
- ):
- y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(o_en.dtype)
- attn = self.generate_attn(dr, x_mask, y_mask)
- o_en_ex = torch.einsum("kmn, kjm -> kjn", [attn.float(), o_en])
- return y_mask, o_en_ex, attn.transpose(1, 2)
-
- def _forward_aligner(
- self,
- x: torch.FloatTensor,
- y: torch.FloatTensor,
- x_mask: torch.IntTensor,
- y_mask: torch.IntTensor,
- attn_priors: torch.FloatTensor,
- ) -> Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
- """Aligner forward pass.
-
- 1. Compute a mask to apply to the attention map.
- 2. Run the alignment network.
- 3. Apply MAS to compute the hard alignment map.
- 4. Compute the durations from the hard alignment map.
-
- Args:
- x (torch.FloatTensor): Input sequence.
- y (torch.FloatTensor): Output sequence.
- x_mask (torch.IntTensor): Input sequence mask.
- y_mask (torch.IntTensor): Output sequence mask.
- attn_priors (torch.FloatTensor): Prior for the aligner network map.
-
- Returns:
- Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
- Durations from the hard alignment map, soft alignment potentials, log scale alignment potentials,
- hard alignment map.
-
- Shapes:
- - x: :math:`[B, T_en, C_en]`
- - y: :math:`[B, T_de, C_de]`
- - x_mask: :math:`[B, 1, T_en]`
- - y_mask: :math:`[B, 1, T_de]`
- - attn_priors: :math:`[B, T_de, T_en]`
-
- - aligner_durations: :math:`[B, T_en]`
- - aligner_soft: :math:`[B, T_de, T_en]`
- - aligner_logprob: :math:`[B, 1, T_de, T_en]`
- - aligner_mas: :math:`[B, T_de, T_en]`
- """
- attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2) # [B, 1, T_en, T_de]
- aligner_soft, aligner_logprob = self.aligner(y.transpose(1, 2), x.transpose(1, 2), x_mask, attn_priors)
- aligner_mas = maximum_path(
- aligner_soft.squeeze(1).transpose(1, 2).contiguous(), attn_mask.squeeze(1).contiguous()
- )
- aligner_durations = torch.sum(aligner_mas, -1).int()
- aligner_soft = aligner_soft.squeeze(1) # [B, T_max2, T_max]
- aligner_mas = aligner_mas.transpose(1, 2) # [B, T_max, T_max2] -> [B, T_max2, T_max]
- return aligner_durations, aligner_soft, aligner_logprob, aligner_mas
-
- def average_utterance_prosody( # pylint: disable=no-self-use
- self, u_prosody_pred: torch.Tensor, src_mask: torch.Tensor
- ) -> torch.Tensor:
- lengths = ((~src_mask) * 1.0).sum(1)
- u_prosody_pred = u_prosody_pred.sum(1, keepdim=True) / lengths.view(-1, 1, 1)
- return u_prosody_pred
-
- def forward(
- self,
- tokens: torch.Tensor,
- src_lens: torch.Tensor,
- mels: torch.Tensor,
- mel_lens: torch.Tensor,
- pitches: torch.Tensor,
- energies: torch.Tensor,
- attn_priors: torch.Tensor,
- use_ground_truth: bool = True,
- d_vectors: torch.Tensor = None,
- speaker_idx: torch.Tensor = None,
- ) -> Dict[str, torch.Tensor]:
- sid, g, lid, _ = self._set_cond_input( # pylint: disable=unused-variable
- {"d_vectors": d_vectors, "speaker_ids": speaker_idx}
- ) # pylint: disable=unused-variable
-
- src_mask = get_mask_from_lengths(src_lens) # [B, T_src]
- mel_mask = get_mask_from_lengths(mel_lens) # [B, T_mel]
-
- # Token embeddings
- token_embeddings = self.src_word_emb(tokens) # [B, T_src, C_hidden]
- token_embeddings = token_embeddings.masked_fill(src_mask.unsqueeze(-1), 0.0)
-
- # Alignment network and durations
- aligner_durations, aligner_soft, aligner_logprob, aligner_mas = self._forward_aligner(
- x=token_embeddings,
- y=mels.transpose(1, 2),
- x_mask=~src_mask[:, None],
- y_mask=~mel_mask[:, None],
- attn_priors=attn_priors,
- )
- dr = aligner_durations # [B, T_en]
-
- # Embeddings
- speaker_embedding = None
- if d_vectors is not None:
- speaker_embedding = g
- elif speaker_idx is not None:
- speaker_embedding = F.normalize(self.emb_g(sid))
-
- pos_encoding = positional_encoding(
- self.emb_dim,
- max(token_embeddings.shape[1], max(mel_lens)),
- device=token_embeddings.device,
- )
- encoder_outputs = self.encoder(
- token_embeddings,
- src_mask,
- speaker_embedding=speaker_embedding,
- encoding=pos_encoding,
- )
-
- u_prosody_ref = self.u_norm(self.utterance_prosody_encoder(mels=mels, mel_lens=mel_lens))
- u_prosody_pred = self.u_norm(
- self.average_utterance_prosody(
- u_prosody_pred=self.utterance_prosody_predictor(x=encoder_outputs, mask=src_mask),
- src_mask=src_mask,
- )
- )
-
- if use_ground_truth:
- encoder_outputs = encoder_outputs + self.u_bottle_out(u_prosody_ref)
- else:
- encoder_outputs = encoder_outputs + self.u_bottle_out(u_prosody_pred)
-
- p_prosody_ref = self.p_norm(
- self.phoneme_prosody_encoder(
- x=encoder_outputs, src_mask=src_mask, mels=mels, mel_lens=mel_lens, encoding=pos_encoding
- )
- )
- p_prosody_pred = self.p_norm(self.phoneme_prosody_predictor(x=encoder_outputs, mask=src_mask))
-
- if use_ground_truth:
- encoder_outputs = encoder_outputs + self.p_bottle_out(p_prosody_ref)
- else:
- encoder_outputs = encoder_outputs + self.p_bottle_out(p_prosody_pred)
-
- encoder_outputs_res = encoder_outputs
-
- pitch_pred, avg_pitch_target, pitch_emb = self.pitch_adaptor.get_pitch_embedding_train(
- x=encoder_outputs,
- target=pitches,
- dr=dr,
- mask=src_mask,
- )
-
- energy_pred, avg_energy_target, energy_emb = self.energy_adaptor.get_energy_embedding_train(
- x=encoder_outputs,
- target=energies,
- dr=dr,
- mask=src_mask,
- )
-
- encoder_outputs = encoder_outputs.transpose(1, 2) + pitch_emb + energy_emb
- log_duration_prediction = self.duration_predictor(x=encoder_outputs_res.detach(), mask=src_mask)
-
- mel_pred_mask, encoder_outputs_ex, alignments = self._expand_encoder_with_durations(
- o_en=encoder_outputs, y_lengths=mel_lens, dr=dr, x_mask=~src_mask[:, None]
- )
-
- x = self.decoder(
- encoder_outputs_ex.transpose(1, 2),
- mel_mask,
- speaker_embedding=speaker_embedding,
- encoding=pos_encoding,
- )
- x = self.to_mel(x)
-
- dr = torch.log(dr + 1)
-
- dr_pred = torch.exp(log_duration_prediction) - 1
- alignments_dp = self.generate_attn(dr_pred, src_mask.unsqueeze(1), mel_pred_mask) # [B, T_max, T_max2']
-
- return {
- "model_outputs": x,
- "pitch_pred": pitch_pred,
- "pitch_target": avg_pitch_target,
- "energy_pred": energy_pred,
- "energy_target": avg_energy_target,
- "u_prosody_pred": u_prosody_pred,
- "u_prosody_ref": u_prosody_ref,
- "p_prosody_pred": p_prosody_pred,
- "p_prosody_ref": p_prosody_ref,
- "alignments_dp": alignments_dp,
- "alignments": alignments, # [B, T_de, T_en]
- "aligner_soft": aligner_soft,
- "aligner_mas": aligner_mas,
- "aligner_durations": aligner_durations,
- "aligner_logprob": aligner_logprob,
- "dr_log_pred": log_duration_prediction.squeeze(1), # [B, T]
- "dr_log_target": dr.squeeze(1), # [B, T]
- "spk_emb": speaker_embedding,
- }
-
- @torch.no_grad()
- def inference(
- self,
- tokens: torch.Tensor,
- speaker_idx: torch.Tensor,
- p_control: float = None, # TODO # pylint: disable=unused-argument
- d_control: float = None, # TODO # pylint: disable=unused-argument
- d_vectors: torch.Tensor = None,
- pitch_transform: Callable = None,
- energy_transform: Callable = None,
- ) -> torch.Tensor:
- src_mask = get_mask_from_lengths(torch.tensor([tokens.shape[1]], dtype=torch.int64, device=tokens.device))
- src_lens = torch.tensor(tokens.shape[1:2]).to(tokens.device) # pylint: disable=unused-variable
- sid, g, lid, _ = self._set_cond_input( # pylint: disable=unused-variable
- {"d_vectors": d_vectors, "speaker_ids": speaker_idx}
- ) # pylint: disable=unused-variable
-
- token_embeddings = self.src_word_emb(tokens)
- token_embeddings = token_embeddings.masked_fill(src_mask.unsqueeze(-1), 0.0)
-
- # Embeddings
- speaker_embedding = None
- if d_vectors is not None:
- speaker_embedding = g
- elif speaker_idx is not None:
- speaker_embedding = F.normalize(self.emb_g(sid))
-
- pos_encoding = positional_encoding(
- self.emb_dim,
- token_embeddings.shape[1],
- device=token_embeddings.device,
- )
- encoder_outputs = self.encoder(
- token_embeddings,
- src_mask,
- speaker_embedding=speaker_embedding,
- encoding=pos_encoding,
- )
-
- u_prosody_pred = self.u_norm(
- self.average_utterance_prosody(
- u_prosody_pred=self.utterance_prosody_predictor(x=encoder_outputs, mask=src_mask),
- src_mask=src_mask,
- )
- )
- encoder_outputs = encoder_outputs + self.u_bottle_out(u_prosody_pred).expand_as(encoder_outputs)
-
- p_prosody_pred = self.p_norm(
- self.phoneme_prosody_predictor(
- x=encoder_outputs,
- mask=src_mask,
- )
- )
- encoder_outputs = encoder_outputs + self.p_bottle_out(p_prosody_pred).expand_as(encoder_outputs)
-
- encoder_outputs_res = encoder_outputs
-
- pitch_emb_pred, pitch_pred = self.pitch_adaptor.get_pitch_embedding(
- x=encoder_outputs,
- mask=src_mask,
- pitch_transform=pitch_transform,
- pitch_mean=self.pitch_mean if hasattr(self, "pitch_mean") else None,
- pitch_std=self.pitch_std if hasattr(self, "pitch_std") else None,
- )
-
- energy_emb_pred, energy_pred = self.energy_adaptor.get_energy_embedding(
- x=encoder_outputs, mask=src_mask, energy_transform=energy_transform
- )
- encoder_outputs = encoder_outputs.transpose(1, 2) + pitch_emb_pred + energy_emb_pred
-
- log_duration_pred = self.duration_predictor(
- x=encoder_outputs_res.detach(), mask=src_mask
- ) # [B, C_hidden, T_src] -> [B, T_src]
- duration_pred = (torch.exp(log_duration_pred) - 1) * (~src_mask) * self.length_scale # -> [B, T_src]
- duration_pred[duration_pred < 1] = 1.0 # -> [B, T_src]
- duration_pred = torch.round(duration_pred) # -> [B, T_src]
- mel_lens = duration_pred.sum(1) # -> [B,]
-
- _, encoder_outputs_ex, alignments = self._expand_encoder_with_durations(
- o_en=encoder_outputs, y_lengths=mel_lens, dr=duration_pred.squeeze(1), x_mask=~src_mask[:, None]
- )
-
- mel_mask = get_mask_from_lengths(
- torch.tensor([encoder_outputs_ex.shape[2]], dtype=torch.int64, device=encoder_outputs_ex.device)
- )
-
- if encoder_outputs_ex.shape[1] > pos_encoding.shape[1]:
- encoding = positional_encoding(self.emb_dim, encoder_outputs_ex.shape[2], device=tokens.device)
-
- # [B, C_hidden, T_src], [B, 1, T_src], [B, C_emb], [B, T_src, C_hidden] -> [B, C_hidden, T_src]
- x = self.decoder(
- encoder_outputs_ex.transpose(1, 2),
- mel_mask,
- speaker_embedding=speaker_embedding,
- encoding=encoding,
- )
- x = self.to_mel(x)
- outputs = {
- "model_outputs": x,
- "alignments": alignments,
- # "pitch": pitch_emb_pred,
- "durations": duration_pred,
- "pitch": pitch_pred,
- "energy": energy_pred,
- "spk_emb": speaker_embedding,
- }
- return outputs
diff --git a/TTS/tts/layers/delightful_tts/conformer.py b/TTS/tts/layers/delightful_tts/conformer.py
deleted file mode 100644
index b2175b3b965c6b100846e87d405a753dc272c9e7..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/conformer.py
+++ /dev/null
@@ -1,450 +0,0 @@
-### credit: https://github.com/dunky11/voicesmith
-import math
-from typing import Tuple
-
-import torch
-import torch.nn as nn # pylint: disable=consider-using-from-import
-import torch.nn.functional as F
-
-from TTS.tts.layers.delightful_tts.conv_layers import Conv1dGLU, DepthWiseConv1d, PointwiseConv1d
-from TTS.tts.layers.delightful_tts.networks import GLUActivation
-
-
-def calc_same_padding(kernel_size: int) -> Tuple[int, int]:
- pad = kernel_size // 2
- return (pad, pad - (kernel_size + 1) % 2)
-
-
-class Conformer(nn.Module):
- def __init__(
- self,
- dim: int,
- n_layers: int,
- n_heads: int,
- speaker_embedding_dim: int,
- p_dropout: float,
- kernel_size_conv_mod: int,
- lrelu_slope: float,
- ):
- """
- A Transformer variant that integrates both CNNs and Transformers components.
- Conformer proposes a novel combination of self-attention and convolution, in which self-attention
- learns the global interaction while the convolutions efficiently capture the local correlations.
-
- Args:
- dim (int): Number of the dimensions for the model.
- n_layers (int): Number of model layers.
- n_heads (int): The number of attention heads.
- speaker_embedding_dim (int): Number of speaker embedding dimensions.
- p_dropout (float): Probabilty of dropout.
- kernel_size_conv_mod (int): Size of kernels for convolution modules.
-
- Inputs: inputs, mask
- - **inputs** (batch, time, dim): Tensor containing input vector
- - **encoding** (batch, time, dim): Positional embedding tensor
- - **mask** (batch, 1, time2) or (batch, time1, time2): Tensor containing indices to be masked
- Returns:
- - **outputs** (batch, time, dim): Tensor produced by Conformer Encoder.
- """
- super().__init__()
- d_k = d_v = dim // n_heads
- self.layer_stack = nn.ModuleList(
- [
- ConformerBlock(
- dim,
- n_heads,
- d_k,
- d_v,
- kernel_size_conv_mod=kernel_size_conv_mod,
- dropout=p_dropout,
- speaker_embedding_dim=speaker_embedding_dim,
- lrelu_slope=lrelu_slope,
- )
- for _ in range(n_layers)
- ]
- )
-
- def forward(
- self,
- x: torch.Tensor,
- mask: torch.Tensor,
- speaker_embedding: torch.Tensor,
- encoding: torch.Tensor,
- ) -> torch.Tensor:
- """
- Shapes:
- - x: :math:`[B, T_src, C]`
- - mask: :math: `[B]`
- - speaker_embedding: :math: `[B, C]`
- - encoding: :math: `[B, T_max2, C]`
- """
-
- attn_mask = mask.view((mask.shape[0], 1, 1, mask.shape[1]))
- for enc_layer in self.layer_stack:
- x = enc_layer(
- x,
- mask=mask,
- slf_attn_mask=attn_mask,
- speaker_embedding=speaker_embedding,
- encoding=encoding,
- )
- return x
-
-
-class ConformerBlock(torch.nn.Module):
- def __init__(
- self,
- d_model: int,
- n_head: int,
- d_k: int, # pylint: disable=unused-argument
- d_v: int, # pylint: disable=unused-argument
- kernel_size_conv_mod: int,
- speaker_embedding_dim: int,
- dropout: float,
- lrelu_slope: float = 0.3,
- ):
- """
- A Conformer block is composed of four modules stacked together,
- A feed-forward module, a self-attention module, a convolution module,
- and a second feed-forward module in the end. The block starts with two Feed forward
- modules sandwiching the Multi-Headed Self-Attention module and the Conv module.
-
- Args:
- d_model (int): The dimension of model
- n_head (int): The number of attention heads.
- kernel_size_conv_mod (int): Size of kernels for convolution modules.
- speaker_embedding_dim (int): Number of speaker embedding dimensions.
- emotion_embedding_dim (int): Number of emotion embedding dimensions.
- dropout (float): Probabilty of dropout.
-
- Inputs: inputs, mask
- - **inputs** (batch, time, dim): Tensor containing input vector
- - **encoding** (batch, time, dim): Positional embedding tensor
- - **slf_attn_mask** (batch, 1, 1, time1): Tensor containing indices to be masked in self attention module
- - **mask** (batch, 1, time2) or (batch, time1, time2): Tensor containing indices to be masked
- Returns:
- - **outputs** (batch, time, dim): Tensor produced by the Conformer Block.
- """
- super().__init__()
- if isinstance(speaker_embedding_dim, int):
- self.conditioning = Conv1dGLU(
- d_model=d_model,
- kernel_size=kernel_size_conv_mod,
- padding=kernel_size_conv_mod // 2,
- embedding_dim=speaker_embedding_dim,
- )
-
- self.ff = FeedForward(d_model=d_model, dropout=dropout, kernel_size=3, lrelu_slope=lrelu_slope)
- self.conformer_conv_1 = ConformerConvModule(
- d_model, kernel_size=kernel_size_conv_mod, dropout=dropout, lrelu_slope=lrelu_slope
- )
- self.ln = nn.LayerNorm(d_model)
- self.slf_attn = ConformerMultiHeadedSelfAttention(d_model=d_model, num_heads=n_head, dropout_p=dropout)
- self.conformer_conv_2 = ConformerConvModule(
- d_model, kernel_size=kernel_size_conv_mod, dropout=dropout, lrelu_slope=lrelu_slope
- )
-
- def forward(
- self,
- x: torch.Tensor,
- speaker_embedding: torch.Tensor,
- mask: torch.Tensor,
- slf_attn_mask: torch.Tensor,
- encoding: torch.Tensor,
- ) -> torch.Tensor:
- """
- Shapes:
- - x: :math:`[B, T_src, C]`
- - mask: :math: `[B]`
- - slf_attn_mask: :math: `[B, 1, 1, T_src]`
- - speaker_embedding: :math: `[B, C]`
- - emotion_embedding: :math: `[B, C]`
- - encoding: :math: `[B, T_max2, C]`
- """
- if speaker_embedding is not None:
- x = self.conditioning(x, embeddings=speaker_embedding)
- x = self.ff(x) + x
- x = self.conformer_conv_1(x) + x
- res = x
- x = self.ln(x)
- x, _ = self.slf_attn(query=x, key=x, value=x, mask=slf_attn_mask, encoding=encoding)
- x = x + res
- x = x.masked_fill(mask.unsqueeze(-1), 0)
-
- x = self.conformer_conv_2(x) + x
- return x
-
-
-class FeedForward(nn.Module):
- def __init__(
- self,
- d_model: int,
- kernel_size: int,
- dropout: float,
- lrelu_slope: float,
- expansion_factor: int = 4,
- ):
- """
- Feed Forward module for conformer block.
-
- Args:
- d_model (int): The dimension of model.
- kernel_size (int): Size of the kernels for conv layers.
- dropout (float): probability of dropout.
- expansion_factor (int): The factor by which to project the number of channels.
- lrelu_slope (int): the negative slope factor for the leaky relu activation.
-
- Inputs: inputs
- - **inputs** (batch, time, dim): Tensor containing input vector
- Returns:
- - **outputs** (batch, time, dim): Tensor produced by the feed forward module.
- """
- super().__init__()
- self.dropout = nn.Dropout(dropout)
- self.ln = nn.LayerNorm(d_model)
- self.conv_1 = nn.Conv1d(
- d_model,
- d_model * expansion_factor,
- kernel_size=kernel_size,
- padding=kernel_size // 2,
- )
- self.act = nn.LeakyReLU(lrelu_slope)
- self.conv_2 = nn.Conv1d(d_model * expansion_factor, d_model, kernel_size=1)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- """
- Shapes:
- x: :math: `[B, T, C]`
- """
- x = self.ln(x)
- x = x.permute((0, 2, 1))
- x = self.conv_1(x)
- x = x.permute((0, 2, 1))
- x = self.act(x)
- x = self.dropout(x)
- x = x.permute((0, 2, 1))
- x = self.conv_2(x)
- x = x.permute((0, 2, 1))
- x = self.dropout(x)
- x = 0.5 * x
- return x
-
-
-class ConformerConvModule(nn.Module):
- def __init__(
- self,
- d_model: int,
- expansion_factor: int = 2,
- kernel_size: int = 7,
- dropout: float = 0.1,
- lrelu_slope: float = 0.3,
- ):
- """
- Convolution module for conformer. Starts with a gating machanism.
- a pointwise convolution and a gated linear unit (GLU). This is followed
- by a single 1-D depthwise convolution layer. Batchnorm is deployed just after the convolution
- to help with training. it also contains an expansion factor to project the number of channels.
-
- Args:
- d_model (int): The dimension of model.
- expansion_factor (int): The factor by which to project the number of channels.
- kernel_size (int): Size of kernels for convolution modules.
- dropout (float): Probabilty of dropout.
- lrelu_slope (float): The slope coefficient for leaky relu activation.
-
- Inputs: inputs
- - **inputs** (batch, time, dim): Tensor containing input vector
- Returns:
- - **outputs** (batch, time, dim): Tensor produced by the conv module.
-
- """
- super().__init__()
- inner_dim = d_model * expansion_factor
- self.ln_1 = nn.LayerNorm(d_model)
- self.conv_1 = PointwiseConv1d(d_model, inner_dim * 2)
- self.conv_act = GLUActivation(slope=lrelu_slope)
- self.depthwise = DepthWiseConv1d(
- inner_dim,
- inner_dim,
- kernel_size=kernel_size,
- padding=calc_same_padding(kernel_size)[0],
- )
- self.ln_2 = nn.GroupNorm(1, inner_dim)
- self.activation = nn.LeakyReLU(lrelu_slope)
- self.conv_2 = PointwiseConv1d(inner_dim, d_model)
- self.dropout = nn.Dropout(dropout)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- """
- Shapes:
- x: :math: `[B, T, C]`
- """
- x = self.ln_1(x)
- x = x.permute(0, 2, 1)
- x = self.conv_1(x)
- x = self.conv_act(x)
- x = self.depthwise(x)
- x = self.ln_2(x)
- x = self.activation(x)
- x = self.conv_2(x)
- x = x.permute(0, 2, 1)
- x = self.dropout(x)
- return x
-
-
-class ConformerMultiHeadedSelfAttention(nn.Module):
- """
- Conformer employ multi-headed self-attention (MHSA) while integrating an important technique from Transformer-XL,
- the relative sinusoidal positional encoding scheme. The relative positional encoding allows the self-attention
- module to generalize better on different input length and the resulting encoder is more robust to the variance of
- the utterance length. Conformer use prenorm residual units with dropout which helps training
- and regularizing deeper models.
- Args:
- d_model (int): The dimension of model
- num_heads (int): The number of attention heads.
- dropout_p (float): probability of dropout
- Inputs: inputs, mask
- - **inputs** (batch, time, dim): Tensor containing input vector
- - **mask** (batch, 1, time2) or (batch, time1, time2): Tensor containing indices to be masked
- Returns:
- - **outputs** (batch, time, dim): Tensor produces by relative multi headed self attention module.
- """
-
- def __init__(self, d_model: int, num_heads: int, dropout_p: float):
- super().__init__()
- self.attention = RelativeMultiHeadAttention(d_model=d_model, num_heads=num_heads)
- self.dropout = nn.Dropout(p=dropout_p)
-
- def forward(
- self,
- query: torch.Tensor,
- key: torch.Tensor,
- value: torch.Tensor,
- mask: torch.Tensor,
- encoding: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- batch_size, seq_length, _ = key.size() # pylint: disable=unused-variable
- encoding = encoding[:, : key.shape[1]]
- encoding = encoding.repeat(batch_size, 1, 1)
- outputs, attn = self.attention(query, key, value, pos_embedding=encoding, mask=mask)
- outputs = self.dropout(outputs)
- return outputs, attn
-
-
-class RelativeMultiHeadAttention(nn.Module):
- """
- Multi-head attention with relative positional encoding.
- This concept was proposed in the "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"
- Args:
- d_model (int): The dimension of model
- num_heads (int): The number of attention heads.
- Inputs: query, key, value, pos_embedding, mask
- - **query** (batch, time, dim): Tensor containing query vector
- - **key** (batch, time, dim): Tensor containing key vector
- - **value** (batch, time, dim): Tensor containing value vector
- - **pos_embedding** (batch, time, dim): Positional embedding tensor
- - **mask** (batch, 1, time2) or (batch, time1, time2): Tensor containing indices to be masked
- Returns:
- - **outputs**: Tensor produces by relative multi head attention module.
- """
-
- def __init__(
- self,
- d_model: int = 512,
- num_heads: int = 16,
- ):
- super().__init__()
- assert d_model % num_heads == 0, "d_model % num_heads should be zero."
- self.d_model = d_model
- self.d_head = int(d_model / num_heads)
- self.num_heads = num_heads
- self.sqrt_dim = math.sqrt(d_model)
-
- self.query_proj = nn.Linear(d_model, d_model)
- self.key_proj = nn.Linear(d_model, d_model, bias=False)
- self.value_proj = nn.Linear(d_model, d_model, bias=False)
- self.pos_proj = nn.Linear(d_model, d_model, bias=False)
-
- self.u_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head))
- self.v_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head))
- torch.nn.init.xavier_uniform_(self.u_bias)
- torch.nn.init.xavier_uniform_(self.v_bias)
- self.out_proj = nn.Linear(d_model, d_model)
-
- def forward(
- self,
- query: torch.Tensor,
- key: torch.Tensor,
- value: torch.Tensor,
- pos_embedding: torch.Tensor,
- mask: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- batch_size = query.shape[0]
- query = self.query_proj(query).view(batch_size, -1, self.num_heads, self.d_head)
- key = self.key_proj(key).view(batch_size, -1, self.num_heads, self.d_head).permute(0, 2, 1, 3)
- value = self.value_proj(value).view(batch_size, -1, self.num_heads, self.d_head).permute(0, 2, 1, 3)
- pos_embedding = self.pos_proj(pos_embedding).view(batch_size, -1, self.num_heads, self.d_head)
- u_bias = self.u_bias.expand_as(query)
- v_bias = self.v_bias.expand_as(query)
- a = (query + u_bias).transpose(1, 2)
- content_score = a @ key.transpose(2, 3)
- b = (query + v_bias).transpose(1, 2)
- pos_score = b @ pos_embedding.permute(0, 2, 3, 1)
- pos_score = self._relative_shift(pos_score)
-
- score = content_score + pos_score
- score = score * (1.0 / self.sqrt_dim)
-
- score.masked_fill_(mask, -1e9)
-
- attn = F.softmax(score, -1)
-
- context = (attn @ value).transpose(1, 2)
- context = context.contiguous().view(batch_size, -1, self.d_model)
-
- return self.out_proj(context), attn
-
- def _relative_shift(self, pos_score: torch.Tensor) -> torch.Tensor: # pylint: disable=no-self-use
- batch_size, num_heads, seq_length1, seq_length2 = pos_score.size()
- zeros = torch.zeros((batch_size, num_heads, seq_length1, 1), device=pos_score.device)
- padded_pos_score = torch.cat([zeros, pos_score], dim=-1)
- padded_pos_score = padded_pos_score.view(batch_size, num_heads, seq_length2 + 1, seq_length1)
- pos_score = padded_pos_score[:, :, 1:].view_as(pos_score)
- return pos_score
-
-
-class MultiHeadAttention(nn.Module):
- """
- input:
- query --- [N, T_q, query_dim]
- key --- [N, T_k, key_dim]
- output:
- out --- [N, T_q, num_units]
- """
-
- def __init__(self, query_dim: int, key_dim: int, num_units: int, num_heads: int):
- super().__init__()
- self.num_units = num_units
- self.num_heads = num_heads
- self.key_dim = key_dim
-
- self.W_query = nn.Linear(in_features=query_dim, out_features=num_units, bias=False)
- self.W_key = nn.Linear(in_features=key_dim, out_features=num_units, bias=False)
- self.W_value = nn.Linear(in_features=key_dim, out_features=num_units, bias=False)
-
- def forward(self, query: torch.Tensor, key: torch.Tensor) -> torch.Tensor:
- querys = self.W_query(query) # [N, T_q, num_units]
- keys = self.W_key(key) # [N, T_k, num_units]
- values = self.W_value(key)
- split_size = self.num_units // self.num_heads
- querys = torch.stack(torch.split(querys, split_size, dim=2), dim=0) # [h, N, T_q, num_units/h]
- keys = torch.stack(torch.split(keys, split_size, dim=2), dim=0) # [h, N, T_k, num_units/h]
- values = torch.stack(torch.split(values, split_size, dim=2), dim=0) # [h, N, T_k, num_units/h]
- # score = softmax(QK^T / (d_k ** 0.5))
- scores = torch.matmul(querys, keys.transpose(2, 3)) # [h, N, T_q, T_k]
- scores = scores / (self.key_dim**0.5)
- scores = F.softmax(scores, dim=3)
- # out = score * V
- out = torch.matmul(scores, values) # [h, N, T_q, num_units/h]
- out = torch.cat(torch.split(out, 1, dim=0), dim=3).squeeze(0) # [N, T_q, num_units]
- return out
diff --git a/TTS/tts/layers/delightful_tts/conv_layers.py b/TTS/tts/layers/delightful_tts/conv_layers.py
deleted file mode 100644
index 354a0336a1f031edc839c103cc01b45fb7642025..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/conv_layers.py
+++ /dev/null
@@ -1,670 +0,0 @@
-from typing import Tuple
-
-import torch
-import torch.nn as nn # pylint: disable=consider-using-from-import
-import torch.nn.functional as F
-
-from TTS.tts.layers.delightful_tts.kernel_predictor import KernelPredictor
-
-
-def calc_same_padding(kernel_size: int) -> Tuple[int, int]:
- pad = kernel_size // 2
- return (pad, pad - (kernel_size + 1) % 2)
-
-
-class ConvNorm(nn.Module):
- """A 1-dimensional convolutional layer with optional weight normalization.
-
- This layer wraps a 1D convolutional layer from PyTorch and applies
- optional weight normalization. The layer can be used in a similar way to
- the convolutional layers in PyTorch's `torch.nn` module.
-
- Args:
- in_channels (int): The number of channels in the input signal.
- out_channels (int): The number of channels in the output signal.
- kernel_size (int, optional): The size of the convolving kernel.
- Defaults to 1.
- stride (int, optional): The stride of the convolution. Defaults to 1.
- padding (int, optional): Zero-padding added to both sides of the input.
- If `None`, the padding will be calculated so that the output has
- the same length as the input. Defaults to `None`.
- dilation (int, optional): Spacing between kernel elements. Defaults to 1.
- bias (bool, optional): If `True`, add bias after convolution. Defaults to `True`.
- w_init_gain (str, optional): The weight initialization function to use.
- Can be either 'linear' or 'relu'. Defaults to 'linear'.
- use_weight_norm (bool, optional): If `True`, apply weight normalization
- to the convolutional weights. Defaults to `False`.
-
- Shapes:
- - Input: :math:`[N, D, T]`
-
- - Output: :math:`[N, out_dim, T]` where `out_dim` is the number of output dimensions.
-
- """
-
- def __init__(
- self,
- in_channels,
- out_channels,
- kernel_size=1,
- stride=1,
- padding=None,
- dilation=1,
- bias=True,
- w_init_gain="linear",
- use_weight_norm=False,
- ):
- super(ConvNorm, self).__init__() # pylint: disable=super-with-arguments
- if padding is None:
- assert kernel_size % 2 == 1
- padding = int(dilation * (kernel_size - 1) / 2)
- self.kernel_size = kernel_size
- self.dilation = dilation
- self.use_weight_norm = use_weight_norm
- conv_fn = nn.Conv1d
- self.conv = conv_fn(
- in_channels,
- out_channels,
- kernel_size=kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- bias=bias,
- )
- nn.init.xavier_uniform_(self.conv.weight, gain=nn.init.calculate_gain(w_init_gain))
- if self.use_weight_norm:
- self.conv = nn.utils.weight_norm(self.conv)
-
- def forward(self, signal, mask=None):
- conv_signal = self.conv(signal)
- if mask is not None:
- # always re-zero output if mask is
- # available to match zero-padding
- conv_signal = conv_signal * mask
- return conv_signal
-
-
-class ConvLSTMLinear(nn.Module):
- def __init__(
- self,
- in_dim,
- out_dim,
- n_layers=2,
- n_channels=256,
- kernel_size=3,
- p_dropout=0.1,
- lstm_type="bilstm",
- use_linear=True,
- ):
- super(ConvLSTMLinear, self).__init__() # pylint: disable=super-with-arguments
- self.out_dim = out_dim
- self.lstm_type = lstm_type
- self.use_linear = use_linear
- self.dropout = nn.Dropout(p=p_dropout)
-
- convolutions = []
- for i in range(n_layers):
- conv_layer = ConvNorm(
- in_dim if i == 0 else n_channels,
- n_channels,
- kernel_size=kernel_size,
- stride=1,
- padding=int((kernel_size - 1) / 2),
- dilation=1,
- w_init_gain="relu",
- )
- conv_layer = nn.utils.weight_norm(conv_layer.conv, name="weight")
- convolutions.append(conv_layer)
-
- self.convolutions = nn.ModuleList(convolutions)
-
- if not self.use_linear:
- n_channels = out_dim
-
- if self.lstm_type != "":
- use_bilstm = False
- lstm_channels = n_channels
- if self.lstm_type == "bilstm":
- use_bilstm = True
- lstm_channels = int(n_channels // 2)
-
- self.bilstm = nn.LSTM(n_channels, lstm_channels, 1, batch_first=True, bidirectional=use_bilstm)
- lstm_norm_fn_pntr = nn.utils.spectral_norm
- self.bilstm = lstm_norm_fn_pntr(self.bilstm, "weight_hh_l0")
- if self.lstm_type == "bilstm":
- self.bilstm = lstm_norm_fn_pntr(self.bilstm, "weight_hh_l0_reverse")
-
- if self.use_linear:
- self.dense = nn.Linear(n_channels, out_dim)
-
- def run_padded_sequence(self, context, lens):
- context_embedded = []
- for b_ind in range(context.size()[0]): # TODO: speed up
- curr_context = context[b_ind : b_ind + 1, :, : lens[b_ind]].clone()
- for conv in self.convolutions:
- curr_context = self.dropout(F.relu(conv(curr_context)))
- context_embedded.append(curr_context[0].transpose(0, 1))
- context = nn.utils.rnn.pad_sequence(context_embedded, batch_first=True)
- return context
-
- def run_unsorted_inputs(self, fn, context, lens): # pylint: disable=no-self-use
- lens_sorted, ids_sorted = torch.sort(lens, descending=True)
- unsort_ids = [0] * lens.size(0)
- for i in range(len(ids_sorted)): # pylint: disable=consider-using-enumerate
- unsort_ids[ids_sorted[i]] = i
- lens_sorted = lens_sorted.long().cpu()
-
- context = context[ids_sorted]
- context = nn.utils.rnn.pack_padded_sequence(context, lens_sorted, batch_first=True)
- context = fn(context)[0]
- context = nn.utils.rnn.pad_packed_sequence(context, batch_first=True)[0]
-
- # map back to original indices
- context = context[unsort_ids]
- return context
-
- def forward(self, context, lens):
- if context.size()[0] > 1:
- context = self.run_padded_sequence(context, lens)
- # to B, D, T
- context = context.transpose(1, 2)
- else:
- for conv in self.convolutions:
- context = self.dropout(F.relu(conv(context)))
-
- if self.lstm_type != "":
- context = context.transpose(1, 2)
- self.bilstm.flatten_parameters()
- if lens is not None:
- context = self.run_unsorted_inputs(self.bilstm, context, lens)
- else:
- context = self.bilstm(context)[0]
- context = context.transpose(1, 2)
-
- x_hat = context
- if self.use_linear:
- x_hat = self.dense(context.transpose(1, 2)).transpose(1, 2)
-
- return x_hat
-
-
-class DepthWiseConv1d(nn.Module):
- def __init__(self, in_channels: int, out_channels: int, kernel_size: int, padding: int):
- super().__init__()
- self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, padding=padding, groups=in_channels)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return self.conv(x)
-
-
-class PointwiseConv1d(nn.Module):
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- stride: int = 1,
- padding: int = 0,
- bias: bool = True,
- ):
- super().__init__()
- self.conv = nn.Conv1d(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=1,
- stride=stride,
- padding=padding,
- bias=bias,
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return self.conv(x)
-
-
-class BSConv1d(nn.Module):
- """https://arxiv.org/pdf/2003.13549.pdf"""
-
- def __init__(self, channels_in: int, channels_out: int, kernel_size: int, padding: int):
- super().__init__()
- self.pointwise = nn.Conv1d(channels_in, channels_out, kernel_size=1)
- self.depthwise = nn.Conv1d(
- channels_out,
- channels_out,
- kernel_size=kernel_size,
- padding=padding,
- groups=channels_out,
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x1 = self.pointwise(x)
- x2 = self.depthwise(x1)
- return x2
-
-
-class BSConv2d(nn.Module):
- """https://arxiv.org/pdf/2003.13549.pdf"""
-
- def __init__(self, channels_in: int, channels_out: int, kernel_size: int, padding: int):
- super().__init__()
- self.pointwise = nn.Conv2d(channels_in, channels_out, kernel_size=1)
- self.depthwise = nn.Conv2d(
- channels_out,
- channels_out,
- kernel_size=kernel_size,
- padding=padding,
- groups=channels_out,
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x1 = self.pointwise(x)
- x2 = self.depthwise(x1)
- return x2
-
-
-class Conv1dGLU(nn.Module):
- """From DeepVoice 3"""
-
- def __init__(self, d_model: int, kernel_size: int, padding: int, embedding_dim: int):
- super().__init__()
- self.conv = BSConv1d(d_model, 2 * d_model, kernel_size=kernel_size, padding=padding)
- self.embedding_proj = nn.Linear(embedding_dim, d_model)
- self.register_buffer("sqrt", torch.sqrt(torch.FloatTensor([0.5])).squeeze(0))
- self.softsign = torch.nn.Softsign()
-
- def forward(self, x: torch.Tensor, embeddings: torch.Tensor) -> torch.Tensor:
- x = x.permute((0, 2, 1))
- residual = x
- x = self.conv(x)
- splitdim = 1
- a, b = x.split(x.size(splitdim) // 2, dim=splitdim)
- embeddings = self.embedding_proj(embeddings).unsqueeze(2)
- softsign = self.softsign(embeddings)
- softsign = softsign.expand_as(a)
- a = a + softsign
- x = a * torch.sigmoid(b)
- x = x + residual
- x = x * self.sqrt
- x = x.permute((0, 2, 1))
- return x
-
-
-class ConvTransposed(nn.Module):
- """
- A 1D convolutional transposed layer for PyTorch.
