mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
giga speech dataloader
This commit is contained in:
Guo Liyong
parent
42b437bea6
commit
4ae1701bba
335
egs/librispeech/ASR/conformer_ctc/gigaspeech_datamodule.py
Normal file
335
egs/librispeech/ASR/conformer_ctc/gigaspeech_datamodule.py
Normal file
@ -0,0 +1,335 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021 Xiaomi Corp. (authors: Liyong Guo)
|
||||
#
|
||||
# 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 List, Union
|
||||
|
||||
from lhotse import CutSet, Fbank, FbankConfig, load_manifest
|
||||
from lhotse.dataset import (
|
||||
BucketingSampler,
|
||||
CutConcatenate,
|
||||
CutMix,
|
||||
K2SpeechRecognitionDataset,
|
||||
PrecomputedFeatures,
|
||||
SingleCutSampler,
|
||||
SpecAugment,
|
||||
)
|
||||
from lhotse.dataset.input_strategies import OnTheFlyFeatures
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from icefall.dataset.datamodule import DataModule
|
||||
from icefall.utils import str2bool
|
||||
|
||||
|
||||
class GigaSpeechAsrDataModule(DataModule):
|
||||
"""
|
||||
DataModule for k2 ASR experiments.
|
||||
It assumes there is always one train and valid dataloader,
|
||||
|
||||
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.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def add_arguments(cls, parser: argparse.ArgumentParser):
|
||||
super().add_arguments(parser)
|
||||
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-giga",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="When enabled, use XL part of GigaSpeech. "
|
||||
"Otherwise, use XS subset.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--feature-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 BucketingSampler"
|
||||
"(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(
|
||||
"--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(
|
||||
"--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.",
|
||||
)
|
||||
|
||||
def train_dataloaders(self) -> DataLoader:
|
||||
logging.info("About to get train cuts")
|
||||
cuts_train = self.train_cuts()
|
||||
|
||||
logging.info("About to create train dataset")
|
||||
transforms = None
|
||||
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
|
||||
)
|
||||
]
|
||||
|
||||
train = K2SpeechRecognitionDataset(
|
||||
cut_transforms=transforms,
|
||||
# input_transforms=input_transforms,
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
|
||||
if self.args.on_the_fly_feats:
|
||||
# NOTE: the PerturbSpeed transform should be added only if we
|
||||
# remove it from data prep stage.
|
||||
# Add on-the-fly speed perturbation; since originally it would
|
||||
# have increased epoch size by 3, we will apply prob 2/3 and use
|
||||
# 3x more epochs.
|
||||
# Speed perturbation probably should come first before
|
||||
# concatenation, but in principle the transforms order doesn't have
|
||||
# to be strict (e.g. could be randomized)
|
||||
# transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa
|
||||
# Drop feats to be on the safe side.
|
||||
train = K2SpeechRecognitionDataset(
|
||||
cut_transforms=transforms,
|
||||
input_strategy=OnTheFlyFeatures(
|
||||
Fbank(FbankConfig(num_mel_bins=80))
|
||||
),
|
||||
input_transforms=input_transforms,
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
|
||||
if self.args.bucketing_sampler:
|
||||
logging.info("Using BucketingSampler.")
|
||||
train_sampler = BucketingSampler(
|
||||
cuts_train,
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
num_buckets=self.args.num_buckets,
|
||||
bucket_method="equal_duration",
|
||||
drop_last=True,
|
||||
)
|
||||
else:
|
||||
logging.info("Using SingleCutSampler.")
|
||||
train_sampler = SingleCutSampler(
|
||||
cuts_train,
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
)
|
||||
logging.info("About to create train dataloader")
|
||||
|
||||
train_dl = DataLoader(
|
||||
train,
|
||||
sampler=train_sampler,
|
||||
batch_size=None,
|
||||
num_workers=self.args.num_workers,
|
||||
persistent_workers=False,
|
||||
)
|
||||
|
||||
return train_dl
|
||||
|
||||
def valid_dataloaders(self) -> DataLoader:
|
||||
logging.info("About to get dev cuts")
|
||||
cuts_valid = self.valid_cuts()
|
||||
|
||||
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")
|
||||
if self.args.on_the_fly_feats:
|
||||
validate = K2SpeechRecognitionDataset(
|
||||
cut_transforms=transforms,
|
||||
input_strategy=OnTheFlyFeatures(
|
||||
Fbank(FbankConfig(num_mel_bins=80))
|
||||
),
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
else:
|
||||
validate = K2SpeechRecognitionDataset(
|
||||
cut_transforms=transforms,
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
valid_sampler = BucketingSampler(
|
||||
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) -> Union[DataLoader, List[DataLoader]]:
|
||||
cuts = self.test_cuts()
|
||||
is_list = isinstance(cuts, list)
|
||||
test_loaders = []
|
||||
if not is_list:
|
||||
cuts = [cuts]
|
||||
|
||||
for cuts_test in cuts:
|
||||
logging.debug("About to create test dataset")
|
||||
test = K2SpeechRecognitionDataset(
|
||||
input_strategy=OnTheFlyFeatures(
|
||||
Fbank(FbankConfig(num_mel_bins=80))
|
||||
)
|
||||
if self.args.on_the_fly_feats
|
||||
else PrecomputedFeatures(),
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
sampler = BucketingSampler(
|
||||
cuts_test, 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,
|
||||
)
|
||||
test_loaders.append(test_dl)
|
||||
|
||||
if is_list:
|
||||
return test_loaders
|
||||
else:
|
||||
return test_loaders[0]
|
||||
|
||||
@lru_cache()
|
||||
def train_cuts(self) -> CutSet:
|
||||
logging.info("About to get train cuts")
|
||||
# TODO(Liyong Guo): Support S, M, L if needed
|
||||
if self.args.full_giga:
|
||||
cuts_train = load_manifest(
|
||||
self.args.feature_dir / "cuts_XL.json.gz"
|
||||
)
|
||||
else:
|
||||
cuts_train = load_manifest(
|
||||
self.args.feature_dir / "cuts_XS.json.gz"
|
||||
)
|
||||
return cuts_train
|
||||
|
||||
@lru_cache()
|
||||
def valid_cuts(self) -> CutSet:
|
||||
logging.info("About to get dev cuts")
|
||||
cuts_valid = load_manifest(
|
||||
self.args.feature_dir / "cuts_DEV.json.gz"
|
||||
)
|
||||
return cuts_valid
|
||||
|
||||
@lru_cache()
|
||||
def test_cuts(self) -> List[CutSet]:
|
||||
logging.info("About to get dev cuts")
|
||||
cuts_test = load_manifest(
|
||||
self.args.feature_dir / "cuts_TEST.json.gz"
|
||||
)
|
||||
return cuts_test
|
||||
97
egs/librispeech/ASR/local/compute_fbank_gigaspeech.py
Executable file
97
egs/librispeech/ASR/local/compute_fbank_gigaspeech.py
Executable file
@ -0,0 +1,97 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021 Xiaomi Corp. (authors: Liyong Guo)
