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236
egs/mls/ASR/prepare.sh Executable file
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#!/usr/bin/env bash
# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
set -eou pipefail
nj=15
# run step 0 to step 5 by default
stage=0
stop_stage=5
# Note: This script just prepare the minimal requirements that needed by a
# transducer training with bpe units.
#
# If you want to use ngram or nnlm, please continue running prepare_lm.sh after
# you succeed running this script.
#
# This script also contains the steps to generate phone based units, but they
# will not run automatically, you can generate the phone based units by
# bash prepare.sh --stage -1 --stop-stage -1
# bash prepare.sh --stage 6 --stop-stage 6
# We assume dl_dir (download dir) contains the following
# directories and files. If not, they will be downloaded
# by this script automatically.
#
# - $dl_dir/LibriSpeech
# You can find BOOKS.TXT, test-clean, train-clean-360, etc, inside it.
# You can download them from https://www.openslr.org/12
#
# - $dl_dir/musan
# This directory contains the following directories downloaded from
# http://www.openslr.org/17/
#
# - music
# - noise
# - speech
#
# lm directory is not necessary for transducer training with bpe units, but it
# is needed by phone based modeling, you can download it by running
# bash prepare.sh --stage -1 --stop-stage -1
# then you can see the following files in the directory.
# - $dl_dir/lm
# This directory contains the following files downloaded from
# http://www.openslr.org/resources/11
#
# - 3-gram.pruned.1e-7.arpa.gz
# - 3-gram.pruned.1e-7.arpa
# - 4-gram.arpa.gz
# - 4-gram.arpa
# - librispeech-vocab.txt
# - librispeech-lexicon.txt
# - librispeech-lm-norm.txt.gz
dl_dir=$PWD/download
. shared/parse_options.sh || exit 1
# vocab size for sentence piece models.
# It will generate data/lang_bpe_xxx,
# data/lang_bpe_yyy if the array contains xxx, yyy
vocab_sizes=(
# 5000
# 2000
# 1000
500
)
# All files generated by this script are saved in "data".
# You can safely remove "data" and rerun this script to regenerate it.
mkdir -p data
log() {
# This function is from espnet
local fname=${BASH_SOURCE[1]##*/}
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}
log "Running prepare.sh"
log "dl_dir: $dl_dir"
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
log "Stage 0: Download data"
# If you have pre-downloaded it to /path/to/LibriSpeech,
# you can create a symlink
#
# ln -sfv /path/to/LibriSpeech $dl_dir/LibriSpeech
#
if [ ! -d $dl_dir/LibriSpeech/train-other-500 ]; then
lhotse download librispeech --full $dl_dir
fi
# If you have pre-downloaded it to /path/to/musan,
# you can create a symlink
#
# ln -sfv /path/to/musan $dl_dir/
#
if [ ! -d $dl_dir/musan ]; then
lhotse download musan $dl_dir
fi
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
log "Stage 1: Prepare LibriSpeech manifest"
# We assume that you have downloaded the LibriSpeech corpus
# to $dl_dir/LibriSpeech
mkdir -p data/manifests
if [ ! -e data/manifests/.librispeech.done ]; then
lhotse prepare librispeech -j $nj $dl_dir/LibriSpeech data/manifests
touch data/manifests/.librispeech.done
fi
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
log "Stage 2: Prepare musan manifest"
# We assume that you have downloaded the musan corpus
# to $dl_dir/musan
mkdir -p data/manifests
if [ ! -e data/manifests/.musan.done ]; then
lhotse prepare musan $dl_dir/musan data/manifests
touch data/manifests/.musan.done
fi
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute fbank for librispeech"
mkdir -p data/fbank
if [ ! -e data/fbank/.librispeech.done ]; then
./local/compute_fbank_librispeech.py
touch data/fbank/.librispeech.done
fi
if [ ! -f data/fbank/librispeech_cuts_train-all-shuf.jsonl.gz ]; then
cat <(gunzip -c data/fbank/librispeech_cuts_train-clean-100.jsonl.gz) \
<(gunzip -c data/fbank/librispeech_cuts_train-clean-360.jsonl.gz) \
<(gunzip -c data/fbank/librispeech_cuts_train-other-500.jsonl.gz) | \
shuf | gzip -c > data/fbank/librispeech_cuts_train-all-shuf.jsonl.gz
fi
if [ ! -e data/fbank/.librispeech-validated.done ]; then
log "Validating data/fbank for LibriSpeech"
parts=(
train-clean-100
train-clean-360
train-other-500
test-clean
test-other
dev-clean
dev-other
)
for part in ${parts[@]}; do
python3 ./local/validate_manifest.py \
data/fbank/librispeech_cuts_${part}.jsonl.gz
done
touch data/fbank/.librispeech-validated.done
fi
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Compute fbank for musan"
mkdir -p data/fbank
if [ ! -e data/fbank/.musan.done ]; then
./local/compute_fbank_musan.py
touch data/fbank/.musan.done
fi
fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Prepare BPE based lang"
for vocab_size in ${vocab_sizes[@]}; do
lang_dir=data/lang_bpe_${vocab_size}
mkdir -p $lang_dir
if [ ! -f $lang_dir/transcript_words.txt ]; then
log "Generate data for BPE training"
files=$(
find "$dl_dir/LibriSpeech/train-clean-100" -name "*.trans.txt"
find "$dl_dir/LibriSpeech/train-clean-360" -name "*.trans.txt"
find "$dl_dir/LibriSpeech/train-other-500" -name "*.trans.txt"
)
for f in ${files[@]}; do
cat $f | cut -d " " -f 2-
done > $lang_dir/transcript_words.txt
fi