- This layer applies a 1D convolutional transpose operation to its input tensor,
- where the number of channels of the input tensor is the same as the number of channels of the output tensor.
-
- Attributes:
- in_channels (int): The number of channels in the input tensor.
- out_channels (int): The number of channels in the output tensor.
- kernel_size (int): The size of the convolutional kernel. Default: 1.
- padding (int): The number of padding elements to add to the input tensor. Default: 0.
- conv (BSConv1d): The 1D convolutional transpose layer.
- """
-
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int = 1,
- padding: int = 0,
- ):
- super().__init__()
- self.conv = BSConv1d(
- in_channels,
- out_channels,
- kernel_size=kernel_size,
- padding=padding,
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = x.contiguous().transpose(1, 2)
- x = self.conv(x)
- x = x.contiguous().transpose(1, 2)
- return x
-
-
-class DepthwiseConvModule(nn.Module):
- def __init__(self, dim: int, kernel_size: int = 7, expansion: int = 4, lrelu_slope: float = 0.3):
- super().__init__()
- padding = calc_same_padding(kernel_size)
- self.depthwise = nn.Conv1d(
- dim,
- dim * expansion,
- kernel_size=kernel_size,
- padding=padding[0],
- groups=dim,
- )
- self.act = nn.LeakyReLU(lrelu_slope)
- self.out = nn.Conv1d(dim * expansion, dim, 1, 1, 0)
- self.ln = nn.LayerNorm(dim)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.ln(x)
- x = x.permute((0, 2, 1))
- x = self.depthwise(x)
- x = self.act(x)
- x = self.out(x)
- x = x.permute((0, 2, 1))
- return x
-
-
-class AddCoords(nn.Module):
- def __init__(self, rank: int, with_r: bool = False):
- super().__init__()
- self.rank = rank
- self.with_r = with_r
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- if self.rank == 1:
- batch_size_shape, channel_in_shape, dim_x = x.shape # pylint: disable=unused-variable
- xx_range = torch.arange(dim_x, dtype=torch.int32)
- xx_channel = xx_range[None, None, :]
-
- xx_channel = xx_channel.float() / (dim_x - 1)
- xx_channel = xx_channel * 2 - 1
- xx_channel = xx_channel.repeat(batch_size_shape, 1, 1)
-
- xx_channel = xx_channel.to(x.device)
- out = torch.cat([x, xx_channel], dim=1)
-
- if self.with_r:
- rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2))
- out = torch.cat([out, rr], dim=1)
-
- elif self.rank == 2:
- batch_size_shape, channel_in_shape, dim_y, dim_x = x.shape
- xx_ones = torch.ones([1, 1, 1, dim_x], dtype=torch.int32)
- yy_ones = torch.ones([1, 1, 1, dim_y], dtype=torch.int32)
-
- xx_range = torch.arange(dim_y, dtype=torch.int32)
- yy_range = torch.arange(dim_x, dtype=torch.int32)
- xx_range = xx_range[None, None, :, None]
- yy_range = yy_range[None, None, :, None]
-
- xx_channel = torch.matmul(xx_range, xx_ones)
- yy_channel = torch.matmul(yy_range, yy_ones)
-
- # transpose y
- yy_channel = yy_channel.permute(0, 1, 3, 2)
-
- xx_channel = xx_channel.float() / (dim_y - 1)
- yy_channel = yy_channel.float() / (dim_x - 1)
-
- xx_channel = xx_channel * 2 - 1
- yy_channel = yy_channel * 2 - 1
-
- xx_channel = xx_channel.repeat(batch_size_shape, 1, 1, 1)
- yy_channel = yy_channel.repeat(batch_size_shape, 1, 1, 1)
-
- xx_channel = xx_channel.to(x.device)
- yy_channel = yy_channel.to(x.device)
-
- out = torch.cat([x, xx_channel, yy_channel], dim=1)
-
- if self.with_r:
- rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2) + torch.pow(yy_channel - 0.5, 2))
- out = torch.cat([out, rr], dim=1)
-
- elif self.rank == 3:
- batch_size_shape, channel_in_shape, dim_z, dim_y, dim_x = x.shape
- xx_ones = torch.ones([1, 1, 1, 1, dim_x], dtype=torch.int32)
- yy_ones = torch.ones([1, 1, 1, 1, dim_y], dtype=torch.int32)
- zz_ones = torch.ones([1, 1, 1, 1, dim_z], dtype=torch.int32)
-
- xy_range = torch.arange(dim_y, dtype=torch.int32)
- xy_range = xy_range[None, None, None, :, None]
-
- yz_range = torch.arange(dim_z, dtype=torch.int32)
- yz_range = yz_range[None, None, None, :, None]
-
- zx_range = torch.arange(dim_x, dtype=torch.int32)
- zx_range = zx_range[None, None, None, :, None]
-
- xy_channel = torch.matmul(xy_range, xx_ones)
- xx_channel = torch.cat([xy_channel + i for i in range(dim_z)], dim=2)
-
- yz_channel = torch.matmul(yz_range, yy_ones)
- yz_channel = yz_channel.permute(0, 1, 3, 4, 2)
- yy_channel = torch.cat([yz_channel + i for i in range(dim_x)], dim=4)
-
- zx_channel = torch.matmul(zx_range, zz_ones)
- zx_channel = zx_channel.permute(0, 1, 4, 2, 3)
- zz_channel = torch.cat([zx_channel + i for i in range(dim_y)], dim=3)
-
- xx_channel = xx_channel.to(x.device)
- yy_channel = yy_channel.to(x.device)
- zz_channel = zz_channel.to(x.device)
- out = torch.cat([x, xx_channel, yy_channel, zz_channel], dim=1)
-
- if self.with_r:
- rr = torch.sqrt(
- torch.pow(xx_channel - 0.5, 2) + torch.pow(yy_channel - 0.5, 2) + torch.pow(zz_channel - 0.5, 2)
- )
- out = torch.cat([out, rr], dim=1)
- else:
- raise NotImplementedError
-
- return out
-
-
-class CoordConv1d(nn.modules.conv.Conv1d):
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int,
- stride: int = 1,
- padding: int = 0,
- dilation: int = 1,
- groups: int = 1,
- bias: bool = True,
- with_r: bool = False,
- ):
- super().__init__(
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- dilation,
- groups,
- bias,
- )
- self.rank = 1
- self.addcoords = AddCoords(self.rank, with_r)
- self.conv = nn.Conv1d(
- in_channels + self.rank + int(with_r),
- out_channels,
- kernel_size,
- stride,
- padding,
- dilation,
- groups,
- bias,
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.addcoords(x)
- x = self.conv(x)
- return x
-
-
-class CoordConv2d(nn.modules.conv.Conv2d):
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- kernel_size: int,
- stride: int = 1,
- padding: int = 0,
- dilation: int = 1,
- groups: int = 1,
- bias: bool = True,
- with_r: bool = False,
- ):
- super().__init__(
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- dilation,
- groups,
- bias,
- )
- self.rank = 2
- self.addcoords = AddCoords(self.rank, with_r)
- self.conv = nn.Conv2d(
- in_channels + self.rank + int(with_r),
- out_channels,
- kernel_size,
- stride,
- padding,
- dilation,
- groups,
- bias,
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.addcoords(x)
- x = self.conv(x)
- return x
-
-
-class LVCBlock(torch.nn.Module):
- """the location-variable convolutions"""
-
- def __init__( # pylint: disable=dangerous-default-value
- self,
- in_channels,
- cond_channels,
- stride,
- dilations=[1, 3, 9, 27],
- lReLU_slope=0.2,
- conv_kernel_size=3,
- cond_hop_length=256,
- kpnet_hidden_channels=64,
- kpnet_conv_size=3,
- kpnet_dropout=0.0,
- ):
- super().__init__()
-
- self.cond_hop_length = cond_hop_length
- self.conv_layers = len(dilations)
- self.conv_kernel_size = conv_kernel_size
-
- self.kernel_predictor = KernelPredictor(
- cond_channels=cond_channels,
- conv_in_channels=in_channels,
- conv_out_channels=2 * in_channels,
- conv_layers=len(dilations),
- conv_kernel_size=conv_kernel_size,
- kpnet_hidden_channels=kpnet_hidden_channels,
- kpnet_conv_size=kpnet_conv_size,
- kpnet_dropout=kpnet_dropout,
- kpnet_nonlinear_activation_params={"negative_slope": lReLU_slope},
- )
-
- self.convt_pre = nn.Sequential(
- nn.LeakyReLU(lReLU_slope),
- nn.utils.weight_norm(
- nn.ConvTranspose1d(
- in_channels,
- in_channels,
- 2 * stride,
- stride=stride,
- padding=stride // 2 + stride % 2,
- output_padding=stride % 2,
- )
- ),
- )
-
- self.conv_blocks = nn.ModuleList()
- for dilation in dilations:
- self.conv_blocks.append(
- nn.Sequential(
- nn.LeakyReLU(lReLU_slope),
- nn.utils.weight_norm(
- nn.Conv1d(
- in_channels,
- in_channels,
- conv_kernel_size,
- padding=dilation * (conv_kernel_size - 1) // 2,
- dilation=dilation,
- )
- ),
- nn.LeakyReLU(lReLU_slope),
- )
- )
-
- def forward(self, x, c):
- """forward propagation of the location-variable convolutions.
- Args:
- x (Tensor): the input sequence (batch, in_channels, in_length)
- c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
-
- Returns:
- Tensor: the output sequence (batch, in_channels, in_length)
- """
- _, in_channels, _ = x.shape # (B, c_g, L')
-
- x = self.convt_pre(x) # (B, c_g, stride * L')
- kernels, bias = self.kernel_predictor(c)
-
- for i, conv in enumerate(self.conv_blocks):
- output = conv(x) # (B, c_g, stride * L')
-
- k = kernels[:, i, :, :, :, :] # (B, 2 * c_g, c_g, kernel_size, cond_length)
- b = bias[:, i, :, :] # (B, 2 * c_g, cond_length)
-
- output = self.location_variable_convolution(
- output, k, b, hop_size=self.cond_hop_length
- ) # (B, 2 * c_g, stride * L'): LVC
- x = x + torch.sigmoid(output[:, :in_channels, :]) * torch.tanh(
- output[:, in_channels:, :]
- ) # (B, c_g, stride * L'): GAU
-
- return x
-
- def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=256): # pylint: disable=no-self-use
- """perform location-variable convolution operation on the input sequence (x) using the local convolution kernl.
- Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100.
- Args:
- x (Tensor): the input sequence (batch, in_channels, in_length).
- kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length)
- bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length)
- dilation (int): the dilation of convolution.
- hop_size (int): the hop_size of the conditioning sequence.
- Returns:
- (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length).
- """
- batch, _, in_length = x.shape
- batch, _, out_channels, kernel_size, kernel_length = kernel.shape
- assert in_length == (kernel_length * hop_size), "length of (x, kernel) is not matched"
-
- padding = dilation * int((kernel_size - 1) / 2)
- x = F.pad(x, (padding, padding), "constant", 0) # (batch, in_channels, in_length + 2*padding)
- x = x.unfold(2, hop_size + 2 * padding, hop_size) # (batch, in_channels, kernel_length, hop_size + 2*padding)
-
- if hop_size < dilation:
- x = F.pad(x, (0, dilation), "constant", 0)
- x = x.unfold(
- 3, dilation, dilation
- ) # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation)
- x = x[:, :, :, :, :hop_size]
- x = x.transpose(3, 4) # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation)
- x = x.unfold(4, kernel_size, 1) # (batch, in_channels, kernel_length, dilation, _, kernel_size)
-
- o = torch.einsum("bildsk,biokl->bolsd", x, kernel)
- o = o.to(memory_format=torch.channels_last_3d)
- bias = bias.unsqueeze(-1).unsqueeze(-1).to(memory_format=torch.channels_last_3d)
- o = o + bias
- o = o.contiguous().view(batch, out_channels, -1)
-
- return o
-
- def remove_weight_norm(self):
- self.kernel_predictor.remove_weight_norm()
- nn.utils.remove_weight_norm(self.convt_pre[1])
- for block in self.conv_blocks:
- nn.utils.remove_weight_norm(block[1])
diff --git a/TTS/tts/layers/delightful_tts/encoders.py b/TTS/tts/layers/delightful_tts/encoders.py
deleted file mode 100644
index 0878f0677a29d092597a46e8a3b11e4a521769b8..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/encoders.py
+++ /dev/null
@@ -1,261 +0,0 @@
-from typing import List, Tuple, Union
-
-import torch
-import torch.nn as nn # pylint: disable=consider-using-from-import
-import torch.nn.functional as F
-
-from TTS.tts.layers.delightful_tts.conformer import ConformerMultiHeadedSelfAttention
-from TTS.tts.layers.delightful_tts.conv_layers import CoordConv1d
-from TTS.tts.layers.delightful_tts.networks import STL
-
-
-def get_mask_from_lengths(lengths: torch.Tensor) -> torch.Tensor:
- batch_size = lengths.shape[0]
- max_len = torch.max(lengths).item()
- ids = torch.arange(0, max_len, device=lengths.device).unsqueeze(0).expand(batch_size, -1)
- mask = ids >= lengths.unsqueeze(1).expand(-1, max_len)
- return mask
-
-
-def stride_lens(lens: torch.Tensor, stride: int = 2) -> torch.Tensor:
- return torch.ceil(lens / stride).int()
-
-
-class ReferenceEncoder(nn.Module):
- """
- Referance encoder for utterance and phoneme prosody encoders. Reference encoder
- made up of convolution and RNN layers.
-
- Args:
- num_mels (int): Number of mel frames to produce.
- ref_enc_filters (list[int]): List of channel sizes for encoder layers.
- ref_enc_size (int): Size of the kernel for the conv layers.
- ref_enc_strides (List[int]): List of strides to use for conv layers.
- ref_enc_gru_size (int): Number of hidden features for the gated recurrent unit.
-
- Inputs: inputs, mask
- - **inputs** (batch, dim, time): Tensor containing mel vector
- - **lengths** (batch): Tensor containing the mel lengths.
- Returns:
- - **outputs** (batch, time, dim): Tensor produced by Reference Encoder.
- """
-
- def __init__(
- self,
- num_mels: int,
- ref_enc_filters: List[Union[int, int, int, int, int, int]],
- ref_enc_size: int,
- ref_enc_strides: List[Union[int, int, int, int, int]],
- ref_enc_gru_size: int,
- ):
- super().__init__()
-
- n_mel_channels = num_mels
- self.n_mel_channels = n_mel_channels
- K = len(ref_enc_filters)
- filters = [self.n_mel_channels] + ref_enc_filters
- strides = [1] + ref_enc_strides
- # Use CoordConv at the first layer to better preserve positional information: https://arxiv.org/pdf/1811.02122.pdf
- convs = [
- CoordConv1d(
- in_channels=filters[0],
- out_channels=filters[0 + 1],
- kernel_size=ref_enc_size,
- stride=strides[0],
- padding=ref_enc_size // 2,
- with_r=True,
- )
- ]
- convs2 = [
- nn.Conv1d(
- in_channels=filters[i],
- out_channels=filters[i + 1],
- kernel_size=ref_enc_size,
- stride=strides[i],
- padding=ref_enc_size // 2,
- )
- for i in range(1, K)
- ]
- convs.extend(convs2)
- self.convs = nn.ModuleList(convs)
-
- self.norms = nn.ModuleList([nn.InstanceNorm1d(num_features=ref_enc_filters[i], affine=True) for i in range(K)])
-
- self.gru = nn.GRU(
- input_size=ref_enc_filters[-1],
- hidden_size=ref_enc_gru_size,
- batch_first=True,
- )
-
- def forward(self, x: torch.Tensor, mel_lens: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """
- inputs --- [N, n_mels, timesteps]
- outputs --- [N, E//2]
- """
-
- mel_masks = get_mask_from_lengths(mel_lens).unsqueeze(1)
- x = x.masked_fill(mel_masks, 0)
- for conv, norm in zip(self.convs, self.norms):
- x = conv(x)
- x = F.leaky_relu(x, 0.3) # [N, 128, Ty//2^K, n_mels//2^K]
- x = norm(x)
-
- for _ in range(2):
- mel_lens = stride_lens(mel_lens)
-
- mel_masks = get_mask_from_lengths(mel_lens)
-
- x = x.masked_fill(mel_masks.unsqueeze(1), 0)
- x = x.permute((0, 2, 1))
- x = torch.nn.utils.rnn.pack_padded_sequence(x, mel_lens.cpu().int(), batch_first=True, enforce_sorted=False)
-
- self.gru.flatten_parameters()
- x, memory = self.gru(x) # memory --- [N, Ty, E//2], out --- [1, N, E//2]
- x, _ = torch.nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
-
- return x, memory, mel_masks
-
- def calculate_channels( # pylint: disable=no-self-use
- self, L: int, kernel_size: int, stride: int, pad: int, n_convs: int
- ) -> int:
- for _ in range(n_convs):
- L = (L - kernel_size + 2 * pad) // stride + 1
- return L
-
-
-class UtteranceLevelProsodyEncoder(nn.Module):
- def __init__(
- self,
- num_mels: int,
- ref_enc_filters: List[Union[int, int, int, int, int, int]],
- ref_enc_size: int,
- ref_enc_strides: List[Union[int, int, int, int, int]],
- ref_enc_gru_size: int,
- dropout: float,
- n_hidden: int,
- bottleneck_size_u: int,
- token_num: int,
- ):
- """
- Encoder to extract prosody from utterance. it is made up of a reference encoder
- with a couple of linear layers and style token layer with dropout.
-
- Args:
- num_mels (int): Number of mel frames to produce.
- ref_enc_filters (list[int]): List of channel sizes for ref encoder layers.
- ref_enc_size (int): Size of the kernel for the ref encoder conv layers.
- ref_enc_strides (List[int]): List of strides to use for teh ref encoder conv layers.
- ref_enc_gru_size (int): Number of hidden features for the gated recurrent unit.
- dropout (float): Probability of dropout.
- n_hidden (int): Size of hidden layers.
- bottleneck_size_u (int): Size of the bottle neck layer.
-
- Inputs: inputs, mask
- - **inputs** (batch, dim, time): Tensor containing mel vector
- - **lengths** (batch): Tensor containing the mel lengths.
- Returns:
- - **outputs** (batch, 1, dim): Tensor produced by Utterance Level Prosody Encoder.
- """
- super().__init__()
-
- self.E = n_hidden
- self.d_q = self.d_k = n_hidden
- bottleneck_size = bottleneck_size_u
-
- self.encoder = ReferenceEncoder(
- ref_enc_filters=ref_enc_filters,
- ref_enc_gru_size=ref_enc_gru_size,
- ref_enc_size=ref_enc_size,
- ref_enc_strides=ref_enc_strides,
- num_mels=num_mels,
- )
- self.encoder_prj = nn.Linear(ref_enc_gru_size, self.E // 2)
- self.stl = STL(n_hidden=n_hidden, token_num=token_num)
- self.encoder_bottleneck = nn.Linear(self.E, bottleneck_size)
- self.dropout = nn.Dropout(dropout)
-
- def forward(self, mels: torch.Tensor, mel_lens: torch.Tensor) -> torch.Tensor:
- """
- Shapes:
- mels: :math: `[B, C, T]`
- mel_lens: :math: `[B]`
-
- out --- [N, seq_len, E]
- """
- _, embedded_prosody, _ = self.encoder(mels, mel_lens)
-
- # Bottleneck
- embedded_prosody = self.encoder_prj(embedded_prosody)
-
- # Style Token
- out = self.encoder_bottleneck(self.stl(embedded_prosody))
- out = self.dropout(out)
-
- out = out.view((-1, 1, out.shape[3]))
- return out
-
-
-class PhonemeLevelProsodyEncoder(nn.Module):
- def __init__(
- self,
- num_mels: int,
- ref_enc_filters: List[Union[int, int, int, int, int, int]],
- ref_enc_size: int,
- ref_enc_strides: List[Union[int, int, int, int, int]],
- ref_enc_gru_size: int,
- dropout: float,
- n_hidden: int,
- n_heads: int,
- bottleneck_size_p: int,
- ):
- super().__init__()
-
- self.E = n_hidden
- self.d_q = self.d_k = n_hidden
- bottleneck_size = bottleneck_size_p
-
- self.encoder = ReferenceEncoder(
- ref_enc_filters=ref_enc_filters,
- ref_enc_gru_size=ref_enc_gru_size,
- ref_enc_size=ref_enc_size,
- ref_enc_strides=ref_enc_strides,
- num_mels=num_mels,
- )
- self.encoder_prj = nn.Linear(ref_enc_gru_size, n_hidden)
- self.attention = ConformerMultiHeadedSelfAttention(
- d_model=n_hidden,
- num_heads=n_heads,
- dropout_p=dropout,
- )
- self.encoder_bottleneck = nn.Linear(n_hidden, bottleneck_size)
-
- def forward(
- self,
- x: torch.Tensor,
- src_mask: torch.Tensor,
- mels: torch.Tensor,
- mel_lens: torch.Tensor,
- encoding: torch.Tensor,
- ) -> torch.Tensor:
- """
- x --- [N, seq_len, encoder_embedding_dim]
- mels --- [N, Ty/r, n_mels*r], r=1
- out --- [N, seq_len, bottleneck_size]
- attn --- [N, seq_len, ref_len], Ty/r = ref_len
- """
- embedded_prosody, _, mel_masks = self.encoder(mels, mel_lens)
-
- # Bottleneck
- embedded_prosody = self.encoder_prj(embedded_prosody)
-
- attn_mask = mel_masks.view((mel_masks.shape[0], 1, 1, -1))
- x, _ = self.attention(
- query=x,
- key=embedded_prosody,
- value=embedded_prosody,
- mask=attn_mask,
- encoding=encoding,
- )
- x = self.encoder_bottleneck(x)
- x = x.masked_fill(src_mask.unsqueeze(-1), 0.0)
- return x
diff --git a/TTS/tts/layers/delightful_tts/energy_adaptor.py b/TTS/tts/layers/delightful_tts/energy_adaptor.py
deleted file mode 100644
index ea0d1e47214d81a42b934bbaaa4b3ebb9f63bcc6..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/energy_adaptor.py
+++ /dev/null
@@ -1,82 +0,0 @@
-from typing import Callable, Tuple
-
-import torch
-import torch.nn as nn # pylint: disable=consider-using-from-import
-
-from TTS.tts.layers.delightful_tts.variance_predictor import VariancePredictor
-from TTS.tts.utils.helpers import average_over_durations
-
-
-class EnergyAdaptor(nn.Module): # pylint: disable=abstract-method
- """Variance Adaptor with an added 1D conv layer. Used to
- get energy embeddings.
-
- Args:
- channels_in (int): Number of in channels for conv layers.
- channels_out (int): Number of out channels.
- kernel_size (int): Size the kernel for the conv layers.
- dropout (float): Probability of dropout.
- lrelu_slope (float): Slope for the leaky relu.
- emb_kernel_size (int): Size the kernel for the pitch embedding.
-
- Inputs: inputs, mask
- - **inputs** (batch, time1, dim): Tensor containing input vector
- - **target** (batch, 1, time2): Tensor containing the energy target
- - **dr** (batch, time1): Tensor containing aligner durations vector
- - **mask** (batch, time1): Tensor containing indices to be masked
- Returns:
- - **energy prediction** (batch, 1, time1): Tensor produced by energy predictor
- - **energy embedding** (batch, channels, time1): Tensor produced energy adaptor
- - **average energy target(train only)** (batch, 1, time1): Tensor produced after averaging over durations
-
- """
-
- def __init__(
- self,
- channels_in: int,
- channels_hidden: int,
- channels_out: int,
- kernel_size: int,
- dropout: float,
- lrelu_slope: float,
- emb_kernel_size: int,
- ):
- super().__init__()
- self.energy_predictor = VariancePredictor(
- channels_in=channels_in,
- channels=channels_hidden,
- channels_out=channels_out,
- kernel_size=kernel_size,
- p_dropout=dropout,
- lrelu_slope=lrelu_slope,
- )
- self.energy_emb = nn.Conv1d(
- 1,
- channels_hidden,
- kernel_size=emb_kernel_size,
- padding=int((emb_kernel_size - 1) / 2),
- )
-
- def get_energy_embedding_train(
- self, x: torch.Tensor, target: torch.Tensor, dr: torch.IntTensor, mask: torch.Tensor
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """
- Shapes:
- x: :math: `[B, T_src, C]`
- target: :math: `[B, 1, T_max2]`
- dr: :math: `[B, T_src]`
- mask: :math: `[B, T_src]`
- """
- energy_pred = self.energy_predictor(x, mask)
- energy_pred.unsqueeze_(1)
- avg_energy_target = average_over_durations(target, dr)
- energy_emb = self.energy_emb(avg_energy_target)
- return energy_pred, avg_energy_target, energy_emb
-
- def get_energy_embedding(self, x: torch.Tensor, mask: torch.Tensor, energy_transform: Callable) -> torch.Tensor:
- energy_pred = self.energy_predictor(x, mask)
- energy_pred.unsqueeze_(1)
- if energy_transform is not None:
- energy_pred = energy_transform(energy_pred, (~mask).sum(dim=(1, 2)), self.pitch_mean, self.pitch_std)
- energy_emb_pred = self.energy_emb(energy_pred)
- return energy_emb_pred, energy_pred
diff --git a/TTS/tts/layers/delightful_tts/kernel_predictor.py b/TTS/tts/layers/delightful_tts/kernel_predictor.py
deleted file mode 100644
index 19dfd57e7bbb018494aa00ca22f0ae70f1b2e71d..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/kernel_predictor.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import torch.nn as nn # pylint: disable=consider-using-from-import
-
-
-class KernelPredictor(nn.Module):
- """Kernel predictor for the location-variable convolutions
-
- Args:
- cond_channels (int): number of channel for the conditioning sequence,
- conv_in_channels (int): number of channel for the input sequence,
- conv_out_channels (int): number of channel for the output sequence,
- conv_layers (int): number of layers
-
- """
-
- def __init__( # pylint: disable=dangerous-default-value
- self,
- cond_channels,
- conv_in_channels,
- conv_out_channels,
- conv_layers,
- conv_kernel_size=3,
- kpnet_hidden_channels=64,
- kpnet_conv_size=3,
- kpnet_dropout=0.0,
- kpnet_nonlinear_activation="LeakyReLU",
- kpnet_nonlinear_activation_params={"negative_slope": 0.1},
- ):
- super().__init__()
-
- self.conv_in_channels = conv_in_channels
- self.conv_out_channels = conv_out_channels
- self.conv_kernel_size = conv_kernel_size
- self.conv_layers = conv_layers
-
- kpnet_kernel_channels = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers # l_w
- kpnet_bias_channels = conv_out_channels * conv_layers # l_b
-
- self.input_conv = nn.Sequential(
- nn.utils.weight_norm(nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)),
- getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
- )
-
- self.residual_convs = nn.ModuleList()
- padding = (kpnet_conv_size - 1) // 2
- for _ in range(3):
- self.residual_convs.append(
- nn.Sequential(
- nn.Dropout(kpnet_dropout),
- nn.utils.weight_norm(
- nn.Conv1d(
- kpnet_hidden_channels,
- kpnet_hidden_channels,
- kpnet_conv_size,
- padding=padding,
- bias=True,
- )
- ),
- getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
- nn.utils.weight_norm(
- nn.Conv1d(
- kpnet_hidden_channels,
- kpnet_hidden_channels,
- kpnet_conv_size,
- padding=padding,
- bias=True,
- )
- ),
- getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
- )
- )
- self.kernel_conv = nn.utils.weight_norm(
- nn.Conv1d(
- kpnet_hidden_channels,
- kpnet_kernel_channels,
- kpnet_conv_size,
- padding=padding,
- bias=True,
- )
- )
- self.bias_conv = nn.utils.weight_norm(
- nn.Conv1d(
- kpnet_hidden_channels,
- kpnet_bias_channels,
- kpnet_conv_size,
- padding=padding,
- bias=True,
- )
- )
-
- def forward(self, c):
- """
- Args:
- c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
- """
- batch, _, cond_length = c.shape
- c = self.input_conv(c)
- for residual_conv in self.residual_convs:
- residual_conv.to(c.device)
- c = c + residual_conv(c)
- k = self.kernel_conv(c)
- b = self.bias_conv(c)
- kernels = k.contiguous().view(
- batch,
- self.conv_layers,
- self.conv_in_channels,
- self.conv_out_channels,
- self.conv_kernel_size,
- cond_length,
- )
- bias = b.contiguous().view(
- batch,
- self.conv_layers,
- self.conv_out_channels,
- cond_length,
- )
-
- return kernels, bias
-
- def remove_weight_norm(self):
- nn.utils.remove_weight_norm(self.input_conv[0])
- nn.utils.remove_weight_norm(self.kernel_conv)
- nn.utils.remove_weight_norm(self.bias_conv)
- for block in self.residual_convs:
- nn.utils.remove_weight_norm(block[1])
- nn.utils.remove_weight_norm(block[3])
diff --git a/TTS/tts/layers/delightful_tts/networks.py b/TTS/tts/layers/delightful_tts/networks.py
deleted file mode 100644
index 4305022f18cf95565b2da2553740276818fb486c..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/networks.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import math
-from typing import Tuple
-
-import numpy as np
-import torch
-import torch.nn as nn # pylint: disable=consider-using-from-import
-import torch.nn.functional as F
-
-from TTS.tts.layers.delightful_tts.conv_layers import ConvNorm
-
-
-def initialize_embeddings(shape: Tuple[int]) -> torch.Tensor:
- assert len(shape) == 2, "Can only initialize 2-D embedding matrices ..."
- # Kaiming initialization
- return torch.randn(shape) * np.sqrt(2 / shape[1])
-
-
-def positional_encoding(d_model: int, length: int, device: torch.device) -> torch.Tensor:
- pe = torch.zeros(length, d_model, device=device)
- position = torch.arange(0, length, dtype=torch.float, device=device).unsqueeze(1)
- div_term = torch.exp(torch.arange(0, d_model, 2, device=device).float() * -(math.log(10000.0) / d_model))
- pe[:, 0::2] = torch.sin(position * div_term)
- pe[:, 1::2] = torch.cos(position * div_term)
- pe = pe.unsqueeze(0)
- return pe
-
-
-class BottleneckLayer(nn.Module):
- """
- Bottleneck layer for reducing the dimensionality of a tensor.
-
- Args:
- in_dim: The number of input dimensions.
- reduction_factor: The factor by which to reduce the number of dimensions.
- norm: The normalization method to use. Can be "weightnorm" or "instancenorm".
- non_linearity: The non-linearity to use. Can be "relu" or "leakyrelu".
- kernel_size: The size of the convolutional kernel.
- use_partial_padding: Whether to use partial padding with the convolutional kernel.
-
- Shape:
- - Input: :math:`[N, in_dim]` where `N` is the batch size and `in_dim` is the number of input dimensions.
-
- - Output: :math:`[N, out_dim]` where `out_dim` is the number of output dimensions.
- """
-
- def __init__(
- self,
- in_dim,
- reduction_factor,
- norm="weightnorm",
- non_linearity="relu",
- kernel_size=3,
- use_partial_padding=False, # pylint: disable=unused-argument
- ):
- super(BottleneckLayer, self).__init__() # pylint: disable=super-with-arguments
-
- self.reduction_factor = reduction_factor
- reduced_dim = int(in_dim / reduction_factor)
- self.out_dim = reduced_dim
- if self.reduction_factor > 1:
- fn = ConvNorm(in_dim, reduced_dim, kernel_size=kernel_size, use_weight_norm=(norm == "weightnorm"))
- if norm == "instancenorm":
- fn = nn.Sequential(fn, nn.InstanceNorm1d(reduced_dim, affine=True))
-
- self.projection_fn = fn
- self.non_linearity = nn.ReLU()
- if non_linearity == "leakyrelu":
- self.non_linearity = nn.LeakyReLU()
-
- def forward(self, x):
- if self.reduction_factor > 1:
- x = self.projection_fn(x)
- x = self.non_linearity(x)
- return x
-
-
-class GLUActivation(nn.Module):
- """Class that implements the Gated Linear Unit (GLU) activation function.
-
- The GLU activation function is a variant of the Leaky ReLU activation function,
- where the output of the activation function is gated by an input tensor.
-
- """
-
- def __init__(self, slope: float):
- super().__init__()
- self.lrelu = nn.LeakyReLU(slope)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- out, gate = x.chunk(2, dim=1)
- x = out * self.lrelu(gate)
- return x
-
-
-class StyleEmbedAttention(nn.Module):
- def __init__(self, query_dim: int, key_dim: int, num_units: int, num_heads: int):
- super().__init__()
- self.num_units = num_units
- self.num_heads = num_heads
- self.key_dim = key_dim
-
- self.W_query = nn.Linear(in_features=query_dim, out_features=num_units, bias=False)
- self.W_key = nn.Linear(in_features=key_dim, out_features=num_units, bias=False)
- self.W_value = nn.Linear(in_features=key_dim, out_features=num_units, bias=False)
-
- def forward(self, query: torch.Tensor, key_soft: torch.Tensor) -> torch.Tensor:
- values = self.W_value(key_soft)
- split_size = self.num_units // self.num_heads
- values = torch.stack(torch.split(values, split_size, dim=2), dim=0)
-
- out_soft = scores_soft = None
- querys = self.W_query(query) # [N, T_q, num_units]
- keys = self.W_key(key_soft) # [N, T_k, num_units]
-
- # [h, N, T_q, num_units/h]
- querys = torch.stack(torch.split(querys, split_size, dim=2), dim=0)
- # [h, N, T_k, num_units/h]
- keys = torch.stack(torch.split(keys, split_size, dim=2), dim=0)
- # [h, N, T_k, num_units/h]
-
- # score = softmax(QK^T / (d_k ** 0.5))
- scores_soft = torch.matmul(querys, keys.transpose(2, 3)) # [h, N, T_q, T_k]
- scores_soft = scores_soft / (self.key_dim**0.5)
- scores_soft = F.softmax(scores_soft, dim=3)
-
- # out = score * V
- # [h, N, T_q, num_units/h]
- out_soft = torch.matmul(scores_soft, values)
- out_soft = torch.cat(torch.split(out_soft, 1, dim=0), dim=3).squeeze(0) # [N, T_q, num_units]
-
- return out_soft # , scores_soft
-
-
-class EmbeddingPadded(nn.Module):
- def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
- super().__init__()
- padding_mult = torch.ones((num_embeddings, 1), dtype=torch.int64)
- padding_mult[padding_idx] = 0
- self.register_buffer("padding_mult", padding_mult)
- self.embeddings = nn.parameter.Parameter(initialize_embeddings((num_embeddings, embedding_dim)))
-
- def forward(self, idx: torch.Tensor) -> torch.Tensor:
- embeddings_zeroed = self.embeddings * self.padding_mult
- x = F.embedding(idx, embeddings_zeroed)
- return x
-
-
-class EmbeddingProjBlock(nn.Module):
- def __init__(self, embedding_dim: int):
- super().__init__()
- self.layers = nn.ModuleList(
- [
- nn.Linear(embedding_dim, embedding_dim),
- nn.LeakyReLU(0.3),
- nn.Linear(embedding_dim, embedding_dim),
- nn.LeakyReLU(0.3),
- ]
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- res = x
- for layer in self.layers:
- x = layer(x)
- x = x + res
- return x
-
-
-class LinearNorm(nn.Module):
- def __init__(self, in_features: int, out_features: int, bias: bool = False):
- super().__init__()
- self.linear = nn.Linear(in_features, out_features, bias)
-
- nn.init.xavier_uniform_(self.linear.weight)
- if bias:
- nn.init.constant_(self.linear.bias, 0.0)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.linear(x)
- return x
-
-
-class STL(nn.Module):
- """
- A PyTorch module for the Style Token Layer (STL) as described in
- "A Style-Based Generator Architecture for Generative Adversarial Networks"
- (https://arxiv.org/abs/1812.04948)
-
- The STL applies a multi-headed attention mechanism over the learned style tokens,
- using the text input as the query and the style tokens as the keys and values.
- The output of the attention mechanism is used as the text's style embedding.
-
- Args:
- token_num (int): The number of style tokens.
- n_hidden (int): Number of hidden dimensions.
