mirrors/icefall
1
0
Fork 0
mirror of https://github.com/k2-fsa/icefall.git synced 2025-08-09 10:02:22 +00:00
Code Issues Packages Projects Releases Wiki Activity

update prepare.sh, fix asr_datamodule.py

Browse Source
This commit is contained in:
Kinan Martin 2025-04-11 10:29:27 +09:00
parent 93766fc24f
commit 3eeadd0f3a
3 changed files with 73 additions and 441 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
egs/mls_english/ASR/local/utils/asr_datamodule.py
View File

@ -120,7 +120,7 @@ class MLSEnglishHFAsrDataModule:
group.add_argument( group.add_argument(
"--on-the-fly-feats", "--on-the-fly-feats",
type=str2bool, type=str2bool,
default=True, # Must be True for Lazy HF dataset (?) default=True, # must be true without lhotse feature prep
help="When enabled, use on-the-fly cut mixing and feature " help="When enabled, use on-the-fly cut mixing and feature "
"extraction. Will drop existing precomputed feature manifests " "extraction. Will drop existing precomputed feature manifests "
"if available.", "if available.",
@ -190,7 +190,7 @@ class MLSEnglishHFAsrDataModule:
Intended usage inside a training script: Intended usage inside a training script:
``` ```
mls_english_corpus = MLSEnglishHFAsrDataModule(args) mls_english_corpus = MLSEnglishHFAsrDataModule(args)
mls_english_corpus.load_hf_dataset("fr") mls_english_corpus.load_hf_dataset("parler-tts/mls_eng")
train_cuts = mls_english_corpus.train_cuts() train_cuts = mls_english_corpus.train_cuts()
train_dataloader = mls_english_corpus.train_dataloaders( train_dataloader = mls_english_corpus.train_dataloaders(
train_cuts, sampler_state_dict=sampler_state_dict train_cuts, sampler_state_dict=sampler_state_dict

125
egs/mls_english/ASR/prepare.sh
View File

@ -9,6 +9,10 @@ nj=15
stage=-1 stage=-1
stop_stage=100 stop_stage=100
# vocab_sizes=(500 1000 2000)
vocab_sizes=(2000)
# We assume dl_dir (download dir) contains the following # We assume dl_dir (download dir) contains the following
# directories and files. If not, they will be downloaded # directories and files. If not, they will be downloaded
# by this script automatically. # by this script automatically.
@ -41,74 +45,87 @@ log "dl_dir: $dl_dir"
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
log "Stage 0: Download data" log "Stage 0: Download data"
# If you have pre-downloaded it to /path/to/ReazonSpeech, # If you have pre-downloaded it to /path/to/mls_eng,
# you can create a symlink # you can create a symlink
# #
# ln -sfv /path/to/ReazonSpeech $dl_dir/ReazonSpeech # ln -sfv /path/to/mls_eng $dl_dir/mls_eng
# #
if [ ! -d $dl_dir/ReazonSpeech/downloads ]; then if [ ! -d $dl_dir/mls_english ]; then
# Download small-v1 by default. git clone https://huggingface.co/datasets/parler-tts/mls_eng $dl_dir/mls_eng
lhotse download reazonspeech --subset small-v1 $dl_dir
fi fi
fi fi
## Not necessary to create manifest or pre-compute fbank for on-the-fly feature computation ##
# if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
# log "Stage 1: Prepare MLS English manifest"
# # We assume that you have downloaded the ReazonSpeech corpus
# # to $dl_dir/ReazonSpeech
# mkdir -p data/manifests
# if [ ! -e data/manifests/.reazonspeech.done ]; then
# lhotse prepare reazonspeech -j $nj $dl_dir/ReazonSpeech data/manifests
# touch data/manifests/.reazonspeech.done
# fi
# fi
# if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
# log "Stage 2: Compute ReazonSpeech fbank"
# if [ ! -e data/manifests/.reazonspeech-validated.done ]; then
# python local/compute_fbank_reazonspeech.py --manifest-dir data/manifests
# python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_train.jsonl.gz
# python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_dev.jsonl.gz
# python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_test.jsonl.gz
# touch data/manifests/.reazonspeech-validated.done
# fi
# fi
###############################################################################################
# if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
# log "Stage 3: Prepare ReazonSpeech lang_char"
# python local/prepare_lang_char.py data/manifests/reazonspeech_cuts_train.jsonl.gz
# fi
# if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
# log "Stage 4: Show manifest statistics"
# python local/display_manifest_statistics.py --manifest-dir data/manifests > data/manifests/manifest_statistics.txt
# cat data/manifests/manifest_statistics.txt
# fi
mkdir -p data/lang
lang_dir=data/lang
log "lang_dir: $lang_dir"
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
log "Stage 1: Prepare ReazonSpeech manifest" log "Stage 1: Prepare BPE based lang"
# We assume that you have downloaded the ReazonSpeech corpus
# to $dl_dir/ReazonSpeech if [ ! -f $lang_dir/transcript.txt ]; then
mkdir -p data/manifests log "Generate transcript for BPE training"
if [ ! -e data/manifests/.reazonspeech.done ]; then
lhotse prepare reazonspeech -j $nj $dl_dir/ReazonSpeech data/manifests ./local/utils/generate_transcript.py --lang-dir $lang_dir
