mirror of
https://github.com/k2-fsa/icefall.git
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344 lines
11 KiB
Python
344 lines
11 KiB
Python
# Copyright 2021 Piotr Żelasko
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# Copyright 2022-2024 Xiaomi Corporation (Authors: Mingshuang Luo,
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# Zengwei Yao,
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# Zengrui Jin,)
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# Copyright 2023 (authors: Feiteng Li)
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# Copyright 2024 (Author: Yuekai Zhang)
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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from functools import lru_cache
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from pathlib import Path
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from typing import Any, Dict, Optional
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import torch
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from lhotse import CutSet, Spectrogram, SpectrogramConfig, load_manifest_lazy
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from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures
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CutConcatenate,
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DynamicBucketingSampler,
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PrecomputedFeatures,
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SimpleCutSampler,
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SpeechSynthesisDataset,
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)
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from lhotse.dataset.input_strategies import ( # noqa F401 For AudioSamples
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AudioSamples,
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OnTheFlyFeatures,
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)
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from lhotse.features.io import KaldiReader
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from lhotse.utils import fix_random_seed
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from torch.utils.data import DataLoader
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from icefall.utils import str2bool
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class _SeedWorkers:
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def __init__(self, seed: int):
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self.seed = seed
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def __call__(self, worker_id: int):
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fix_random_seed(self.seed + worker_id)
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class TtsDataModule:
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"""
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DataModule for tts experiments.
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It assumes there is always one train and valid dataloader,
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but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
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and test-other).
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It contains all the common data pipeline modules used in TTS
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experiments, e.g.:
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- dynamic batch size,
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- bucketing samplers,
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- cut concatenation,
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- on-the-fly feature extraction
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This class should be derived for specific corpora used in ASR tasks.
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"""
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def __init__(self, args: argparse.Namespace):
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self.args = args
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@classmethod
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def add_arguments(cls, parser: argparse.ArgumentParser):
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group = parser.add_argument_group(
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title="TTS data related options",
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description="These options are used for the preparation of "
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"PyTorch DataLoaders from Lhotse CutSet's -- they control the "
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"effective batch sizes, sampling strategies, applied data "
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"augmentations, etc.",
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)
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group.add_argument(
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"--manifest-dir",
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type=Path,
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default=Path("data/tokenized"),
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help="Path to directory with train/valid/test cuts.",
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)
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group.add_argument(
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"--speaker-embeds",
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type=Path,
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default=Path("exp/xvector_nnet_1a/"),
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help="Path to directory with speaker embeddings.",
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)
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group.add_argument(
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"--max-duration",
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type=int,
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default=200.0,
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help="Maximum pooled recordings duration (seconds) in a "
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"single batch. You can reduce it if it causes CUDA OOM.",
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)
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group.add_argument(
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"--bucketing-sampler",
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type=str2bool,
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default=True,
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help="When enabled, the batches will come from buckets of "
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"similar duration (saves padding frames).",
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)
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group.add_argument(
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"--num-buckets",
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type=int,
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default=30,
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help="The number of buckets for the DynamicBucketingSampler"
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"(you might want to increase it for larger datasets).",
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)
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group.add_argument(
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"--on-the-fly-feats",
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type=str2bool,
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default=False,
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help="When enabled, use on-the-fly cut mixing and feature "
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"extraction. Will drop existing precomputed feature manifests "
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"if available.",
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)
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group.add_argument(
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"--shuffle",
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type=str2bool,
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default=True,
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help="When enabled (=default), the examples will be "
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"shuffled for each epoch.",
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)
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group.add_argument(
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"--drop-last",
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type=str2bool,
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default=True,
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help="Whether to drop last batch. Used by sampler.",
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)
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group.add_argument(
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"--return-cuts",
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type=str2bool,
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default=True,
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help="When enabled, each batch will have the "
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"field: batch['cut'] with the cuts that "
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"were used to construct it.",
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)
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group.add_argument(
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"--num-workers",
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type=int,
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default=4,
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help="The number of training dataloader workers that "
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"collect the batches.",
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)
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group.add_argument(
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"--enable-spec-aug",
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type=str2bool,
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default=False,
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help="When enabled, use SpecAugment for training dataset.",
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)
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group.add_argument(
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"--input-strategy",
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type=str,
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default="PrecomputedFeatures",
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help="AudioSamples or PrecomputedFeatures",
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)
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group.add_argument(
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"--dataset",
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type=str,
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default="libritts",
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help="--input-strategy PromptedPrecomputedFeatures needs dataset name to prepare prompts.",
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)
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parser.add_argument(
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"--sampling-rate",
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type=int,
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default=24000,
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help="""Audio sampling rate.""",
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)
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def train_dataloaders(
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self,
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cuts_train: CutSet,
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sampler_state_dict: Optional[Dict[str, Any]] = None,
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) -> DataLoader:
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"""
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Args:
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cuts_train:
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CutSet for training.