- """
-
- def __init__(self, n_hidden: int, token_num: int):
- super(STL, self).__init__() # pylint: disable=super-with-arguments
-
- num_heads = 1
- E = n_hidden
- self.token_num = token_num
- self.embed = nn.Parameter(torch.FloatTensor(self.token_num, E // num_heads))
- d_q = E // 2
- d_k = E // num_heads
- self.attention = StyleEmbedAttention(query_dim=d_q, key_dim=d_k, num_units=E, num_heads=num_heads)
-
- torch.nn.init.normal_(self.embed, mean=0, std=0.5)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- N = x.size(0)
- query = x.unsqueeze(1) # [N, 1, E//2]
-
- keys_soft = torch.tanh(self.embed).unsqueeze(0).expand(N, -1, -1) # [N, token_num, E // num_heads]
-
- # Weighted sum
- emotion_embed_soft = self.attention(query, keys_soft)
-
- return emotion_embed_soft
diff --git a/TTS/tts/layers/delightful_tts/phoneme_prosody_predictor.py b/TTS/tts/layers/delightful_tts/phoneme_prosody_predictor.py
deleted file mode 100644
index 28418f7163361120914f277446f76ac9f0363254..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/phoneme_prosody_predictor.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn # pylint: disable=consider-using-from-import
-
-from TTS.tts.layers.delightful_tts.conv_layers import ConvTransposed
-
-
-class PhonemeProsodyPredictor(nn.Module):
- """Non-parallel Prosody Predictor inspired by: https://arxiv.org/pdf/2102.00851.pdf
- It consists of 2 layers of 1D convolutions each followed by a relu activation, layer norm
- and dropout, then finally a linear layer.
-
- Args:
- hidden_size (int): Size of hidden channels.
- kernel_size (int): Kernel size for the conv layers.
- dropout: (float): Probability of dropout.
- bottleneck_size (int): bottleneck size for last linear layer.
- lrelu_slope (float): Slope of the leaky relu.
- """
-
- def __init__(
- self,
- hidden_size: int,
- kernel_size: int,
- dropout: float,
- bottleneck_size: int,
- lrelu_slope: float,
- ):
- super().__init__()
- self.d_model = hidden_size
- self.layers = nn.ModuleList(
- [
- ConvTransposed(
- self.d_model,
- self.d_model,
- kernel_size=kernel_size,
- padding=(kernel_size - 1) // 2,
- ),
- nn.LeakyReLU(lrelu_slope),
- nn.LayerNorm(self.d_model),
- nn.Dropout(dropout),
- ConvTransposed(
- self.d_model,
- self.d_model,
- kernel_size=kernel_size,
- padding=(kernel_size - 1) // 2,
- ),
- nn.LeakyReLU(lrelu_slope),
- nn.LayerNorm(self.d_model),
- nn.Dropout(dropout),
- ]
- )
- self.predictor_bottleneck = nn.Linear(self.d_model, bottleneck_size)
-
- def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
- """
- Shapes:
- x: :math: `[B, T, D]`
- mask: :math: `[B, T]`
- """
- mask = mask.unsqueeze(2)
- for layer in self.layers:
- x = layer(x)
- x = x.masked_fill(mask, 0.0)
- x = self.predictor_bottleneck(x)
- return x
diff --git a/TTS/tts/layers/delightful_tts/pitch_adaptor.py b/TTS/tts/layers/delightful_tts/pitch_adaptor.py
deleted file mode 100644
index 9031369e0f019cf115d0d43b288bb97d9db48467..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/pitch_adaptor.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from typing import Callable, Tuple
-
-import torch
-import torch.nn as nn # pylint: disable=consider-using-from-import
-
-from TTS.tts.layers.delightful_tts.variance_predictor import VariancePredictor
-from TTS.tts.utils.helpers import average_over_durations
-
-
-class PitchAdaptor(nn.Module): # pylint: disable=abstract-method
- """Module to get pitch embeddings via pitch predictor
-
- Args:
- n_input (int): Number of pitch predictor input channels.
- n_hidden (int): Number of pitch predictor hidden channels.
- n_out (int): Number of pitch predictor out channels.
- kernel size (int): Size of the kernel for conv layers.
- emb_kernel_size (int): Size the kernel for the pitch embedding.
- p_dropout (float): Probability of dropout.
- lrelu_slope (float): Slope for the leaky relu.
-
- Inputs: inputs, mask
- - **inputs** (batch, time1, dim): Tensor containing input vector
- - **target** (batch, 1, time2): Tensor containing the pitch target
- - **dr** (batch, time1): Tensor containing aligner durations vector
- - **mask** (batch, time1): Tensor containing indices to be masked
- Returns:
- - **pitch prediction** (batch, 1, time1): Tensor produced by pitch predictor
- - **pitch embedding** (batch, channels, time1): Tensor produced pitch pitch adaptor
- - **average pitch target(train only)** (batch, 1, time1): Tensor produced after averaging over durations
- """
-
- def __init__(
- self,
- n_input: int,
- n_hidden: int,
- n_out: int,
- kernel_size: int,
- emb_kernel_size: int,
- p_dropout: float,
- lrelu_slope: float,
- ):
- super().__init__()
- self.pitch_predictor = VariancePredictor(
- channels_in=n_input,
- channels=n_hidden,
- channels_out=n_out,
- kernel_size=kernel_size,
- p_dropout=p_dropout,
- lrelu_slope=lrelu_slope,
- )
- self.pitch_emb = nn.Conv1d(
- 1,
- n_input,
- kernel_size=emb_kernel_size,
- padding=int((emb_kernel_size - 1) / 2),
- )
-
- def get_pitch_embedding_train(
- self, x: torch.Tensor, target: torch.Tensor, dr: torch.IntTensor, mask: torch.Tensor
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- """
- Shapes:
- x: :math: `[B, T_src, C]`
- target: :math: `[B, 1, T_max2]`
- dr: :math: `[B, T_src]`
- mask: :math: `[B, T_src]`
- """
- pitch_pred = self.pitch_predictor(x, mask) # [B, T_src, C_hidden], [B, T_src] --> [B, T_src]
- pitch_pred.unsqueeze_(1) # --> [B, 1, T_src]
- avg_pitch_target = average_over_durations(target, dr) # [B, 1, T_mel], [B, T_src] --> [B, 1, T_src]
- pitch_emb = self.pitch_emb(avg_pitch_target) # [B, 1, T_src] --> [B, C_hidden, T_src]
- return pitch_pred, avg_pitch_target, pitch_emb
-
- def get_pitch_embedding(
- self,
- x: torch.Tensor,
- mask: torch.Tensor,
- pitch_transform: Callable,
- pitch_mean: torch.Tensor,
- pitch_std: torch.Tensor,
- ) -> torch.Tensor:
- pitch_pred = self.pitch_predictor(x, mask)
- if pitch_transform is not None:
- pitch_pred = pitch_transform(pitch_pred, (~mask).sum(), pitch_mean, pitch_std)
- pitch_pred.unsqueeze_(1)
- pitch_emb_pred = self.pitch_emb(pitch_pred)
- return pitch_emb_pred, pitch_pred
diff --git a/TTS/tts/layers/delightful_tts/variance_predictor.py b/TTS/tts/layers/delightful_tts/variance_predictor.py
deleted file mode 100644
index 68303a1bd1148089eab7ee8be12d4f37ddf420e1..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/delightful_tts/variance_predictor.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import torch
-import torch.nn as nn # pylint: disable=consider-using-from-import
-
-from TTS.tts.layers.delightful_tts.conv_layers import ConvTransposed
-
-
-class VariancePredictor(nn.Module):
- """
- Network is 2-layer 1D convolutions with leaky relu activation and then
- followed by layer normalization then a dropout layer and finally an
- extra linear layer to project the hidden states into the output sequence.
-
- Args:
- channels_in (int): Number of in channels for conv layers.
- channels_out (int): Number of out channels for the last linear layer.
- kernel_size (int): Size the kernel for the conv layers.
- p_dropout (float): Probability of dropout.
- lrelu_slope (float): Slope for the leaky relu.
-
- Inputs: inputs, mask
- - **inputs** (batch, time, dim): Tensor containing input vector
- - **mask** (batch, time): Tensor containing indices to be masked
- Returns:
- - **outputs** (batch, time): Tensor produced by last linear layer.
- """
-
- def __init__(
- self, channels_in: int, channels: int, channels_out: int, kernel_size: int, p_dropout: float, lrelu_slope: float
- ):
- super().__init__()
-
- self.layers = nn.ModuleList(
- [
- ConvTransposed(
- channels_in,
- channels,
- kernel_size=kernel_size,
- padding=(kernel_size - 1) // 2,
- ),
- nn.LeakyReLU(lrelu_slope),
- nn.LayerNorm(channels),
- nn.Dropout(p_dropout),
- ConvTransposed(
- channels,
- channels,
- kernel_size=kernel_size,
- padding=(kernel_size - 1) // 2,
- ),
- nn.LeakyReLU(lrelu_slope),
- nn.LayerNorm(channels),
- nn.Dropout(p_dropout),
- ]
- )
-
- self.linear_layer = nn.Linear(channels, channels_out)
-
- def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
- """
- Shapes:
- x: :math: `[B, T_src, C]`
- mask: :math: `[B, T_src]`
- """
- for layer in self.layers:
- x = layer(x)
- x = self.linear_layer(x)
- x = x.squeeze(-1)
- x = x.masked_fill(mask, 0.0)
- return x
diff --git a/TTS/tts/layers/feed_forward/__init__.py b/TTS/tts/layers/feed_forward/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tts/layers/feed_forward/decoder.py b/TTS/tts/layers/feed_forward/decoder.py
deleted file mode 100644
index 0376e2e3926e65254c3a81d085d48c97df033958..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/feed_forward/decoder.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import torch
-from torch import nn
-
-from TTS.tts.layers.generic.res_conv_bn import Conv1dBN, Conv1dBNBlock, ResidualConv1dBNBlock
-from TTS.tts.layers.generic.transformer import FFTransformerBlock
-from TTS.tts.layers.generic.wavenet import WNBlocks
-from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
-
-
-class WaveNetDecoder(nn.Module):
- """WaveNet based decoder with a prenet and a postnet.
-
- prenet: conv1d_1x1
- postnet: 3 x [conv1d_1x1 -> relu] -> conv1d_1x1
-
- TODO: Integrate speaker conditioning vector.
-
- Note:
- default wavenet parameters;
- params = {
- "num_blocks": 12,
- "hidden_channels":192,
- "kernel_size": 5,
- "dilation_rate": 1,
- "num_layers": 4,
- "dropout_p": 0.05
- }
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels for prenet and postnet.
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, hidden_channels, c_in_channels, params):
- super().__init__()
- # prenet
- self.prenet = torch.nn.Conv1d(in_channels, params["hidden_channels"], 1)
- # wavenet layers
- self.wn = WNBlocks(params["hidden_channels"], c_in_channels=c_in_channels, **params)
- # postnet
- self.postnet = [
- torch.nn.Conv1d(params["hidden_channels"], hidden_channels, 1),
- torch.nn.ReLU(),
- torch.nn.Conv1d(hidden_channels, hidden_channels, 1),
- torch.nn.ReLU(),
- torch.nn.Conv1d(hidden_channels, hidden_channels, 1),
- torch.nn.ReLU(),
- torch.nn.Conv1d(hidden_channels, out_channels, 1),
- ]
- self.postnet = nn.Sequential(*self.postnet)
-
- def forward(self, x, x_mask=None, g=None):
- x = self.prenet(x) * x_mask
- x = self.wn(x, x_mask, g)
- o = self.postnet(x) * x_mask
- return o
-
-
-class RelativePositionTransformerDecoder(nn.Module):
- """Decoder with Relative Positional Transformer.
-
- Note:
- Default params
- params={
- 'hidden_channels_ffn': 128,
- 'num_heads': 2,
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 8,
- "rel_attn_window_size": 4,
- "input_length": None
- }
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels including Transformer layers.
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, hidden_channels, params):
- super().__init__()
- self.prenet = Conv1dBN(in_channels, hidden_channels, 1, 1)
- self.rel_pos_transformer = RelativePositionTransformer(in_channels, out_channels, hidden_channels, **params)
-
- def forward(self, x, x_mask=None, g=None): # pylint: disable=unused-argument
- o = self.prenet(x) * x_mask
- o = self.rel_pos_transformer(o, x_mask)
- return o
-
-
-class FFTransformerDecoder(nn.Module):
- """Decoder with FeedForwardTransformer.
-
- Default params
- params={
- 'hidden_channels_ffn': 1024,
- 'num_heads': 2,
- "dropout_p": 0.1,
- "num_layers": 6,
- }
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels including Transformer layers.
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, params):
- super().__init__()
- self.transformer_block = FFTransformerBlock(in_channels, **params)
- self.postnet = nn.Conv1d(in_channels, out_channels, 1)
-
- def forward(self, x, x_mask=None, g=None): # pylint: disable=unused-argument
- # TODO: handle multi-speaker
- x_mask = 1 if x_mask is None else x_mask
- o = self.transformer_block(x) * x_mask
- o = self.postnet(o) * x_mask
- return o
-
-
-class ResidualConv1dBNDecoder(nn.Module):
- """Residual Convolutional Decoder as in the original Speedy Speech paper
-
- TODO: Integrate speaker conditioning vector.
-
- Note:
- Default params
- params = {
- "kernel_size": 4,
- "dilations": 4 * [1, 2, 4, 8] + [1],
- "num_conv_blocks": 2,
- "num_res_blocks": 17
- }
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels including ResidualConv1dBNBlock layers.
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, hidden_channels, params):
- super().__init__()
- self.res_conv_block = ResidualConv1dBNBlock(in_channels, hidden_channels, hidden_channels, **params)
- self.post_conv = nn.Conv1d(hidden_channels, hidden_channels, 1)
- self.postnet = nn.Sequential(
- Conv1dBNBlock(
- hidden_channels, hidden_channels, hidden_channels, params["kernel_size"], 1, num_conv_blocks=2
- ),
- nn.Conv1d(hidden_channels, out_channels, 1),
- )
-
- def forward(self, x, x_mask=None, g=None): # pylint: disable=unused-argument
- o = self.res_conv_block(x, x_mask)
- o = self.post_conv(o) + x
- return self.postnet(o) * x_mask
-
-
-class Decoder(nn.Module):
- """Decodes the expanded phoneme encoding into spectrograms
- Args:
- out_channels (int): number of output channels.
- in_hidden_channels (int): input and hidden channels. Model keeps the input channels for the intermediate layers.
- decoder_type (str): decoder layer types. 'transformers' or 'residual_conv_bn'. Default 'residual_conv_bn'.
- decoder_params (dict): model parameters for specified decoder type.
- c_in_channels (int): number of channels for conditional input.
-
- Shapes:
- - input: (B, C, T)
- """
-
- # pylint: disable=dangerous-default-value
- def __init__(
- self,
- out_channels,
- in_hidden_channels,
- decoder_type="residual_conv_bn",
- decoder_params={
- "kernel_size": 4,
- "dilations": 4 * [1, 2, 4, 8] + [1],
- "num_conv_blocks": 2,
- "num_res_blocks": 17,
- },
- c_in_channels=0,
- ):
- super().__init__()
-
- if decoder_type.lower() == "relative_position_transformer":
- self.decoder = RelativePositionTransformerDecoder(
- in_channels=in_hidden_channels,
- out_channels=out_channels,
- hidden_channels=in_hidden_channels,
- params=decoder_params,
- )
- elif decoder_type.lower() == "residual_conv_bn":
- self.decoder = ResidualConv1dBNDecoder(
- in_channels=in_hidden_channels,
- out_channels=out_channels,
- hidden_channels=in_hidden_channels,
- params=decoder_params,
- )
- elif decoder_type.lower() == "wavenet":
- self.decoder = WaveNetDecoder(
- in_channels=in_hidden_channels,
- out_channels=out_channels,
- hidden_channels=in_hidden_channels,
- c_in_channels=c_in_channels,
- params=decoder_params,
- )
- elif decoder_type.lower() == "fftransformer":
- self.decoder = FFTransformerDecoder(in_hidden_channels, out_channels, decoder_params)
- else:
- raise ValueError(f"[!] Unknown decoder type - {decoder_type}")
-
- def forward(self, x, x_mask, g=None): # pylint: disable=unused-argument
- """
- Args:
- x: [B, C, T]
- x_mask: [B, 1, T]
- g: [B, C_g, 1]
- """
- # TODO: implement multi-speaker
- o = self.decoder(x, x_mask, g)
- return o
diff --git a/TTS/tts/layers/feed_forward/duration_predictor.py b/TTS/tts/layers/feed_forward/duration_predictor.py
deleted file mode 100644
index 4422648f4337e48aab39671836fcfb5e12ff4be7..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/feed_forward/duration_predictor.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from torch import nn
-
-from TTS.tts.layers.generic.res_conv_bn import Conv1dBN
-
-
-class DurationPredictor(nn.Module):
- """Speedy Speech duration predictor model.
- Predicts phoneme durations from encoder outputs.
-
- Note:
- Outputs interpreted as log(durations)
- To get actual durations, do exp transformation
-
- conv_BN_4x1 -> conv_BN_3x1 -> conv_BN_1x1 -> conv_1x1
-
- Args:
- hidden_channels (int): number of channels in the inner layers.
- """
-
- def __init__(self, hidden_channels):
- super().__init__()
-
- self.layers = nn.ModuleList(
- [
- Conv1dBN(hidden_channels, hidden_channels, 4, 1),
- Conv1dBN(hidden_channels, hidden_channels, 3, 1),
- Conv1dBN(hidden_channels, hidden_channels, 1, 1),
- nn.Conv1d(hidden_channels, 1, 1),
- ]
- )
-
- def forward(self, x, x_mask):
- """
- Shapes:
- x: [B, C, T]
- x_mask: [B, 1, T]
- """
- o = x
- for layer in self.layers:
- o = layer(o) * x_mask
- return o
diff --git a/TTS/tts/layers/feed_forward/encoder.py b/TTS/tts/layers/feed_forward/encoder.py
deleted file mode 100644
index caf939ffc73fedac299228e090b2df3bb4cc553c..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/feed_forward/encoder.py
+++ /dev/null
@@ -1,162 +0,0 @@
-from torch import nn
-
-from TTS.tts.layers.generic.res_conv_bn import ResidualConv1dBNBlock
-from TTS.tts.layers.generic.transformer import FFTransformerBlock
-from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
-
-
-class RelativePositionTransformerEncoder(nn.Module):
- """Speedy speech encoder built on Transformer with Relative Position encoding.
-
- TODO: Integrate speaker conditioning vector.
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, hidden_channels, params):
- super().__init__()
- self.prenet = ResidualConv1dBNBlock(
- in_channels,
- hidden_channels,
- hidden_channels,
- kernel_size=5,
- num_res_blocks=3,
- num_conv_blocks=1,
- dilations=[1, 1, 1],
- )
- self.rel_pos_transformer = RelativePositionTransformer(hidden_channels, out_channels, hidden_channels, **params)
-
- def forward(self, x, x_mask=None, g=None): # pylint: disable=unused-argument
- if x_mask is None:
- x_mask = 1
- o = self.prenet(x) * x_mask
- o = self.rel_pos_transformer(o, x_mask)
- return o
-
-
-class ResidualConv1dBNEncoder(nn.Module):
- """Residual Convolutional Encoder as in the original Speedy Speech paper
-
- TODO: Integrate speaker conditioning vector.
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of hidden channels
- params (dict): dictionary for residual convolutional blocks.
- """
-
- def __init__(self, in_channels, out_channels, hidden_channels, params):
- super().__init__()
- self.prenet = nn.Sequential(nn.Conv1d(in_channels, hidden_channels, 1), nn.ReLU())
- self.res_conv_block = ResidualConv1dBNBlock(hidden_channels, hidden_channels, hidden_channels, **params)
-
- self.postnet = nn.Sequential(
- *[
- nn.Conv1d(hidden_channels, hidden_channels, 1),
- nn.ReLU(),
- nn.BatchNorm1d(hidden_channels),
- nn.Conv1d(hidden_channels, out_channels, 1),
- ]
- )
-
- def forward(self, x, x_mask=None, g=None): # pylint: disable=unused-argument
- if x_mask is None:
- x_mask = 1
- o = self.prenet(x) * x_mask
- o = self.res_conv_block(o, x_mask)
- o = self.postnet(o + x) * x_mask
- return o * x_mask
-
-
-class Encoder(nn.Module):
- # pylint: disable=dangerous-default-value
- """Factory class for Speedy Speech encoder enables different encoder types internally.
-
- Args:
- num_chars (int): number of characters.
- out_channels (int): number of output channels.
- in_hidden_channels (int): input and hidden channels. Model keeps the input channels for the intermediate layers.
- encoder_type (str): encoder layer types. 'transformers' or 'residual_conv_bn'. Default 'residual_conv_bn'.
- encoder_params (dict): model parameters for specified encoder type.
- c_in_channels (int): number of channels for conditional input.
-
- Note:
- Default encoder_params to be set in config.json...
-
- ```python
- # for 'relative_position_transformer'
- encoder_params={
- 'hidden_channels_ffn': 128,
- 'num_heads': 2,
- "kernel_size": 3,
- "dropout_p": 0.1,
- "num_layers": 6,
- "rel_attn_window_size": 4,
- "input_length": None
- },
-
- # for 'residual_conv_bn'
- encoder_params = {
- "kernel_size": 4,
- "dilations": 4 * [1, 2, 4] + [1],
- "num_conv_blocks": 2,
- "num_res_blocks": 13
- }
-
- # for 'fftransformer'
- encoder_params = {
- "hidden_channels_ffn": 1024 ,
- "num_heads": 2,
- "num_layers": 6,
- "dropout_p": 0.1
- }
- ```
- """
-
- def __init__(
- self,
- in_hidden_channels,
- out_channels,
- encoder_type="residual_conv_bn",
- encoder_params={"kernel_size": 4, "dilations": 4 * [1, 2, 4] + [1], "num_conv_blocks": 2, "num_res_blocks": 13},
- c_in_channels=0,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.in_channels = in_hidden_channels
- self.hidden_channels = in_hidden_channels
- self.encoder_type = encoder_type
- self.c_in_channels = c_in_channels
-
- # init encoder
- if encoder_type.lower() == "relative_position_transformer":
- # text encoder
- # pylint: disable=unexpected-keyword-arg
- self.encoder = RelativePositionTransformerEncoder(
- in_hidden_channels, out_channels, in_hidden_channels, encoder_params
- )
- elif encoder_type.lower() == "residual_conv_bn":
- self.encoder = ResidualConv1dBNEncoder(in_hidden_channels, out_channels, in_hidden_channels, encoder_params)
- elif encoder_type.lower() == "fftransformer":
- assert (
- in_hidden_channels == out_channels
- ), "[!] must be `in_channels` == `out_channels` when encoder type is 'fftransformer'"
- # pylint: disable=unexpected-keyword-arg
- self.encoder = FFTransformerBlock(in_hidden_channels, **encoder_params)
- else:
- raise NotImplementedError(" [!] unknown encoder type.")
-
- def forward(self, x, x_mask, g=None): # pylint: disable=unused-argument
- """
- Shapes:
- x: [B, C, T]
- x_mask: [B, 1, T]
- g: [B, C, 1]
- """
- o = self.encoder(x, x_mask)
- return o * x_mask
diff --git a/TTS/tts/layers/generic/__init__.py b/TTS/tts/layers/generic/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tts/layers/generic/aligner.py b/TTS/tts/layers/generic/aligner.py
deleted file mode 100644
index baa6f0e9c4879207695b2de1193c9147b5a3fa4b..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/generic/aligner.py
+++ /dev/null
@@ -1,92 +0,0 @@
-from typing import Tuple
-
-import torch
-from torch import nn
-
-
-class AlignmentNetwork(torch.nn.Module):
- """Aligner Network for learning alignment between the input text and the model output with Gaussian Attention.
-
- ::
-
- query -> conv1d -> relu -> conv1d -> relu -> conv1d -> L2_dist -> softmax -> alignment
- key -> conv1d -> relu -> conv1d -----------------------^
-
- Args:
- in_query_channels (int): Number of channels in the query network. Defaults to 80.
- in_key_channels (int): Number of channels in the key network. Defaults to 512.
- attn_channels (int): Number of inner channels in the attention layers. Defaults to 80.
- temperature (float): Temperature for the softmax. Defaults to 0.0005.
- """
-
- def __init__(
- self,
- in_query_channels=80,
- in_key_channels=512,
- attn_channels=80,
- temperature=0.0005,
- ):
- super().__init__()
- self.temperature = temperature
- self.softmax = torch.nn.Softmax(dim=3)
- self.log_softmax = torch.nn.LogSoftmax(dim=3)
-
- self.key_layer = nn.Sequential(
- nn.Conv1d(
- in_key_channels,
- in_key_channels * 2,
- kernel_size=3,
- padding=1,
- bias=True,
- ),
- torch.nn.ReLU(),
- nn.Conv1d(in_key_channels * 2, attn_channels, kernel_size=1, padding=0, bias=True),
- )
-
- self.query_layer = nn.Sequential(
- nn.Conv1d(
- in_query_channels,
- in_query_channels * 2,
- kernel_size=3,
- padding=1,
- bias=True,
- ),
- torch.nn.ReLU(),
- nn.Conv1d(in_query_channels * 2, in_query_channels, kernel_size=1, padding=0, bias=True),
- torch.nn.ReLU(),
- nn.Conv1d(in_query_channels, attn_channels, kernel_size=1, padding=0, bias=True),
- )
-
- self.init_layers()
-
- def init_layers(self):
- torch.nn.init.xavier_uniform_(self.key_layer[0].weight, gain=torch.nn.init.calculate_gain("relu"))
- torch.nn.init.xavier_uniform_(self.key_layer[2].weight, gain=torch.nn.init.calculate_gain("linear"))
- torch.nn.init.xavier_uniform_(self.query_layer[0].weight, gain=torch.nn.init.calculate_gain("relu"))
- torch.nn.init.xavier_uniform_(self.query_layer[2].weight, gain=torch.nn.init.calculate_gain("linear"))
- torch.nn.init.xavier_uniform_(self.query_layer[4].weight, gain=torch.nn.init.calculate_gain("linear"))
-
- def forward(
- self, queries: torch.tensor, keys: torch.tensor, mask: torch.tensor = None, attn_prior: torch.tensor = None
- ) -> Tuple[torch.tensor, torch.tensor]:
- """Forward pass of the aligner encoder.
- Shapes:
- - queries: :math:`[B, C, T_de]`
- - keys: :math:`[B, C_emb, T_en]`
- - mask: :math:`[B, T_de]`
- Output:
- attn (torch.tensor): :math:`[B, 1, T_en, T_de]` soft attention mask.
- attn_logp (torch.tensor): :math:`[ßB, 1, T_en , T_de]` log probabilities.
- """
- key_out = self.key_layer(keys)
- query_out = self.query_layer(queries)
- attn_factor = (query_out[:, :, :, None] - key_out[:, :, None]) ** 2
- attn_logp = -self.temperature * attn_factor.sum(1, keepdim=True)
- if attn_prior is not None:
- attn_logp = self.log_softmax(attn_logp) + torch.log(attn_prior[:, None] + 1e-8)
-
- if mask is not None:
- attn_logp.data.masked_fill_(~mask.bool().unsqueeze(2), -float("inf"))
-
- attn = self.softmax(attn_logp)
- return attn, attn_logp
diff --git a/TTS/tts/layers/generic/gated_conv.py b/TTS/tts/layers/generic/gated_conv.py
deleted file mode 100644
index 9a29c4499f970db538a4b99c3c05cba22576195f..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/generic/gated_conv.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from torch import nn
-
-from .normalization import LayerNorm
-
-
-class GatedConvBlock(nn.Module):
- """Gated convolutional block as in https://arxiv.org/pdf/1612.08083.pdf
- Args:
- in_out_channels (int): number of input/output channels.
- kernel_size (int): convolution kernel size.
- dropout_p (float): dropout rate.
- """
-
- def __init__(self, in_out_channels, kernel_size, dropout_p, num_layers):
- super().__init__()
- # class arguments
- self.dropout_p = dropout_p
- self.num_layers = num_layers
- # define layers
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.layers = nn.ModuleList()
- for _ in range(num_layers):
- self.conv_layers += [nn.Conv1d(in_out_channels, 2 * in_out_channels, kernel_size, padding=kernel_size // 2)]
- self.norm_layers += [LayerNorm(2 * in_out_channels)]
-
- def forward(self, x, x_mask):
- o = x
- res = x
- for idx in range(self.num_layers):
- o = nn.functional.dropout(o, p=self.dropout_p, training=self.training)
- o = self.conv_layers[idx](o * x_mask)
- o = self.norm_layers[idx](o)
- o = nn.functional.glu(o, dim=1)
- o = res + o
- res = o
- return o
diff --git a/TTS/tts/layers/generic/normalization.py b/TTS/tts/layers/generic/normalization.py
deleted file mode 100644
index c0270e405e4246e47b7bc0787e4cd4b069533f92..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/generic/normalization.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import torch
-from torch import nn
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-4):
- """Layer norm for the 2nd dimension of the input.
- Args:
- channels (int): number of channels (2nd dimension) of the input.
- eps (float): to prevent 0 division
-
- Shapes:
- - input: (B, C, T)
- - output: (B, C, T)
- """
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(1, channels, 1) * 0.1)
- self.beta = nn.Parameter(torch.zeros(1, channels, 1))
-
- def forward(self, x):
- mean = torch.mean(x, 1, keepdim=True)
- variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
- x = (x - mean) * torch.rsqrt(variance + self.eps)
- x = x * self.gamma + self.beta
- return x
-
-
-class LayerNorm2(nn.Module):
- """Layer norm for the 2nd dimension of the input using torch primitive.
- Args:
- channels (int): number of channels (2nd dimension) of the input.
- eps (float): to prevent 0 division
-
- Shapes:
- - input: (B, C, T)
- - output: (B, C, T)
- """
-
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = torch.nn.functional.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class TemporalBatchNorm1d(nn.BatchNorm1d):
- """Normalize each channel separately over time and batch."""
-
- def __init__(self, channels, affine=True, track_running_stats=True, momentum=0.1):
- super().__init__(channels, affine=affine, track_running_stats=track_running_stats, momentum=momentum)
-
- def forward(self, x):
- return super().forward(x.transpose(2, 1)).transpose(2, 1)
-
-
-class ActNorm(nn.Module):
- """Activation Normalization bijector as an alternative to Batch Norm. It computes
- mean and std from a sample data in advance and it uses these values
- for normalization at training.
-
- Args:
- channels (int): input channels.
- ddi (False): data depended initialization flag.
-
- Shapes:
- - inputs: (B, C, T)
- - outputs: (B, C, T)
- """
-
- def __init__(self, channels, ddi=False, **kwargs): # pylint: disable=unused-argument
- super().__init__()
- self.channels = channels
- self.initialized = not ddi
-
- self.logs = nn.Parameter(torch.zeros(1, channels, 1))
- self.bias = nn.Parameter(torch.zeros(1, channels, 1))
-
- def forward(self, x, x_mask=None, reverse=False, **kwargs): # pylint: disable=unused-argument
- if x_mask is None:
- x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype)
- x_len = torch.sum(x_mask, [1, 2])
- if not self.initialized:
- self.initialize(x, x_mask)
- self.initialized = True
-
- if reverse:
- z = (x - self.bias) * torch.exp(-self.logs) * x_mask
- logdet = None
- else:
- z = (self.bias + torch.exp(self.logs) * x) * x_mask
- logdet = torch.sum(self.logs) * x_len # [b]
-
- return z, logdet
-
- def store_inverse(self):
- pass
-
- def set_ddi(self, ddi):
- self.initialized = not ddi
-
- def initialize(self, x, x_mask):
- with torch.no_grad():
- denom = torch.sum(x_mask, [0, 2])
- m = torch.sum(x * x_mask, [0, 2]) / denom
- m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom
- v = m_sq - (m**2)
- logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6))
-
- bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype)
- logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype)
-
- self.bias.data.copy_(bias_init)
- self.logs.data.copy_(logs_init)
diff --git a/TTS/tts/layers/generic/pos_encoding.py b/TTS/tts/layers/generic/pos_encoding.py
deleted file mode 100644
index 913add0d14332bf70c3ecd2a95869d0071310bd4..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/generic/pos_encoding.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import math
-
-import torch
-from torch import nn
-
-
-class PositionalEncoding(nn.Module):
- """Sinusoidal positional encoding for non-recurrent neural networks.
- Implementation based on "Attention Is All You Need"
-
- Args:
- channels (int): embedding size
- dropout_p (float): dropout rate applied to the output.
- max_len (int): maximum sequence length.
- use_scale (bool): whether to use a learnable scaling coefficient.
- """
-
- def __init__(self, channels, dropout_p=0.0, max_len=5000, use_scale=False):
- super().__init__()
- if channels % 2 != 0:
- raise ValueError(
- "Cannot use sin/cos positional encoding with " "odd channels (got channels={:d})".format(channels)
- )
- self.use_scale = use_scale
- if use_scale:
- self.scale = torch.nn.Parameter(torch.ones(1))
- pe = torch.zeros(max_len, channels)
- position = torch.arange(0, max_len).unsqueeze(1)
- div_term = torch.pow(10000, torch.arange(0, channels, 2).float() / channels)
- pe[:, 0::2] = torch.sin(position.float() * div_term)
- pe[:, 1::2] = torch.cos(position.float() * div_term)
- pe = pe.unsqueeze(0).transpose(1, 2)
- self.register_buffer("pe", pe)
- if dropout_p > 0:
- self.dropout = nn.Dropout(p=dropout_p)
- self.channels = channels
-
- def forward(self, x, mask=None, first_idx=None, last_idx=None):
- """
- Shapes:
- x: [B, C, T]
- mask: [B, 1, T]
- first_idx: int
- last_idx: int
- """
-
- x = x * math.sqrt(self.channels)
- if first_idx is None:
- if self.pe.size(2) < x.size(2):
- raise RuntimeError(
- f"Sequence is {x.size(2)} but PositionalEncoding is"
- f" limited to {self.pe.size(2)}. See max_len argument."
- )
- if mask is not None:
- pos_enc = self.pe[:, :, : x.size(2)] * mask
- else:
- pos_enc = self.pe[:, :, : x.size(2)]
- if self.use_scale:
- x = x + self.scale * pos_enc
- else:
- x = x + pos_enc
- else:
- if self.use_scale:
- x = x + self.scale * self.pe[:, :, first_idx:last_idx]
- else:
- x = x + self.pe[:, :, first_idx:last_idx]
- if hasattr(self, "dropout"):
- x = self.dropout(x)
- return x
diff --git a/TTS/tts/layers/generic/res_conv_bn.py b/TTS/tts/layers/generic/res_conv_bn.py
deleted file mode 100644
index 4beda291aa15398024b5b16cd6bf12b88898a0a9..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/generic/res_conv_bn.py
+++ /dev/null
@@ -1,127 +0,0 @@
-from torch import nn
-
-
-class ZeroTemporalPad(nn.Module):
- """Pad sequences to equal lentgh in the temporal dimension"""
-
- def __init__(self, kernel_size, dilation):
- super().__init__()
- total_pad = dilation * (kernel_size - 1)
- begin = total_pad // 2
- end = total_pad - begin
- self.pad_layer = nn.ZeroPad2d((0, 0, begin, end))
-
- def forward(self, x):
- return self.pad_layer(x)
-
-
-class Conv1dBN(nn.Module):
- """1d convolutional with batch norm.
- conv1d -> relu -> BN blocks.
-
- Note:
- Batch normalization is applied after ReLU regarding the original implementation.
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- kernel_size (int): kernel size for convolutional filters.
- dilation (int): dilation for convolution layers.
- """
-
- def __init__(self, in_channels, out_channels, kernel_size, dilation):
- super().__init__()
- padding = dilation * (kernel_size - 1)
- pad_s = padding // 2
- pad_e = padding - pad_s
- self.conv1d = nn.Conv1d(in_channels, out_channels, kernel_size, dilation=dilation)
- self.pad = nn.ZeroPad2d((pad_s, pad_e, 0, 0)) # uneven left and right padding
- self.norm = nn.BatchNorm1d(out_channels)
-
- def forward(self, x):
- o = self.conv1d(x)
- o = self.pad(o)
- o = nn.functional.relu(o)
- o = self.norm(o)
- return o
-
-
-class Conv1dBNBlock(nn.Module):
- """1d convolutional block with batch norm. It is a set of conv1d -> relu -> BN blocks.
-
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of inner convolution channels.
- kernel_size (int): kernel size for convolutional filters.
- dilation (int): dilation for convolution layers.
- num_conv_blocks (int, optional): number of convolutional blocks. Defaults to 2.
- """
-
- def __init__(self, in_channels, out_channels, hidden_channels, kernel_size, dilation, num_conv_blocks=2):
- super().__init__()
- self.conv_bn_blocks = []
- for idx in range(num_conv_blocks):
- layer = Conv1dBN(
- in_channels if idx == 0 else hidden_channels,
- out_channels if idx == (num_conv_blocks - 1) else hidden_channels,
- kernel_size,
- dilation,
- )
- self.conv_bn_blocks.append(layer)
- self.conv_bn_blocks = nn.Sequential(*self.conv_bn_blocks)
-
- def forward(self, x):
- """
- Shapes:
- x: (B, D, T)
- """
- return self.conv_bn_blocks(x)
-
-
-class ResidualConv1dBNBlock(nn.Module):
- """Residual Convolutional Blocks with BN
- Each block has 'num_conv_block' conv layers and 'num_res_blocks' such blocks are connected
- with residual connections.
-
- conv_block = (conv1d -> relu -> bn) x 'num_conv_blocks'
- residuak_conv_block = (x -> conv_block -> + ->) x 'num_res_blocks'
- ' - - - - - - - - - ^
- Args:
- in_channels (int): number of input channels.
- out_channels (int): number of output channels.
- hidden_channels (int): number of inner convolution channels.
- kernel_size (int): kernel size for convolutional filters.
- dilations (list): dilations for each convolution layer.
- num_res_blocks (int, optional): number of residual blocks. Defaults to 13.
- num_conv_blocks (int, optional): number of convolutional blocks in each residual block. Defaults to 2.