if [ ! -f $lang_dir/bpe.model ]; then
./local/train_bpe_model.py \
--lang-dir $lang_dir \
--vocab-size $vocab_size \
--transcript $lang_dir/transcript_words.txt
fi
done
fi
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
log "Stage 6: Prepare phone based lang"
lang_dir=data/lang_phone
mkdir -p $lang_dir
if [ ! -f $dl_dir/lm/librispeech-lexicon.txt ]; then
log "No lexicon file in $dl_dir/lm, please run :"
log "prepare.sh --stage -1 --stop-stage -1"
exit -1
fi
if [ ! -f $lang_dir/lexicon.txt ]; then
(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
cat - $dl_dir/lm/librispeech-lexicon.txt |
sort | uniq > $lang_dir/lexicon.txt
fi
if [ ! -f $lang_dir/L_disambig.pt ]; then
./local/prepare_lang.py --lang-dir $lang_dir
fi
if [ ! -f $lang_dir/L.fst ]; then
log "Converting L.pt to L.fst"
./shared/convert-k2-to-openfst.py \
--olabels aux_labels \
$lang_dir/L.pt \
$lang_dir/L.fst
fi
if [ ! -f $lang_dir/L_disambig.fst ]; then
log "Converting L_disambig.pt to L_disambig.fst"
./shared/convert-k2-to-openfst.py \
--olabels aux_labels \
$lang_dir/L_disambig.pt \
$lang_dir/L_disambig.fst
fi
fi

493
egs/mls/ASR/zipformer/asr_datamodule.py Normal file
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# Copyright 2021 Piotr Żelasko
# Copyright 2024 Xiaomi Corporation (Author: Xiaoyu Yang)
#
# 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, Fbank, FbankConfig, 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 ( # noqa F401 For AudioSamples
AudioSamples,
OnTheFlyFeatures,
)
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 MLSAsrDataModule:
"""
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(
"--language",
type=str2bool,
default="all",
choices=["english", "german", "dutch", "french", "spanish", "italian", "portuguese", "polish"],
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/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(
"--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(
"--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(
"--enable-spec-aug",
type=str2bool,
default=True,
help="When enabled, use SpecAugment for training dataset.",
)
group.add_argument(
"--spec-aug-time-warp-factor",
type=int,
default=80,
help="Used only when --enable-spec-aug is True. "
"It specifies the factor for time warping in SpecAugment. "
"Larger values mean more warping. "
"A value less than 1 means to disable time warp.",
)
group.add_argument(
"--enable-musan",
type=str2bool,
default=True,
help="When enabled, select noise from MUSAN and mix it"
"with training dataset. ",
)
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.
"""
transforms = []
if self.args.enable_musan:
logging.info("Enable MUSAN")
logging.info("About to get Musan cuts")
cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
transforms.append(
CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
)
else:
logging.info("Disable MUSAN")
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
input_transforms = []
if self.args.enable_spec_aug:
logging.info("Enable SpecAugment")
logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
# Set the value of num_frame_masks according to Lhotse's version.
# In different Lhotse's versions, the default of num_frame_masks is
# different.
num_frame_masks = 10
num_frame_masks_parameter = inspect.signature(
SpecAugment.__init__
).parameters["num_frame_masks"]
if num_frame_masks_parameter.default == 1:
num_frame_masks = 2
logging.info(f"Num frame mask: {num_frame_masks}")
input_transforms.append(
SpecAugment(
time_warp_factor=self.args.spec_aug_time_warp_factor,
num_frame_masks=num_frame_masks,
features_mask_size=27,
num_feature_masks=2,
frames_mask_size=100,
)
)
else:
logging.info("Disable SpecAugment")
logging.info("About to create train dataset")
train = K2SpeechRecognitionDataset(
input_strategy=eval(self.args.input_strategy)(),
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 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")
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 = 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=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
if self.args.on_the_fly_feats
else 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 / "librispeech_cuts_train-clean-100.jsonl.gz"
)
@lru_cache()
def train_clean_360_cuts(self) -> CutSet:
logging.info("About to get train-clean-360 cuts")
return load_manifest_lazy(
self.args.manifest_dir / "librispeech_cuts_train-clean-360.jsonl.gz"
)
@lru_cache()
def train_other_500_cuts(self) -> CutSet:
logging.info("About to get train-other-500 cuts")
return load_manifest_lazy(
self.args.manifest_dir / "librispeech_cuts_train-other-500.jsonl.gz"
)
@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"
)
@lru_cache()
def gigaspeech_subset_small_cuts(self) -> CutSet:
logging.info("About to get Gigaspeech subset-S cuts")
return load_manifest_lazy(self.args.manifest_dir / "cuts_S.jsonl.gz")
@lru_cache()
def gigaspeech_dev_cuts(self) -> CutSet:
logging.info("About to get Gigaspeech dev cuts")
return load_manifest_lazy(self.args.manifest_dir / "cuts_DEV.jsonl.gz")
@lru_cache()
def gigaspeech_test_cuts(self) -> CutSet:
logging.info("About to get Gigaspeech test cuts")
return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST.jsonl.gz")