|
||||
#
|
||||
# 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.
|
||||
|
||||
|
||||
"""
|
||||
This file computes fbank features of the GigaSpeech dataset.
|
||||
It looks for manifests in the directory data/manifests.
|
||||
|
||||
The generated fbank features are saved in data/fbank.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer
|
||||
from lhotse.recipes.utils import read_manifests_if_cached
|
||||
|
||||
from icefall.utils import get_executor
|
||||
|
||||
# Torch's multithreaded behavior needs to be disabled or
|
||||
# it wastes a lot of CPU and slow things down.
|
||||
# Do this outside of main() in case it needs to take effect
|
||||
# even when we are not invoking the main (e.g. when spawning subprocesses).
|
||||
torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
|
||||
|
||||
def compute_fbank_gigaspeech():
|
||||
manifests_dir = Path("data/manifests")
|
||||
output_dir = Path("data/fbank")
|
||||
num_jobs = min(15, os.cpu_count())
|
||||
num_mel_bins = 80
|
||||
|
||||
dataset_parts = (
|
||||
"XS",
|
||||
"S",
|
||||
"M",
|
||||
"L",
|
||||
"XL",
|
||||
"DEV",
|
||||
"TEST",
|
||||
)
|
||||
|
||||
manifests = read_manifests_if_cached(
|
||||
dataset_parts=dataset_parts,
|
||||
output_dir=manifests_dir,
|
||||
prefix="gigaspeech",
|
||||
suffix="jsonl.gz",
|
||||
)
|
||||
assert manifests is not None
|
||||
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
|
||||
with get_executor() as ex: # Initialize the executor only once.
|
||||
for partition, m in manifests.items():
|
||||
if (output_dir / f"cuts_{partition}.json.gz").is_file():
|
||||
logging.info(f"{partition} already exists - skipping.")
|
||||
continue
|
||||
logging.info(f"Processing {partition}")
|
||||
cut_set = CutSet.from_manifests(
|
||||
recordings=m["recordings"],
|
||||
supervisions=m["supervisions"],
|
||||
)
|
||||
cut_set = cut_set.compute_and_store_features(
|
||||
extractor=extractor,
|
||||
storage_path=f"{output_dir}/feats_{partition}",
|
||||
# when an executor is specified, make more partitions
|
||||
num_jobs=num_jobs if ex is None else 80,
|
||||
executor=ex,
|
||||
storage_type=LilcomHdf5Writer,
|
||||
)
|
||||
cut_set.to_json(output_dir / f"cuts_{partition}.json.gz")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = (
|
||||
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
)
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
compute_fbank_gigaspeech()
|
||||
30
egs/librispeech/ASR/prepare_giga.sh
Executable file
30
egs/librispeech/ASR/prepare_giga.sh
Executable file
@ -0,0 +1,30 @@
|
||||
|
||||
dl_dir='/home/storage07/zhangjunbo/data/'
|
||||
output_dir=/ceph-hw/ly/data/gigaspeech_nb/
|
||||
|
||||
mkdir -p $output_dir/manifests
|
||||
|
||||
stage=2
|
||||
stop_stage=2
|
||||
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
|
||||
echo "Implement and verify gigaspeech downloading later"
|
||||
fi
|
||||
|
||||
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
|
||||
# subset could be: ["XS", "S", "M", "L", "XL", "DEV" "TEST"]
|
||||
# Currently only XS DEV TEST are verified
|
||||
# Others SHOULD also work
|
||||
subsets="XS DEV TEST"
|
||||
for subset in $subsets; do
|
||||
lhotse prepare gigaspeech \
|
||||
-j 60 \
|
||||
--subset=$subset \
|
||||
$dl_dir/GigaSpeech $output_dir/manifests
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
|
||||
log "Stage 3: Compute fbank for gigaspeech"
|
||||
mkdir -p $output_dir/fbank
|
||||
./local/compute_fbank_gigaspeech.py
|
||||
fi
|
||||
Loading…
x
Reference in New Issue
Block a user