- """
-
- def __init__(
- self, in_channels, out_channels, hidden_channels, kernel_size, dilations, num_res_blocks=13, num_conv_blocks=2
- ):
- super().__init__()
- assert len(dilations) == num_res_blocks
- self.res_blocks = nn.ModuleList()
- for idx, dilation in enumerate(dilations):
- block = Conv1dBNBlock(
- in_channels if idx == 0 else hidden_channels,
- out_channels if (idx + 1) == len(dilations) else hidden_channels,
- hidden_channels,
- kernel_size,
- dilation,
- num_conv_blocks,
- )
- self.res_blocks.append(block)
-
- def forward(self, x, x_mask=None):
- if x_mask is None:
- x_mask = 1.0
- o = x * x_mask
- for block in self.res_blocks:
- res = o
- o = block(o)
- o = o + res
- if x_mask is not None:
- o = o * x_mask
- return o
diff --git a/TTS/tts/layers/generic/time_depth_sep_conv.py b/TTS/tts/layers/generic/time_depth_sep_conv.py
deleted file mode 100644
index 186cea02e75e156c40923de91086c369a9ea02ee..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/generic/time_depth_sep_conv.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import torch
-from torch import nn
-
-
-class TimeDepthSeparableConv(nn.Module):
- """Time depth separable convolution as in https://arxiv.org/pdf/1904.02619.pdf
- It shows competative results with less computation and memory footprint."""
-
- def __init__(self, in_channels, hid_channels, out_channels, kernel_size, bias=True):
- super().__init__()
-
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hid_channels = hid_channels
- self.kernel_size = kernel_size
-
- self.time_conv = nn.Conv1d(
- in_channels,
- 2 * hid_channels,
- kernel_size=1,
- stride=1,
- padding=0,
- bias=bias,
- )
- self.norm1 = nn.BatchNorm1d(2 * hid_channels)
- self.depth_conv = nn.Conv1d(
- hid_channels,
- hid_channels,
- kernel_size,
- stride=1,
- padding=(kernel_size - 1) // 2,
- groups=hid_channels,
- bias=bias,
- )
- self.norm2 = nn.BatchNorm1d(hid_channels)
- self.time_conv2 = nn.Conv1d(
- hid_channels,
- out_channels,
- kernel_size=1,
- stride=1,
- padding=0,
- bias=bias,
- )
- self.norm3 = nn.BatchNorm1d(out_channels)
-
- def forward(self, x):
- x_res = x
- x = self.time_conv(x)
- x = self.norm1(x)
- x = nn.functional.glu(x, dim=1)
- x = self.depth_conv(x)
- x = self.norm2(x)
- x = x * torch.sigmoid(x)
- x = self.time_conv2(x)
- x = self.norm3(x)
- x = x_res + x
- return x
-
-
-class TimeDepthSeparableConvBlock(nn.Module):
- def __init__(self, in_channels, hid_channels, out_channels, num_layers, kernel_size, bias=True):
- super().__init__()
- assert (kernel_size - 1) % 2 == 0
- assert num_layers > 1
-
- self.layers = nn.ModuleList()
- layer = TimeDepthSeparableConv(
- in_channels, hid_channels, out_channels if num_layers == 1 else hid_channels, kernel_size, bias
- )
- self.layers.append(layer)
- for idx in range(num_layers - 1):
- layer = TimeDepthSeparableConv(
- hid_channels,
- hid_channels,
- out_channels if (idx + 1) == (num_layers - 1) else hid_channels,
- kernel_size,
- bias,
- )
- self.layers.append(layer)
-
- def forward(self, x, mask):
- for layer in self.layers:
- x = layer(x * mask)
- return x
diff --git a/TTS/tts/layers/generic/transformer.py b/TTS/tts/layers/generic/transformer.py
deleted file mode 100644
index 9b7ecee2bacb68cd330e18630531c97bc6f2e6a3..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/generic/transformer.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-class FFTransformer(nn.Module):
- def __init__(self, in_out_channels, num_heads, hidden_channels_ffn=1024, kernel_size_fft=3, dropout_p=0.1):
- super().__init__()
- self.self_attn = nn.MultiheadAttention(in_out_channels, num_heads, dropout=dropout_p)
-
- padding = (kernel_size_fft - 1) // 2
- self.conv1 = nn.Conv1d(in_out_channels, hidden_channels_ffn, kernel_size=kernel_size_fft, padding=padding)
- self.conv2 = nn.Conv1d(hidden_channels_ffn, in_out_channels, kernel_size=kernel_size_fft, padding=padding)
-
- self.norm1 = nn.LayerNorm(in_out_channels)
- self.norm2 = nn.LayerNorm(in_out_channels)
-
- self.dropout1 = nn.Dropout(dropout_p)
- self.dropout2 = nn.Dropout(dropout_p)
-
- def forward(self, src, src_mask=None, src_key_padding_mask=None):
- """😦 ugly looking with all the transposing"""
- src = src.permute(2, 0, 1)
- src2, enc_align = self.self_attn(src, src, src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)
- src = src + self.dropout1(src2)
- src = self.norm1(src + src2)
- # T x B x D -> B x D x T
- src = src.permute(1, 2, 0)
- src2 = self.conv2(F.relu(self.conv1(src)))
- src2 = self.dropout2(src2)
- src = src + src2
- src = src.transpose(1, 2)
- src = self.norm2(src)
- src = src.transpose(1, 2)
- return src, enc_align
-
-
-class FFTransformerBlock(nn.Module):
- def __init__(self, in_out_channels, num_heads, hidden_channels_ffn, num_layers, dropout_p):
- super().__init__()
- self.fft_layers = nn.ModuleList(
- [
- FFTransformer(
- in_out_channels=in_out_channels,
- num_heads=num_heads,
- hidden_channels_ffn=hidden_channels_ffn,
- dropout_p=dropout_p,
- )
- for _ in range(num_layers)
- ]
- )
-
- def forward(self, x, mask=None, g=None): # pylint: disable=unused-argument
- """
- TODO: handle multi-speaker
- Shapes:
- - x: :math:`[B, C, T]`
- - mask: :math:`[B, 1, T] or [B, T]`
- """
- if mask is not None and mask.ndim == 3:
- mask = mask.squeeze(1)
- # mask is negated, torch uses 1s and 0s reversely.
- mask = ~mask.bool()
- alignments = []
- for layer in self.fft_layers:
- x, align = layer(x, src_key_padding_mask=mask)
- alignments.append(align.unsqueeze(1))
- alignments = torch.cat(alignments, 1)
- return x
-
-
-class FFTDurationPredictor:
- def __init__(
- self, in_channels, hidden_channels, num_heads, num_layers, dropout_p=0.1, cond_channels=None
- ): # pylint: disable=unused-argument
- self.fft = FFTransformerBlock(in_channels, num_heads, hidden_channels, num_layers, dropout_p)
- self.proj = nn.Linear(in_channels, 1)
-
- def forward(self, x, mask=None, g=None): # pylint: disable=unused-argument
- """
- Shapes:
- - x: :math:`[B, C, T]`
- - mask: :math:`[B, 1, T]`
-
- TODO: Handle the cond input
- """
- x = self.fft(x, mask=mask)
- x = self.proj(x)
- return x
diff --git a/TTS/tts/layers/generic/wavenet.py b/TTS/tts/layers/generic/wavenet.py
deleted file mode 100644
index bc89da4fbe6b2425f2a39a578b8fad105a18da38..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/generic/wavenet.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import torch
-from torch import nn
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-class WN(torch.nn.Module):
- """Wavenet layers with weight norm and no input conditioning.
-
- |-----------------------------------------------------------------------------|
- | |-> tanh -| |
- res -|- conv1d(dilation) -> dropout -> + -| * -> conv1d1x1 -> split -|- + -> res
- g -------------------------------------| |-> sigmoid -| |
- o --------------------------------------------------------------------------- + --------- o
-
- Args:
- in_channels (int): number of input channels.
- hidden_channes (int): number of hidden channels.
- kernel_size (int): filter kernel size for the first conv layer.
- dilation_rate (int): dilations rate to increase dilation per layer.
- If it is 2, dilations are 1, 2, 4, 8 for the next 4 layers.
- num_layers (int): number of wavenet layers.
- c_in_channels (int): number of channels of conditioning input.
- dropout_p (float): dropout rate.
- weight_norm (bool): enable/disable weight norm for convolution layers.
- """
-
- def __init__(
- self,
- in_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- num_layers,
- c_in_channels=0,
- dropout_p=0,
- weight_norm=True,
- ):
- super().__init__()
- assert kernel_size % 2 == 1
- assert hidden_channels % 2 == 0
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.num_layers = num_layers
- self.c_in_channels = c_in_channels
- self.dropout_p = dropout_p
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.dropout = nn.Dropout(dropout_p)
-
- # init conditioning layer
- if c_in_channels > 0:
- cond_layer = torch.nn.Conv1d(c_in_channels, 2 * hidden_channels * num_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
- # intermediate layers
- for i in range(num_layers):
- dilation = dilation_rate**i
- padding = int((kernel_size * dilation - dilation) / 2)
- if i == 0:
- in_layer = torch.nn.Conv1d(
- in_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding
- )
- else:
- in_layer = torch.nn.Conv1d(
- hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding
- )
- in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
- self.in_layers.append(in_layer)
-
- if i < num_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
- self.res_skip_layers.append(res_skip_layer)
- # setup weight norm
- if not weight_norm:
- self.remove_weight_norm()
-
- def forward(self, x, x_mask=None, g=None, **kwargs): # pylint: disable=unused-argument
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
- x_mask = 1.0 if x_mask is None else x_mask
- if g is not None:
- g = self.cond_layer(g)
- for i in range(self.num_layers):
- x_in = self.in_layers[i](x)
- x_in = self.dropout(x_in)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
- else:
- g_l = torch.zeros_like(x_in)
- acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.num_layers - 1:
- x = (x + res_skip_acts[:, : self.hidden_channels, :]) * x_mask
- output = output + res_skip_acts[:, self.hidden_channels :, :]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.c_in_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class WNBlocks(nn.Module):
- """Wavenet blocks.
-
- Note: After each block dilation resets to 1 and it increases in each block
- along the dilation rate.
-
- Args:
- in_channels (int): number of input channels.
- hidden_channes (int): number of hidden channels.
- kernel_size (int): filter kernel size for the first conv layer.
- dilation_rate (int): dilations rate to increase dilation per layer.
- If it is 2, dilations are 1, 2, 4, 8 for the next 4 layers.
- num_blocks (int): number of wavenet blocks.
- num_layers (int): number of wavenet layers.
- c_in_channels (int): number of channels of conditioning input.
- dropout_p (float): dropout rate.
- weight_norm (bool): enable/disable weight norm for convolution layers.
- """
-
- def __init__(
- self,
- in_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- num_blocks,
- num_layers,
- c_in_channels=0,
- dropout_p=0,
- weight_norm=True,
- ):
- super().__init__()
- self.wn_blocks = nn.ModuleList()
- for idx in range(num_blocks):
- layer = WN(
- in_channels=in_channels if idx == 0 else hidden_channels,
- hidden_channels=hidden_channels,
- kernel_size=kernel_size,
- dilation_rate=dilation_rate,
- num_layers=num_layers,
- c_in_channels=c_in_channels,
- dropout_p=dropout_p,
- weight_norm=weight_norm,
- )
- self.wn_blocks.append(layer)
-
- def forward(self, x, x_mask=None, g=None):
- o = x
- for layer in self.wn_blocks:
- o = layer(o, x_mask, g)
- return o
diff --git a/TTS/tts/layers/glow_tts/__init__.py b/TTS/tts/layers/glow_tts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tts/layers/glow_tts/decoder.py b/TTS/tts/layers/glow_tts/decoder.py
deleted file mode 100644
index 61c5174ac5e67885288043885290c2906656c99c..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/glow_tts/decoder.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import torch
-from torch import nn
-
-from TTS.tts.layers.generic.normalization import ActNorm
-from TTS.tts.layers.glow_tts.glow import CouplingBlock, InvConvNear
-
-
-def squeeze(x, x_mask=None, num_sqz=2):
- """GlowTTS squeeze operation
- Increase number of channels and reduce number of time steps
- by the same factor.
-
- Note:
- each 's' is a n-dimensional vector.
- ``[s1,s2,s3,s4,s5,s6] --> [[s1, s3, s5], [s2, s4, s6]]``
- """
- b, c, t = x.size()
-
- t = (t // num_sqz) * num_sqz
- x = x[:, :, :t]
- x_sqz = x.view(b, c, t // num_sqz, num_sqz)
- x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * num_sqz, t // num_sqz)
-
- if x_mask is not None:
- x_mask = x_mask[:, :, num_sqz - 1 :: num_sqz]
- else:
- x_mask = torch.ones(b, 1, t // num_sqz).to(device=x.device, dtype=x.dtype)
- return x_sqz * x_mask, x_mask
-
-
-def unsqueeze(x, x_mask=None, num_sqz=2):
- """GlowTTS unsqueeze operation (revert the squeeze)
-
- Note:
- each 's' is a n-dimensional vector.
- ``[[s1, s3, s5], [s2, s4, s6]] --> [[s1, s3, s5, s2, s4, s6]]``
- """
- b, c, t = x.size()
-
- x_unsqz = x.view(b, num_sqz, c // num_sqz, t)
- x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // num_sqz, t * num_sqz)
-
- if x_mask is not None:
- x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, num_sqz).view(b, 1, t * num_sqz)
- else:
- x_mask = torch.ones(b, 1, t * num_sqz).to(device=x.device, dtype=x.dtype)
- return x_unsqz * x_mask, x_mask
-
-
-class Decoder(nn.Module):
- """Stack of Glow Decoder Modules.
-
- ::
-
- Squeeze -> ActNorm -> InvertibleConv1x1 -> AffineCoupling -> Unsqueeze
-
- Args:
- in_channels (int): channels of input tensor.
- hidden_channels (int): hidden decoder channels.
- kernel_size (int): Coupling block kernel size. (Wavenet filter kernel size.)
- dilation_rate (int): rate to increase dilation by each layer in a decoder block.
- num_flow_blocks (int): number of decoder blocks.
- num_coupling_layers (int): number coupling layers. (number of wavenet layers.)
- dropout_p (float): wavenet dropout rate.
- sigmoid_scale (bool): enable/disable sigmoid scaling in coupling layer.
- """
-
- def __init__(
- self,
- in_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- num_flow_blocks,
- num_coupling_layers,
- dropout_p=0.0,
- num_splits=4,
- num_squeeze=2,
- sigmoid_scale=False,
- c_in_channels=0,
- ):
- super().__init__()
-
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.num_flow_blocks = num_flow_blocks
- self.num_coupling_layers = num_coupling_layers
- self.dropout_p = dropout_p
- self.num_splits = num_splits
- self.num_squeeze = num_squeeze
- self.sigmoid_scale = sigmoid_scale
- self.c_in_channels = c_in_channels
-
- self.flows = nn.ModuleList()
- for _ in range(num_flow_blocks):
- self.flows.append(ActNorm(channels=in_channels * num_squeeze))
- self.flows.append(InvConvNear(channels=in_channels * num_squeeze, num_splits=num_splits))
- self.flows.append(
- CouplingBlock(
- in_channels * num_squeeze,
- hidden_channels,
- kernel_size=kernel_size,
- dilation_rate=dilation_rate,
- num_layers=num_coupling_layers,
- c_in_channels=c_in_channels,
- dropout_p=dropout_p,
- sigmoid_scale=sigmoid_scale,
- )
- )
-
- def forward(self, x, x_mask, g=None, reverse=False):
- """
- Shapes:
- - x: :math:`[B, C, T]`
- - x_mask: :math:`[B, 1 ,T]`
- - g: :math:`[B, C]`
- """
- if not reverse:
- flows = self.flows
- logdet_tot = 0
- else:
- flows = reversed(self.flows)
- logdet_tot = None
-
- if self.num_squeeze > 1:
- x, x_mask = squeeze(x, x_mask, self.num_squeeze)
- for f in flows:
- if not reverse:
- x, logdet = f(x, x_mask, g=g, reverse=reverse)
- logdet_tot += logdet
- else:
- x, logdet = f(x, x_mask, g=g, reverse=reverse)
- if self.num_squeeze > 1:
- x, x_mask = unsqueeze(x, x_mask, self.num_squeeze)
- return x, logdet_tot
-
- def store_inverse(self):
- for f in self.flows:
- f.store_inverse()
diff --git a/TTS/tts/layers/glow_tts/duration_predictor.py b/TTS/tts/layers/glow_tts/duration_predictor.py
deleted file mode 100644
index e766ed6ab5a0348eaca8d1482be124003d8b8c68..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/glow_tts/duration_predictor.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import torch
-from torch import nn
-
-from ..generic.normalization import LayerNorm
-
-
-class DurationPredictor(nn.Module):
- """Glow-TTS duration prediction model.
-
- ::
-
- [2 x (conv1d_kxk -> relu -> layer_norm -> dropout)] -> conv1d_1x1 -> durs
-
- Args:
- in_channels (int): Number of channels of the input tensor.
- hidden_channels (int): Number of hidden channels of the network.
- kernel_size (int): Kernel size for the conv layers.
- dropout_p (float): Dropout rate used after each conv layer.
- """
-
- def __init__(self, in_channels, hidden_channels, kernel_size, dropout_p, cond_channels=None, language_emb_dim=None):
- super().__init__()
-
- # add language embedding dim in the input
- if language_emb_dim:
- in_channels += language_emb_dim
-
- # class arguments
- self.in_channels = in_channels
- self.filter_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dropout_p = dropout_p
- # layers
- self.drop = nn.Dropout(dropout_p)
- self.conv_1 = nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)
- self.norm_1 = LayerNorm(hidden_channels)
- self.conv_2 = nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)
- self.norm_2 = LayerNorm(hidden_channels)
- # output layer
- self.proj = nn.Conv1d(hidden_channels, 1, 1)
- if cond_channels is not None and cond_channels != 0:
- self.cond = nn.Conv1d(cond_channels, in_channels, 1)
-
- if language_emb_dim != 0 and language_emb_dim is not None:
- self.cond_lang = nn.Conv1d(language_emb_dim, in_channels, 1)
-
- def forward(self, x, x_mask, g=None, lang_emb=None):
- """
- Shapes:
- - x: :math:`[B, C, T]`
- - x_mask: :math:`[B, 1, T]`
- - g: :math:`[B, C, 1]`
- """
- if g is not None:
- x = x + self.cond(g)
-
- if lang_emb is not None:
- x = x + self.cond_lang(lang_emb)
-
- x = self.conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.norm_1(x)
- x = self.drop(x)
- x = self.conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.norm_2(x)
- x = self.drop(x)
- x = self.proj(x * x_mask)
- return x * x_mask
diff --git a/TTS/tts/layers/glow_tts/encoder.py b/TTS/tts/layers/glow_tts/encoder.py
deleted file mode 100644
index 3b43e527f5e9ca2bd0880bf204e04a1526bc8dfb..0000000000000000000000000000000000000000
--- a/TTS/tts/layers/glow_tts/encoder.py
+++ /dev/null
@@ -1,179 +0,0 @@
-import math
-
-import torch
-from torch import nn
-
-from TTS.tts.layers.generic.gated_conv import GatedConvBlock
-from TTS.tts.layers.generic.res_conv_bn import ResidualConv1dBNBlock
-from TTS.tts.layers.generic.time_depth_sep_conv import TimeDepthSeparableConvBlock
-from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor
-from TTS.tts.layers.glow_tts.glow import ResidualConv1dLayerNormBlock
-from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
-from TTS.tts.utils.helpers import sequence_mask
-
-
-class Encoder(nn.Module):
- """Glow-TTS encoder module.
-
- ::
-
- embedding ->
-
- {% if show_details == true %}
-
-
- Model details
-
-
-
-
-
-
-
-
-
- CLI arguments:
-| CLI key | -Value | -
| {{ key }} | -{{ value }} | -
-
- {% if model_config != None %}
-
-
-
-
-
-
-
-
-
-
- {% endif %}
-
- Model config:
- -| Key | -Value | -
| {{ key }} | -{{ value }} | -
- {% if vocoder_config != None %}
-
-
- {% else %}
-
-
-
-
- {% endif %}
- Vocoder model config:
- -| Key | -Value | -
| {{ key }} | -{{ value }} | -
- Please start server with --show_details=true to see details.
-
-
- {% endif %}
-
-
-
-
\ No newline at end of file
diff --git a/TTS/server/templates/index.html b/TTS/server/templates/index.html
deleted file mode 100644
index 6354d3919d9a1e9c1e22e9866c84c4eb8284bc13..0000000000000000000000000000000000000000
--- a/TTS/server/templates/index.html
+++ /dev/null
@@ -1,154 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/TTS/setup.cfg b/TTS/setup.cfg
deleted file mode 100644
index 1f31cb5decf4c9997bc26769d9cef18e19d759f8..0000000000000000000000000000000000000000
--- a/TTS/setup.cfg
+++ /dev/null
@@ -1,8 +0,0 @@
-[build_py]
-build_lib=temp_build
-
-[bdist_wheel]
-bdist_dir=temp_build
-
-[install_lib]
-build_dir=temp_build
diff --git a/TTS/setup.py b/TTS/setup.py
deleted file mode 100644
index df14b41adcdda02932894e0bdad8acad9bc05c27..0000000000000000000000000000000000000000
--- a/TTS/setup.py
+++ /dev/null
@@ -1,141 +0,0 @@
-#!/usr/bin/env python
-# ,*++++++*, ,*++++++*,
-# *++. .+++ *++. .++*
-# *+* ,++++* *+* *+* ,++++, *+*
-# ,+, .++++++++++* ,++,,,,*+, ,++++++++++. *+,
-# *+. .++++++++++++..++ *+.,++++++++++++. .+*
-# .+* ++++++++++++.*+, .+*.++++++++++++ *+,
-# .++ *++++++++* ++, .++.*++++++++* ++,
-# ,+++*. . .*++, ,++*. .*+++*
-# *+, .,*++**. .**++**. ,+*
-# .+* *+,
-# *+. Coqui .+*
-# *+* +++ TTS +++ *+*
-# .+++*. . . *+++.
-# ,+* *+++*... ...*+++* *+,
-# .++. .""""+++++++****+++++++"""". ++.
-# ,++. .++,
-# .++* *++.
-# *+++, ,+++*
-# .,*++++::::::++++*,.
-# ``````
-
-import os
-import subprocess
-import sys
-from packaging.version import Version
-
-import numpy
-import setuptools.command.build_py
-import setuptools.command.develop
-from Cython.Build import cythonize
-from setuptools import Extension, find_packages, setup
-
-python_version = sys.version.split()[0]
-if Version(python_version) < Version("3.9") or Version(python_version) >= Version("3.12"):
- raise RuntimeError("TTS requires python >= 3.9 and < 3.12 " "but your Python version is {}".format(sys.version))
-
-
-cwd = os.path.dirname(os.path.abspath(__file__))
-with open(os.path.join(cwd, "TTS", "VERSION")) as fin:
- version = fin.read().strip()
-
-
-class build_py(setuptools.command.build_py.build_py): # pylint: disable=too-many-ancestors
- def run(self):
- setuptools.command.build_py.build_py.run(self)
-
-
-class develop(setuptools.command.develop.develop):
- def run(self):
- setuptools.command.develop.develop.run(self)
-
-
-# The documentation for this feature is in server/README.md
-package_data = ["TTS/server/templates/*"]
-
-
-def pip_install(package_name):
- subprocess.call([sys.executable, "-m", "pip", "install", package_name])
-
-
-requirements = open(os.path.join(cwd, "requirements.txt"), "r").readlines()
-with open(os.path.join(cwd, "requirements.notebooks.txt"), "r") as f:
- requirements_notebooks = f.readlines()
-with open(os.path.join(cwd, "requirements.dev.txt"), "r") as f:
- requirements_dev = f.readlines()
-with open(os.path.join(cwd, "requirements.ja.txt"), "r") as f:
- requirements_ja = f.readlines()
-requirements_all = requirements_dev + requirements_notebooks + requirements_ja
-
-with open("README.md", "r", encoding="utf-8") as readme_file:
- README = readme_file.read()
-
-exts = [
- Extension(
- name="TTS.tts.utils.monotonic_align.core",
- sources=["TTS/tts/utils/monotonic_align/core.pyx"],
- )
-]
-setup(
- name="TTS",
- version=version,
- url="https://github.com/coqui-ai/TTS",
- author="Eren Gölge",
- author_email="egolge@coqui.ai",
- description="Deep learning for Text to Speech by Coqui.",
- long_description=README,
- long_description_content_type="text/markdown",
- license="MPL-2.0",
- # cython
- include_dirs=numpy.get_include(),
- ext_modules=cythonize(exts, language_level=3),
- # ext_modules=find_cython_extensions(),
- # package
- include_package_data=True,
- packages=find_packages(include=["TTS"], exclude=["*.tests", "*tests.*", "tests.*", "*tests", "tests"]),
- package_data={
- "TTS": [
- "VERSION",
- ]
- },
- project_urls={
- "Documentation": "https://github.com/coqui-ai/TTS/wiki",
- "Tracker": "https://github.com/coqui-ai/TTS/issues",
- "Repository": "https://github.com/coqui-ai/TTS",
- "Discussions": "https://github.com/coqui-ai/TTS/discussions",
- },
- cmdclass={
- "build_py": build_py,
- "develop": develop,
- # 'build_ext': build_ext
- },
- install_requires=requirements,
- extras_require={
- "all": requirements_all,
- "dev": requirements_dev,
- "notebooks": requirements_notebooks,
- "ja": requirements_ja,
- },
- python_requires=">=3.9.0, <3.12",
- entry_points={"console_scripts": ["tts=TTS.bin.synthesize:main", "tts-server = TTS.server.server:main"]},
- classifiers=[
- "Programming Language :: Python",
- "Programming Language :: Python :: 3",
- "Programming Language :: Python :: 3.9",
- "Programming Language :: Python :: 3.10",
- "Programming Language :: Python :: 3.11",
- "Development Status :: 3 - Alpha",
- "Intended Audience :: Science/Research",
- "Intended Audience :: Developers",
- "Operating System :: POSIX :: Linux",
- "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
- "Topic :: Software Development",
- "Topic :: Software Development :: Libraries :: Python Modules",
- "Topic :: Multimedia :: Sound/Audio :: Speech",
- "Topic :: Multimedia :: Sound/Audio",
- "Topic :: Multimedia",
- "Topic :: Scientific/Engineering :: Artificial Intelligence",
- ],
- zip_safe=False,
-)
diff --git a/TTS/tests/__init__.py b/TTS/tests/__init__.py
deleted file mode 100644
index e102a2dfeeb424b703ac5cbe0832ccadda745a00..0000000000000000000000000000000000000000
--- a/TTS/tests/__init__.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-
-from TTS.config import BaseDatasetConfig
-from TTS.utils.generic_utils import get_cuda
-
-
-def get_device_id():
- use_cuda, _ = get_cuda()
- if use_cuda:
- if "CUDA_VISIBLE_DEVICES" in os.environ and os.environ["CUDA_VISIBLE_DEVICES"] != "":
- GPU_ID = os.environ["CUDA_VISIBLE_DEVICES"].split(",")[0]
- else:
- GPU_ID = "0"
- else:
- GPU_ID = ""
- return GPU_ID
-
-
-def get_tests_path():
- """Returns the path to the test directory."""
- return os.path.dirname(os.path.realpath(__file__))
-
-
-def get_tests_input_path():
- """Returns the path to the test data directory."""
- return os.path.join(get_tests_path(), "inputs")
-
-
-def get_tests_data_path():
- """Returns the path to the test data directory."""
- return os.path.join(get_tests_path(), "data")
-
-
-def get_tests_output_path():
- """Returns the path to the directory for test outputs."""
- path = os.path.join(get_tests_path(), "outputs")
- os.makedirs(path, exist_ok=True)
- return path
-
-
-def run_cli(command):
- exit_status = os.system(command)
- assert exit_status == 0, f" [!] command `{command}` failed."
-
-
-def get_test_data_config():
- return BaseDatasetConfig(formatter="ljspeech", path="tests/data/ljspeech/", meta_file_train="metadata.csv")
-
-
-def assertHasAttr(test_obj, obj, intendedAttr):
- # from https://stackoverflow.com/questions/48078636/pythons-unittest-lacks-an-asserthasattr-method-what-should-i-use-instead
- testBool = hasattr(obj, intendedAttr)
- test_obj.assertTrue(testBool, msg=f"obj lacking an attribute. obj: {obj}, intendedAttr: {intendedAttr}")
-
-
-def assertHasNotAttr(test_obj, obj, intendedAttr):
- testBool = hasattr(obj, intendedAttr)
- test_obj.assertFalse(testBool, msg=f"obj should not have an attribute. obj: {obj}, intendedAttr: {intendedAttr}")
diff --git a/TTS/tests/api_tests/__init__.py b/TTS/tests/api_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/api_tests/test_python_api.py b/TTS/tests/api_tests/test_python_api.py
deleted file mode 100644
index 2025fcd9c6b6558742779083efa96008d383cd80..0000000000000000000000000000000000000000
--- a/TTS/tests/api_tests/test_python_api.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import os
-import unittest
-
-from tests import get_tests_data_path, get_tests_output_path
-from TTS.api import CS_API, TTS
-
-OUTPUT_PATH = os.path.join(get_tests_output_path(), "test_python_api.wav")
-cloning_test_wav_path = os.path.join(get_tests_data_path(), "ljspeech/wavs/LJ001-0028.wav")
-
-
-is_coqui_available = os.environ.get("COQUI_STUDIO_TOKEN")
-
-
-if is_coqui_available:
-
- class CS_APITest(unittest.TestCase):
- def test_speakers(self):
- tts = CS_API()
- self.assertGreater(len(tts.speakers), 1)
-
- def test_emotions(self):
- tts = CS_API()
- self.assertGreater(len(tts.emotions), 1)
-
- def test_list_calls(self):
- tts = CS_API()
- self.assertGreater(len(tts.list_voices()), 1)
- self.assertGreater(len(tts.list_speakers()), 1)
- self.assertGreater(len(tts.list_all_speakers()), 1)
- self.assertGreater(len(tts.list_speakers_as_tts_models()), 1)
-
- def test_name_to_speaker(self):
- tts = CS_API()
- speaker_name = tts.list_speakers_as_tts_models()[0].split("/")[2]
- speaker = tts.name_to_speaker(speaker_name)
- self.assertEqual(speaker.name, speaker_name)
-
- def test_tts(self):
- tts = CS_API()
- wav, sr = tts.tts(text="This is a test.", speaker_name=tts.list_speakers()[0].name)
- self.assertEqual(sr, 44100)
- self.assertGreater(len(wav), 1)
-
- class TTSTest(unittest.TestCase):
- def test_single_speaker_model(self):
- tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False)
-
- error_raised = False
- try:
- tts.tts_to_file(text="Ich bin eine Testnachricht.", speaker="Thorsten", language="de")
- except ValueError:
- error_raised = True
-
- tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path=OUTPUT_PATH)
-
- self.assertTrue(error_raised)
- self.assertFalse(tts.is_multi_speaker)
- self.assertFalse(tts.is_multi_lingual)
- self.assertIsNone(tts.speakers)
- self.assertIsNone(tts.languages)
-
- def test_studio_model(self):
- tts = TTS(model_name="coqui_studio/en/Zacharie Aimilios/coqui_studio")
- tts.tts_to_file(text="This is a test.")
-
- # check speed > 2.0 raises error
- raised_error = False
- try:
- _ = tts.tts(text="This is a test.", speed=4.0, emotion="Sad") # should raise error with speed > 2.0
- except ValueError:
- raised_error = True
- self.assertTrue(raised_error)
-
- # check emotion is invalid
- raised_error = False
- try:
- _ = tts.tts(text="This is a test.", speed=2.0, emotion="No Emo") # should raise error with speed > 2.0
- except ValueError:
- raised_error = True
- self.assertTrue(raised_error)
-
- # check valid call
- wav = tts.tts(text="This is a test.", speed=2.0, emotion="Sad")
- self.assertGreater(len(wav), 0)
-
- def test_fairseq_model(self): # pylint: disable=no-self-use
- tts = TTS(model_name="tts_models/eng/fairseq/vits")
- tts.tts_to_file(text="This is a test.")
-
- def test_multi_speaker_multi_lingual_model(self):
- tts = TTS()
- tts.load_tts_model_by_name(tts.models[0]) # YourTTS
- tts.tts_to_file(
- text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path=OUTPUT_PATH
- )
-
- self.assertTrue(tts.is_multi_speaker)
- self.assertTrue(tts.is_multi_lingual)
- self.assertGreater(len(tts.speakers), 1)
- self.assertGreater(len(tts.languages), 1)
-
- def test_voice_cloning(self): # pylint: disable=no-self-use
- tts = TTS()
- tts.load_tts_model_by_name("tts_models/multilingual/multi-dataset/your_tts")
- tts.tts_to_file("Hello world!", speaker_wav=cloning_test_wav_path, language="en", file_path=OUTPUT_PATH)
-
- def test_voice_conversion(self): # pylint: disable=no-self-use
- tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=False)
- tts.voice_conversion_to_file(
- source_wav=cloning_test_wav_path,
- target_wav=cloning_test_wav_path,
- file_path=OUTPUT_PATH,
- )
diff --git a/TTS/tests/api_tests/test_synthesize_api.py b/TTS/tests/api_tests/test_synthesize_api.py
deleted file mode 100644
index a96c8beab6bd682c735c01c3f299beba4e17a8e8..0000000000000000000000000000000000000000
--- a/TTS/tests/api_tests/test_synthesize_api.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-
-from tests import get_tests_output_path, run_cli
-
-
-def test_synthesize():
- """Test synthesize.py with diffent arguments."""
- output_path = os.path.join(get_tests_output_path(), "output.wav")
-
- # 🐸 Coqui studio model
- run_cli(
- 'tts --model_name "coqui_studio/en/Torcull Diarmuid/coqui_studio" '
- '--text "This is it" '
- f'--out_path "{output_path}"'
- )
diff --git a/TTS/tests/aux_tests/__init__.py b/TTS/tests/aux_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/aux_tests/test_audio_processor.py b/TTS/tests/aux_tests/test_audio_processor.py
deleted file mode 100644
index 5b1fa9d38a0026ecfc8cf257fb90168cf44a20b6..0000000000000000000000000000000000000000
--- a/TTS/tests/aux_tests/test_audio_processor.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import os
-import unittest
-
-from tests import get_tests_input_path, get_tests_output_path, get_tests_path
-from TTS.config import BaseAudioConfig
-from TTS.utils.audio.processor import AudioProcessor
-
-TESTS_PATH = get_tests_path()
-OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests")
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-
-os.makedirs(OUT_PATH, exist_ok=True)
-conf = BaseAudioConfig(mel_fmax=8000, pitch_fmax=640, pitch_fmin=1)
-
-
-# pylint: disable=protected-access
-class TestAudio(unittest.TestCase):
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.ap = AudioProcessor(**conf)
-
- def test_audio_synthesis(self):
- """1. load wav
- 2. set normalization parameters
- 3. extract mel-spec
- 4. invert to wav and save the output
- """
- print(" > Sanity check for the process wav -> mel -> wav")
-
- def _test(max_norm, signal_norm, symmetric_norm, clip_norm):
- self.ap.max_norm = max_norm
- self.ap.signal_norm = signal_norm
- self.ap.symmetric_norm = symmetric_norm
- self.ap.clip_norm = clip_norm
- wav = self.ap.load_wav(WAV_FILE)
- mel = self.ap.melspectrogram(wav)
- wav_ = self.ap.inv_melspectrogram(mel)
- file_name = "/audio_test-melspec_max_norm_{}-signal_norm_{}-symmetric_{}-clip_norm_{}.wav".format(
- max_norm, signal_norm, symmetric_norm, clip_norm
- )
- print(" | > Creating wav file at : ", file_name)
- self.ap.save_wav(wav_, OUT_PATH + file_name)
-
- # maxnorm = 1.0
- _test(1.0, False, False, False)
- _test(1.0, True, False, False)
- _test(1.0, True, True, False)
- _test(1.0, True, False, True)
- _test(1.0, True, True, True)
- # maxnorm = 4.0
- _test(4.0, False, False, False)
- _test(4.0, True, False, False)
- _test(4.0, True, True, False)
- _test(4.0, True, False, True)
- _test(4.0, True, True, True)
-
- def test_normalize(self):
- """Check normalization and denormalization for range values and consistency"""
- print(" > Testing normalization and denormalization.")
- wav = self.ap.load_wav(WAV_FILE)
- wav = self.ap.sound_norm(wav) # normalize audio to get abetter normalization range below.
- self.ap.signal_norm = False
- x = self.ap.melspectrogram(wav)
- x_old = x
-
- self.ap.signal_norm = True
- self.ap.symmetric_norm = False
- self.ap.clip_norm = False
- self.ap.max_norm = 4.0
- x_norm = self.ap.normalize(x)
- print(
- f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} -- {x_norm.min()}"
- )
- assert (x_old - x).sum() == 0
- # check value range
- assert x_norm.max() <= self.ap.max_norm + 1, x_norm.max()
- assert x_norm.min() >= 0 - 1, x_norm.min()
- # check denorm.
- x_ = self.ap.denormalize(x_norm)
- assert (x - x_).sum() < 1e-3, (x - x_).mean()
-
- self.ap.signal_norm = True
- self.ap.symmetric_norm = False
- self.ap.clip_norm = True
- self.ap.max_norm = 4.0
- x_norm = self.ap.normalize(x)
- print(
- f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} -- {x_norm.min()}"
- )
-
- assert (x_old - x).sum() == 0
- # check value range
- assert x_norm.max() <= self.ap.max_norm, x_norm.max()
- assert x_norm.min() >= 0, x_norm.min()
- # check denorm.
- x_ = self.ap.denormalize(x_norm)
- assert (x - x_).sum() < 1e-3, (x - x_).mean()
-
- self.ap.signal_norm = True
- self.ap.symmetric_norm = True
- self.ap.clip_norm = False
- self.ap.max_norm = 4.0
- x_norm = self.ap.normalize(x)
- print(
- f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} -- {x_norm.min()}"
- )
-
- assert (x_old - x).sum() == 0
- # check value range
- assert x_norm.max() <= self.ap.max_norm + 1, x_norm.max()
- assert x_norm.min() >= -self.ap.max_norm - 2, x_norm.min() # pylint: disable=invalid-unary-operand-type
- assert x_norm.min() <= 0, x_norm.min()
- # check denorm.
