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import json
import random
import re
from importlib.resources import files
import torch
import torch.nn.functional as F
import torchaudio
from datasets import Dataset as Dataset_
from datasets import load_from_disk
from torch import nn
from torch.utils.data import Dataset, Sampler
from tqdm import tqdm
from f5_tts.model.modules import MelSpec
from f5_tts.model.utils import default
def get_speaker_id(path):
parts = path.split("/")
speaker_id = parts[-3]
return speaker_id
class CustomDataset(Dataset):
def __init__(
self,
custom_dataset: Dataset,
durations=None,
target_sample_rate=24_000,
hop_length=256,
n_mel_channels=100,
n_fft=1024,
win_length=1024,
mel_spec_type="vocos",
preprocessed_mel=False,
mel_spec_module: nn.Module | None = None,
validation=False,
validation_num=5000,
data_augmentation=False,
return_wavform=False,
remove_starting_space=True,
need_prompt_speech=False,
prompt_repository: dict = None,
):
self.data = custom_dataset
self.durations = durations
self.target_sample_rate = target_sample_rate
self.hop_length = hop_length
self.n_fft = n_fft
self.win_length = win_length
self.mel_spec_type = mel_spec_type
self.preprocessed_mel = preprocessed_mel
if not preprocessed_mel:
self.mel_spectrogram = default(
mel_spec_module,
MelSpec(
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
n_mel_channels=n_mel_channels,
target_sample_rate=target_sample_rate,
mel_spec_type=mel_spec_type,
),
)
self.validation = validation
self.validation_num = validation_num
if (not validation) and data_augmentation:
print("Using data augmentation.")
self.augment = Compose(
[
AddBackgroundNoise(
sounds_path="/data5/ESC-50-master",
min_snr_db=3.0,
max_snr_db=30.0,
noise_transform=PolarityInversion(),
p=0.5,
),
AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=0.5),
PitchShift(min_semitones=-12.0, max_semitones=12.0, p=0.8),
ApplyImpulseResponse(ir_path="/data5/Audio", p=1.0),
Aliasing(min_sample_rate=4000, max_sample_rate=30000, p=0.3),
BandPassFilter(min_center_freq=100.0, max_center_freq=6000, p=0.2),
SevenBandParametricEQ(p=0.2),
TanhDistortion(min_distortion=0.01, max_distortion=0.7, p=0.2),
]
)
else:
print("No data augmentation.")
self.augment = None
self.return_wavform = return_wavform
self.remove_starting_space = remove_starting_space
if need_prompt_speech:
if prompt_repository == None:
self.prompt_repository = {}
for row in tqdm(self.data):
audio_path = row["audio_path"]
text = row["text"]
duration = row["duration"]
spk_id = get_speaker_id(audio_path)
assert spk_id != None and spk_id != "mp3"
if spk_id not in self.prompt_repository:
self.prompt_repository[spk_id] = [row]
else:
self.prompt_repository[spk_id].append(row)
else:
self.prompt_repository = prompt_repository
print(
f"Grouped samples into {len(self.prompt_repository.keys())} speakers."
)
self.need_prompt_speech = True
else:
self.need_prompt_speech = False
def get_frame_len(self, index):
if self.validation:
index += len(self.data) - self.validation_num
if (
self.durations is not None
): # Please make sure the separately provided durations are correct, otherwise 99.99% OOM
return self.durations[index] * self.target_sample_rate / self.hop_length
return self.data[index]["duration"] * self.target_sample_rate / self.hop_length
def __len__(self):
if not self.validation:
return len(self.data) - self.validation_num
return self.validation_num
def __getitem__(self, index, return_row=True, return_path=False):
if self.validation:
index += len(self.data) - self.validation_num
out = {}
while True:
row = self.data[index]
audio_path = row["audio_path"]
text = row["text"]
duration = row["duration"]
if not isinstance(text, list):
text = list(text)
# filter by given length
if (0.3 <= duration <= 30) and (0 < len(text) < 2048):
break # valid
index = (index + 1) % len(self.data)
if self.remove_starting_space:
while len(text) > 1 and text[0] == " ":
text = text[1:]
if self.preprocessed_mel:
mel_spec = torch.tensor(row["mel_spec"])
else:
audio, source_sample_rate = torchaudio.load(audio_path)
# make sure mono input
if audio.shape[0] > 1:
audio = torch.mean(audio, dim=0, keepdim=True)
# resample if necessary
if source_sample_rate != self.target_sample_rate:
resampler = torchaudio.transforms.Resample(
source_sample_rate, self.target_sample_rate
)
audio = resampler(audio)
if not self.validation:
if self.augment != None:
audio = self.augment(
audio.squeeze().numpy(), sample_rate=self.target_sample_rate
)
audio = torch.from_numpy(audio).float().unsqueeze(0)
# to mel spectrogram
mel_spec = self.mel_spectrogram(audio)
