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# Copyright 2021 Piotr Żelasko
# Copyright 2022-2024 Xiaomi Corporation (Authors: Mingshuang Luo,
# Zengwei Yao,
# Zengrui Jin,
# Han Zhu,
# Wei Kang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
from functools import lru_cache
from pathlib import Path
from typing import Any, Dict, Optional
import torch
from lhotse import CutSet, load_manifest_lazy
from lhotse.dataset import DynamicBucketingSampler, SimpleCutSampler
from lhotse.dataset.input_strategies import OnTheFlyFeatures, PrecomputedFeatures
from lhotse.utils import fix_random_seed
from torch.utils.data import DataLoader
from zipvoice.dataset.dataset import SpeechSynthesisDataset
from zipvoice.utils.common import str2bool
from zipvoice.utils.feature import VocosFbank
class _SeedWorkers:
def __init__(self, seed: int):
self.seed = seed
def __call__(self, worker_id: int):
fix_random_seed(self.seed + worker_id)
SAMPLING_RATE = 24000
class TtsDataModule:
"""
DataModule for tts experiments.
It assumes there is always one train and valid dataloader,
but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
and test-other).
It contains all the common data pipeline modules used in ASR
experiments, e.g.:
- dynamic batch size,
- bucketing samplers,
- cut concatenation,
- on-the-fly feature extraction
This class should be derived for specific corpora used in ASR tasks.
"""
def __init__(self, args: argparse.Namespace):
self.args = args
@classmethod
def add_arguments(cls, parser: argparse.ArgumentParser):
group = parser.add_argument_group(
title="TTS data related options",
description="These options are used for the preparation of "
"PyTorch DataLoaders from Lhotse CutSet's -- they control the "
"effective batch sizes, sampling strategies, applied data "
"augmentations, etc.",
)
group.add_argument(
"--manifest-dir",
type=Path,
default=Path("data/fbank"),
help="Path to directory with train/valid/test cuts.",
)
group.add_argument(
"--max-duration",
type=int,
default=200.0,
help="Maximum pooled recordings duration (seconds) in a "
"single batch. You can reduce it if it causes CUDA OOM.",
)
group.add_argument(
"--bucketing-sampler",
type=str2bool,
default=True,
help="When enabled, the batches will come from buckets of "
"similar duration (saves padding frames).",
)
group.add_argument(
"--num-buckets",
type=int,
default=30,
help="The number of buckets for the DynamicBucketingSampler"
"(you might want to increase it for larger datasets).",
)
group.add_argument(
"--on-the-fly-feats",
type=str2bool,
default=False,
help="When enabled, use on-the-fly cut mixing and feature "
"extraction. Will drop existing precomputed feature manifests "
"if available.",
)
group.add_argument(
"--shuffle",
type=str2bool,
default=True,
help="When enabled (=default), the examples will be "
"shuffled for each epoch.",
)
group.add_argument(
"--drop-last",
type=str2bool,
default=True,
help="Whether to drop last batch. Used by sampler.",
)
group.add_argument(
"--return-cuts",
type=str2bool,
default=False,
help="When enabled, each batch will have the "
"field: batch['cut'] with the cuts that "
"were used to construct it.",
)
group.add_argument(
"--num-workers",
type=int,
default=8,
help="The number of training dataloader workers that "
"collect the batches.",
)
group.add_argument(
"--input-strategy",
type=str,
default="PrecomputedFeatures",
help="AudioSamples or PrecomputedFeatures",
)
def train_dataloaders(
self,
cuts_train: CutSet,
sampler_state_dict: Optional[Dict[str, Any]] = None,
) -> DataLoader:
"""
Args:
cuts_train:
CutSet for training.
sampler_state_dict:
The state dict for the training sampler.