- x_ = self.ap.denormalize(x_norm)
- assert (x - x_).sum() < 1e-3, (x - x_).mean()
-
- self.ap.signal_norm = True
- self.ap.symmetric_norm = True
- self.ap.clip_norm = True
- self.ap.max_norm = 4.0
- x_norm = self.ap.normalize(x)
- print(
- f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} -- {x_norm.min()}"
- )
-
- assert (x_old - x).sum() == 0
- # check value range
- assert x_norm.max() <= self.ap.max_norm, x_norm.max()
- assert x_norm.min() >= -self.ap.max_norm, x_norm.min() # pylint: disable=invalid-unary-operand-type
- assert x_norm.min() <= 0, x_norm.min()
- # check denorm.
- x_ = self.ap.denormalize(x_norm)
- assert (x - x_).sum() < 1e-3, (x - x_).mean()
-
- self.ap.signal_norm = True
- self.ap.symmetric_norm = False
- self.ap.max_norm = 1.0
- x_norm = self.ap.normalize(x)
- print(
- f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} -- {x_norm.min()}"
- )
-
- assert (x_old - x).sum() == 0
- assert x_norm.max() <= self.ap.max_norm, x_norm.max()
- assert x_norm.min() >= 0, x_norm.min()
- x_ = self.ap.denormalize(x_norm)
- assert (x - x_).sum() < 1e-3
-
- self.ap.signal_norm = True
- self.ap.symmetric_norm = True
- self.ap.max_norm = 1.0
- x_norm = self.ap.normalize(x)
- print(
- f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} -- {x_norm.min()}"
- )
-
- assert (x_old - x).sum() == 0
- assert x_norm.max() <= self.ap.max_norm, x_norm.max()
- assert x_norm.min() >= -self.ap.max_norm, x_norm.min() # pylint: disable=invalid-unary-operand-type
- assert x_norm.min() < 0, x_norm.min()
- x_ = self.ap.denormalize(x_norm)
- assert (x - x_).sum() < 1e-3
-
- def test_scaler(self):
- scaler_stats_path = os.path.join(get_tests_input_path(), "scale_stats.npy")
- conf.stats_path = scaler_stats_path
- conf.preemphasis = 0.0
- conf.do_trim_silence = True
- conf.signal_norm = True
-
- ap = AudioProcessor(**conf)
- mel_mean, mel_std, linear_mean, linear_std, _ = ap.load_stats(scaler_stats_path)
- ap.setup_scaler(mel_mean, mel_std, linear_mean, linear_std)
-
- self.ap.signal_norm = False
- self.ap.preemphasis = 0.0
-
- # test scaler forward and backward transforms
- wav = self.ap.load_wav(WAV_FILE)
- mel_reference = self.ap.melspectrogram(wav)
- mel_norm = ap.melspectrogram(wav)
- mel_denorm = ap.denormalize(mel_norm)
- assert abs(mel_reference - mel_denorm).max() < 1e-4
-
- def test_compute_f0(self): # pylint: disable=no-self-use
- ap = AudioProcessor(**conf)
- wav = ap.load_wav(WAV_FILE)
- pitch = ap.compute_f0(wav)
- mel = ap.melspectrogram(wav)
- assert pitch.shape[0] == mel.shape[1]
diff --git a/TTS/tests/aux_tests/test_embedding_manager.py b/TTS/tests/aux_tests/test_embedding_manager.py
deleted file mode 100644
index 7392150163dd92bfa65e43774db9b6684d807237..0000000000000000000000000000000000000000
--- a/TTS/tests/aux_tests/test_embedding_manager.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import os
-import unittest
-
-import numpy as np
-import torch
-
-from tests import get_tests_input_path
-from TTS.config import load_config
-from TTS.encoder.utils.generic_utils import setup_encoder_model
-from TTS.encoder.utils.io import save_checkpoint
-from TTS.tts.utils.managers import EmbeddingManager
-from TTS.utils.audio import AudioProcessor
-
-encoder_config_path = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
-encoder_model_path = os.path.join(get_tests_input_path(), "checkpoint_0.pth")
-sample_wav_path = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0001.wav")
-sample_wav_path2 = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0002.wav")
-embedding_file_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.json")
-embeddings_file_path2 = os.path.join(get_tests_input_path(), "../data/dummy_speakers2.json")
-embeddings_file_pth_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.pth")
-
-
-class EmbeddingManagerTest(unittest.TestCase):
- """Test emEeddingManager for loading embedding files and computing embeddings from waveforms"""
-
- @staticmethod
- def test_speaker_embedding():
- # load config
- config = load_config(encoder_config_path)
- config.audio.resample = True
-
- # create a dummy speaker encoder
- model = setup_encoder_model(config)
- save_checkpoint(model, None, None, get_tests_input_path(), 0)
-
- # load audio processor and speaker encoder
- manager = EmbeddingManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path)
-
- # load a sample audio and compute embedding
- ap = AudioProcessor(**config.audio)
- waveform = ap.load_wav(sample_wav_path)
- mel = ap.melspectrogram(waveform)
- embedding = manager.compute_embeddings(mel)
- assert embedding.shape[1] == 256
-
- # compute embedding directly from an input file
- embedding = manager.compute_embedding_from_clip(sample_wav_path)
- embedding2 = manager.compute_embedding_from_clip(sample_wav_path)
- embedding = torch.FloatTensor(embedding)
- embedding2 = torch.FloatTensor(embedding2)
- assert embedding.shape[0] == 256
- assert (embedding - embedding2).sum() == 0.0
-
- # compute embedding from a list of wav files.
- embedding3 = manager.compute_embedding_from_clip([sample_wav_path, sample_wav_path2])
- embedding3 = torch.FloatTensor(embedding3)
- assert embedding3.shape[0] == 256
- assert (embedding - embedding3).sum() != 0.0
-
- # remove dummy model
- os.remove(encoder_model_path)
-
- def test_embedding_file_processing(self): # pylint: disable=no-self-use
- manager = EmbeddingManager(embedding_file_path=embeddings_file_pth_path)
- # test embedding querying
- embedding = manager.get_embedding_by_clip(manager.clip_ids[0])
- assert len(embedding) == 256
- embeddings = manager.get_embeddings_by_name(manager.embedding_names[0])
- assert len(embeddings[0]) == 256
- embedding1 = manager.get_mean_embedding(manager.embedding_names[0], num_samples=2, randomize=True)
- assert len(embedding1) == 256
- embedding2 = manager.get_mean_embedding(manager.embedding_names[0], num_samples=2, randomize=False)
- assert len(embedding2) == 256
- assert np.sum(np.array(embedding1) - np.array(embedding2)) != 0
-
- def test_embedding_file_loading(self):
- # test loading a json file
- manager = EmbeddingManager(embedding_file_path=embedding_file_path)
- self.assertEqual(manager.num_embeddings, 384)
- self.assertEqual(manager.embedding_dim, 256)
- # test loading a pth file
- manager = EmbeddingManager(embedding_file_path=embeddings_file_pth_path)
- self.assertEqual(manager.num_embeddings, 384)
- self.assertEqual(manager.embedding_dim, 256)
- # test loading a pth files with duplicate embedding keys
- with self.assertRaises(Exception) as context:
- manager = EmbeddingManager(embedding_file_path=[embeddings_file_pth_path, embeddings_file_pth_path])
- self.assertTrue("Duplicate embedding names" in str(context.exception))
- # test loading embedding files with different embedding keys
- manager = EmbeddingManager(embedding_file_path=[embeddings_file_pth_path, embeddings_file_path2])
- self.assertEqual(manager.embedding_dim, 256)
- self.assertEqual(manager.num_embeddings, 384 * 2)
diff --git a/TTS/tests/aux_tests/test_extract_tts_spectrograms.py b/TTS/tests/aux_tests/test_extract_tts_spectrograms.py
deleted file mode 100644
index f2d119ac350e81797cf3d0d7157cf6a81d1c98d6..0000000000000000000000000000000000000000
--- a/TTS/tests/aux_tests/test_extract_tts_spectrograms.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import os
-import unittest
-
-import torch
-
-from tests import get_tests_input_path, get_tests_output_path, run_cli
-from TTS.config import load_config
-from TTS.tts.models import setup_model
-
-torch.manual_seed(1)
-
-
-# pylint: disable=protected-access
-class TestExtractTTSSpectrograms(unittest.TestCase):
- @staticmethod
- def test_GlowTTS():
- # set paths
- config_path = os.path.join(get_tests_input_path(), "test_glow_tts.json")
- checkpoint_path = os.path.join(get_tests_output_path(), "glowtts.pth")
- output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
- # load config
- c = load_config(config_path)
- # create model
- model = setup_model(c)
- # save model
- torch.save({"model": model.state_dict()}, checkpoint_path)
- # run test
- run_cli(
- f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"'
- )
- run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')
-
- @staticmethod
- def test_Tacotron2():
- # set paths
- config_path = os.path.join(get_tests_input_path(), "test_tacotron2_config.json")
- checkpoint_path = os.path.join(get_tests_output_path(), "tacotron2.pth")
- output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
- # load config
- c = load_config(config_path)
- # create model
- model = setup_model(c)
- # save model
- torch.save({"model": model.state_dict()}, checkpoint_path)
- # run test
- run_cli(
- f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"'
- )
- run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')
-
- @staticmethod
- def test_Tacotron():
- # set paths
- config_path = os.path.join(get_tests_input_path(), "test_tacotron_config.json")
- checkpoint_path = os.path.join(get_tests_output_path(), "tacotron.pth")
- output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
- # load config
- c = load_config(config_path)
- # create model
- model = setup_model(c)
- # save model
- torch.save({"model": model.state_dict()}, checkpoint_path)
- # run test
- run_cli(
- f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"'
- )
- run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')
diff --git a/TTS/tests/aux_tests/test_find_unique_phonemes.py b/TTS/tests/aux_tests/test_find_unique_phonemes.py
deleted file mode 100644
index 018679f573020075fa77cd0b917fbbe75e4627c0..0000000000000000000000000000000000000000
--- a/TTS/tests/aux_tests/test_find_unique_phonemes.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import os
-import unittest
-
-import torch
-
-from tests import get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.configs.vits_config import VitsConfig
-
-torch.manual_seed(1)
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-
-dataset_config_en = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- meta_file_val="metadata.csv",
- path="tests/data/ljspeech",
- language="en",
-)
-
-"""
-dataset_config_pt = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- meta_file_val="metadata.csv",
- path="tests/data/ljspeech",
- language="pt-br",
-)
-"""
-
-
-# pylint: disable=protected-access
-class TestFindUniquePhonemes(unittest.TestCase):
- @staticmethod
- def test_espeak_phonemes():
- # prepare the config
- config = VitsConfig(
- batch_size=2,
- eval_batch_size=2,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- datasets=[dataset_config_en],
- )
- config.save_json(config_path)
-
- # run test
- run_cli(f'CUDA_VISIBLE_DEVICES="" python TTS/bin/find_unique_phonemes.py --config_path "{config_path}"')
-
- @staticmethod
- def test_no_espeak_phonemes():
- # prepare the config
- config = VitsConfig(
- batch_size=2,
- eval_batch_size=2,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- text_cleaner="english_cleaners",
- use_phonemes=True,
- phoneme_language="en-us",
- phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- print_step=1,
- print_eval=True,
- datasets=[dataset_config_en],
- )
- config.save_json(config_path)
-
- # run test
- run_cli(f'CUDA_VISIBLE_DEVICES="" python TTS/bin/find_unique_phonemes.py --config_path "{config_path}"')
diff --git a/TTS/tests/aux_tests/test_numpy_transforms.py b/TTS/tests/aux_tests/test_numpy_transforms.py
deleted file mode 100644
index 00597a0f88038e97ace965234703f43fad872d0f..0000000000000000000000000000000000000000
--- a/TTS/tests/aux_tests/test_numpy_transforms.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import math
-import os
-import unittest
-from dataclasses import dataclass
-
-import librosa
-import numpy as np
-from coqpit import Coqpit
-
-from tests import get_tests_input_path, get_tests_output_path, get_tests_path
-from TTS.utils.audio import numpy_transforms as np_transforms
-
-TESTS_PATH = get_tests_path()
-OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests")
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-
-os.makedirs(OUT_PATH, exist_ok=True)
-
-
-# pylint: disable=no-self-use
-
-
-class TestNumpyTransforms(unittest.TestCase):
- def setUp(self) -> None:
- @dataclass
- class AudioConfig(Coqpit):
- sample_rate: int = 22050
- fft_size: int = 1024
- num_mels: int = 256
- mel_fmax: int = 1800
- mel_fmin: int = 0
- hop_length: int = 256
- win_length: int = 1024
- pitch_fmax: int = 640
- pitch_fmin: int = 1
- trim_db: int = -1
- min_silence_sec: float = 0.01
- gain: float = 1.0
- base: float = 10.0
-
- self.config = AudioConfig()
- self.sample_wav, _ = librosa.load(WAV_FILE, sr=self.config.sample_rate)
-
- def test_build_mel_basis(self):
- """Check if the mel basis is correctly built"""
- print(" > Testing mel basis building.")
- mel_basis = np_transforms.build_mel_basis(**self.config)
- self.assertEqual(mel_basis.shape, (self.config.num_mels, self.config.fft_size // 2 + 1))
-
- def test_millisec_to_length(self):
- """Check if the conversion from milliseconds to length is correct"""
- print(" > Testing millisec to length conversion.")
- win_len, hop_len = np_transforms.millisec_to_length(
- frame_length_ms=1000, frame_shift_ms=12.5, sample_rate=self.config.sample_rate
- )
- self.assertEqual(hop_len, int(12.5 / 1000.0 * self.config.sample_rate))
- self.assertEqual(win_len, self.config.sample_rate)
-
- def test_amplitude_db_conversion(self):
- di = np.random.rand(11)
- o1 = np_transforms.amp_to_db(x=di, gain=1.0, base=10)
- o2 = np_transforms.db_to_amp(x=o1, gain=1.0, base=10)
- np.testing.assert_almost_equal(di, o2, decimal=5)
-
- def test_preemphasis_deemphasis(self):
- di = np.random.rand(11)
- o1 = np_transforms.preemphasis(x=di, coeff=0.95)
- o2 = np_transforms.deemphasis(x=o1, coeff=0.95)
- np.testing.assert_almost_equal(di, o2, decimal=5)
-
- def test_spec_to_mel(self):
- mel_basis = np_transforms.build_mel_basis(**self.config)
- spec = np.random.rand(self.config.fft_size // 2 + 1, 20) # [C, T]
- mel = np_transforms.spec_to_mel(spec=spec, mel_basis=mel_basis)
- self.assertEqual(mel.shape, (self.config.num_mels, 20))
-
- def mel_to_spec(self):
- mel_basis = np_transforms.build_mel_basis(**self.config)
- mel = np.random.rand(self.config.num_mels, 20) # [C, T]
- spec = np_transforms.mel_to_spec(mel=mel, mel_basis=mel_basis)
- self.assertEqual(spec.shape, (self.config.fft_size // 2 + 1, 20))
-
- def test_wav_to_spec(self):
- spec = np_transforms.wav_to_spec(wav=self.sample_wav, **self.config)
- self.assertEqual(
- spec.shape, (self.config.fft_size // 2 + 1, math.ceil(self.sample_wav.shape[0] / self.config.hop_length))
- )
-
- def test_wav_to_mel(self):
- mel_basis = np_transforms.build_mel_basis(**self.config)
- mel = np_transforms.wav_to_mel(wav=self.sample_wav, mel_basis=mel_basis, **self.config)
- self.assertEqual(
- mel.shape, (self.config.num_mels, math.ceil(self.sample_wav.shape[0] / self.config.hop_length))
- )
-
- def test_compute_f0(self):
- pitch = np_transforms.compute_f0(x=self.sample_wav, **self.config)
- mel_basis = np_transforms.build_mel_basis(**self.config)
- mel = np_transforms.wav_to_mel(wav=self.sample_wav, mel_basis=mel_basis, **self.config)
- assert pitch.shape[0] == mel.shape[1]
-
- def test_load_wav(self):
- wav = np_transforms.load_wav(filename=WAV_FILE, resample=False, sample_rate=22050)
- wav_resample = np_transforms.load_wav(filename=WAV_FILE, resample=True, sample_rate=16000)
- self.assertEqual(wav.shape, (self.sample_wav.shape[0],))
- self.assertNotEqual(wav_resample.shape, (self.sample_wav.shape[0],))
diff --git a/TTS/tests/aux_tests/test_speaker_encoder.py b/TTS/tests/aux_tests/test_speaker_encoder.py
deleted file mode 100644
index 01f6118ad714a3711be6cea98647d6c57564485e..0000000000000000000000000000000000000000
--- a/TTS/tests/aux_tests/test_speaker_encoder.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import unittest
-
-import torch as T
-
-from tests import get_tests_input_path
-from TTS.encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss
-from TTS.encoder.models.lstm import LSTMSpeakerEncoder
-from TTS.encoder.models.resnet import ResNetSpeakerEncoder
-
-file_path = get_tests_input_path()
-
-
-class LSTMSpeakerEncoderTests(unittest.TestCase):
- # pylint: disable=R0201
- def test_in_out(self):
- dummy_input = T.rand(4, 80, 20) # B x D x T
- dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
- model = LSTMSpeakerEncoder(input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3)
- # computing d vectors
- output = model.forward(dummy_input)
- assert output.shape[0] == 4
- assert output.shape[1] == 256
- output = model.inference(dummy_input)
- assert output.shape[0] == 4
- assert output.shape[1] == 256
- # compute d vectors by passing LSTM hidden
- # output = model.forward(dummy_input, dummy_hidden)
- # assert output.shape[0] == 4
- # assert output.shape[1] == 20
- # assert output.shape[2] == 256
- # check normalization
- output_norm = T.nn.functional.normalize(output, dim=1, p=2)
- assert_diff = (output_norm - output).sum().item()
- assert output.type() == "torch.FloatTensor"
- assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
- # compute d for a given batch
- dummy_input = T.rand(1, 80, 240) # B x T x D
- output = model.compute_embedding(dummy_input, num_frames=160, num_eval=5)
- assert output.shape[0] == 1
- assert output.shape[1] == 256
- assert len(output.shape) == 2
-
-
-class ResNetSpeakerEncoderTests(unittest.TestCase):
- # pylint: disable=R0201
- def test_in_out(self):
- dummy_input = T.rand(4, 80, 20) # B x D x T
- dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
- model = ResNetSpeakerEncoder(input_dim=80, proj_dim=256)
- # computing d vectors
- output = model.forward(dummy_input)
- assert output.shape[0] == 4
- assert output.shape[1] == 256
- output = model.forward(dummy_input, l2_norm=True)
- assert output.shape[0] == 4
- assert output.shape[1] == 256
-
- # check normalization
- output_norm = T.nn.functional.normalize(output, dim=1, p=2)
- assert_diff = (output_norm - output).sum().item()
- assert output.type() == "torch.FloatTensor"
- assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
- # compute d for a given batch
- dummy_input = T.rand(1, 80, 240) # B x D x T
- output = model.compute_embedding(dummy_input, num_frames=160, num_eval=10)
- assert output.shape[0] == 1
- assert output.shape[1] == 256
- assert len(output.shape) == 2
-
-
-class GE2ELossTests(unittest.TestCase):
- # pylint: disable=R0201
- def test_in_out(self):
- # check random input
- dummy_input = T.rand(4, 5, 64) # num_speaker x num_utterance x dim
- loss = GE2ELoss(loss_method="softmax")
- output = loss.forward(dummy_input)
- assert output.item() >= 0.0
- # check all zeros
- dummy_input = T.ones(4, 5, 64) # num_speaker x num_utterance x dim
- loss = GE2ELoss(loss_method="softmax")
- output = loss.forward(dummy_input)
- assert output.item() >= 0.0
- # check speaker loss with orthogonal d-vectors
- dummy_input = T.empty(3, 64)
- dummy_input = T.nn.init.orthogonal_(dummy_input)
- dummy_input = T.cat(
- [
- dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
- dummy_input[1].repeat(5, 1, 1).transpose(0, 1),
- dummy_input[2].repeat(5, 1, 1).transpose(0, 1),
- ]
- ) # num_speaker x num_utterance x dim
- loss = GE2ELoss(loss_method="softmax")
- output = loss.forward(dummy_input)
- assert output.item() < 0.005
-
-
-class AngleProtoLossTests(unittest.TestCase):
- # pylint: disable=R0201
- def test_in_out(self):
- # check random input
- dummy_input = T.rand(4, 5, 64) # num_speaker x num_utterance x dim
- loss = AngleProtoLoss()
- output = loss.forward(dummy_input)
- assert output.item() >= 0.0
-
- # check all zeros
- dummy_input = T.ones(4, 5, 64) # num_speaker x num_utterance x dim
- loss = AngleProtoLoss()
- output = loss.forward(dummy_input)
- assert output.item() >= 0.0
-
- # check speaker loss with orthogonal d-vectors
- dummy_input = T.empty(3, 64)
- dummy_input = T.nn.init.orthogonal_(dummy_input)
- dummy_input = T.cat(
- [
- dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
- dummy_input[1].repeat(5, 1, 1).transpose(0, 1),
- dummy_input[2].repeat(5, 1, 1).transpose(0, 1),
- ]
- ) # num_speaker x num_utterance x dim
- loss = AngleProtoLoss()
- output = loss.forward(dummy_input)
- assert output.item() < 0.005
-
-
-class SoftmaxAngleProtoLossTests(unittest.TestCase):
- # pylint: disable=R0201
- def test_in_out(self):
- embedding_dim = 64
- num_speakers = 5
- batch_size = 4
-
- dummy_label = T.randint(low=0, high=num_speakers, size=(batch_size, num_speakers))
- # check random input
- dummy_input = T.rand(batch_size, num_speakers, embedding_dim) # num_speaker x num_utterance x dim
- loss = SoftmaxAngleProtoLoss(embedding_dim=embedding_dim, n_speakers=num_speakers)
- output = loss.forward(dummy_input, dummy_label)
- assert output.item() >= 0.0
-
- # check all zeros
- dummy_input = T.ones(batch_size, num_speakers, embedding_dim) # num_speaker x num_utterance x dim
- loss = SoftmaxAngleProtoLoss(embedding_dim=embedding_dim, n_speakers=num_speakers)
- output = loss.forward(dummy_input, dummy_label)
- assert output.item() >= 0.0
diff --git a/TTS/tests/aux_tests/test_speaker_encoder_train.py b/TTS/tests/aux_tests/test_speaker_encoder_train.py
deleted file mode 100644
index 5d8626faa665f7195222be8e50c26c31b4af73cd..0000000000000000000000000000000000000000
--- a/TTS/tests/aux_tests/test_speaker_encoder_train.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.encoder.configs.speaker_encoder_config import SpeakerEncoderConfig
-
-
-def run_test_train():
- command = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --config_path {config_path} "
- f"--coqpit.output_path {output_path} "
- "--coqpit.datasets.0.formatter ljspeech_test "
- "--coqpit.datasets.0.meta_file_train metadata.csv "
- "--coqpit.datasets.0.meta_file_val metadata.csv "
- "--coqpit.datasets.0.path tests/data/ljspeech "
- )
- run_cli(command)
-
-
-config_path = os.path.join(get_tests_output_path(), "test_speaker_encoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = SpeakerEncoderConfig(
- batch_size=4,
- num_classes_in_batch=4,
- num_utter_per_class=2,
- eval_num_classes_in_batch=4,
- eval_num_utter_per_class=2,
- num_loader_workers=1,
- epochs=1,
- print_step=1,
- save_step=2,
- print_eval=True,
- run_eval=True,
- audio=BaseAudioConfig(num_mels=80),
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.loss = "ge2e"
-config.save_json(config_path)
-
-print(config)
-# train the model for one epoch
-run_test_train()
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
-
-# test resnet speaker encoder
-config.model_params["model_name"] = "resnet"
-config.save_json(config_path)
-
-# train the model for one epoch
-run_test_train()
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
-
-# test model with ge2e loss function
-# config.loss = "ge2e"
-# config.save_json(config_path)
-# run_test_train()
-
-# test model with angleproto loss function
-# config.loss = "angleproto"
-# config.save_json(config_path)
-# run_test_train()
-
-# test model with softmaxproto loss function
-config.loss = "softmaxproto"
-config.save_json(config_path)
-run_test_train()
diff --git a/TTS/tests/aux_tests/test_speaker_manager.py b/TTS/tests/aux_tests/test_speaker_manager.py
deleted file mode 100644
index 397f9c81f67fea675ee2e6ce665313785597b536..0000000000000000000000000000000000000000
--- a/TTS/tests/aux_tests/test_speaker_manager.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import unittest
-
-import numpy as np
-import torch
-
-from tests import get_tests_input_path
-from TTS.config import load_config
-from TTS.encoder.utils.generic_utils import setup_encoder_model
-from TTS.encoder.utils.io import save_checkpoint
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.utils.audio import AudioProcessor
-
-encoder_config_path = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
-encoder_model_path = os.path.join(get_tests_input_path(), "checkpoint_0.pth")
-sample_wav_path = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0001.wav")
-sample_wav_path2 = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0002.wav")
-d_vectors_file_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.json")
-d_vectors_file_pth_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.pth")
-
-
-class SpeakerManagerTest(unittest.TestCase):
- """Test SpeakerManager for loading embedding files and computing d_vectors from waveforms"""
-
- @staticmethod
- def test_speaker_embedding():
- # load config
- config = load_config(encoder_config_path)
- config.audio.resample = True
-
- # create a dummy speaker encoder
- model = setup_encoder_model(config)
- save_checkpoint(model, None, None, get_tests_input_path(), 0)
-
- # load audio processor and speaker encoder
- ap = AudioProcessor(**config.audio)
- manager = SpeakerManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path)
-
- # load a sample audio and compute embedding
- waveform = ap.load_wav(sample_wav_path)
- mel = ap.melspectrogram(waveform)
- d_vector = manager.compute_embeddings(mel)
- assert d_vector.shape[1] == 256
-
- # compute d_vector directly from an input file
- d_vector = manager.compute_embedding_from_clip(sample_wav_path)
- d_vector2 = manager.compute_embedding_from_clip(sample_wav_path)
- d_vector = torch.FloatTensor(d_vector)
- d_vector2 = torch.FloatTensor(d_vector2)
- assert d_vector.shape[0] == 256
- assert (d_vector - d_vector2).sum() == 0.0
-
- # compute d_vector from a list of wav files.
- d_vector3 = manager.compute_embedding_from_clip([sample_wav_path, sample_wav_path2])
- d_vector3 = torch.FloatTensor(d_vector3)
- assert d_vector3.shape[0] == 256
- assert (d_vector - d_vector3).sum() != 0.0
-
- # remove dummy model
- os.remove(encoder_model_path)
-
- def test_dvector_file_processing(self):
- manager = SpeakerManager(d_vectors_file_path=d_vectors_file_path)
- self.assertEqual(manager.num_speakers, 1)
- self.assertEqual(manager.embedding_dim, 256)
- manager = SpeakerManager(d_vectors_file_path=d_vectors_file_pth_path)
- self.assertEqual(manager.num_speakers, 1)
- self.assertEqual(manager.embedding_dim, 256)
- d_vector = manager.get_embedding_by_clip(manager.clip_ids[0])
- assert len(d_vector) == 256
- d_vectors = manager.get_embeddings_by_name(manager.speaker_names[0])
- assert len(d_vectors[0]) == 256
- d_vector1 = manager.get_mean_embedding(manager.speaker_names[0], num_samples=2, randomize=True)
- assert len(d_vector1) == 256
- d_vector2 = manager.get_mean_embedding(manager.speaker_names[0], num_samples=2, randomize=False)
- assert len(d_vector2) == 256
- assert np.sum(np.array(d_vector1) - np.array(d_vector2)) != 0
diff --git a/TTS/tests/aux_tests/test_stft_torch.py b/TTS/tests/aux_tests/test_stft_torch.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/bash_tests/test_compute_statistics.sh b/TTS/tests/bash_tests/test_compute_statistics.sh
deleted file mode 100644
index d7f0ab9d4c7d1ded0c1584941cb949bb711ad430..0000000000000000000000000000000000000000
--- a/TTS/tests/bash_tests/test_compute_statistics.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/usr/bin/env bash
-set -xe
-BASEDIR=$(dirname "$0")
-echo "$BASEDIR"
-# run training
-CUDA_VISIBLE_DEVICES="" python TTS/bin/compute_statistics.py --config_path $BASEDIR/../inputs/test_glow_tts.json --out_path $BASEDIR/../outputs/scale_stats.npy
-
diff --git a/TTS/tests/bash_tests/test_demo_server.sh b/TTS/tests/bash_tests/test_demo_server.sh
deleted file mode 100644
index ebd0bc8b89f2ba450a569be4c147ec4959efca18..0000000000000000000000000000000000000000
--- a/TTS/tests/bash_tests/test_demo_server.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-set -xe
-
-python -m TTS.server.server &
-SERVER_PID=$!
-
-echo 'Waiting for server...'
-sleep 30
-
-curl -o /tmp/audio.wav "http://localhost:5002/api/tts?text=synthesis%20schmynthesis"
-python -c 'import sys; import wave; print(wave.open(sys.argv[1]).getnframes())' /tmp/audio.wav
-
-kill $SERVER_PID
-
-rm /tmp/audio.wav
diff --git a/TTS/tests/data/dummy_speakers.json b/TTS/tests/data/dummy_speakers.json
deleted file mode 100644
index 233533b7968a053c0868f9db2446749e9c0effd1..0000000000000000000000000000000000000000
--- a/TTS/tests/data/dummy_speakers.json
+++ /dev/null
@@ -1,100226 +0,0 @@
-{
- "p244_302.wav": {
- "name": "p244",
- "embedding": [
- 0.05539746582508087,
- 0.08493061363697052,
- -0.010013150051236153,
- 0.04369359463453293,
- -0.05871078372001648,
- 0.07792330533266068,
- -0.12001194059848785,
- 0.09205509722232819,
- -0.053687505424022675,
- 0.13110113143920898,
- -0.0672345906496048,
- 0.09076011180877686,
- -0.012022187933325768,
- -0.1773194968700409,
- -0.03690509498119354,
- 0.052139587700366974,
- -0.06511855870485306,
- -0.014169753529131413,
- -0.0788075178861618,
- -0.022713735699653625,
- 0.026002388447523117,
- 0.04142642393708229,
- 0.06633599102497101,
- -0.040966324508190155,
- 0.05216488242149353,
- 0.043708473443984985,
- 0.008947450667619705,
- 0.043884553015232086,
- 0.015242422930896282,
- -0.07271697372198105,
- -0.03943272680044174,
- 0.11445401608943939,
- -0.01976911909878254,
- -0.001584329642355442,
- 0.03226276487112045,
- -0.002877067308872938,
- 0.006218053866177797,
- -0.09210439026355743,
- -0.023884698748588562,
- 0.019102394580841064,
- -0.023189997300505638,
- 0.07678322494029999,
- 0.04511963576078415,
- -0.028598245233297348,
- 0.02654365450143814,
- -0.026303084567189217,
- -0.036059144884347916,
- -0.04994352161884308,
- -0.10899694263935089,
- 0.16808779537677765,
- 0.0568464957177639,
- 0.017774248495697975,
- -0.0766686350107193,
- -0.08056356757879257,
- 0.11318203061819077,
- -0.0009237118065357208,
- -0.11983267217874527,
- -0.04011853411793709,
- 0.06481920927762985,
- 0.18528658151626587,
- -0.020618144422769547,
- 0.0030966848134994507,
- 0.030582068488001823,
- 0.11048240959644318,
- 0.026203282177448273,
- 0.08886025100946426,
- 0.0776662528514862,
- 0.08468905836343765,
- 0.02009391225874424,
- 0.053141623735427856,
- 0.04102938249707222,
- 0.059041380882263184,
- -0.006237464025616646,
- -0.018360337242484093,
- 0.015418153256177902,
- -0.03559226542711258,
- -0.05805520713329315,
- -0.00861218199133873,
- -0.021234268322587013,
- -0.025556275621056557,
- -0.012332704849541187,
- -0.009777471423149109,
- 0.03721384331583977,
- 0.010376224294304848,
- -0.05210898444056511,
- 0.035450324416160583,
- 0.0026437342166900635,
- -0.03329150378704071,
- 0.07028764486312866,
- 0.03101171739399433,
- 0.003101848065853119,
- 0.029428653419017792,
- -0.03445912152528763,
- -0.11992329359054565,
- -0.006469260435551405,
- 0.02472860924899578,
- -0.0021879260893911123,
- 0.06576769798994064,
- 0.04159736633300781,
- -0.044104330241680145,
- 0.10868340730667114,
- 0.06065361574292183,
- -0.00814537052065134,
- 0.029497724026441574,
- -0.0820949599146843,
- 0.09694784879684448,
- 0.10299994796514511,
- 0.007466038689017296,
- 0.0573151595890522,
- -0.04003140702843666,
- 0.0748046338558197,
- 0.07954449951648712,
- -0.14061805605888367,
- -0.07225356996059418,
- 0.030713198706507683,
- -0.01169175747781992,
- 0.015277700498700142,
- 0.101996049284935,
- 0.0023796744644641876,
- 0.013835912570357323,
- 0.08836984634399414,
- -0.08798637241125107,
- -0.053786784410476685,
- -0.025867177173495293,
- 0.07090725004673004,
- -0.05228910967707634,
- 0.024839768186211586,
- 0.0543626993894577,
- -0.048099253326654434,
- -0.01027676835656166,
- 0.04654526337981224,
- -0.0034045036882162094,
- 0.003895972855389118,
- 0.04250902682542801,
- -0.05232023075222969,
- 0.06287448853254318,
- -0.04146592691540718,
- -0.0022073618602007627,
- 0.07169511169195175,
- 0.057035692036151886,
- 0.04202979430556297,
- -0.01752091944217682,
- -0.03615778684616089,
- -0.07597745209932327,
- 0.0076013305224478245,
- 0.03388708084821701,
- 0.06191568076610565,
- -0.01607775315642357,
- 0.004401837941259146,
- -0.06070601940155029,
- -0.07674850523471832,
- 0.059249889105558395,
- -0.02222420647740364,
- 0.10215721279382706,
- -0.000883960397914052,
- 0.010600706562399864,
- 0.09869417548179626,
- 0.011313805356621742,
- -0.01187396701425314,
- -0.04851905256509781,
- -0.020747501403093338,
- 0.043711841106414795,
- 0.04022590070962906,
- -0.06653523445129395,
- -0.04014153778553009,
- 0.012923783622682095,
- 0.0024894566740840673,
- -0.03801071271300316,
- 0.017412755638360977,
- 0.03090047463774681,
- 0.021060986444354057,
- 0.04588426649570465,
- -0.061013057827949524,
- 0.022323710843920708,
- -0.0921829417347908,
- -0.009262383915483952,
- -0.0024641728959977627,
- -0.04311069846153259,
- -0.02953970432281494,
- 0.11183556914329529,
- 0.041883185505867004,
- 0.01362229697406292,
- -0.009713159874081612,
- -0.07398185133934021,
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diff --git a/TTS/tests/data/ljspeech/f0_cache/pitch_stats.npy b/TTS/tests/data/ljspeech/f0_cache/pitch_stats.npy
deleted file mode 100644
index 6203bda43d1728040b40b0726066fcaf2d834e90..0000000000000000000000000000000000000000
--- a/TTS/tests/data/ljspeech/f0_cache/pitch_stats.npy
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b81d6735e13d1401a8228aa04c4de1762edb82e0ad50fc0b0066e0710b7dddaf
-size 424
diff --git a/TTS/tests/data/ljspeech/metadata.csv b/TTS/tests/data/ljspeech/metadata.csv
deleted file mode 100644
index 6c65ca0d80fb2133b57ed16fe2708953ce6595d6..0000000000000000000000000000000000000000
--- a/TTS/tests/data/ljspeech/metadata.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-LJ001-0001|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition
-LJ001-0002|in being comparatively modern.|in being comparatively modern.
-LJ001-0003|For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process|For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process
-LJ001-0004|produced the block books, which were the immediate predecessors of the true printed book,|produced the block books, which were the immediate predecessors of the true printed book,
-LJ001-0005|the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.|the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.
-LJ001-0006|And it is worth mention in passing that, as an example of fine typography,|And it is worth mention in passing that, as an example of fine typography,
-LJ001-0007|the earliest book printed with movable types, the Gutenberg, or "forty-two line Bible" of about 1455,|the earliest book printed with movable types, the Gutenberg, or "forty-two line Bible" of about fourteen fifty-five,
-LJ001-0008|has never been surpassed.|has never been surpassed.
diff --git a/TTS/tests/data/ljspeech/metadata_attn_mask.txt b/TTS/tests/data/ljspeech/metadata_attn_mask.txt
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diff --git a/TTS/tests/data/ljspeech/speakers.json b/TTS/tests/data/ljspeech/speakers.json
deleted file mode 100644
index 461435c2a1ffa3f6c9e2cd219bab58c6ee0c59cd..0000000000000000000000000000000000000000
--- a/TTS/tests/data/ljspeech/speakers.json
+++ /dev/null
@@ -1,2612 +0,0 @@
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diff --git a/TTS/tests/data_tests/__init__.py b/TTS/tests/data_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/data_tests/test_dataset_formatters.py b/TTS/tests/data_tests/test_dataset_formatters.py
deleted file mode 100644
index 30fb79a8e4f64fbefbd6c19427f38d3003409733..0000000000000000000000000000000000000000
--- a/TTS/tests/data_tests/test_dataset_formatters.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import os
-import unittest
-
-from tests import get_tests_input_path
-from TTS.tts.datasets.formatters import common_voice
-
-
-class TestTTSFormatters(unittest.TestCase):
- def test_common_voice_preprocessor(self): # pylint: disable=no-self-use
- root_path = get_tests_input_path()
- meta_file = "common_voice.tsv"
- items = common_voice(root_path, meta_file)
- assert items[0]["text"] == "The applicants are invited for coffee and visa is given immediately."
