mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
5dfc3ed7f9
* Fix buffer size * Fix for flake8 --------- Co-authored-by: yifanyeung <yifanyeung@yifanyeung.local>
372 lines
12 KiB
Python
372 lines
12 KiB
Python
# Copyright 2021 Piotr Żelasko
|
|
# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo)
|
|
# 2023 John Hopkins University (author: Dongji Gao)
|
|
#
|
|
# 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 inspect
|
|
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, load_manifest_lazy
|
|
from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures
|
|
CutConcatenate,
|
|
CutMix,
|
|
DynamicBucketingSampler,
|
|
K2SpeechRecognitionDataset,
|
|
PrecomputedFeatures,
|
|
SimpleCutSampler,
|
|
SpecAugment,
|
|
)
|
|
from lhotse.dataset.input_strategies import AudioSamples # noqa F401 For AudioSamples
|
|
from lhotse.utils import fix_random_seed
|
|
from torch.utils.data import DataLoader
|
|
|
|
from icefall.utils import str2bool
|
|
|
|
|
|
class _SeedWorkers:
|
|
def __init__(self, seed: int):
|
|
self.seed = seed
|
|
|
|
def __call__(self, worker_id: int):
|
|
fix_random_seed(self.seed + worker_id)
|
|
|
|
|
|
class LibriSpeechAsrDataModule:
|
|
"""
|
|
DataModule for k2 ASR 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,
|
|
- augmentation,
|
|
- 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="ASR 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(
|
|
"--full-libri",
|
|
type=str2bool,
|
|
default=False,
|
|
help="""Used only when --mini-libri is False.When enabled,
|
|
use 960h LibriSpeech. Otherwise, use 100h subset.""",
|
|
)
|
|
group.add_argument(
|
|
"--mini-libri",
|
|
type=str2bool,
|
|
default=False,
|
|
help="True for mini librispeech",
|
|
)
|
|
group.add_argument(
|
|
"--manifest-dir",
|
|
type=Path,
|
|
default=Path("data/ssl"),
|
|
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(
|
|
"--concatenate-cuts",
|
|
type=str2bool,
|
|
default=False,
|
|
help="When enabled, utterances (cuts) will be concatenated "
|
|
"to minimize the amount of padding.",
|
|
)
|
|
group.add_argument(
|
|
"--duration-factor",
|
|
type=float,
|
|
default=1.0,
|
|
help="Determines the maximum duration of a concatenated cut "
|
|
"relative to the duration of the longest cut in a batch.",
|
|
)
|
|
group.add_argument(
|
|
"--gap",
|
|
type=float,
|
|
default=1.0,
|
|
help="The amount of padding (in seconds) inserted between "
|
|
"concatenated cuts. This padding is filled with noise when "
|
|
"noise augmentation is used.",
|
|
)
|
|
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=True,
|
|
help="When enabled, each batch will have the "
|
|
"field: batch['supervisions']['cut'] with the cuts that "
|
|
"were used to construct it.",
|
|
)
|
|
|
|
group.add_argument(
|
|
"--num-workers",
|
|
type=int,
|
|
default=2,
|
|
help="The number of training dataloader workers that "
|
|
"collect the batches.",
|
|
)
|
|
|
|
group.add_argument(
|
|
"--input-strategy",
|
|
type=str,
|
|
default="PrecomputedFeatures",
|
|
help="AudioSamples or PrecomputedFeatures",
|
|
)
|
|
|
|
group.add_argument(
|
|
"--train-manifest",
|
|
type=str,
|
|
default="librispeech_cuts_train-clean-100.jsonl.gz",
|
|
help="Train manifest file.",
|
|
)
|
|
|
|
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.
|
|
"""
|
|
transforms = []
|
|
if self.args.concatenate_cuts:
|
|
logging.info(
|
|
f"Using cut concatenation with duration factor "
|
|
f"{self.args.duration_factor} and gap {self.args.gap}."
|
|
)
|
|
# Cut concatenation should be the first transform in the list,
|
|
# so that if we e.g. mix noise in, it will fill the gaps between
|
|
# different utterances.
|
|
transforms = [
|
|
CutConcatenate(
|
|
duration_factor=self.args.duration_factor, gap=self.args.gap
|
|
)
|
|
] + transforms
|
|
|
|
logging.info("About to create train dataset")
|
|
train = K2SpeechRecognitionDataset(
|
|
input_strategy=eval(self.args.input_strategy)(),
|
|
cut_transforms=transforms,
|
|
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 valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
|
|
transforms = []
|
|
if self.args.concatenate_cuts:
|
|
transforms = [
|
|
CutConcatenate(
|
|
duration_factor=self.args.duration_factor, gap=self.args.gap
|
|
)
|
|
] + transforms
|
|
|
|
logging.info("About to create dev dataset")
|
|
|
|
validate = K2SpeechRecognitionDataset(
|
|
cut_transforms=transforms,
|
|
return_cuts=self.args.return_cuts,
|
|
)
|
|
|
|
valid_sampler = DynamicBucketingSampler(
|
|
cuts_valid,
|
|
max_duration=self.args.max_duration,
|
|
shuffle=False,
|
|
)
|
|
|
|
logging.info("About to create dev dataloader")
|
|
valid_dl = DataLoader(
|
|
validate,
|
|
sampler=valid_sampler,
|
|
batch_size=None,
|
|
num_workers=2,
|
|
persistent_workers=False,
|
|
)
|
|
|
|
return valid_dl
|
|
|
|
def test_dataloaders(self, cuts: CutSet) -> DataLoader:
|
|
logging.debug("About to create test dataset")
|
|
test = K2SpeechRecognitionDataset(
|
|
input_strategy=eval(self.args.input_strategy)(),
|
|
return_cuts=self.args.return_cuts,
|
|
)
|
|
sampler = DynamicBucketingSampler(
|
|
cuts,
|
|
max_duration=self.args.max_duration,
|
|
shuffle=False,
|
|
)
|
|
logging.debug("About to create test dataloader")
|
|
test_dl = DataLoader(
|
|
test,
|
|
batch_size=None,
|
|
sampler=sampler,
|
|
num_workers=self.args.num_workers,
|
|
)
|
|
return test_dl
|
|
|
|
@lru_cache()
|
|
def train_clean_5_cuts(self) -> CutSet:
|
|
logging.info("mini_librispeech: About to get train-clean-5 cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_train-clean-5.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def train_clean_100_cuts(self) -> CutSet:
|
|
logging.info("About to get train-clean-100 cuts")
|
|
return load_manifest_lazy(self.args.manifest_dir / self.args.train_manifest)
|
|
|
|
@lru_cache()
|
|
def train_all_shuf_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 / "librispeech_cuts_train-all-shuf.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def dev_clean_2_cuts(self) -> CutSet:
|
|
logging.info("mini_librispeech: About to get dev-clean-2 cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_dev-clean-2.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def dev_clean_cuts(self) -> CutSet:
|
|
logging.info("About to get dev-clean cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_dev-clean.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def dev_other_cuts(self) -> CutSet:
|
|
logging.info("About to get dev-other cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_dev-other.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def test_clean_cuts(self) -> CutSet:
|
|
logging.info("About to get test-clean cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz"
|
|
)
|
|
|
|
@lru_cache()
|
|
def test_other_cuts(self) -> CutSet:
|
|
logging.info("About to get test-other cuts")
|
|
return load_manifest_lazy(
|
|
self.args.manifest_dir / "librispeech_cuts_test-other.jsonl.gz"
|
|
)
|