"""
logging.info("About to create train dataset")
train = SpeechSynthesisDataset(
return_text=True,
return_tokens=True,
return_spk_ids=True,
feature_input_strategy=OnTheFlyFeatures(VocosFbank())
if self.args.on_the_fly_feats
else PrecomputedFeatures(),
return_cuts=self.args.return_cuts,
)
if self.args.bucketing_sampler:
logging.info("Using DynamicBucketingSampler.")
train_sampler = DynamicBucketingSampler(
cuts_train,
max_duration=self.args.max_duration,
shuffle=self.args.shuffle,
num_buckets=self.args.num_buckets,
buffer_size=self.args.num_buckets * 2000,
shuffle_buffer_size=self.args.num_buckets * 5000,
drop_last=self.args.drop_last,
)
else:
logging.info("Using SimpleCutSampler.")
train_sampler = SimpleCutSampler(
cuts_train,
max_duration=self.args.max_duration,
shuffle=self.args.shuffle,
)
logging.info("About to create train dataloader")
if sampler_state_dict is not None:
logging.info("Loading sampler state dict")
train_sampler.load_state_dict(sampler_state_dict)
# 'seed' is derived from the current random state, which will have
# previously been set in the main process.
seed = torch.randint(0, 100000, ()).item()
worker_init_fn = _SeedWorkers(seed)
train_dl = DataLoader(
train,
sampler=train_sampler,
batch_size=None,
num_workers=self.args.num_workers,
persistent_workers=False,
worker_init_fn=worker_init_fn,
)
return train_dl
def dev_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
logging.info("About to create dev dataset")
validate = SpeechSynthesisDataset(
return_text=True,
return_tokens=True,
return_spk_ids=True,
feature_input_strategy=OnTheFlyFeatures(VocosFbank())
if self.args.on_the_fly_feats
else PrecomputedFeatures(),
return_cuts=self.args.return_cuts,
)
dev_sampler = DynamicBucketingSampler(
cuts_valid,
max_duration=self.args.max_duration,
shuffle=False,
)
logging.info("About to create valid dataloader")
dev_dl = DataLoader(
validate,
sampler=dev_sampler,
batch_size=None,
num_workers=2,
persistent_workers=False,
)
return dev_dl
def test_dataloaders(self, cuts: CutSet) -> DataLoader:
logging.info("About to create test dataset")
test = SpeechSynthesisDataset(
return_text=True,
return_tokens=True,
return_spk_ids=True,
feature_input_strategy=OnTheFlyFeatures(VocosFbank())
if self.args.on_the_fly_feats
else PrecomputedFeatures(),
return_cuts=self.args.return_cuts,
return_audio=True,
)
test_sampler = DynamicBucketingSampler(
cuts,
max_duration=self.args.max_duration,
shuffle=False,
)
logging.info("About to create test dataloader")
test_dl = DataLoader(
test,
batch_size=None,
sampler=test_sampler,
num_workers=self.args.num_workers,
)
return test_dl
@lru_cache()
def train_custom_cuts(self, manifest_file) -> CutSet:
logging.info(f"About to get the custom training cuts {manifest_file}")
return load_manifest_lazy(manifest_file)
@lru_cache()
def dev_custom_cuts(self, manifest_file) -> CutSet:
logging.info(f"About to get the custom validation cuts {manifest_file}")
return load_manifest_lazy(manifest_file)
@lru_cache()
def train_emilia_EN_cuts(self) -> CutSet:
logging.info("About to get train the EN subset")
return load_manifest_lazy(self.args.manifest_dir / "emilia_cuts_EN.jsonl.gz")
@lru_cache()
def train_emilia_ZH_cuts(self) -> CutSet:
logging.info("About to get train the ZH subset")
return load_manifest_lazy(self.args.manifest_dir / "emilia_cuts_ZH.jsonl.gz")
@lru_cache()
def dev_emilia_EN_cuts(self) -> CutSet:
logging.info("About to get dev the EN subset")
return load_manifest_lazy(
self.args.manifest_dir / "emilia_cuts_EN-dev.jsonl.gz"
)
@lru_cache()
def dev_emilia_ZH_cuts(self) -> CutSet:
logging.info("About to get dev the ZH subset")
return load_manifest_lazy(
self.args.manifest_dir / "emilia_cuts_ZH-dev.jsonl.gz"
)
@lru_cache()
def train_libritts_cuts(self) -> CutSet:
logging.info(
"About to get the shuffled train-clean-100, \
train-clean-360 and train-other-500 cuts"
)
return load_manifest_lazy(
self.args.manifest_dir / "libritts_cuts_train-all-shuf.jsonl.gz"
)
@lru_cache()
def dev_libritts_cuts(self) -> CutSet:
logging.info("About to get dev-clean cuts")
return load_manifest_lazy(
self.args.manifest_dir / "libritts_cuts_dev-clean.jsonl.gz"
)
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