- assert items[0]["audio_file"] == os.path.join(get_tests_input_path(), "clips", "common_voice_en_20005954.wav")
-
- assert items[-1]["text"] == "Competition for limited resources has also resulted in some local conflicts."
- assert items[-1]["audio_file"] == os.path.join(get_tests_input_path(), "clips", "common_voice_en_19737074.wav")
diff --git a/TTS/tests/data_tests/test_loader.py b/TTS/tests/data_tests/test_loader.py
deleted file mode 100644
index cbd98fc0c5cd27344699a5166bf67998d44886ae..0000000000000000000000000000000000000000
--- a/TTS/tests/data_tests/test_loader.py
+++ /dev/null
@@ -1,242 +0,0 @@
-import os
-import shutil
-import unittest
-
-import numpy as np
-import torch
-from torch.utils.data import DataLoader
-
-from tests import get_tests_data_path, get_tests_output_path
-from TTS.tts.configs.shared_configs import BaseDatasetConfig, BaseTTSConfig
-from TTS.tts.datasets import TTSDataset, load_tts_samples
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-
-# pylint: disable=unused-variable
-
-OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
-os.makedirs(OUTPATH, exist_ok=True)
-
-# create a dummy config for testing data loaders.
-c = BaseTTSConfig(text_cleaner="english_cleaners", num_loader_workers=0, batch_size=2, use_noise_augment=False)
-c.r = 5
-c.data_path = os.path.join(get_tests_data_path(), "ljspeech/")
-ok_ljspeech = os.path.exists(c.data_path)
-
-dataset_config = BaseDatasetConfig(
- formatter="ljspeech_test", # ljspeech_test to multi-speaker
- meta_file_train="metadata.csv",
- meta_file_val=None,
- path=c.data_path,
- language="en",
-)
-
-DATA_EXIST = True
-if not os.path.exists(c.data_path):
- DATA_EXIST = False
-
-print(" > Dynamic data loader test: {}".format(DATA_EXIST))
-
-
-class TestTTSDataset(unittest.TestCase):
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.max_loader_iter = 4
- self.ap = AudioProcessor(**c.audio)
-
- def _create_dataloader(self, batch_size, r, bgs, start_by_longest=False):
- # load dataset
- meta_data_train, meta_data_eval = load_tts_samples(dataset_config, eval_split=True, eval_split_size=0.2)
- items = meta_data_train + meta_data_eval
-
- tokenizer, _ = TTSTokenizer.init_from_config(c)
- dataset = TTSDataset(
- outputs_per_step=r,
- compute_linear_spec=True,
- return_wav=True,
- tokenizer=tokenizer,
- ap=self.ap,
- samples=items,
- batch_group_size=bgs,
- min_text_len=c.min_text_len,
- max_text_len=c.max_text_len,
- min_audio_len=c.min_audio_len,
- max_audio_len=c.max_audio_len,
- start_by_longest=start_by_longest,
- )
- dataloader = DataLoader(
- dataset,
- batch_size=batch_size,
- shuffle=False,
- collate_fn=dataset.collate_fn,
- drop_last=True,
- num_workers=c.num_loader_workers,
- )
- return dataloader, dataset
-
- def test_loader(self):
- if ok_ljspeech:
- dataloader, dataset = self._create_dataloader(1, 1, 0)
-
- for i, data in enumerate(dataloader):
- if i == self.max_loader_iter:
- break
- text_input = data["token_id"]
- _ = data["token_id_lengths"]
- speaker_name = data["speaker_names"]
- linear_input = data["linear"]
- mel_input = data["mel"]
- mel_lengths = data["mel_lengths"]
- _ = data["stop_targets"]
- _ = data["item_idxs"]
- wavs = data["waveform"]
-
- neg_values = text_input[text_input < 0]
- check_count = len(neg_values)
-
- # check basic conditions
- self.assertEqual(check_count, 0)
- self.assertEqual(linear_input.shape[0], mel_input.shape[0], c.batch_size)
- self.assertEqual(linear_input.shape[2], self.ap.fft_size // 2 + 1)
- self.assertEqual(mel_input.shape[2], c.audio["num_mels"])
- self.assertEqual(wavs.shape[1], mel_input.shape[1] * c.audio.hop_length)
- self.assertIsInstance(speaker_name[0], str)
-
- # make sure that the computed mels and the waveform match and correctly computed
- mel_new = self.ap.melspectrogram(wavs[0].squeeze().numpy())
- # remove padding in mel-spectrogram
- mel_dataloader = mel_input[0].T.numpy()[:, : mel_lengths[0]]
- # guarantee that both mel-spectrograms have the same size and that we will remove waveform padding
- mel_new = mel_new[:, : mel_lengths[0]]
- ignore_seg = -(1 + c.audio.win_length // c.audio.hop_length)
- mel_diff = (mel_new[:, : mel_input.shape[1]] - mel_input[0].T.numpy())[:, 0:ignore_seg]
- self.assertLess(abs(mel_diff.sum()), 1e-5)
-
- # check normalization ranges
- if self.ap.symmetric_norm:
- self.assertLessEqual(mel_input.max(), self.ap.max_norm)
- self.assertGreaterEqual(
- mel_input.min(), -self.ap.max_norm # pylint: disable=invalid-unary-operand-type
- )
- self.assertLess(mel_input.min(), 0)
- else:
- self.assertLessEqual(mel_input.max(), self.ap.max_norm)
- self.assertGreaterEqual(mel_input.min(), 0)
-
- def test_batch_group_shuffle(self):
- if ok_ljspeech:
- dataloader, dataset = self._create_dataloader(2, c.r, 16)
- last_length = 0
- frames = dataset.samples
- for i, data in enumerate(dataloader):
- if i == self.max_loader_iter:
- break
- mel_lengths = data["mel_lengths"]
- avg_length = mel_lengths.numpy().mean()
- dataloader.dataset.preprocess_samples()
- is_items_reordered = False
- for idx, item in enumerate(dataloader.dataset.samples):
- if item != frames[idx]:
- is_items_reordered = True
- break
- self.assertGreaterEqual(avg_length, last_length)
- self.assertTrue(is_items_reordered)
-
- def test_start_by_longest(self):
- """Test start_by_longest option.
-
- Ther first item of the fist batch must be longer than all the other items.
- """
- if ok_ljspeech:
- dataloader, _ = self._create_dataloader(2, c.r, 0, True)
- dataloader.dataset.preprocess_samples()
- for i, data in enumerate(dataloader):
- if i == self.max_loader_iter:
- break
- mel_lengths = data["mel_lengths"]
- if i == 0:
- max_len = mel_lengths[0]
- print(mel_lengths)
- self.assertTrue(all(max_len >= mel_lengths))
-
- def test_padding_and_spectrograms(self):
- def check_conditions(idx, linear_input, mel_input, stop_target, mel_lengths):
- self.assertNotEqual(linear_input[idx, -1].sum(), 0) # check padding
- self.assertNotEqual(linear_input[idx, -2].sum(), 0)
- self.assertNotEqual(mel_input[idx, -1].sum(), 0)
- self.assertNotEqual(mel_input[idx, -2].sum(), 0)
- self.assertEqual(stop_target[idx, -1], 1)
- self.assertEqual(stop_target[idx, -2], 0)
- self.assertEqual(stop_target[idx].sum(), 1)
- self.assertEqual(len(mel_lengths.shape), 1)
- self.assertEqual(mel_lengths[idx], linear_input[idx].shape[0])
- self.assertEqual(mel_lengths[idx], mel_input[idx].shape[0])
-
- if ok_ljspeech:
- dataloader, _ = self._create_dataloader(1, 1, 0)
-
- for i, data in enumerate(dataloader):
- if i == self.max_loader_iter:
- break
- linear_input = data["linear"]
- mel_input = data["mel"]
- mel_lengths = data["mel_lengths"]
- stop_target = data["stop_targets"]
- item_idx = data["item_idxs"]
-
- # check mel_spec consistency
- wav = np.asarray(self.ap.load_wav(item_idx[0]), dtype=np.float32)
- mel = self.ap.melspectrogram(wav).astype("float32")
- mel = torch.FloatTensor(mel).contiguous()
- mel_dl = mel_input[0]
- # NOTE: Below needs to check == 0 but due to an unknown reason
- # there is a slight difference between two matrices.
- # TODO: Check this assert cond more in detail.
- self.assertLess(abs(mel.T - mel_dl).max(), 1e-5)
-
- # check mel-spec correctness
- mel_spec = mel_input[0].cpu().numpy()
- wav = self.ap.inv_melspectrogram(mel_spec.T)
- self.ap.save_wav(wav, OUTPATH + "/mel_inv_dataloader.wav")
- shutil.copy(item_idx[0], OUTPATH + "/mel_target_dataloader.wav")
-
- # check linear-spec
- linear_spec = linear_input[0].cpu().numpy()
- wav = self.ap.inv_spectrogram(linear_spec.T)
- self.ap.save_wav(wav, OUTPATH + "/linear_inv_dataloader.wav")
- shutil.copy(item_idx[0], OUTPATH + "/linear_target_dataloader.wav")
-
- # check the outputs
- check_conditions(0, linear_input, mel_input, stop_target, mel_lengths)
-
- # Test for batch size 2
- dataloader, _ = self._create_dataloader(2, 1, 0)
-
- for i, data in enumerate(dataloader):
- if i == self.max_loader_iter:
- break
- linear_input = data["linear"]
- mel_input = data["mel"]
- mel_lengths = data["mel_lengths"]
- stop_target = data["stop_targets"]
- item_idx = data["item_idxs"]
-
- # set id to the longest sequence in the batch
- if mel_lengths[0] > mel_lengths[1]:
- idx = 0
- else:
- idx = 1
-
- # check the longer item in the batch
- check_conditions(idx, linear_input, mel_input, stop_target, mel_lengths)
-
- # check the other item in the batch
- self.assertEqual(linear_input[1 - idx, -1].sum(), 0)
- self.assertEqual(mel_input[1 - idx, -1].sum(), 0)
- self.assertEqual(stop_target[1, mel_lengths[1] - 1], 1)
- self.assertEqual(stop_target[1, mel_lengths[1] :].sum(), stop_target.shape[1] - mel_lengths[1])
- self.assertEqual(len(mel_lengths.shape), 1)
-
- # check batch zero-frame conditions (zero-frame disabled)
- # assert (linear_input * stop_target.unsqueeze(2)).sum() == 0
- # assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
diff --git a/TTS/tests/data_tests/test_samplers.py b/TTS/tests/data_tests/test_samplers.py
deleted file mode 100644
index 0975d5edcb12f32e2cdc4ae99730ad9144cac303..0000000000000000000000000000000000000000
--- a/TTS/tests/data_tests/test_samplers.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import functools
-import random
-import unittest
-
-import torch
-
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.utils.data import get_length_balancer_weights
-from TTS.tts.utils.languages import get_language_balancer_weights
-from TTS.tts.utils.speakers import get_speaker_balancer_weights
-from TTS.utils.samplers import BucketBatchSampler, PerfectBatchSampler
-
-# Fixing random state to avoid random fails
-torch.manual_seed(0)
-
-dataset_config_en = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- meta_file_val="metadata.csv",
- path="tests/data/ljspeech",
- language="en",
-)
-
-dataset_config_pt = BaseDatasetConfig(
- formatter="ljspeech",
- meta_file_train="metadata.csv",
- meta_file_val="metadata.csv",
- path="tests/data/ljspeech",
- language="pt-br",
-)
-
-# Adding the EN samples twice to create a language unbalanced dataset
-train_samples, eval_samples = load_tts_samples(
- [dataset_config_en, dataset_config_en, dataset_config_pt], eval_split=True
-)
-
-# gerenate a speaker unbalanced dataset
-for i, sample in enumerate(train_samples):
- if i < 5:
- sample["speaker_name"] = "ljspeech-0"
- else:
- sample["speaker_name"] = "ljspeech-1"
-
-
-def is_balanced(lang_1, lang_2):
- return 0.85 < lang_1 / lang_2 < 1.2
-
-
-class TestSamplers(unittest.TestCase):
- def test_language_random_sampler(self): # pylint: disable=no-self-use
- random_sampler = torch.utils.data.RandomSampler(train_samples)
- ids = functools.reduce(lambda a, b: a + b, [list(random_sampler) for i in range(100)])
- en, pt = 0, 0
- for index in ids:
- if train_samples[index]["language"] == "en":
- en += 1
- else:
- pt += 1
-
- assert not is_balanced(en, pt), "Random sampler is supposed to be unbalanced"
-
- def test_language_weighted_random_sampler(self): # pylint: disable=no-self-use
- weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(
- get_language_balancer_weights(train_samples), len(train_samples)
- )
- ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
- en, pt = 0, 0
- for index in ids:
- if train_samples[index]["language"] == "en":
- en += 1
- else:
- pt += 1
-
- assert is_balanced(en, pt), "Language Weighted sampler is supposed to be balanced"
-
- def test_speaker_weighted_random_sampler(self): # pylint: disable=no-self-use
- weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(
- get_speaker_balancer_weights(train_samples), len(train_samples)
- )
- ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
- spk1, spk2 = 0, 0
- for index in ids:
- if train_samples[index]["speaker_name"] == "ljspeech-0":
- spk1 += 1
- else:
- spk2 += 1
-
- assert is_balanced(spk1, spk2), "Speaker Weighted sampler is supposed to be balanced"
-
- def test_perfect_sampler(self): # pylint: disable=no-self-use
- classes = set()
- for item in train_samples:
- classes.add(item["speaker_name"])
-
- sampler = PerfectBatchSampler(
- train_samples,
- classes,
- batch_size=2 * 3, # total batch size
- num_classes_in_batch=2,
- label_key="speaker_name",
- shuffle=False,
- drop_last=True,
- )
- batchs = functools.reduce(lambda a, b: a + b, [list(sampler) for i in range(100)])
- for batch in batchs:
- spk1, spk2 = 0, 0
- # for in each batch
- for index in batch:
- if train_samples[index]["speaker_name"] == "ljspeech-0":
- spk1 += 1
- else:
- spk2 += 1
- assert spk1 == spk2, "PerfectBatchSampler is supposed to be perfectly balanced"
-
- def test_perfect_sampler_shuffle(self): # pylint: disable=no-self-use
- classes = set()
- for item in train_samples:
- classes.add(item["speaker_name"])
-
- sampler = PerfectBatchSampler(
- train_samples,
- classes,
- batch_size=2 * 3, # total batch size
- num_classes_in_batch=2,
- label_key="speaker_name",
- shuffle=True,
- drop_last=False,
- )
- batchs = functools.reduce(lambda a, b: a + b, [list(sampler) for i in range(100)])
- for batch in batchs:
- spk1, spk2 = 0, 0
- # for in each batch
- for index in batch:
- if train_samples[index]["speaker_name"] == "ljspeech-0":
- spk1 += 1
- else:
- spk2 += 1
- assert spk1 == spk2, "PerfectBatchSampler is supposed to be perfectly balanced"
-
- def test_length_weighted_random_sampler(self): # pylint: disable=no-self-use
- for _ in range(1000):
- # gerenate a lenght unbalanced dataset with random max/min audio lenght
- min_audio = random.randrange(1, 22050)
- max_audio = random.randrange(44100, 220500)
- for idx, item in enumerate(train_samples):
- # increase the diversity of durations
- random_increase = random.randrange(100, 1000)
- if idx < 5:
- item["audio_length"] = min_audio + random_increase
- else:
- item["audio_length"] = max_audio + random_increase
-
- weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(
- get_length_balancer_weights(train_samples, num_buckets=2), len(train_samples)
- )
- ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
- len1, len2 = 0, 0
- for index in ids:
- if train_samples[index]["audio_length"] < max_audio:
- len1 += 1
- else:
- len2 += 1
- assert is_balanced(len1, len2), "Length Weighted sampler is supposed to be balanced"
-
- def test_bucket_batch_sampler(self):
- bucket_size_multiplier = 2
- sampler = range(len(train_samples))
- sampler = BucketBatchSampler(
- sampler,
- data=train_samples,
- batch_size=7,
- drop_last=True,
- sort_key=lambda x: len(x["text"]),
- bucket_size_multiplier=bucket_size_multiplier,
- )
-
- # check if the samples are sorted by text lenght whuile bucketing
- min_text_len_in_bucket = 0
- bucket_items = []
- for batch_idx, batch in enumerate(list(sampler)):
- if (batch_idx + 1) % bucket_size_multiplier == 0:
- for bucket_item in bucket_items:
- self.assertLessEqual(min_text_len_in_bucket, len(train_samples[bucket_item]["text"]))
- min_text_len_in_bucket = len(train_samples[bucket_item]["text"])
- min_text_len_in_bucket = 0
- bucket_items = []
- else:
- bucket_items += batch
-
- # check sampler length
- self.assertEqual(len(sampler), len(train_samples) // 7)
diff --git a/TTS/tests/inference_tests/__init__.py b/TTS/tests/inference_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/inference_tests/test_synthesize.py b/TTS/tests/inference_tests/test_synthesize.py
deleted file mode 100644
index 28a4088c964acd01df70837d0d5ee00523526f03..0000000000000000000000000000000000000000
--- a/TTS/tests/inference_tests/test_synthesize.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-
-from tests import get_tests_output_path, run_cli
-
-
-def test_synthesize():
- """Test synthesize.py with diffent arguments."""
- output_path = os.path.join(get_tests_output_path(), "output.wav")
- run_cli("tts --list_models")
-
- # single speaker model
- run_cli(f'tts --text "This is an example." --out_path "{output_path}"')
- run_cli(
- "tts --model_name tts_models/en/ljspeech/glow-tts " f'--text "This is an example." --out_path "{output_path}"'
- )
- run_cli(
- "tts --model_name tts_models/en/ljspeech/glow-tts "
- "--vocoder_name vocoder_models/en/ljspeech/multiband-melgan "
- f'--text "This is an example." --out_path "{output_path}"'
- )
diff --git a/TTS/tests/inference_tests/test_synthesizer.py b/TTS/tests/inference_tests/test_synthesizer.py
deleted file mode 100644
index 40e830178c20493f5b1a6c670a9aeb1d2ce8992f..0000000000000000000000000000000000000000
--- a/TTS/tests/inference_tests/test_synthesizer.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import os
-import unittest
-
-from tests import get_tests_input_path
-from TTS.config import load_config
-from TTS.tts.models import setup_model
-from TTS.utils.io import save_checkpoint
-from TTS.utils.synthesizer import Synthesizer
-
-
-class SynthesizerTest(unittest.TestCase):
- # pylint: disable=R0201
- def _create_random_model(self):
- # pylint: disable=global-statement
- config = load_config(os.path.join(get_tests_input_path(), "dummy_model_config.json"))
- model = setup_model(config)
- output_path = os.path.join(get_tests_input_path())
- save_checkpoint(config, model, None, None, 10, 1, output_path)
-
- def test_in_out(self):
- self._create_random_model()
- tts_root_path = get_tests_input_path()
- tts_checkpoint = os.path.join(tts_root_path, "checkpoint_10.pth")
- tts_config = os.path.join(tts_root_path, "dummy_model_config.json")
- synthesizer = Synthesizer(tts_checkpoint, tts_config, None, None)
- synthesizer.tts("Better this test works!!")
-
- def test_split_into_sentences(self):
- """Check demo server sentences split as expected"""
- print("\n > Testing demo server sentence splitting")
- # pylint: disable=attribute-defined-outside-init, protected-access
- self.seg = Synthesizer._get_segmenter("en")
- sis = Synthesizer.split_into_sentences
- assert sis(self, "Hello. Two sentences") == ["Hello.", "Two sentences"]
- assert sis(self, "He went to meet the adviser from Scott, Waltman & Co. next morning.") == [
- "He went to meet the adviser from Scott, Waltman & Co. next morning."
- ]
- assert sis(self, "Let's run it past Sarah and co. They'll want to see this.") == [
- "Let's run it past Sarah and co.",
- "They'll want to see this.",
- ]
- assert sis(self, "Where is Bobby Jr.'s rabbit?") == ["Where is Bobby Jr.'s rabbit?"]
- assert sis(self, "Please inform the U.K. authorities right away.") == [
- "Please inform the U.K. authorities right away."
- ]
- assert sis(self, "Were David and co. at the event?") == ["Were David and co. at the event?"]
- assert sis(self, "paging dr. green, please come to theatre four immediately.") == [
- "paging dr. green, please come to theatre four immediately."
- ]
- assert sis(self, "The email format is Firstname.Lastname@example.com. I think you reversed them.") == [
- "The email format is Firstname.Lastname@example.com.",
- "I think you reversed them.",
- ]
- assert sis(
- self,
- "The demo site is: https://top100.example.com/subsection/latestnews.html. Please send us your feedback.",
- ) == [
- "The demo site is: https://top100.example.com/subsection/latestnews.html.",
- "Please send us your feedback.",
- ]
- assert sis(self, "Scowling at him, 'You are not done yet!' she yelled.") == [
- "Scowling at him, 'You are not done yet!' she yelled."
- ] # with the final lowercase "she" we see it's all one sentence
- assert sis(self, "Hey!! So good to see you.") == ["Hey!!", "So good to see you."]
- assert sis(self, "He went to Yahoo! but I don't know the division.") == [
- "He went to Yahoo! but I don't know the division."
- ]
- assert sis(self, "If you can't remember a quote, “at least make up a memorable one that's plausible...\"") == [
- "If you can't remember a quote, “at least make up a memorable one that's plausible...\""
- ]
- assert sis(self, "The address is not google.com.") == ["The address is not google.com."]
- assert sis(self, "1.) The first item 2.) The second item") == ["1.) The first item", "2.) The second item"]
- assert sis(self, "1) The first item 2) The second item") == ["1) The first item", "2) The second item"]
- assert sis(self, "a. The first item b. The second item c. The third list item") == [
- "a. The first item",
- "b. The second item",
- "c. The third list item",
- ]
diff --git a/TTS/tests/inputs/common_voice.tsv b/TTS/tests/inputs/common_voice.tsv
deleted file mode 100644
index 39fc4190acff0267c220895db29c49eb2a2903a3..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/common_voice.tsv
+++ /dev/null
@@ -1,6 +0,0 @@
-client_id path sentence up_votes down_votes age gender accent locale segment
-95324d489b122a800b840e0b0d068f7363a1a6c2cd2e7365672cc7033e38deaa794bd59edcf8196aa35c9791652b9085ac3839a98bb50ebab4a1e8538a94846b common_voice_en_20005954.mp3 The applicants are invited for coffee and visa is given immediately. 3 0 en
-95324d489b122a800b840e0b0d068f7363a1a6c2cd2e7365672cc7033e38deaa794bd59edcf8196aa35c9791652b9085ac3839a98bb50ebab4a1e8538a94846b common_voice_en_20005955.mp3 Developmental robotics is related to, but differs from, evolutionary robotics. 2 0 en
-95324d489b122a800b840e0b0d068f7363a1a6c2cd2e7365672cc7033e38deaa794bd59edcf8196aa35c9791652b9085ac3839a98bb50ebab4a1e8538a94846b common_voice_en_20005956.mp3 The musical was originally directed and choreographed by Alan Lund. 2 0 en
-954a4181ae9fba89d1b1570f2ae148b3ee18ee2311de978e698f598db859f830d93d35574596d713518e8c96cdae01fce7a08c60c2e0a22bcf01e020924440a6 common_voice_en_19737073.mp3 He graduated from Columbia High School, in Brown County, South Dakota. 2 0 en
-954a4181ae9fba89d1b1570f2ae148b3ee18ee2311de978e698f598db859f830d93d35574596d713518e8c96cdae01fce7a08c60c2e0a22bcf01e020924440a6 common_voice_en_19737074.mp3 Competition for limited resources has also resulted in some local conflicts. 2 0 en
diff --git a/TTS/tests/inputs/dummy_model_config.json b/TTS/tests/inputs/dummy_model_config.json
deleted file mode 100644
index 6504aebc78b0776db9f09ff183c1773978a3099d..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/dummy_model_config.json
+++ /dev/null
@@ -1,102 +0,0 @@
-{
- "run_name": "mozilla-no-loc-fattn-stopnet-sigmoid-loss_masking",
- "run_description": "using forward attention, with original prenet, loss masking,separate stopnet, sigmoid. Compare this with 4817. Pytorch DPP",
-
- "audio":{
- // Audio processing parameters
- "num_mels": 80, // size of the mel spec frame.
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
- "hop_length": 256,
- "win_length": 1024,
- "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "min_level_db": -100, // normalization range
- "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
- "power": 1.5, // value to sharpen wav signals after GL algorithm.
- "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
- // Normalization parameters
- "signal_norm": true, // normalize the spec values in range [0, 1]
- "symmetric_norm": false, // move normalization to range [-1, 1]
- "max_norm": 1, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
- "do_trim_silence": true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
- },
-
- "distributed":{
- "backend": "nccl",
- "url": "tcp:\/\/localhost:54321"
- },
-
- "reinit_layers": [],
-
- "model": "Tacotron2", // one of the model in models/
- "grad_clip": 1, // upper limit for gradients for clipping.
- "epochs": 1000, // total number of epochs to train.
- "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
- "lr_decay": false, // if true, Noam learning rate decaying is applied through training.
- "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
- "windowing": false, // Enables attention windowing. Used only in eval mode.
- "memory_size": 5, // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5.
- "attention_norm": "sigmoid", // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
- "prenet_type": "original", // ONLY TACOTRON2 - "original" or "bn".
- "prenet_dropout": true, // ONLY TACOTRON2 - enable/disable dropout at prenet.
- "use_forward_attn": true, // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
- "forward_attn_mask": false,
- "attention_type": "original",
- "attention_heads": 5,
- "bidirectional_decoder": false,
- "transition_agent": false, // ONLY TACOTRON2 - enable/disable transition agent of forward attention.
- "location_attn": false, // ONLY TACOTRON2 - enable_disable location sensitive attention. It is enabled for TACOTRON by default.
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
- "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
- "stopnet": true, // Train stopnet predicting the end of synthesis.
- "separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
- "use_gst": false,
- "double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
- "ddc_r": 7, // reduction rate for coarse decoder.
-
- "batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
- "eval_batch_size":16,
- "r": 1, // Number of frames to predict for step.
- "wd": 0.000001, // Weight decay weight.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
- "print_step": 10, // Number of steps to log traning on console.
- "batch_group_size": 0, //Number of batches to shuffle after bucketing.
-
- "run_eval": true,
- "test_delay_epochs": 5, //Until attention is aligned, testing only wastes computation time.
- "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
- "data_path": "/media/erogol/data_ssd/Data/Mozilla/", // DATASET-RELATED: can overwritten from command argument
- "meta_file_train": "metadata_train.txt", // DATASET-RELATED: metafile for training dataloader.
- "meta_file_val": "metadata_val.txt", // DATASET-RELATED: metafile for evaluation dataloader.
- "dataset": "mozilla", // DATASET-RELATED: one of mozilla_voice_tts.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
- "min_seq_len": 0, // DATASET-RELATED: minimum text length to use in training
- "max_seq_len": 150, // DATASET-RELATED: maximum text length
- "output_path": "../keep/", // DATASET-RELATED: output path for all training outputs.
- "num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_val_loader_workers": 4, // number of evaluation data loader processes.
- "phoneme_cache_path": "mozilla_us_phonemes", // phoneme computation is slow, therefore, it caches results in the given folder.
- "use_phonemes": false, // use phonemes instead of raw characters. It is suggested for better pronounciation.
- "phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
- "text_cleaner": "phoneme_cleaners",
- "use_speaker_embedding": false, // whether to use additional embeddings for separate speakers
-
- // MULTI-SPEAKER and GST
- "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
- "gst": { // gst parameter if gst is enabled
- "gst_style_input": null, // Condition the style input either on a
- // -> wave file [path to wave] or
- // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
- // with the dictionary being len(dict) <= len(gst_style_tokens).
- "gst_use_speaker_embedding": true, // if true pass speaker embedding in attention input GST.
- "gst_embedding_dim": 512,
- "gst_num_heads": 4,
- "gst_style_tokens": 10
- }
-}
-
-
diff --git a/TTS/tests/inputs/example_1.wav b/TTS/tests/inputs/example_1.wav
deleted file mode 100644
index b1a0ed110ab9763dab7428f6273d696fecb4205d..0000000000000000000000000000000000000000
Binary files a/TTS/tests/inputs/example_1.wav and /dev/null differ
diff --git a/TTS/tests/inputs/language_ids.json b/TTS/tests/inputs/language_ids.json
deleted file mode 100644
index 27bb15206f1b06db9f2f14451caa7f5f43bdb7f1..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/language_ids.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
- "en": 0,
- "fr-fr": 1,
- "pt-br": 2
-}
\ No newline at end of file
diff --git a/TTS/tests/inputs/scale_stats.npy b/TTS/tests/inputs/scale_stats.npy
deleted file mode 100644
index 74be37553ee6204095a6f791ebe10f8f10140fba..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/scale_stats.npy
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:66e84c8c947d3cdead90cc37710c7b426562e2520e59500bc8e53c435152506c
-size 10479
diff --git a/TTS/tests/inputs/server_config.json b/TTS/tests/inputs/server_config.json
deleted file mode 100644
index f0a922836adbebc2b488c218f0969c707bb7d4ed..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/server_config.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
- "tts_checkpoint":"checkpoint_10.pth", // tts checkpoint file
- "tts_config":"dummy_model_config.json", // tts config.json file
- "tts_speakers": null, // json file listing speaker ids. null if no speaker embedding.
- "wavernn_lib_path": null, // Rootpath to wavernn project folder to be imported. If this is null, model uses GL for speech synthesis.
- "wavernn_file": null, // wavernn checkpoint file name
- "wavernn_config": null, // wavernn config file
- "vocoder_config":null,
- "vocoder_checkpoint": null,
- "is_wavernn_batched":true,
- "port": 5002,
- "use_cuda": false,
- "debug": true
-}
diff --git a/TTS/tests/inputs/test_align_tts.json b/TTS/tests/inputs/test_align_tts.json
deleted file mode 100644
index 3f928c7e922fd3abb431880ae65db73b22f04974..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_align_tts.json
+++ /dev/null
@@ -1,158 +0,0 @@
-{
- "model": "align_tts",
- "run_name": "test_sample_dataset_run",
- "run_description": "sample dataset test run",
-
- // AUDIO PARAMETERS
- "audio":{
- // stft parameters
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "win_length": 1024, // stft window length in ms.
- "hop_length": 256, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
- // Audio processing parameters
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate.
- "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
-
- // Silence trimming
- "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
- "trim_db": 60, // threshold for timming silence. Set this according to your dataset.
-
- // Griffin-Lim
- "power": 1.5, // value to sharpen wav signals after GL algorithm.
- "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
- // MelSpectrogram parameters
- "num_mels": 80, // size of the mel spec frame.
- "mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
- "spec_gain": 1,
-
- // Normalization parameters
- "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
- "min_level_db": -100, // lower bound for normalization
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
- },
-
- // VOCABULARY PARAMETERS
- // if custom character set is not defined,
- // default set in symbols.py is used
- // "characters":{
- // "pad": "_",
- // "eos": "&",
- // "bos": "*",
- // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZÇÃÀÁÂÊÉÍÓÔÕÚÛabcdefghijklmnopqrstuvwxyzçãàáâêéíóôõúû!(),-.:;? ",
- // "punctuations":"!'(),-.:;? ",
- // "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ'̃' "
- // },
-
- "add_blank": false, // if true add a new token after each token of the sentence. This increases the size of the input sequence, but has considerably improved the prosody of the GlowTTS model.
-
- // DISTRIBUTED TRAINING
- "distributed":{
- "backend": "nccl",
- "url": "tcp:\/\/localhost:54321"
- },
-
- "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
- // MODEL PARAMETERS
- "positional_encoding": true,
- "hidden_channels": 256,
- "hidden_channels_dp": 256,
- "encoder_type": "fftransformer",
- "encoder_params":{
- "hidden_channels_ffn": 1024 ,
- "num_heads": 2,
- "num_layers": 6,
- "dropout_p": 0.1
- },
- "decoder_type": "fftransformer",
- "decoder_params":{
- "hidden_channels_ffn": 1024 ,
- "num_heads": 2,
- "num_layers": 6,
- "dropout_p": 0.1
- },
-
-
- // TRAINING
- "batch_size":2, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
- "eval_batch_size":1,
- "r": 1, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
- "phase_start_steps": null,
-
-
- // LOSS PARAMETERS
- "ssim_alpha": 1,
- "spec_loss_alpha": 1,
- "dur_loss_alpha": 1,
- "mdn_alpha": 1,
-
- // VALIDATION
- "run_eval": true,
- "test_delay_epochs": -1, //Until attention is aligned, testing only wastes computation time.
- "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
- // OPTIMIZER
- "noam_schedule": true, // use noam warmup and lr schedule.
- "grad_clip": 1.0, // upper limit for gradients for clipping.
- "epochs": 1, // total number of epochs to train.
- "lr": 0.002, // Initial learning rate. If Noam decay is active, maximum learning rate.
- "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
-
- // TENSORBOARD and LOGGING
- "print_step": 1, // Number of steps to log training on console.
- "tb_plot_step": 100, // Number of steps to plot TB training figures.
- "print_eval": false, // If True, it prints intermediate loss values in evalulation.
- "save_step": 5000, // Number of training steps expected to save traninpg stats and checkpoints.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "keep_all_best": true, // If true, keeps all best_models after keep_after steps
- "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.:set n
- "mixed_precision": false,
-
- // DATA LOADING
- "text_cleaner": "english_cleaners",
- "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
- "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_eval_loader_workers": 0, // number of evaluation data loader processes.
- "batch_group_size": 0, //Number of batches to shuffle after bucketing.
- "min_seq_len": 2, // DATASET-RELATED: minimum text length to use in training
- "max_seq_len": 300, // DATASET-RELATED: maximum text length
- "compute_f0": false, // compute f0 values in data-loader
- "compute_input_seq_cache": false, // if true, text sequences are computed before starting training. If phonemes are enabled, they are also computed at this stage.
-
- // PATHS
- "output_path": "tests/train_outputs/",
-
- // PHONEMES
- "phoneme_cache_path": "tests/train_outputs/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder.
- "use_phonemes": false, // use phonemes instead of raw characters. It is suggested for better pronoun[ciation.
- "phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
-
- // MULTI-SPEAKER and GST
- "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
- "use_d_vector_file": false, // if true, forces the model to use external embedding per sample instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
- "d_vector_file": "/home/erogol/Data/libritts/speakers.json", // if not null and use_d_vector_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
-
-
- // DATASETS
- "datasets": // List of datasets. They all merged and they get different speaker_ids.
- [
- {
- "formatter": "ljspeech",
- "path": "tests/data/ljspeech/",
- "meta_file_train": "metadata.csv",
- "meta_file_val": "metadata.csv",
- "meta_file_attn_mask": null
- }
- ]
-}
\ No newline at end of file
diff --git a/TTS/tests/inputs/test_config.json b/TTS/tests/inputs/test_config.json
deleted file mode 100644
index 8f8810d17f1a3871c50fa5cd0ba093096a9b4d04..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_config.json
+++ /dev/null
@@ -1,69 +0,0 @@
- {
- "audio":{
- "audio_processor": "audio",
- "num_mels": 80,
- "fft_size": 1024,
- "sample_rate": 22050,
- "frame_length_ms": null,
- "frame_shift_ms": null,
- "hop_length": 256,
- "win_length": 1024,
- "preemphasis": 0.97,
- "min_level_db": -100,
- "ref_level_db": 20,
- "power": 1.5,
- "griffin_lim_iters": 30,
- "signal_norm": true,
- "symmetric_norm": true,
- "clip_norm": true,
- "max_norm": 4,
- "mel_fmin": 0,
- "mel_fmax": 8000,
- "do_trim_silence": false,
- "spec_gain": 20
- },
-
- "characters":{
- "pad": "_",
- "eos": "~",
- "bos": "^",
- "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
- "punctuations":"!'(),-.:;? ",
- "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫʲ"
- },
-
- "hidden_size": 128,
- "embedding_size": 256,
- "text_cleaner": "english_cleaners",
-
- "epochs": 2000,
- "lr": 0.003,
- "lr_patience": 5,
- "lr_decay": 0.5,
- "batch_size": 2,
- "r": 5,
- "mk": 1.0,
- "num_loader_workers": 0,
- "memory_size": 5,
-
- "save_step": 200,
- "data_path": "tests/data/ljspeech/",
- "output_path": "result",
- "min_seq_len": 0,
- "max_seq_len": 300,
- "log_dir": "tests/outputs/",
-
-
- "use_speaker_embedding": false,
- "use_gst": true,
- "gst": {
- "gst_style_input": null,
-
-
-
- "gst_use_speaker_embedding": true,
- "gst_embedding_dim": 512,
- "gst_num_heads": 4,
- "gst_num_style_tokens": 10
- }
-}
diff --git a/TTS/tests/inputs/test_glow_tts.json b/TTS/tests/inputs/test_glow_tts.json
deleted file mode 100644
index 8c0ab864b71a1672a4bd520707224fcce3b72be3..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_glow_tts.json
+++ /dev/null
@@ -1,149 +0,0 @@
-{
- "model": "glow_tts",
- "run_name": "glow-tts-gatedconv",
- "run_description": "glow-tts model training with gated conv.",
-
- // AUDIO PARAMETERS
- "audio":{
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "win_length": 1024, // stft window length in ms.
- "hop_length": 256, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
- // Audio processing parameters
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
- "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "ref_level_db": 0, // reference level db, theoretically 20db is the sound of air.
-
- // Griffin-Lim
- "power": 1.1, // value to sharpen wav signals after GL algorithm.
- "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
- // Silence trimming
- "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
- "trim_db": 60, // threshold for timming silence. Set this according to your dataset.
-
- // MelSpectrogram parameters
- "num_mels": 80, // size of the mel spec frame.
- "mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
- "spec_gain": 1.0, // scaler value appplied after log transform of spectrogram.