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sampler_state_dict:
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The state dict for the training sampler.
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"""
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logging.info("About to create train dataset")
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train = SpeechSynthesisDataset(
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return_text=True,
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return_tokens=True,
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return_spk_ids=False,
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feature_input_strategy=eval(self.args.input_strategy)(),
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return_cuts=self.args.return_cuts,
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)
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if self.args.on_the_fly_feats:
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raise NotImplementedError
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if self.args.bucketing_sampler:
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logging.info("Using DynamicBucketingSampler.")
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train_sampler = DynamicBucketingSampler(
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cuts_train,
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max_duration=self.args.max_duration,
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shuffle=self.args.shuffle,
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num_buckets=self.args.num_buckets,
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buffer_size=self.args.num_buckets * 2000,
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shuffle_buffer_size=self.args.num_buckets * 5000,
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drop_last=self.args.drop_last,
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)
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else:
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logging.info("Using SimpleCutSampler.")
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train_sampler = SimpleCutSampler(
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cuts_train,
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max_duration=self.args.max_duration,
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shuffle=self.args.shuffle,
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)
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logging.info("About to create train dataloader")
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if sampler_state_dict is not None:
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logging.info("Loading sampler state dict")
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train_sampler.load_state_dict(sampler_state_dict)
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# 'seed' is derived from the current random state, which will have
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# previously been set in the main process.
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seed = torch.randint(0, 100000, ()).item()
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worker_init_fn = _SeedWorkers(seed)
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train_dl = DataLoader(
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train,
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sampler=train_sampler,
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batch_size=None,
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num_workers=self.args.num_workers,
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persistent_workers=False,
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worker_init_fn=worker_init_fn,
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)
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return train_dl
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def dev_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
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logging.info("About to create dev dataset")
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if self.args.on_the_fly_feats:
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raise NotImplementedError
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else:
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validate = SpeechSynthesisDataset(
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return_text=True,
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return_tokens=True,
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return_spk_ids=False,
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feature_input_strategy=eval(self.args.input_strategy)(),
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return_cuts=self.args.return_cuts,
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)
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dev_sampler = DynamicBucketingSampler(
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cuts_valid,
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max_duration=self.args.max_duration,
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shuffle=False,
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)
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logging.info("About to create valid dataloader")
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dev_dl = DataLoader(
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validate,
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sampler=dev_sampler,
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batch_size=None,
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num_workers=self.args.num_workers,
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persistent_workers=False,
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)
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return dev_dl
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def test_dataloaders(self, cuts: CutSet) -> DataLoader:
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logging.info("About to create test dataset")
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if self.args.on_the_fly_feats:
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raise NotImplementedError
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else:
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test = SpeechSynthesisDataset(
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return_text=True,
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return_tokens=True,
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return_spk_ids=False,
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feature_input_strategy=eval(self.args.input_strategy)(),
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return_cuts=self.args.return_cuts,
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)
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test_sampler = DynamicBucketingSampler(
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cuts,
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max_duration=self.args.max_duration,
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shuffle=False,
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)
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logging.info("About to create test dataloader")
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test_dl = DataLoader(
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test,
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batch_size=None,
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sampler=test_sampler,
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num_workers=self.args.num_workers,
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)
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return test_dl
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@lru_cache()
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def train_cuts(self) -> CutSet:
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logging.info("About to get train cuts")
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return load_manifest_lazy(self.args.manifest_dir / "cuts_train.jsonl.gz")
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@lru_cache()
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def dev_cuts(self) -> CutSet:
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logging.info("About to get dev cuts")
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return load_manifest_lazy(self.args.manifest_dir / "cuts_dev.jsonl.gz")
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@lru_cache()
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def test_cuts(self) -> CutSet:
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logging.info("About to get test cuts")
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return load_manifest_lazy(self.args.manifest_dir / "cuts_test.jsonl.gz")
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@lru_cache()
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def dev_clean_cuts(self) -> CutSet:
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logging.info("About to get dev-clean cuts")
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return load_manifest_lazy(
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self.args.manifest_dir / "libritts_cuts_dev-clean.jsonl.gz"
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)
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@lru_cache()
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def dev_other_cuts(self) -> CutSet:
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logging.info("About to get dev-other cuts")
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return load_manifest_lazy(
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self.args.manifest_dir / "libritts_cuts_dev-other.jsonl.gz"
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)
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@lru_cache()
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def test_clean_cuts(self) -> CutSet:
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logging.info("About to get test-clean cuts")
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return load_manifest_lazy(
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self.args.manifest_dir / "libritts_cuts_test-clean.jsonl.gz"
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)
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@lru_cache()
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def test_other_cuts(self) -> CutSet:
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logging.info("About to get test-other cuts")
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return load_manifest_lazy(
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self.args.manifest_dir / "libritts_cuts_test-other.jsonl.gz"
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)
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