mel_spec = mel_spec.squeeze(0) # '1 d t -> d t'
out["mel_spec"] = mel_spec
out["text"] = text
out["duration"] = duration
out["target_text"] = self.data[(index + len(self.data) // 2) % len(self.data)][
"text"
]
if self.return_wavform:
out["wav"] = audio
if return_path:
out["path"] = audio_path
if return_row:
out["row"] = row
# Sample a prompt speech of the same speaker
# From prompt_repository
if self.need_prompt_speech:
spk = get_speaker_id(audio_path)
spk_repository = self.prompt_repository[spk]
_count = 100
while True:
pmt_row = random.choice(spk_repository)
pmt_audio_path = pmt_row["audio_path"]
pmt_text = pmt_row["text"]
pmt_duration = pmt_row["duration"]
if not isinstance(pmt_text, list):
pmt_text = list(pmt_text)
# filter by given length
if 0.3 <= pmt_duration <= 30 and (0 < len(pmt_text) < 2048):
if pmt_text != text:
break
_count = _count - 1
if _count <= 0:
break
if self.remove_starting_space:
while len(pmt_text) > 1 and pmt_text[0] == " ":
pmt_text = pmt_text[1:]
if self.preprocessed_mel:
pmt_mel_spec = torch.tensor(pmt_row["mel_spec"])
else:
pmt_audio, source_sample_rate = torchaudio.load(pmt_audio_path)
# make sure mono input
if pmt_audio.shape[0] > 1:
pmt_audio = torch.mean(pmt_audio, dim=0, keepdim=True)
# resample if necessary
if source_sample_rate != self.target_sample_rate:
resampler = torchaudio.transforms.Resample(
source_sample_rate, self.target_sample_rate
)
pmt_audio = resampler(pmt_audio)
if not self.validation:
if self.augment != None:
pmt_audio = self.augment(
pmt_audio.squeeze().numpy(),
sample_rate=self.target_sample_rate,
)
pmt_audio = torch.from_numpy(pmt_audio).float().unsqueeze(0)
# to mel spectrogram
pmt_mel_spec = self.mel_spectrogram(pmt_audio)
pmt_mel_spec = pmt_mel_spec.squeeze(0) # '1 d t -> d t'
out["pmt_mel_spec"] = pmt_mel_spec
out["pmt_text"] = pmt_text
out["pmt_duration"] = pmt_duration
if self.return_wavform:
out["pmt_wav"] = pmt_audio
if return_path:
out["pmt_path"] = pmt_audio_path
if return_row:
out["pmt_row"] = pmt_row
return out
# Dynamic Batch Sampler
class DynamicBatchSampler(Sampler[list[int]]):
"""Extension of Sampler that will do the following:
1. Change the batch size (essentially number of sequences)
in a batch to ensure that the total number of frames are less
than a certain threshold.
2. Make sure the padding efficiency in the batch is high.
"""
def __init__(
self,
sampler: Sampler[int],
frames_threshold: int,
max_samples=0,
random_seed=None,
drop_last: bool = False,
):
self.sampler = sampler
self.frames_threshold = frames_threshold
self.max_samples = max_samples
indices, batches = [], []
data_source = self.sampler.data_source
# for idx in tqdm(
# self.sampler, desc="Sorting with sampler... if slow, check whether dataset is provided with duration"
# ):
for idx in self.sampler:
indices.append((idx, data_source.get_frame_len(idx)))
indices.sort(key=lambda elem: elem[1])
batch = []
batch_frames = 0
# for idx, frame_len in tqdm(
# indices, desc=f"Creating dynamic batches with {frames_threshold} audio frames per gpu"
# ):
for idx, frame_len in indices:
if batch_frames + frame_len <= self.frames_threshold and (
max_samples == 0 or len(batch) < max_samples
):
batch.append(idx)
batch_frames += frame_len
else:
if len(batch) > 0:
batches.append(batch)
if frame_len <= self.frames_threshold:
batch = [idx]
batch_frames = frame_len
else:
batch = []
batch_frames = 0
if not drop_last and len(batch) > 0:
batches.append(batch)
del indices
# if want to have different batches between epochs, may just set a seed and log it in ckpt
# cuz during multi-gpu training, although the batch on per gpu not change between epochs, the formed general minibatch is different
# e.g. for epoch n, use (random_seed + n)
random.seed(random_seed)
random.shuffle(batches)
self.batches = batches
def __iter__(self):
return iter(self.batches)
def __len__(self):
return len(self.batches)
# Load dataset
def load_dataset(
dataset_name: str,
tokenizer: str = "pinyin",
dataset_type: str = "CustomDataset",
audio_type: str = "raw",
mel_spec_module: nn.Module | None = None,
mel_spec_kwargs: dict = dict(),
split: str = "train",
data_augmentation: bool = False,
return_wavform: bool = False,
remove_starting_space: bool = True,
need_prompt_speech: bool = False,
prompt_repository: dict = None,
) -> CustomDataset:
"""
dataset_type - "CustomDataset" if you want to use tokenizer name and default data path to load for train_dataset
- "CustomDatasetPath" if you just want to pass the full path to a preprocessed dataset without relying on tokenizer
"""
print("Loading dataset ...")