-
- // Normalization parameters
- "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
- "min_level_db": -100, // lower bound for normalization
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 1.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
- },
-
- // VOCABULARY PARAMETERS
- // if custom character set is not defined,
- // default set in symbols.py is used
- // "characters":{
- // "pad": "_",
- // "eos": "~",
- // "bos": "^",
- // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
- // "punctuations":"!'(),-.:;? ",
- // "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
- // },
-
- "add_blank": false, // if true add a new token after each token of the sentence. This increases the size of the input sequence, but has considerably improved the prosody of the GlowTTS model.
-
- // DISTRIBUTED TRAINING
- "mixed_precision": false,
- "distributed":{
- "backend": "nccl",
- "url": "tcp:\/\/localhost:54323"
- },
-
- "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
- // MODEL PARAMETERS
- "use_mas": false, // use Monotonic Alignment Search if true. Otherwise use pre-computed attention alignments.
-
- // TRAINING
- "batch_size": 8, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
- "eval_batch_size": 8,
- "r": 1, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
- "data_dep_init_iter": 1,
-
- // VALIDATION
- "run_eval": true,
- "test_delay_epochs": 0, //Until attention is aligned, testing only wastes computation time.
- "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
- // OPTIMIZER
- "noam_schedule": true, // use noam warmup and lr schedule.
- "grad_clip": 5.0, // upper limit for gradients for clipping.
- "epochs": 1, // total number of epochs to train.
- "lr": 1e-3, // Initial learning rate. If Noam decay is active, maximum learning rate.
- "wd": 0.000001, // Weight decay weight.
- "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
- "seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
-
- "hidden_channels_encoder": 192,
- "hidden_channels_decoder": 192,
- "hidden_channels_duration_predictor": 256,
- "use_encoder_prenet": true,
- "encoder_type": "rel_pos_transformer",
- "encoder_params": {
- "kernel_size":3,
- "dropout_p": 0.1,
- "num_layers": 6,
- "num_heads": 2,
- "hidden_channels_ffn": 768,
- "input_length": null
- },
-
- // TENSORBOARD and LOGGING
- "print_step": 25, // Number of steps to log training on console.
- "tb_plot_step": 100, // Number of steps to plot TB training figures.
- "print_eval": false, // If True, it prints intermediate loss values in evalulation.
- "save_step": 5000, // Number of training steps expected to save traninpg stats and checkpoints.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "keep_all_best": true, // If true, keeps all best_models after keep_after steps
- "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
- "apex_amp_level": null,
-
- // DATA LOADING
- "text_cleaner": "phoneme_cleaners",
- "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
- "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_eval_loader_workers": 0, // number of evaluation data loader processes.
- "batch_group_size": 0, //Number of batches to shuffle after bucketing.
- "min_seq_len": 3, // DATASET-RELATED: minimum text length to use in training
- "max_seq_len": 500, // DATASET-RELATED: maximum text length
- "compute_f0": false, // compute f0 values in data-loader
- "compute_input_seq_cache": true,
- "use_noise_augment": true,
-
- // PATHS
- "output_path": "tests/train_outputs/",
-
- // PHONEMES
- "phoneme_cache_path": "tests/outputs/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder.
- "use_phonemes": false, // use phonemes instead of raw characters. It is suggested for better pronounciation.
- "phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
-
- // MULTI-SPEAKER and GST
- "use_d_vector_file": false,
- "d_vector_file": null,
- "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
-
- // DATASETS
- "datasets": // List of datasets. They all merged and they get different speaker_ids.
- [
- {
- "formatter": "ljspeech",
- "path": "tests/data/ljspeech/",
- "meta_file_train": "metadata.csv",
- "meta_file_val": "metadata.csv"
- }
- ]
-}
diff --git a/TTS/tests/inputs/test_speaker_encoder_config.json b/TTS/tests/inputs/test_speaker_encoder_config.json
deleted file mode 100644
index bfcc17ab0e6390bdd00830f2a8c0ffc7e6f14032..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_speaker_encoder_config.json
+++ /dev/null
@@ -1,61 +0,0 @@
-
-{
- "model": "speaker_encoder",
- "run_name": "test_speaker_encoder",
- "run_description": "test speaker encoder.",
- "audio":{
- // Audio processing parameters
- "num_mels": 40, // size of the mel spec frame.
- "fft_size": 400, // number of stft frequency levels. Size of the linear spectogram frame.
- "sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
- "win_length": 400, // stft window length in ms.
- "hop_length": 160, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
- "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "min_level_db": -100, // normalization range
- "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
- "power": 1.5, // value to sharpen wav signals after GL algorithm.
- "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
- // Normalization parameters
- "signal_norm": true, // normalize the spec values in range [0, 1]
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
- "do_trim_silence": true, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
- "trim_db": 60 // threshold for timming silence. Set this according to your dataset.
- },
- "reinit_layers": [],
- "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
- "grad_clip": 3.0, // upper limit for gradients for clipping.
- "epochs": 1000, // total number of epochs to train.
- "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
- "lr_decay": false, // if true, Noam learning rate decaying is applied through training.
- "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
- "steps_plot_stats": 10, // number of steps to plot embeddings.
- "num_classes_in_batch": 64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
- "num_utter_per_class": 10, //
- "num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "wd": 0.000001, // Weight decay weight.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
- "print_step": 20, // Number of steps to log traning on console.
- "batch_size": 32,
- "output_path": "", // DATASET-RELATED: output path for all training outputs.
- "model_params": {
- "model_name": "lstm",
- "input_dim": 40,
- "proj_dim": 256,
- "lstm_dim": 768,
- "num_lstm_layers": 3,
- "use_lstm_with_projection": true
- },
- "storage": {
- "sample_from_storage_p": 0.66, // the probability with which we'll sample from the DataSet in-memory storage
- "storage_size": 15 // the size of the in-memory storage with respect to a single batch
- },
- "datasets":null
-}
\ No newline at end of file
diff --git a/TTS/tests/inputs/test_speedy_speech.json b/TTS/tests/inputs/test_speedy_speech.json
deleted file mode 100644
index 4a7eea5ded0ce2e89a13882864ab9dea226239fd..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_speedy_speech.json
+++ /dev/null
@@ -1,155 +0,0 @@
-{
- "model": "speedy_speech",
- "run_name": "test_sample_dataset_run",
- "run_description": "sample dataset test run",
-
- // AUDIO PARAMETERS
- "audio":{
- // stft parameters
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "win_length": 1024, // stft window length in ms.
- "hop_length": 256, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
- // Audio processing parameters
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate.
- "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
-
- // Silence trimming
- "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
- "trim_db": 60, // threshold for timming silence. Set this according to your dataset.
-
- // Griffin-Lim
- "power": 1.5, // value to sharpen wav signals after GL algorithm.
- "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
- // MelSpectrogram parameters
- "num_mels": 80, // size of the mel spec frame.
- "mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
- "spec_gain": 1,
-
- // Normalization parameters
- "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
- "min_level_db": -100, // lower bound for normalization
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
- },
-
- // VOCABULARY PARAMETERS
- // if custom character set is not defined,
- // default set in symbols.py is used
- // "characters":{
- // "pad": "_",
- // "eos": "&",
- // "bos": "*",
- // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZÇÃÀÁÂÊÉÍÓÔÕÚÛabcdefghijklmnopqrstuvwxyzçãàáâêéíóôõúû!(),-.:;? ",
- // "punctuations":"!'(),-.:;? ",
- // "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ'̃' "
- // },
-
- "add_blank": false, // if true add a new token after each token of the sentence. This increases the size of the input sequence, but has considerably improved the prosody of the GlowTTS model.
-
- // DISTRIBUTED TRAINING
- "distributed":{
- "backend": "nccl",
- "url": "tcp:\/\/localhost:54321"
- },
-
- "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
- // MODEL PARAMETERS
- "positional_encoding": true,
- "hidden_channels": 128,
- "encoder_type": "residual_conv_bn",
- "encoder_type": "residual_conv_bn",
- "encoder_params":{
- "kernel_size": 4,
- "dilations": [1, 2, 4, 1, 2, 4, 1, 2, 4, 1, 2, 4, 1],
- "num_conv_blocks": 2,
- "num_res_blocks": 13
- },
- "decoder_type": "residual_conv_bn",
- "decoder_params":{
- "kernel_size": 4,
- "dilations": [1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1],
- "num_conv_blocks": 2,
- "num_res_blocks": 17
- },
-
-
- // TRAINING
- "batch_size":64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
- "eval_batch_size":32,
- "r": 1, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
-
- // LOSS PARAMETERS
- "ssim_alpha": 1,
- "l1_alpha": 1,
- "huber_alpha": 1,
-
- // VALIDATION
- "run_eval": true,
- "test_delay_epochs": -1, //Until attention is aligned, testing only wastes computation time.
- "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
- // OPTIMIZER
- "noam_schedule": true, // use noam warmup and lr schedule.
- "grad_clip": 1.0, // upper limit for gradients for clipping.
- "epochs": 1, // total number of epochs to train.
- "lr": 0.002, // Initial learning rate. If Noam decay is active, maximum learning rate.
- "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
-
- // TENSORBOARD and LOGGING
- "print_step": 1, // Number of steps to log training on console.
- "tb_plot_step": 100, // Number of steps to plot TB training figures.
- "print_eval": false, // If True, it prints intermediate loss values in evalulation.
- "save_step": 5000, // Number of training steps expected to save traninpg stats and checkpoints.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "keep_all_best": true, // If true, keeps all best_models after keep_after steps
- "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.:set n
- "mixed_precision": false,
-
- // DATA LOADING
- "text_cleaner": "english_cleaners",
- "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
- "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_eval_loader_workers": 0, // number of evaluation data loader processes.
- "batch_group_size": 0, //Number of batches to shuffle after bucketing.
- "min_seq_len": 2, // DATASET-RELATED: minimum text length to use in training
- "max_seq_len": 300, // DATASET-RELATED: maximum text length
- "compute_f0": false, // compute f0 values in data-loader
- "compute_input_seq_cache": false, // if true, text sequences are computed before starting training. If phonemes are enabled, they are also computed at this stage.
-
- // PATHS
- "output_path": "tests/train_outputs/",
-
- // PHONEMES
- "phoneme_cache_path": "tests/train_outputs/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder.
- "use_phonemes": false, // use phonemes instead of raw characters. It is suggested for better pronoun[ciation.
- "phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
-
- // MULTI-SPEAKER and GST
- "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
- "use_d_vector_file": false, // if true, forces the model to use external embedding per sample instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
- "d_vector_file": "/home/erogol/Data/libritts/speakers.json", // if not null and use_d_vector_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
-
-
- // DATASETS
- "datasets": // List of datasets. They all merged and they get different speaker_ids.
- [
- {
- "formatter": "ljspeech",
- "path": "tests/data/ljspeech/",
- "meta_file_train": "metadata.csv",
- "meta_file_val": "metadata.csv",
- "meta_file_attn_mask": "tests/data/ljspeech/metadata_attn_mask.txt"
- }
- ]
-}
\ No newline at end of file
diff --git a/TTS/tests/inputs/test_tacotron2_config.json b/TTS/tests/inputs/test_tacotron2_config.json
deleted file mode 100644
index 30e5fa7a3754bc4ec5e11e46d18254134ea465bb..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_tacotron2_config.json
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "model": "Tacotron2",
- "run_name": "test_sample_dataset_run",
- "run_description": "sample dataset test run",
-
- // AUDIO PARAMETERS
- "audio":{
- // stft parameters
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "win_length": 1024, // stft window length in ms.
- "hop_length": 256, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
- // Audio processing parameters
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate.
- "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
-
- // Silence trimming
- "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
- "trim_db": 60, // threshold for timming silence. Set this according to your dataset.
-
- // Griffin-Lim
- "power": 1.5, // value to sharpen wav signals after GL algorithm.
- "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
- // MelSpectrogram parameters
- "num_mels": 80, // size of the mel spec frame.
- "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
- "spec_gain": 20.0,
-
- // Normalization parameters
- "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
- "min_level_db": -100, // lower bound for normalization
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
- },
-
- // VOCABULARY PARAMETERS
- // if custom character set is not defined,
- // default set in symbols.py is used
- // "characters":{
- // "pad": "_",
- // "eos": "~",
- // "bos": "^",
- // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
- // "punctuations":"!'(),-.:;? ",
- // "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
- // },
-
- // DISTRIBUTED TRAINING
- "distributed":{
- "backend": "nccl",
- "url": "tcp:\/\/localhost:54321"
- },
-
- "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
- // TRAINING
- "batch_size": 8, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
- "eval_batch_size": 8,
- "r": 7, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
- "gradual_training": [[0, 7, 4], [1, 5, 2]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
- "ga_alpha": 10.0, // weight for guided attention loss. If > 0, guided attention is enabled.
- "mixed_precision": false,
-
- // VALIDATION
- "run_eval": true,
- "test_delay_epochs": 0, //Until attention is aligned, testing only wastes computation time.
- "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
- // LOSS SETTINGS
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
- "decoder_loss_alpha": 0.5, // original decoder loss weight. If > 0, it is enabled
- "postnet_loss_alpha": 0.25, // original postnet loss weight. If > 0, it is enabled
- "postnet_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
- "decoder_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
- "decoder_ssim_alpha": 0.5, // decoder ssim loss weight. If > 0, it is enabled
- "postnet_ssim_alpha": 0.25, // postnet ssim loss weight. If > 0, it is enabled
- "ga_alpha": 5.0, // weight for guided attention loss. If > 0, guided attention is enabled.
- "stopnet_pos_weight": 15.0, // pos class weight for stopnet loss since there are way more negative samples than positive samples.
-
- // OPTIMIZER
- "noam_schedule": false, // use noam warmup and lr schedule.
- "grad_clip": 1.0, // upper limit for gradients for clipping.
- "epochs": 1, // total number of epochs to train.
- "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
- "wd": 0.000001, // Weight decay weight.
- "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
- "seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
-
- // TACOTRON PRENET
- "memory_size": -1, // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
- "prenet_type": "bn", // "original" or "bn".
- "prenet_dropout": false, // enable/disable dropout at prenet.
-
- // TACOTRON ATTENTION
- "attention_type": "original", // 'original' , 'graves', 'dynamic_convolution'
- "attention_heads": 4, // number of attention heads (only for 'graves')
- "attention_norm": "sigmoid", // softmax or sigmoid.
- "windowing": false, // Enables attention windowing. Used only in eval mode.
- "use_forward_attn": false, // if it uses forward attention. In general, it aligns faster.
- "forward_attn_mask": false, // Additional masking forcing monotonicity only in eval mode.
- "transition_agent": false, // enable/disable transition agent of forward attention.
- "location_attn": true, // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
- "bidirectional_decoder": false, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
- "double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
- "ddc_r": 7, // reduction rate for coarse decoder.
-
- // STOPNET
- "stopnet": true, // Train stopnet predicting the end of synthesis.
- "separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
-
- // TENSORBOARD and LOGGING
- "print_step": 1, // Number of steps to log training on console.
- "tb_plot_step": 100, // Number of steps to plot TB training figures.
- "print_eval": false, // If True, it prints intermediate loss values in evalulation.
- "save_step": 10000, // Number of training steps expected to save traninpg stats and checkpoints.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "keep_all_best": true, // If true, keeps all best_models after keep_after steps
- "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
- // DATA LOADING
- "text_cleaner": "phoneme_cleaners",
- "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
- "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_eval_loader_workers": 0, // number of evaluation data loader processes.
- "batch_group_size": 0, //Number of batches to shuffle after bucketing.
- "min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training
- "max_seq_len": 153, // DATASET-RELATED: maximum text length
- "compute_input_seq_cache": true,
-
- // PATHS
- "output_path": "tests/train_outputs/",
-
- // PHONEMES
- "phoneme_cache_path": "tests/train_outputs/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder.
- "use_phonemes": false, // use phonemes instead of raw characters. It is suggested for better pronounciation.
- "phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
-
- // MULTI-SPEAKER and GST
- "use_d_vector_file": false,
- "d_vector_file": null,
- "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
- "use_gst": true, // use global style tokens
- "gst": { // gst parameter if gst is enabled
- "gst_style_input": null, // Condition the style input either on a
- // -> wave file [path to wave] or
- // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
- // with the dictionary being len(dict) == len(gst_num_style_tokens).
- "gst_use_speaker_embedding": true, // if true pass speaker embedding in attention input GST.
- "gst_embedding_dim": 512,
- "gst_num_heads": 4,
- "gst_num_style_tokens": 10
- },
-
- // DATASETS
- "train_portion": 0.1, // dataset portion used for training. It is mainly for internal experiments.
- "eval_portion": 0.1, // dataset portion used for training. It is mainly for internal experiments.
- "datasets": // List of datasets. They all merged and they get different speaker_ids.
- [
- {
- "formatter": "ljspeech",
- "path": "tests/data/ljspeech/",
- "meta_file_train": "metadata.csv",
- "meta_file_val": "metadata.csv"
- }
- ]
-
-}
diff --git a/TTS/tests/inputs/test_tacotron_bd_config.json b/TTS/tests/inputs/test_tacotron_bd_config.json
deleted file mode 100644
index 6239d40b391dba096c0dbf4e428f360adaf4ef4b..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_tacotron_bd_config.json
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "model": "Tacotron",
- "run_name": "test_sample_dataset_run",
- "run_description": "sample dataset test run",
-
- // AUDIO PARAMETERS
- "audio":{
- // stft parameters
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "win_length": 1024, // stft window length in ms.
- "hop_length": 256, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
- // Audio processing parameters
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate.
- "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
-
- // Silence trimming
- "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
- "trim_db": 60, // threshold for timming silence. Set this according to your dataset.
-
- // Griffin-Lim
- "power": 1.5, // value to sharpen wav signals after GL algorithm.
- "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
- // MelSpectrogram parameters
- "num_mels": 80, // size of the mel spec frame.
- "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
- "spec_gain": 20.0,
-
- // Normalization parameters
- "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
- "min_level_db": -100, // lower bound for normalization
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
- },
-
- // VOCABULARY PARAMETERS
- // if custom character set is not defined,
- // default set in symbols.py is used
- // "characters":{
- // "pad": "_",
- // "eos": "~",
- // "bos": "^",
- // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
- // "punctuations":"!'(),-.:;? ",
- // "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
- // },
-
- // DISTRIBUTED TRAINING
- "distributed":{
- "backend": "nccl",
- "url": "tcp:\/\/localhost:54321"
- },
-
- "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
- // TRAINING
- "batch_size": 1, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
- "eval_batch_size":1,
- "r": 7, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
- "gradual_training": [[0, 7, 4]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
- "ga_alpha": 10.0, // weight for guided attention loss. If > 0, guided attention is enabled.
- "mixed_precision": false,
-
- // VALIDATION
- "run_eval": true,
- "test_delay_epochs": 0, //Until attention is aligned, testing only wastes computation time.
- "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
- // LOSS SETTINGS
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
- "decoder_loss_alpha": 0.5, // original decoder loss weight. If > 0, it is enabled
- "postnet_loss_alpha": 0.25, // original postnet loss weight. If > 0, it is enabled
- "postnet_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
- "decoder_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
- "decoder_ssim_alpha": 0.5, // decoder ssim loss weight. If > 0, it is enabled
- "postnet_ssim_alpha": 0.25, // postnet ssim loss weight. If > 0, it is enabled
- "ga_alpha": 5.0, // weight for guided attention loss. If > 0, guided attention is enabled.
- "stopnet_pos_weight": 15.0, // pos class weight for stopnet loss since there are way more negative samples than positive samples.
-
- // OPTIMIZER
- "noam_schedule": false, // use noam warmup and lr schedule.
- "grad_clip": 1.0, // upper limit for gradients for clipping.
- "epochs": 1, // total number of epochs to train.
- "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
- "wd": 0.000001, // Weight decay weight.
- "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
- "seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
-
- // TACOTRON PRENET
- "memory_size": -1, // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
- "prenet_type": "bn", // "original" or "bn".
- "prenet_dropout": false, // enable/disable dropout at prenet.
-
- // TACOTRON ATTENTION
- "attention_type": "original", // 'original' , 'graves', 'dynamic_convolution'
- "attention_heads": 4, // number of attention heads (only for 'graves')
- "attention_norm": "sigmoid", // softmax or sigmoid.
- "windowing": false, // Enables attention windowing. Used only in eval mode.
- "use_forward_attn": false, // if it uses forward attention. In general, it aligns faster.
- "forward_attn_mask": false, // Additional masking forcing monotonicity only in eval mode.
- "transition_agent": false, // enable/disable transition agent of forward attention.
- "location_attn": true, // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
- "bidirectional_decoder": true, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
- "double_decoder_consistency": false, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
- "ddc_r": 7, // reduction rate for coarse decoder.
-
- // STOPNET
- "stopnet": true, // Train stopnet predicting the end of synthesis.
- "separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
-
- // TENSORBOARD and LOGGING
- "print_step": 1, // Number of steps to log training on console.
- "tb_plot_step": 100, // Number of steps to plot TB training figures.
- "print_eval": false, // If True, it prints intermediate loss values in evalulation.
- "save_step": 10000, // Number of training steps expected to save traninpg stats and checkpoints.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "keep_all_best": true, // If true, keeps all best_models after keep_after steps
- "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
- // DATA LOADING
- "text_cleaner": "phoneme_cleaners",
- "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
- "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_eval_loader_workers": 0, // number of evaluation data loader processes.
- "batch_group_size": 0, //Number of batches to shuffle after bucketing.
- "min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training
- "max_seq_len": 153, // DATASET-RELATED: maximum text length
- "compute_input_seq_cache": true,
-
- // PATHS
- "output_path": "tests/train_outputs/",
-
- // PHONEMES
- "phoneme_cache_path": "tests/train_outputs/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder.
- "use_phonemes": false, // use phonemes instead of raw characters. It is suggested for better pronounciation.
- "phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
-
- // MULTI-SPEAKER and GST
- "use_d_vector_file": false,
- "d_vector_file": null,
- "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
- "use_gst": true, // use global style tokens
- "gst": { // gst parameter if gst is enabled
- "gst_style_input": null, // Condition the style input either on a
- // -> wave file [path to wave] or
- // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
- // with the dictionary being len(dict) == len(gst_style_tokens).
- "gst_use_speaker_embedding": true, // if true pass speaker embedding in attention input GST.
- "gst_embedding_dim": 512,
- "gst_num_heads": 4,
- "gst_style_tokens": 10
- },
-
- // DATASETS
- "train_portion": 0.1, // dataset portion used for training. It is mainly for internal experiments.
- "eval_portion": 0.1, // dataset portion used for training. It is mainly for internal experiments.
- "datasets": // List of datasets. They all merged and they get different speaker_ids.
- [
- {
- "formatter": "ljspeech",
- "path": "tests/data/ljspeech/",
- "meta_file_train": "metadata.csv",
- "meta_file_val": "metadata.csv"
- }
- ]
-
-}
diff --git a/TTS/tests/inputs/test_tacotron_config.json b/TTS/tests/inputs/test_tacotron_config.json
deleted file mode 100644
index 70d66cb0ece567bdd688f61390dbc7826cd69e3c..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_tacotron_config.json
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "model": "Tacotron",
- "run_name": "test_sample_dataset_run",
- "run_description": "sample dataset test run",
-
- // AUDIO PARAMETERS
- "audio":{
- // stft parameters
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "win_length": 1024, // stft window length in ms.
- "hop_length": 256, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
- // Audio processing parameters
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate.
- "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
-
- // Silence trimming
- "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
- "trim_db": 60, // threshold for timming silence. Set this according to your dataset.
-
- // Griffin-Lim
- "power": 1.5, // value to sharpen wav signals after GL algorithm.
- "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
- // MelSpectrogram parameters
- "num_mels": 80, // size of the mel spec frame.
- "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
- "spec_gain": 20.0,
-
- // Normalization parameters
- "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
- "min_level_db": -100, // lower bound for normalization
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
- },
-
- // VOCABULARY PARAMETERS
- // if custom character set is not defined,
- // default set in symbols.py is used
- // "characters":{
- // "pad": "_",
- // "eos": "~",
- // "bos": "^",
- // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
- // "punctuations":"!'(),-.:;? ",
- // "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
- // },
-
- // DISTRIBUTED TRAINING
- "distributed":{
- "backend": "nccl",
- "url": "tcp:\/\/localhost:54321"
- },
-
- "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
- // TRAINING
- "batch_size": 8, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
- "eval_batch_size": 8,
- "r": 7, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
- "gradual_training": [[0, 7, 4], [1, 5, 2]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
- "ga_alpha": 10.0, // weight for guided attention loss. If > 0, guided attention is enabled.
- "mixed_precision": false,
-
- // VALIDATION
- "run_eval": true,
- "test_delay_epochs": 0, //Until attention is aligned, testing only wastes computation time.
- "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
- // LOSS SETTINGS
- "loss_masking": true, // enable / disable loss masking against the sequence padding.
- "decoder_loss_alpha": 0.5, // original decoder loss weight. If > 0, it is enabled
- "postnet_loss_alpha": 0.25, // original postnet loss weight. If > 0, it is enabled
- "postnet_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
- "decoder_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
- "decoder_ssim_alpha": 0.5, // decoder ssim loss weight. If > 0, it is enabled
- "postnet_ssim_alpha": 0.25, // postnet ssim loss weight. If > 0, it is enabled
- "ga_alpha": 5.0, // weight for guided attention loss. If > 0, guided attention is enabled.
- "stopnet_pos_weight": 15.0, // pos class weight for stopnet loss since there are way more negative samples than positive samples.
-
- // OPTIMIZER
- "noam_schedule": false, // use noam warmup and lr schedule.
- "grad_clip": 1.0, // upper limit for gradients for clipping.
- "epochs": 1, // total number of epochs to train.
- "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
- "wd": 0.000001, // Weight decay weight.
- "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
- "seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
-
- // TACOTRON PRENET
- "memory_size": -1, // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
- "prenet_type": "bn", // "original" or "bn".
- "prenet_dropout": false, // enable/disable dropout at prenet.
-
- // TACOTRON ATTENTION
- "attention_type": "original", // 'original' , 'graves', 'dynamic_convolution'
- "attention_heads": 4, // number of attention heads (only for 'graves')
- "attention_norm": "sigmoid", // softmax or sigmoid.
- "windowing": false, // Enables attention windowing. Used only in eval mode.
- "use_forward_attn": false, // if it uses forward attention. In general, it aligns faster.
- "forward_attn_mask": false, // Additional masking forcing monotonicity only in eval mode.
- "transition_agent": false, // enable/disable transition agent of forward attention.
- "location_attn": true, // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
- "bidirectional_decoder": false, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
- "double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
- "ddc_r": 7, // reduction rate for coarse decoder.
-
- // STOPNET
- "stopnet": true, // Train stopnet predicting the end of synthesis.
- "separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
-
- // TENSORBOARD and LOGGING
- "print_step": 1, // Number of steps to log training on console.
- "tb_plot_step": 100, // Number of steps to plot TB training figures.
- "print_eval": false, // If True, it prints intermediate loss values in evalulation.
- "save_step": 10000, // Number of training steps expected to save traninpg stats and checkpoints.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "keep_all_best": true, // If true, keeps all best_models after keep_after steps
- "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
- // DATA LOADING
- "text_cleaner": "phoneme_cleaners",
- "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
- "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_eval_loader_workers": 0, // number of evaluation data loader processes.
- "batch_group_size": 0, //Number of batches to shuffle after bucketing.
- "min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training
- "max_seq_len": 153, // DATASET-RELATED: maximum text length
- "compute_input_seq_cache": true,
-
- // PATHS
- "output_path": "tests/train_outputs/",
-
- // PHONEMES
- "phoneme_cache_path": "tests/train_outputs/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder.
- "use_phonemes": false, // use phonemes instead of raw characters. It is suggested for better pronounciation.
- "phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
-
- // MULTI-SPEAKER and GST
- "use_d_vector_file": false,
- "d_vector_file": null,
- "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
- "use_gst": true, // use global style tokens
- "gst": { // gst parameter if gst is enabled
- "gst_style_input": null, // Condition the style input either on a
- // -> wave file [path to wave] or
- // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
- // with the dictionary being len(dict) == len(gst_style_tokens).
- "gst_use_speaker_embedding": true, // if true pass speaker embedding in attention input GST.
- "gst_embedding_dim": 512,
- "gst_num_heads": 4,
- "gst_style_tokens": 10
- },
-
- // DATASETS
- "train_portion": 0.1, // dataset portion used for training. It is mainly for internal experiments.
- "eval_portion": 0.1, // dataset portion used for training. It is mainly for internal experiments.
- "datasets": // List of datasets. They all merged and they get different speaker_ids.
- [
- {
- "formatter": "ljspeech",
- "path": "tests/data/ljspeech/",
- "meta_file_train": "metadata.csv",
- "meta_file_val": "metadata.csv"
- }
- ]
-
-}
diff --git a/TTS/tests/inputs/test_vocoder_audio_config.json b/TTS/tests/inputs/test_vocoder_audio_config.json
deleted file mode 100644
index 08acc48cd34296c4549931ce440fda8d1882ba66..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_vocoder_audio_config.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "audio":{
- "num_mels": 80, // size of the mel spec frame.
- "num_freq": 513, // number of stft frequency levels. Size of the linear spectogram frame.
- "sample_rate": 22050, // wav sample-rate. If different than the original data, it is resampled.
- "frame_length_ms": null, // stft window length in ms.
- "frame_shift_ms": null, // stft window hop-lengh in ms.
- "hop_length": 256,
- "win_length": 1024,
- "preemphasis": 0.97, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "min_level_db": -100, // normalization range
- "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
- "power": 1.5, // value to sharpen wav signals after GL algorithm.
- "griffin_lim_iters": 30,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
- "signal_norm": true, // normalize the spec values in range [0, 1]
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "clip_norm": true, // clip normalized values into the range.
- "max_norm": 4, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "mel_fmin": 0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 8000, // maximum freq level for mel-spec. Tune for dataset!!
- "do_trim_silence": false
- }
-}
-
diff --git a/TTS/tests/inputs/test_vocoder_multiband_melgan_config.json b/TTS/tests/inputs/test_vocoder_multiband_melgan_config.json
deleted file mode 100644
index 82afc977271c20d46b3a4d5e67cca52a21b98d7e..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_vocoder_multiband_melgan_config.json
+++ /dev/null
@@ -1,166 +0,0 @@
-{
- "run_name": "multiband-melgan",
- "run_description": "multiband melgan mean-var scaling",
-
- // AUDIO PARAMETERS
- "audio":{
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "win_length": 1024, // stft window length in ms.
- "hop_length": 256, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
- // Audio processing parameters
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
- "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
- "log_func": "np.log10",
- "do_sound_norm": true,
-
- // Silence trimming
- "do_trim_silence": false,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
- "trim_db": 60, // threshold for timming silence. Set this according to your dataset.
-
- // MelSpectrogram parameters
- "num_mels": 80, // size of the mel spec frame.
- "mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
- "spec_gain": 1.0, // scaler value appplied after log transform of spectrogram.
-
- // Normalization parameters
- "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
- "min_level_db": -100, // lower bound for normalization
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "stats_path": null
- },
-
- // DISTRIBUTED TRAINING
- // "distributed":{
- // "backend": "nccl",
- // "url": "tcp:\/\/localhost:54321"
- // },
-
- // MODEL PARAMETERS
- "use_pqmf": true,
-
- // LOSS PARAMETERS
- "use_stft_loss": true,
- "use_subband_stft_loss": true,
- "use_mse_gan_loss": true,
- "use_hinge_gan_loss": false,
- "use_feat_match_loss": false, // use only with melgan discriminators
- "use_l1_spec_loss": true,
-
- // loss weights
- "stft_loss_weight": 0.5,
- "subband_stft_loss_weight": 0.5,
- "mse_G_loss_weight": 2.5,
- "hinge_G_loss_weight": 2.5,
- "feat_match_loss_weight": 25,
- "l1_spec_loss_weight": 2.5,
-
- // multiscale stft loss parameters
- "stft_loss_params": {
- "n_ffts": [1024, 2048, 512],
- "hop_lengths": [120, 240, 50],
- "win_lengths": [600, 1200, 240]
- },
-
- // subband multiscale stft loss parameters
- "subband_stft_loss_params":{
- "n_ffts": [384, 683, 171],
- "hop_lengths": [30, 60, 10],
- "win_lengths": [150, 300, 60]
- },
-
- "l1_spec_loss_params": {
- "use_mel": true,
- "sample_rate": 22050,
- "n_fft": 1024,
- "hop_length": 256,
- "win_length": 1024,
- "n_mels": 80,
- "mel_fmin": 0.0,
- "mel_fmax": null
- },
-
- "target_loss": "G_avg_loss", // loss value to pick the best model to save after each epoch
-
- // DISCRIMINATOR
- "discriminator_model": "melgan_multiscale_discriminator",
- "discriminator_model_params":{
- "base_channels": 16,
- "max_channels":512,
- "downsample_factors":[4, 4, 4]
- },
- "steps_to_start_discriminator": 200000, // steps required to start GAN trainining.1
-
- // GENERATOR
- "generator_model": "multiband_melgan_generator",
- "generator_model_params": {
- "upsample_factors":[8, 4, 2],
- "num_res_blocks": 4
- },
-
- // DATASET
- "data_path": "tests/data/ljspeech/wavs/",
- "feature_path": null,
- "seq_len": 16384,
- "pad_short": 2000,
- "conv_pad": 0,
- "use_noise_augment": false,
- "use_cache": true,
-
- "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
- // TRAINING
- "batch_size": 4, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-
- // VALIDATION
- "run_eval": true,
- "test_delay_epochs": 10, //Until attention is aligned, testing only wastes computation time.
- "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
- // OPTIMIZER
- "epochs": 1, // total number of epochs to train.
- "wd": 0.0, // Weight decay weight.
- "gen_clip_grad": -1, // Generator gradient clipping threshold. Apply gradient clipping if > 0
- "disc_clip_grad": -1, // Discriminator gradient clipping threshold.
- "optimizer": "AdamW",
- "optimizer_params":{
- "betas": [0.8, 0.99],
- "weight_decay": 0.0
- },
- "lr_scheduler_gen": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
- "lr_scheduler_gen_params": {
- "gamma": 0.5,
- "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
- },
- "lr_scheduler_disc": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
- "lr_scheduler_disc_params": {
- "gamma": 0.5,
- "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
- },
- "lr_gen": 1e-4, // Initial learning rate. If Noam decay is active, maximum learning rate.
- "lr_disc": 1e-4,
-
- // TENSORBOARD and LOGGING
- "print_step": 1, // Number of steps to log traning on console.
- "print_eval": false, // If True, it prints loss values for each step in eval run.
- "save_step": 25000, // Number of training steps expected to plot training stats on TB and save model checkpoints.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "keep_all_best": true, // If true, keeps all best_models after keep_after steps
- "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
- // DATA LOADING
- "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_eval_loader_workers": 0, // number of evaluation data loader processes.
- "eval_split_size": 10,
-
- // PATHS
- "output_path": "tests/train_outputs/"
-}
-
diff --git a/TTS/tests/inputs/test_vocoder_wavegrad.json b/TTS/tests/inputs/test_vocoder_wavegrad.json
deleted file mode 100644
index 6378c07a6dee8d9d52e0f1341b0105b3ed119abe..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_vocoder_wavegrad.json
+++ /dev/null
@@ -1,116 +0,0 @@
-{
- "run_name": "wavegrad-ljspeech",
- "run_description": "wavegrad ljspeech",
-
- "audio":{
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "win_length": 1024, // stft window length in ms.
- "hop_length": 256, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
- // Audio processing parameters
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
- "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "ref_level_db": 0, // reference level db, theoretically 20db is the sound of air.
-
- // Silence trimming
- "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
- "trim_db": 60, // threshold for timming silence. Set this according to your dataset.
-
- // MelSpectrogram parameters
- "num_mels": 80, // size of the mel spec frame.
- "mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
- "spec_gain": 1.0, // scaler value appplied after log transform of spectrogram.
-
- // Normalization parameters
- "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
- "min_level_db": -100, // lower bound for normalization
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
- },
-
- // DISTRIBUTED TRAINING
- "mixed_precision": false,
- "distributed":{
- "backend": "nccl",
- "url": "tcp:\/\/localhost:54322"
- },
-
- "target_loss": "avg_wavegrad_loss", // loss value to pick the best model to save after each epoch
-
- // MODEL PARAMETERS
- "generator_model": "wavegrad",
- "model_params":{
- "y_conv_channels":32,
- "x_conv_channels":768,
- "ublock_out_channels": [512, 512, 256, 128, 128],
- "dblock_out_channels": [128, 128, 256, 512],
- "upsample_factors": [4, 4, 4, 2, 2],
- "upsample_dilations": [
- [1, 2, 1, 2],
- [1, 2, 1, 2],
- [1, 2, 4, 8],
- [1, 2, 4, 8],
- [1, 2, 4, 8]],
- "use_weight_norm": true
- },
-
- // DATASET
- "data_path": "tests/data/ljspeech/wavs/", // root data path. It finds all wav files recursively from there.
- "feature_path": null, // if you use precomputed features
- "seq_len": 6144, // 24 * hop_length
- "pad_short": 0, // additional padding for short wavs
- "conv_pad": 0, // additional padding against convolutions applied to spectrograms
- "use_noise_augment": false, // add noise to the audio signal for augmentation
- "use_cache": true, // use in memory cache to keep the computed features. This might cause OOM.
-
- "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
- // TRAINING
- "batch_size": 1, // Batch size for training.
- "train_noise_schedule":{
- "min_val": 1e-6,
- "max_val": 1e-2,
- "num_steps": 1000
- },
- "test_noise_schedule":{
- "min_val": 1e-6,
- "max_val": 1e-2,
- "num_steps": 2
- },
-
- // VALIDATION
- "run_eval": true, // enable/disable evaluation run
-
- // OPTIMIZER
- "epochs": 1, // total number of epochs to train.
- "grad_clip": 1.0, // Generator gradient clipping threshold. Apply gradient clipping if > 0
- "lr_scheduler": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
- "lr_scheduler_params": {
- "gamma": 0.5,
- "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
- },
- "lr": 1e-4, // Initial learning rate. If Noam decay is active, maximum learning rate.
-
- // TENSORBOARD and LOGGING
- "print_step": 250, // Number of steps to log traning on console.