touch data/manifests/.reazonspeech.done # files=$(
# 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 fi
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
log "Stage 2: Compute ReazonSpeech fbank"
if [ ! -e data/manifests/.reazonspeech-validated.done ]; then
python local/compute_fbank_reazonspeech.py --manifest-dir data/manifests
python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_train.jsonl.gz
python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_dev.jsonl.gz
python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_test.jsonl.gz
touch data/manifests/.reazonspeech-validated.done
fi
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Prepare ReazonSpeech lang_char"
python local/prepare_lang_char.py data/manifests/reazonspeech_cuts_train.jsonl.gz
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Show manifest statistics"
python local/display_manifest_statistics.py --manifest-dir data/manifests > data/manifests/manifest_statistics.txt
cat data/manifests/manifest_statistics.txt
fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Prepare BPE based lang"
for vocab_size in ${vocab_sizes[@]}; do for vocab_size in ${vocab_sizes[@]}; do
lang_dir=data/lang_bpe_${vocab_size} log "Train BPE model with vocab_size: $vocab_size"
mkdir -p $lang_dir bpe_dir=data/lang/bpe_${vocab_size}
mkdir -p $bpe_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 if [ ! -f $bpe_dir/bpe.model ]; then
./local/train_bpe_model.py \ ./local/train_bpe_model.py \
--lang-dir $lang_dir \ --lang-dir $bpe_dir \
--vocab-size $vocab_size \ --vocab-size $vocab_size \
--transcript $lang_dir/transcript_words.txt --transcript $lang_dir/transcript.txt
fi fi
done done
fi fi

386
egs/mls_english/ASR/zipformer/asr_datamodule.py
View File

@ -1,386 +0,0 @@
# Copyright 2021 Piotr Żelasko
# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo)
#
# 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, List, Optional
from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
from lhotse.dataset import (
CutConcatenate,
CutMix,
DynamicBucketingSampler,
K2SpeechRecognitionDataset,
PrecomputedFeatures,
SimpleCutSampler,
SpecAugment,
)
from lhotse.dataset.input_strategies import OnTheFlyFeatures
from lhotse.utils import is_module_available
from torch.utils.data import DataLoader
from icefall.utils import str2bool
class MLSEnglishHFAsrDataModule:
"""
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(
"--manifest-dir",
type=Path,
default=Path("data/manifests"),
help="Path to directory with train/dev/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=True,
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['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=False,
help="When enabled, select noise from MUSAN and mix it"
"with training dataset. ",
)
def load_hf_dataset(
self, mls_eng_hf_dataset_path: str = "parler-tts/mls_eng",
):
"""
Method to load HF dataset with datasets.load_dataset
and save it in this DataModule.
Intended usage inside a training script:
```
mls_english_corpus = MLSEnglishHFAsrDataModule(args)
mls_english_corpus.load_hf_dataset("parler-tts/mls_eng")
train_cuts = mls_english_corpus.train_cuts()
train_dataloader = mls_english_corpus.train_dataloaders(
train_cuts, sampler_state_dict=sampler_state_dict
)
...
for epoch in range(...):
train_one_epoch(
...,
train_dl=train_dl,
...,
)
```
"""
if not is_module_available("datasets"):
raise ImportError(
"To process the MLS English HF corpus, please install optional dependency: pip install datasets"
)
from datasets import load_dataset
self.dataset = load_dataset(mls_eng_hf_dataset_path) #, split="test")
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 = []
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(
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,
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)
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, 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.info("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 = DynamicBucketingSampler(
cuts,
max_duration=self.args.max_duration,
shuffle=False,
)
test_dl = DataLoader(
test,
batch_size=None,
sampler=sampler,
num_workers=self.args.num_workers,
)
return test_dl
@lru_cache()
def train_cuts(self) -> CutSet:
logging.info("About to get train cuts")
cutset = CutSet.from_huggingface_dataset(self.dataset["train"], text_key="transcript")
return cutset
@lru_cache()
def valid_cuts(self) -> CutSet:
logging.info("About to get dev cuts")
cutset = CutSet.from_huggingface_dataset(self.dataset["dev"], text_key="transcript")
return cutset
@lru_cache()
def test_cuts(self) -> List[CutSet]:
logging.info("About to get test cuts")
cutset = CutSet.from_huggingface_dataset(self.dataset["test"], text_key="transcript")
return cutset

1
egs/mls_english/ASR/zipformer/asr_datamodule.py Symbolic link
View File

@ -0,0 +1 @@
local/utils/asr_datamodule.py