if dataset_type == "CustomDataset":
rel_data_path = str(
f"/home/yl4579/F5-TTS-diff/F5-TTS-DMD-flow-ds/data/{dataset_name}_{tokenizer}"
)
if "LibriTTS_100_360_500_char_pinyin" in rel_data_path:
rel_data_path = rel_data_path.replace(
"LibriTTS_100_360_500_char_pinyin", "LibriTTS_100_360_500_char"
)
if audio_type == "raw":
try:
train_dataset = load_from_disk(f"{rel_data_path}/raw")
except: # noqa: E722
train_dataset = Dataset_.from_file(f"{rel_data_path}/raw.arrow")
preprocessed_mel = False
elif audio_type == "mel":
train_dataset = Dataset_.from_file(f"{rel_data_path}/mel.arrow")
preprocessed_mel = True
with open(f"{rel_data_path}/duration.json", "r", encoding="utf-8") as f:
data_dict = json.load(f)
durations = data_dict["duration"]
train_dataset = CustomDataset(
train_dataset,
durations=durations,
preprocessed_mel=preprocessed_mel,
mel_spec_module=mel_spec_module,
**mel_spec_kwargs,
validation=split == "val",
data_augmentation=data_augmentation,
return_wavform=return_wavform,
remove_starting_space=remove_starting_space,
need_prompt_speech=need_prompt_speech,
prompt_repository=prompt_repository,
)
elif dataset_type == "CustomDatasetPath":
try:
train_dataset = load_from_disk(f"{dataset_name}/raw")
except: # noqa: E722
train_dataset = Dataset_.from_file(f"{dataset_name}/raw.arrow")
with open(f"{dataset_name}/duration.json", "r", encoding="utf-8") as f:
data_dict = json.load(f)
durations = data_dict["duration"]
train_dataset = CustomDataset(
train_dataset,
durations=durations,
preprocessed_mel=preprocessed_mel,
**mel_spec_kwargs,
)
return train_dataset
# collation
def collate_fn(batch):
# Extract mel_specs and their lengths
mel_specs = [item["mel_spec"].squeeze(0) for item in batch]
mel_lengths = torch.LongTensor([spec.shape[-1] for spec in mel_specs])
max_mel_length = mel_lengths.amax()
# Pad mel_specs
padded_mel_specs = []
for spec in mel_specs: # TODO. maybe records mask for attention here
padding = (0, max_mel_length - spec.size(-1))
padded_spec = F.pad(spec, padding, value=0)
padded_mel_specs.append(padded_spec)
mel_specs = torch.stack(padded_mel_specs)
text = [item["text"] for item in batch]
target_text = [item["target_text"] for item in batch]
text_lengths = torch.LongTensor([len(item) for item in text])
out = dict(
mel=mel_specs,
mel_lengths=mel_lengths,
text=text,
text_lengths=text_lengths,
target_text=target_text,
)
if "pmt_mel_spec" in batch[0]:
pmt_mel_specs = [item["pmt_mel_spec"].squeeze(0) for item in batch]
pmt_mel_lengths = torch.LongTensor([spec.shape[-1] for spec in pmt_mel_specs])
max_pmt_mel_length = pmt_mel_lengths.amax()
# Pad mel_specs
padded_pmt_mel_specs = []
for spec in pmt_mel_specs:
padding = (0, max_pmt_mel_length - spec.size(-1))
padded_spec = F.pad(spec, padding, value=0)
padded_pmt_mel_specs.append(padded_spec)
pmt_mel_specs = torch.stack(padded_pmt_mel_specs)
out["pmt_mel_specs"] = pmt_mel_specs
if "pmt_text" in batch[0]:
pmt_text = [item["pmt_text"] for item in batch]
pmt_text_lengths = torch.LongTensor([len(item) for item in pmt_text])
out["pmt_text"] = pmt_text
out["pmt_text_lengths"] = pmt_text_lengths
return out
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