- "print_eval": false, // If True, it prints loss values for each step in eval run.
- "save_step": 10000, // Number of training steps expected to plot training stats on TB and save model checkpoints.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "keep_all_best": true, // If true, keeps all best_models after keep_after steps
- "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
- "tb_model_param_stats": true, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
- // DATA LOADING
- "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_eval_loader_workers": 0, // number of evaluation data loader processes.
- "eval_split_size": 4,
-
- // PATHS
- "output_path": "tests/train_outputs/"
-}
-
diff --git a/TTS/tests/inputs/test_vocoder_wavernn_config.json b/TTS/tests/inputs/test_vocoder_wavernn_config.json
deleted file mode 100644
index ee4e5f8e42b3f07e0a6ab3b131988a0d6cd15475..0000000000000000000000000000000000000000
--- a/TTS/tests/inputs/test_vocoder_wavernn_config.json
+++ /dev/null
@@ -1,112 +0,0 @@
-{
- "run_name": "wavernn_test",
- "run_description": "wavernn_test training",
-
- // AUDIO PARAMETERS
- "audio":{
- "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
- "win_length": 1024, // stft window length in ms.
- "hop_length": 256, // stft window hop-lengh in ms.
- "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
- "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
- // Audio processing parameters
- "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
- "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
- "ref_level_db": 0, // reference level db, theoretically 20db is the sound of air.
-
- // Silence trimming
- "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
- "trim_db": 60, // threshold for timming silence. Set this according to your dataset.
-
- // MelSpectrogram parameters
- "num_mels": 80, // size of the mel spec frame.
- "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
- "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
- "spec_gain": 20.0, // scaler value appplied after log transform of spectrogram.
-
- // Normalization parameters
- "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
- "min_level_db": -100, // lower bound for normalization
- "symmetric_norm": true, // move normalization to range [-1, 1]
- "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
- "clip_norm": true, // clip normalized values into the range.
- "stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
- },
-
- // Generating / Synthesizing
- "batched": true,
- "target_samples": 11000, // target number of samples to be generated in each batch entry
- "overlap_samples": 550, // number of samples for crossfading between batches
-
- // DISTRIBUTED TRAINING
- // "distributed":{
- // "backend": "nccl",
- // "url": "tcp:\/\/localhost:54321"
- // },
-
- // MODEL PARAMETERS
- "use_aux_net": true,
- "use_upsample_net": true,
- "upsample_factors": [4, 8, 8], // this needs to correctly factorise hop_length
- "seq_len": 1280, // has to be devideable by hop_length
- "mode": "mold", // mold [string], gauss [string], bits [int]
- "mulaw": false, // apply mulaw if mode is bits
- "padding": 2, // pad the input for resnet to see wider input length
-
- // GENERATOR - for backward compatibility
- "generator_model": "Wavernn",
-
- // DATASET
- //"use_gta": true, // use computed gta features from the tts model
- "data_path": "tests/data/ljspeech/wavs/", // path containing training wav files
- "feature_path": null, // path containing computed features from wav files if null compute them
-
- // MODEL PARAMETERS
- "wavernn_model_params": {
- "rnn_dims": 512,
- "fc_dims": 512,
- "compute_dims": 128,
- "res_out_dims": 128,
- "num_res_blocks": 10,
- "use_aux_net": true,
- "use_upsample_net": true,
- "upsample_factors": [4, 8, 8] // this needs to correctly factorise hop_length
- },
- "mixed_precision": false,
-
- // TRAINING
- "batch_size": 4, // Batch size for training. Lower values than 32 might cause hard to learn attention.
- "epochs": 1, // total number of epochs to train.
-
- // VALIDATION
- "run_eval": true,
- "test_every_epochs": 10, // Test after set number of epochs (Test every 20 epochs for example)
-
- // OPTIMIZER
- "grad_clip": 4, // apply gradient clipping if > 0
- "lr_scheduler": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
- "lr_scheduler_params": {
- "gamma": 0.5,
- "milestones": [200000, 400000, 600000]
- },
- "lr": 1e-4, // initial learning rate
-
- // TENSORBOARD and LOGGING
- "print_step": 25, // Number of steps to log traning on console.
- "print_eval": false, // If True, it prints loss values for each step in eval run.
- "save_step": 25000, // Number of training steps expected to plot training stats on TB and save model checkpoints.
- "checkpoint": true, // If true, it saves checkpoints per "save_step"
- "keep_all_best": true, // If true, keeps all best_models after keep_after steps
- "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
- "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
- // DATA LOADING
- "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
- "num_eval_loader_workers": 0, // number of evaluation data loader processes.
- "eval_split_size": 10, // number of samples for testing
-
- // PATHS
- "output_path": "tests/train_outputs/"
-}
-
diff --git a/TTS/tests/text_tests/__init__.py b/TTS/tests/text_tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/TTS/tests/text_tests/test_characters.py b/TTS/tests/text_tests/test_characters.py
deleted file mode 100644
index 8f40656ad7ae0c862835e00c627224bab7b5d35c..0000000000000000000000000000000000000000
--- a/TTS/tests/text_tests/test_characters.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import unittest
-
-from TTS.tts.utils.text.characters import BaseCharacters, BaseVocabulary, Graphemes, IPAPhonemes
-
-# pylint: disable=protected-access
-
-
-class BaseVocabularyTest(unittest.TestCase):
- def setUp(self):
- self.phonemes = IPAPhonemes()
- self.base_vocab = BaseVocabulary(
- vocab=self.phonemes._vocab,
- pad=self.phonemes.pad,
- blank=self.phonemes.blank,
- bos=self.phonemes.bos,
- eos=self.phonemes.eos,
- )
- self.empty_vocab = BaseVocabulary({})
-
- def test_pad_id(self):
- self.assertEqual(self.empty_vocab.pad_id, 0)
- self.assertEqual(self.base_vocab.pad_id, self.phonemes.pad_id)
-
- def test_blank_id(self):
- self.assertEqual(self.empty_vocab.blank_id, 0)
- self.assertEqual(self.base_vocab.blank_id, self.phonemes.blank_id)
-
- def test_vocab(self):
- self.assertEqual(self.empty_vocab.vocab, {})
- self.assertEqual(self.base_vocab.vocab, self.phonemes._vocab)
-
- # def test_init_from_config(self):
- # ...
-
- def test_num_chars(self):
- self.assertEqual(self.empty_vocab.num_chars, 0)
- self.assertEqual(self.base_vocab.num_chars, self.phonemes.num_chars)
-
- def test_char_to_id(self):
- try:
- self.empty_vocab.char_to_id("a")
- raise Exception("Should have raised KeyError")
- except:
- pass
- for k in self.phonemes.vocab:
- self.assertEqual(self.base_vocab.char_to_id(k), self.phonemes.char_to_id(k))
-
- def test_id_to_char(self):
- try:
- self.empty_vocab.id_to_char(0)
- raise Exception("Should have raised KeyError")
- except:
- pass
- for k in self.phonemes.vocab:
- v = self.phonemes.char_to_id(k)
- self.assertEqual(self.base_vocab.id_to_char(v), self.phonemes.id_to_char(v))
-
-
-class BaseCharacterTest(unittest.TestCase):
- def setUp(self):
- self.characters_empty = BaseCharacters("", "", pad="", eos="", bos="", blank="", is_unique=True, is_sorted=True)
-
- def test_default_character_sets(self): # pylint: disable=no-self-use
- """Test initiation of default character sets"""
- _ = IPAPhonemes()
- _ = Graphemes()
-
- def test_unique(self):
- """Test if the unique option works"""
- self.characters_empty.characters = "abcc"
- self.characters_empty.punctuations = ".,;:!? "
- self.characters_empty.pad = "[PAD]"
- self.characters_empty.eos = "[EOS]"
- self.characters_empty.bos = "[BOS]"
- self.characters_empty.blank = "[BLANK]"
-
- self.assertEqual(
- self.characters_empty.num_chars,
- len(["[PAD]", "[EOS]", "[BOS]", "[BLANK]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "]),
- )
-
- def test_unique_sorted(self):
- """Test if the unique and sorted option works"""
- self.characters_empty.characters = "cba"
- self.characters_empty.punctuations = ".,;:!? "
- self.characters_empty.pad = "[PAD]"
- self.characters_empty.eos = "[EOS]"
- self.characters_empty.bos = "[BOS]"
- self.characters_empty.blank = "[BLANK]"
-
- self.assertEqual(
- self.characters_empty.num_chars,
- len(["[PAD]", "[EOS]", "[BOS]", "[BLANK]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "]),
- )
-
- def test_setters_getters(self):
- """Test the class setters behaves as expected"""
- self.characters_empty.characters = "abc"
- self.assertEqual(self.characters_empty._characters, "abc")
- self.assertEqual(self.characters_empty.vocab, ["a", "b", "c"])
-
- self.characters_empty.punctuations = ".,;:!? "
- self.assertEqual(self.characters_empty._punctuations, ".,;:!? ")
- self.assertEqual(self.characters_empty.vocab, ["a", "b", "c", ".", ",", ";", ":", "!", "?", " "])
-
- self.characters_empty.pad = "[PAD]"
- self.assertEqual(self.characters_empty._pad, "[PAD]")
- self.assertEqual(self.characters_empty.vocab, ["[PAD]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "])
-
- self.characters_empty.eos = "[EOS]"
- self.assertEqual(self.characters_empty._eos, "[EOS]")
- self.assertEqual(
- self.characters_empty.vocab, ["[PAD]", "[EOS]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "]
- )
-
- self.characters_empty.bos = "[BOS]"
- self.assertEqual(self.characters_empty._bos, "[BOS]")
- self.assertEqual(
- self.characters_empty.vocab, ["[PAD]", "[EOS]", "[BOS]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "]
- )
-
- self.characters_empty.blank = "[BLANK]"
- self.assertEqual(self.characters_empty._blank, "[BLANK]")
- self.assertEqual(
- self.characters_empty.vocab,
- ["[PAD]", "[EOS]", "[BOS]", "[BLANK]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "],
- )
- self.assertEqual(
- self.characters_empty.num_chars,
- len(["[PAD]", "[EOS]", "[BOS]", "[BLANK]", "a", "b", "c", ".", ",", ";", ":", "!", "?", " "]),
- )
-
- self.characters_empty.print_log()
-
- def test_char_lookup(self):
- """Test char to ID and ID to char conversion"""
- self.characters_empty.characters = "abc"
- self.characters_empty.punctuations = ".,;:!? "
- self.characters_empty.pad = "[PAD]"
- self.characters_empty.eos = "[EOS]"
- self.characters_empty.bos = "[BOS]"
- self.characters_empty.blank = "[BLANK]"
-
- # char to ID
- self.assertEqual(self.characters_empty.char_to_id("[PAD]"), 0)
- self.assertEqual(self.characters_empty.char_to_id("[EOS]"), 1)
- self.assertEqual(self.characters_empty.char_to_id("[BOS]"), 2)
- self.assertEqual(self.characters_empty.char_to_id("[BLANK]"), 3)
- self.assertEqual(self.characters_empty.char_to_id("a"), 4)
- self.assertEqual(self.characters_empty.char_to_id("b"), 5)
- self.assertEqual(self.characters_empty.char_to_id("c"), 6)
- self.assertEqual(self.characters_empty.char_to_id("."), 7)
- self.assertEqual(self.characters_empty.char_to_id(","), 8)
- self.assertEqual(self.characters_empty.char_to_id(";"), 9)
- self.assertEqual(self.characters_empty.char_to_id(":"), 10)
- self.assertEqual(self.characters_empty.char_to_id("!"), 11)
- self.assertEqual(self.characters_empty.char_to_id("?"), 12)
- self.assertEqual(self.characters_empty.char_to_id(" "), 13)
-
- # ID to char
- self.assertEqual(self.characters_empty.id_to_char(0), "[PAD]")
- self.assertEqual(self.characters_empty.id_to_char(1), "[EOS]")
- self.assertEqual(self.characters_empty.id_to_char(2), "[BOS]")
- self.assertEqual(self.characters_empty.id_to_char(3), "[BLANK]")
- self.assertEqual(self.characters_empty.id_to_char(4), "a")
- self.assertEqual(self.characters_empty.id_to_char(5), "b")
- self.assertEqual(self.characters_empty.id_to_char(6), "c")
- self.assertEqual(self.characters_empty.id_to_char(7), ".")
- self.assertEqual(self.characters_empty.id_to_char(8), ",")
- self.assertEqual(self.characters_empty.id_to_char(9), ";")
- self.assertEqual(self.characters_empty.id_to_char(10), ":")
- self.assertEqual(self.characters_empty.id_to_char(11), "!")
- self.assertEqual(self.characters_empty.id_to_char(12), "?")
- self.assertEqual(self.characters_empty.id_to_char(13), " ")
diff --git a/TTS/tests/text_tests/test_japanese_phonemizer.py b/TTS/tests/text_tests/test_japanese_phonemizer.py
deleted file mode 100644
index 423b79b9ce5d5d7e7ddb20317b48fa711fad8f92..0000000000000000000000000000000000000000
--- a/TTS/tests/text_tests/test_japanese_phonemizer.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import unittest
-
-from TTS.tts.utils.text.japanese.phonemizer import japanese_text_to_phonemes
-
-_TEST_CASES = """
-どちらに行きますか？/dochiraniikimasuka?
-今日は温泉に、行きます。/kyo:waoNseNni,ikimasu.
-「A」から「Z」までです。/e:karazeqtomadedesu.
-そうですね！/so:desune!
-クジラは哺乳類です。/kujirawahonyu:ruidesu.
-ヴィディオを見ます。/bidioomimasu.
-今日は８月22日です/kyo:wahachigatsuniju:ninichidesu
-xyzとαβγ/eqkusuwaizeqtotoarufabe:tagaNma
-値段は$12.34です/nedaNwaju:niteNsaNyoNdorudesu
-"""
-
-
-class TestText(unittest.TestCase):
- def test_japanese_text_to_phonemes(self):
- for line in _TEST_CASES.strip().split("\n"):
- text, phone = line.split("/")
- self.assertEqual(japanese_text_to_phonemes(text), phone)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/TTS/tests/text_tests/test_korean_phonemizer.py b/TTS/tests/text_tests/test_korean_phonemizer.py
deleted file mode 100644
index 7d651d1d693a5675e688741b0cb2ee22f14d70d6..0000000000000000000000000000000000000000
--- a/TTS/tests/text_tests/test_korean_phonemizer.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import unittest
-
-from TTS.tts.utils.text.korean.phonemizer import korean_text_to_phonemes
-
-_TEST_CASES = """
-포상은 열심히 한 아이에게만 주어지기 때문에 포상인 것입니다./포상으 녈심히 하 나이에게만 주어지기 때무네 포상인 거심니다.
-오늘은 8월 31일 입니다./오느른 파뤌 삼시비리 림니다.
-친구 100명 만들기가 목표입니다./친구 뱅명 만들기가 목표임니다.
-A부터 Z까지 입니다./에이부터 제트까지 임니다.
-이게 제 마음이에요./이게 제 마으미에요.
-"""
-_TEST_CASES_EN = """
-이제야 이쪽을 보는구나./IJeYa IJjoGeul BoNeunGuNa.
-크고 맛있는 cake를 부탁해요./KeuGo MaSinNeun KeIKeuLeul BuTaKaeYo.
-전부 거짓말이야./JeonBu GeoJinMaLiYa.
-좋은 노래를 찾았어요./JoEun NoLaeLeul ChaJaSseoYo.
-"""
-
-
-class TestText(unittest.TestCase):
- def test_korean_text_to_phonemes(self):
- for line in _TEST_CASES.strip().split("\n"):
- text, phone = line.split("/")
- self.assertEqual(korean_text_to_phonemes(text), phone)
- for line in _TEST_CASES_EN.strip().split("\n"):
- text, phone = line.split("/")
- self.assertEqual(korean_text_to_phonemes(text, character="english"), phone)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/TTS/tests/text_tests/test_phonemizer.py b/TTS/tests/text_tests/test_phonemizer.py
deleted file mode 100644
index 8810554421957d47dd2f07440719ac57178b26c3..0000000000000000000000000000000000000000
--- a/TTS/tests/text_tests/test_phonemizer.py
+++ /dev/null
@@ -1,295 +0,0 @@
-import unittest
-
-from packaging.version import Version
-
-from TTS.tts.utils.text.phonemizers import ESpeak, Gruut, JA_JP_Phonemizer, ZH_CN_Phonemizer
-from TTS.tts.utils.text.phonemizers.bangla_phonemizer import BN_Phonemizer
-from TTS.tts.utils.text.phonemizers.multi_phonemizer import MultiPhonemizer
-
-EXAMPLE_TEXTs = [
- "Recent research at Harvard has shown meditating",
- "for as little as 8 weeks can actually increase, the grey matter",
- "in the parts of the brain responsible",
- "for emotional regulation and learning!",
-]
-
-
-EXPECTED_ESPEAK_PHONEMES = [
- "ɹ|ˈiː|s|ə|n|t ɹ|ɪ|s|ˈɜː|tʃ æ|t h|ˈɑːɹ|v|ɚ|d h|ɐ|z ʃ|ˈoʊ|n m|ˈɛ|d|ɪ|t|ˌeɪ|ɾ|ɪ|ŋ",
- "f|ɔː|ɹ æ|z l|ˈɪ|ɾ|əl æ|z ˈeɪ|t w|ˈiː|k|s k|æ|n ˈæ|k|tʃ|uː|əl|i| ˈɪ|n|k|ɹ|iː|s, ð|ə ɡ|ɹ|ˈeɪ m|ˈæ|ɾ|ɚ",
- "ɪ|n|ð|ə p|ˈɑːɹ|t|s ʌ|v|ð|ə b|ɹ|ˈeɪ|n ɹ|ɪ|s|p|ˈɑː|n|s|ə|b|əl",
- "f|ɔː|ɹ ɪ|m|ˈoʊ|ʃ|ə|n|əl ɹ|ˌɛ|ɡ|j|uː|l|ˈeɪ|ʃ|ə|n|| æ|n|d l|ˈɜː|n|ɪ|ŋ!",
-]
-
-
-EXPECTED_ESPEAK_v1_48_15_PHONEMES = [
- "ɹ|ˈiː|s|ə|n|t ɹ|ɪ|s|ˈɜː|tʃ æ|t h|ˈɑːɹ|v|ɚ|d h|ɐ|z ʃ|ˈoʊ|n m|ˈɛ|d|ᵻ|t|ˌeɪ|ɾ|ɪ|ŋ",
- "f|ɔː|ɹ æ|z l|ˈɪ|ɾ|əl æ|z ˈeɪ|t w|ˈiː|k|s k|æ|n ˈæ|k|tʃ|uː|əl|i| ˈɪ|n|k|ɹ|iː|s, ð|ə ɡ|ɹ|ˈeɪ m|ˈæ|ɾ|ɚ",
- "ɪ|n|ð|ə p|ˈɑːɹ|t|s ʌ|v|ð|ə b|ɹ|ˈeɪ|n ɹ|ɪ|s|p|ˈɑː|n|s|ə|b|əl",
- "f|ɔː|ɹ ɪ|m|ˈoʊ|ʃ|ə|n|əl ɹ|ˌɛ|ɡ|j|uː|l|ˈeɪ|ʃ|ə|n|| æ|n|d l|ˈɜː|n|ɪ|ŋ!",
-]
-
-
-EXPECTED_ESPEAKNG_PHONEMES = [
- "ɹ|ˈiː|s|ə|n|t ɹ|ᵻ|s|ˈɜː|tʃ æ|t h|ˈɑːɹ|v|ɚ|d h|ɐ|z ʃ|ˈoʊ|n m|ˈɛ|d|ᵻ|t|ˌeɪ|ɾ|ɪ|ŋ",
- "f|ɔː|ɹ æ|z l|ˈɪ|ɾ|əl æ|z ˈeɪ|t w|ˈiː|k|s k|æ|n ˈæ|k|tʃ|uː|əl|i| ˈɪ|ŋ|k|ɹ|iː|s, ð|ə ɡ|ɹ|ˈeɪ m|ˈæ|ɾ|ɚ",
- "ɪ|n|ð|ə p|ˈɑːɹ|t|s ʌ|v|ð|ə b|ɹ|ˈeɪ|n ɹ|ᵻ|s|p|ˈɑː|n|s|ᵻ|b|əl",
- "f|ɔː|ɹ ɪ|m|ˈoʊ|ʃ|ə|n|əl ɹ|ˌɛ|ɡ|j|ʊ|l|ˈeɪ|ʃ|ə|n|| æ|n|d l|ˈɜː|n|ɪ|ŋ!",
-]
-
-
-class TestEspeakPhonemizer(unittest.TestCase):
- def setUp(self):
- self.phonemizer = ESpeak(language="en-us", backend="espeak")
-
- if Version(self.phonemizer.backend_version) >= Version("1.48.15"):
- target_phonemes = EXPECTED_ESPEAK_v1_48_15_PHONEMES
- else:
- target_phonemes = EXPECTED_ESPEAK_PHONEMES
-
- for text, ph in zip(EXAMPLE_TEXTs, target_phonemes):
- phonemes = self.phonemizer.phonemize(text)
- self.assertEqual(phonemes, ph)
-
- # multiple punctuations
- text = "Be a voice, not an! echo?"
- gt = "biː ɐ vˈɔɪs, nˈɑːt ɐn! ˈɛkoʊ?"
- if Version(self.phonemizer.backend_version) >= Version("1.48.15"):
- gt = "biː ɐ vˈɔɪs, nˈɑːt æn! ˈɛkoʊ?"
- output = self.phonemizer.phonemize(text, separator="|")
- output = output.replace("|", "")
- self.assertEqual(output, gt)
-
- # not ending with punctuation
- text = "Be a voice, not an! echo"
- gt = "biː ɐ vˈɔɪs, nˈɑːt ɐn! ˈɛkoʊ"
- if Version(self.phonemizer.backend_version) >= Version("1.48.15"):
- gt = "biː ɐ vˈɔɪs, nˈɑːt æn! ˈɛkoʊ"
- output = self.phonemizer.phonemize(text, separator="")
- self.assertEqual(output, gt)
-
- # extra space after the sentence
- text = "Be a voice, not an! echo. "
- gt = "biː ɐ vˈɔɪs, nˈɑːt ɐn! ˈɛkoʊ."
- if Version(self.phonemizer.backend_version) >= Version("1.48.15"):
- gt = "biː ɐ vˈɔɪs, nˈɑːt æn! ˈɛkoʊ."
- output = self.phonemizer.phonemize(text, separator="")
- self.assertEqual(output, gt)
-
- def test_name(self):
- self.assertEqual(self.phonemizer.name(), "espeak")
-
- def test_get_supported_languages(self):
- self.assertIsInstance(self.phonemizer.supported_languages(), dict)
-
- def test_get_version(self):
- self.assertIsInstance(self.phonemizer.version(), str)
-
- def test_is_available(self):
- self.assertTrue(self.phonemizer.is_available())
-
-
-class TestEspeakNgPhonemizer(unittest.TestCase):
- def setUp(self):
- self.phonemizer = ESpeak(language="en-us", backend="espeak-ng")
-
- for text, ph in zip(EXAMPLE_TEXTs, EXPECTED_ESPEAKNG_PHONEMES):
- phonemes = self.phonemizer.phonemize(text)
- self.assertEqual(phonemes, ph)
-
- # multiple punctuations
- text = "Be a voice, not an! echo?"
- gt = "biː ɐ vˈɔɪs, nˈɑːt æn! ˈɛkoʊ?"
- output = self.phonemizer.phonemize(text, separator="|")
- output = output.replace("|", "")
- self.assertEqual(output, gt)
-
- # not ending with punctuation
- text = "Be a voice, not an! echo"
- gt = "biː ɐ vˈɔɪs, nˈɑːt æn! ˈɛkoʊ"
- output = self.phonemizer.phonemize(text, separator="")
- self.assertEqual(output, gt)
-
- # extra space after the sentence
- text = "Be a voice, not an! echo. "
- gt = "biː ɐ vˈɔɪs, nˈɑːt æn! ˈɛkoʊ."
- output = self.phonemizer.phonemize(text, separator="")
- self.assertEqual(output, gt)
-
- def test_name(self):
- self.assertEqual(self.phonemizer.name(), "espeak")
-
- def test_get_supported_languages(self):
- self.assertIsInstance(self.phonemizer.supported_languages(), dict)
-
- def test_get_version(self):
- self.assertIsInstance(self.phonemizer.version(), str)
-
- def test_is_available(self):
- self.assertTrue(self.phonemizer.is_available())
-
-
-class TestGruutPhonemizer(unittest.TestCase):
- def setUp(self):
- self.phonemizer = Gruut(language="en-us", use_espeak_phonemes=True, keep_stress=False)
- self.EXPECTED_PHONEMES = [
- "ɹ|i|ː|s|ə|n|t| ɹ|ᵻ|s|ɜ|ː|t|ʃ| æ|ɾ| h|ɑ|ː|ɹ|v|ɚ|d| h|ɐ|z| ʃ|o|ʊ|n| m|ɛ|d|ᵻ|t|e|ɪ|ɾ|ɪ|ŋ",
- "f|ɔ|ː|ɹ| æ|z| l|ɪ|ɾ|ə|l| æ|z| e|ɪ|t| w|i|ː|k|s| k|æ|ŋ| æ|k|t|ʃ|u|ː|ə|l|i| ɪ|ŋ|k|ɹ|i|ː|s, ð|ə| ɡ|ɹ|e|ɪ| m|æ|ɾ|ɚ",
- "ɪ|n| ð|ə| p|ɑ|ː|ɹ|t|s| ʌ|v| ð|ə| b|ɹ|e|ɪ|n| ɹ|ᵻ|s|p|ɑ|ː|n|s|ᵻ|b|ə|l",
- "f|ɔ|ː|ɹ| ɪ|m|o|ʊ|ʃ|ə|n|ə|l| ɹ|ɛ|ɡ|j|ʊ|l|e|ɪ|ʃ|ə|n| æ|n|d| l|ɜ|ː|n|ɪ|ŋ!",
- ]
-
- def test_phonemize(self):
- for text, ph in zip(EXAMPLE_TEXTs, self.EXPECTED_PHONEMES):
- phonemes = self.phonemizer.phonemize(text, separator="|")
- self.assertEqual(phonemes, ph)
-
- # multiple punctuations
- text = "Be a voice, not an! echo?"
- gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ?"
- output = self.phonemizer.phonemize(text, separator="|")
- output = output.replace("|", "")
- self.assertEqual(output, gt)
-
- # not ending with punctuation
- text = "Be a voice, not an! echo"
- gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ"
- output = self.phonemizer.phonemize(text, separator="")
- self.assertEqual(output, gt)
-
- # extra space after the sentence
- text = "Be a voice, not an! echo. "
- gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ."
- output = self.phonemizer.phonemize(text, separator="")
- self.assertEqual(output, gt)
-
- def test_name(self):
- self.assertEqual(self.phonemizer.name(), "gruut")
-
- def test_get_supported_languages(self):
- self.assertIsInstance(self.phonemizer.supported_languages(), list)
-
- def test_get_version(self):
- self.assertIsInstance(self.phonemizer.version(), str)
-
- def test_is_available(self):
- self.assertTrue(self.phonemizer.is_available())
-
-
-class TestJA_JPPhonemizer(unittest.TestCase):
- def setUp(self):
- self.phonemizer = JA_JP_Phonemizer()
- self._TEST_CASES = """
- どちらに行きますか？/dochiraniikimasuka?
- 今日は温泉に、行きます。/kyo:waoNseNni,ikimasu.
- 「A」から「Z」までです。/e:karazeqtomadedesu.
- そうですね！/so:desune!
- クジラは哺乳類です。/kujirawahonyu:ruidesu.
- ヴィディオを見ます。/bidioomimasu.
- 今日は８月22日です/kyo:wahachigatsuniju:ninichidesu
- xyzとαβγ/eqkusuwaizeqtotoarufabe:tagaNma
- 値段は$12.34です/nedaNwaju:niteNsaNyoNdorudesu
- """
-
- def test_phonemize(self):
- for line in self._TEST_CASES.strip().split("\n"):
- text, phone = line.split("/")
- self.assertEqual(self.phonemizer.phonemize(text, separator=""), phone)
-
- def test_name(self):
- self.assertEqual(self.phonemizer.name(), "ja_jp_phonemizer")
-
- def test_get_supported_languages(self):
- self.assertIsInstance(self.phonemizer.supported_languages(), dict)
-
- def test_get_version(self):
- self.assertIsInstance(self.phonemizer.version(), str)
-
- def test_is_available(self):
- self.assertTrue(self.phonemizer.is_available())
-
-
-class TestZH_CN_Phonemizer(unittest.TestCase):
- def setUp(self):
- self.phonemizer = ZH_CN_Phonemizer()
- self._TEST_CASES = ""
-
- def test_phonemize(self):
- # TODO: implement ZH phonemizer tests
- pass
-
- def test_name(self):
- self.assertEqual(self.phonemizer.name(), "zh_cn_phonemizer")
-
- def test_get_supported_languages(self):
- self.assertIsInstance(self.phonemizer.supported_languages(), dict)
-
- def test_get_version(self):
- self.assertIsInstance(self.phonemizer.version(), str)
-
- def test_is_available(self):
- self.assertTrue(self.phonemizer.is_available())
-
-
-class TestBN_Phonemizer(unittest.TestCase):
- def setUp(self):
- self.phonemizer = BN_Phonemizer()
- self._TEST_CASES = "রাসূলুল্লাহ সাল্লাল্লাহু আলাইহি ওয়া সাল্লাম শিক্ষা দিয়েছেন যে, কেউ যদি কোন খারাপ কিছুর সম্মুখীন হয়, তখনও যেন"
- self._EXPECTED = "রাসূলুল্লাহ সাল্লাল্লাহু আলাইহি ওয়া সাল্লাম শিক্ষা দিয়েছেন যে কেউ যদি কোন খারাপ কিছুর সম্মুখীন হয় তখনও যেন।"
-
- def test_phonemize(self):
- self.assertEqual(self.phonemizer.phonemize(self._TEST_CASES, separator=""), self._EXPECTED)
-
- def test_name(self):
- self.assertEqual(self.phonemizer.name(), "bn_phonemizer")
-
- def test_get_supported_languages(self):
- self.assertIsInstance(self.phonemizer.supported_languages(), dict)
-
- def test_get_version(self):
- self.assertIsInstance(self.phonemizer.version(), str)
-
- def test_is_available(self):
- self.assertTrue(self.phonemizer.is_available())
-
-
-class TestMultiPhonemizer(unittest.TestCase):
- def setUp(self):
- self.phonemizer = MultiPhonemizer({"tr": "espeak", "en-us": "", "de": "gruut", "zh-cn": ""})
-
- def test_phonemize(self):
- # Enlish espeak
- text = "Be a voice, not an! echo?"
- gt = "biː ɐ vˈɔɪs, nˈɑːt æn! ˈɛkoʊ?"
- output = self.phonemizer.phonemize(text, separator="|", language="en-us")
- output = output.replace("|", "")
- self.assertEqual(output, gt)
-
- # German gruut
- text = "Hallo, das ist ein Deutches Beipiel!"
- gt = "haloː, das ɪst aeːn dɔɔʏ̯tçəs bəʔiːpiːl!"
- output = self.phonemizer.phonemize(text, separator="|", language="de")
- output = output.replace("|", "")
- self.assertEqual(output, gt)
-
- def test_phonemizer_initialization(self):
- # test with unsupported language
- with self.assertRaises(ValueError):
- MultiPhonemizer({"tr": "espeak", "xx": ""})
-
- # test with unsupported phonemizer
- with self.assertRaises(ValueError):
- MultiPhonemizer({"tr": "espeak", "fr": "xx"})
-
- def test_sub_phonemizers(self):
- for lang in self.phonemizer.lang_to_phonemizer_name.keys():
- self.assertEqual(lang, self.phonemizer.lang_to_phonemizer[lang].language)
- self.assertEqual(
- self.phonemizer.lang_to_phonemizer_name[lang], self.phonemizer.lang_to_phonemizer[lang].name()
- )
-
- def test_name(self):
- self.assertEqual(self.phonemizer.name(), "multi-phonemizer")
-
- def test_get_supported_languages(self):
- self.assertIsInstance(self.phonemizer.supported_languages(), list)
diff --git a/TTS/tests/text_tests/test_punctuation.py b/TTS/tests/text_tests/test_punctuation.py
deleted file mode 100644
index 141c10e48f814b3843bea25d7456189716647ce0..0000000000000000000000000000000000000000
--- a/TTS/tests/text_tests/test_punctuation.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import unittest
-
-from TTS.tts.utils.text.punctuation import _DEF_PUNCS, Punctuation
-
-
-class PunctuationTest(unittest.TestCase):
- def setUp(self):
- self.punctuation = Punctuation()
- self.test_texts = [
- ("This, is my text ... to be striped !! from text?", "This is my text to be striped from text"),
- ("This, is my text ... to be striped !! from text", "This is my text to be striped from text"),
- ("This, is my text ... to be striped from text?", "This is my text to be striped from text"),
- ("This, is my text to be striped from text", "This is my text to be striped from text"),
- ]
-
- def test_get_set_puncs(self):
- self.punctuation.puncs = "-="
- self.assertEqual(self.punctuation.puncs, "-=")
-
- self.punctuation.puncs = _DEF_PUNCS
- self.assertEqual(self.punctuation.puncs, _DEF_PUNCS)
-
- def test_strip_punc(self):
- for text, gt in self.test_texts:
- text_striped = self.punctuation.strip(text)
- self.assertEqual(text_striped, gt)
-
- def test_strip_restore(self):
- for text, gt in self.test_texts:
- text_striped, puncs_map = self.punctuation.strip_to_restore(text)
- text_restored = self.punctuation.restore(text_striped, puncs_map)
- self.assertEqual(" ".join(text_striped), gt)
- self.assertEqual(text_restored[0], text)
diff --git a/TTS/tests/text_tests/test_text_cleaners.py b/TTS/tests/text_tests/test_text_cleaners.py
deleted file mode 100644
index fcfa71e77dde8daa6002aa71a56e4f8ca96a51a7..0000000000000000000000000000000000000000
--- a/TTS/tests/text_tests/test_text_cleaners.py
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/usr/bin/env python3
-
-from TTS.tts.utils.text.cleaners import english_cleaners, phoneme_cleaners
-
-
-def test_time() -> None:
- assert english_cleaners("It's 11:00") == "it's eleven a m"
- assert english_cleaners("It's 9:01") == "it's nine oh one a m"
- assert english_cleaners("It's 16:00") == "it's four p m"
- assert english_cleaners("It's 00:00 am") == "it's twelve a m"
-
-
-def test_currency() -> None:
- assert phoneme_cleaners("It's $10.50") == "It's ten dollars fifty cents"
- assert phoneme_cleaners("£1.1") == "one pound sterling one penny"
- assert phoneme_cleaners("¥1") == "one yen"
-
-
-def test_expand_numbers() -> None:
- assert phoneme_cleaners("-1") == "minus one"
- assert phoneme_cleaners("1") == "one"
diff --git a/TTS/tests/text_tests/test_tokenizer.py b/TTS/tests/text_tests/test_tokenizer.py
deleted file mode 100644
index dfa213d9d527e21de2e19f5c069bcd247954dd99..0000000000000000000000000000000000000000
--- a/TTS/tests/text_tests/test_tokenizer.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import unittest
-from dataclasses import dataclass, field
-
-from coqpit import Coqpit
-
-from TTS.tts.utils.text.characters import Graphemes, IPAPhonemes, _blank, _bos, _eos, _pad, _phonemes, _punctuations
-from TTS.tts.utils.text.phonemizers import ESpeak
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-
-
-class TestTTSTokenizer(unittest.TestCase):
- def setUp(self):
- self.tokenizer = TTSTokenizer(use_phonemes=False, characters=Graphemes())
-
- self.ph = ESpeak("tr", backend="espeak")
- self.tokenizer_ph = TTSTokenizer(use_phonemes=True, characters=IPAPhonemes(), phonemizer=self.ph)
-
- def test_encode_decode_graphemes(self):
- text = "This is, a test."
- ids = self.tokenizer.encode(text)
- test_hat = self.tokenizer.decode(ids)
- self.assertEqual(text, test_hat)
- self.assertEqual(len(ids), len(text))
-
- def test_text_to_ids_phonemes(self):
- # TODO: note sure how to extend to cover all the languages and phonemizer.
- text = "Bu bir Örnek."
- text_ph = self.ph.phonemize(text, separator="")
- ids = self.tokenizer_ph.text_to_ids(text)
- test_hat = self.tokenizer_ph.ids_to_text(ids)
- self.assertEqual(text_ph, test_hat)
-
- def test_text_to_ids_phonemes_punctuation(self):
- text = "..."
- text_ph = self.ph.phonemize(text, separator="")
- ids = self.tokenizer_ph.text_to_ids(text)
- test_hat = self.tokenizer_ph.ids_to_text(ids)
- self.assertEqual(text_ph, test_hat)
-
- def test_text_to_ids_phonemes_with_eos_bos(self):
- text = "Bu bir Örnek."
- self.tokenizer_ph.use_eos_bos = True
- text_ph = IPAPhonemes().bos + self.ph.phonemize(text, separator="") + IPAPhonemes().eos
- ids = self.tokenizer_ph.text_to_ids(text)
- test_hat = self.tokenizer_ph.ids_to_text(ids)
- self.assertEqual(text_ph, test_hat)
-
- def test_text_to_ids_phonemes_with_eos_bos_and_blank(self):
- text = "Bu bir Örnek."
- self.tokenizer_ph.use_eos_bos = True
- self.tokenizer_ph.add_blank = True
- text_ph = "
-
-
-
-
-
- - {%if use_multi_speaker%} - Choose a speaker: -
- {%endif%} - - {%if use_multi_language%} - Choose a language: -
- {%endif%} - - - {%if show_details%} -
- {%endif%} - - -
-
-
- -
-
- - {%if use_multi_speaker%} - Choose a speaker: -
- {%endif%} - - {%if use_multi_language%} - Choose a language: -
- {%endif%} - - - {%if show_details%} -
- {%endif%} - - -