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[Do not merge] example of using LibriSpeech + Lhotse Shar

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This commit is contained in:
Piotr Żelasko 2023-08-21 14:46:44 -04:00
parent fc2df07841
commit 924aa0b3bf
3 changed files with 511 additions and 143 deletions
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101
egs/librispeech/ASR/prepare_shar.sh Executable file
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@ -0,0 +1,101 @@
#!/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
set -x
nj=15
stage=-1
stop_stage=100
# 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/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
#
# - $dl_dir/musan
# This directory contains the following directories downloaded from
# http://www.openslr.org/17/
#
# - music
# - noise
# - speech
dl_dir=$PWD/download
. shared/parse_options.sh || exit 1
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]}) $*"
}
# Run data downloading and core manifest preparation
./prepare.sh --nj $nj --stage $stage --stop-stage 3
# Split the data into shards and compute the features on shard level
# This step leverages Lhotse Shar format for optimized sequential I/O
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: [Shar] Split manifests into shards and compute fbank features"
mkdir -p data/shar
if [ ! -e data/shar/.librispeech.done ]; then
for part in dev-clean dev-other test-clean test-other train-clean-100 train-clean-360 train-other-500; do
lhotse cut simple \
-r data/manifests/librispeech_recordings_${part}.jsonl.gz \
-s data/manifests/librispeech_supervisions_${part}.jsonl.gz \
data/manifests/librispeech_cuts_${part}.jsonl.gz
done
lhotse combine \
data/manifests/librispeech_cuts_train-{clean-100,clean-360,other-500}.jsonl.gz - \
| shuf \
| gzip -c \
> data/manifests/librispeech_cuts_train_all.jsonl.gz
lhotse shar export -j$nj -v -a flac -s 1000 \
data/manifests/librispeech_cuts_train_all.jsonl.gz \
data/shar
lhotse shar compute-features -v -j$nj data/shar
touch data/shar/.librispeech.done
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
# Run the rest of data preparation steps
./prepare.sh --stage $stage --stop-stage $stop_stage

528
egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
View File

@ -29,10 +29,12 @@ from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures
CutConcatenate,
CutMix,
DynamicBucketingSampler,
IterableDatasetWrapper,
K2SpeechRecognitionDataset,
PrecomputedFeatures,
SingleCutSampler,
SpecAugment,
make_worker_init_fn,
)
from lhotse.dataset.input_strategies import ( # noqa F401 For AudioSamples
AudioSamples,
@ -52,6 +54,155 @@ class _SeedWorkers:
fix_random_seed(self.seed + worker_id)
def add_dataloading_arguments(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=True,
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 (returns audio + audio lens), or "
"OnTheFlyFeatures/PrecomputedFeatures (both return features + feature lens)",
)
group.add_argument(
"--shar-dir",
type=Path,
default=Path("data/shar"),
help="Path to directory with data in Lhotse Shar format (if used)",
)
class LibriSpeechAsrDataModule:
"""
DataModule for k2 ASR experiments.
@ -75,145 +226,7 @@ class LibriSpeechAsrDataModule:
@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=True,
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",
)
return add_dataloading_arguments(parser)
def train_dataloaders(
self,
@ -473,3 +486,240 @@ class LibriSpeechAsrDataModule:
return load_manifest_lazy(
self.args.manifest_dir / "librispeech_cuts_test-other.jsonl.gz"
)
class LibriSpeechSharAsrDataModule:
"""
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
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.
"""
if sampler_state_dict is not None:
logging.warning(
"Loading sampler state dict is not supported for Lhotse Shar -- ignoring this."
)
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, prob=0.5, snr=(10, 20), preserve_id=True)
)
else:
logging.info("Disable MUSAN")
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")
if self.args.input_strategy == "OnTheFlyFeatures":
# 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,
)
else:
train = K2SpeechRecognitionDataset(
input_strategy=eval(self.args.input_strategy)(),
cut_transforms=transforms,
input_transforms=input_transforms,
)
logging.info("Using DynamicBucketingSampler.")
train_sampler = DynamicBucketingSampler(
cuts_train.repeat(), # sample infinite CutSet
max_duration=self.args.max_duration,
shuffle=True,
num_buckets=self.args.num_buckets,
# DDP auto-detection is disabled for Lhotse Shar
# instead, each worker process will initialize sampling
# with a different random seed using worker_init_fn,
# and CutSet.from_shar is going to react to this change.
rank=0,
world_size=1,
)
logging.info("About to create train dataloader")
# 'seed' is derived from the current random state, which will have
# previously been set in the main process.
seed = torch.randint(0, 100000, ()).item()
rank, world_size = None, None
if torch.distributed.is_initialized():
rank, world_size = (
torch.distributed.get_rank(),
torch.distributed.get_world_size(),
)
train_dl = DataLoader(
IterableDatasetWrapper(dataset=train, sampler=train_sampler),
num_workers=self.args.num_workers,
batch_size=None,
worker_init_fn=make_worker_init_fn(
rank=rank, world_size=world_size, seed=seed
),
)
return train_dl
def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
logging.info("About to create dev dataset")
if self.args.input_strategy == "OnTheFlyFeatures":
validate = K2SpeechRecognitionDataset(
input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
)
else:
validate = K2SpeechRecognitionDataset()
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.input_strategy == "OnTheFlyFeatures"
else eval(self.args.input_strategy)(),
)
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_all_shuf_cuts(self) -> CutSet:
logging.info(
"About to get the shuffled and sharded cuts for full (960h) LibriSpeech using Lhotse Shar"
)
# Below we'll figure out which files to read.
# Since we only use either (precomputed) features or recordings,
# we shouldn't iterate over both at the same time.
shar_dir = Path(self.args.shar_dir)
fields = {"cuts": sorted(shar_dir.glob("cuts.*.jsonl*"))}
if self.args.input_strategy == "PrecomputedFeatures":
logging.info(
"Requested PrecomputedFeatures, we'll only read features.XXXXXX.tar files."
)
fields["features"] = sorted(shar_dir.glob("features.*.tar"))
else: # AudioSamples / OnTheFlyFeatures
logging.info(
f"Requested {self.args.input_strategy}, we'll only read recording.XXXXXX.tar files."
)
fields["recording"] = sorted(shar_dir.glob("recording.*.tar"))
return CutSet.from_shar(fields=fields, shuffle_shards=True, seed="randomized")
@lru_cache()
def train_clean_100_cuts(self) -> CutSet:
raise NotImplementedError(
"LibriSpeech 100h subset support for Lhotse Shar is not implemented."
)
@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"
)

25
egs/librispeech/ASR/tdnn_lstm_ctc/train.py
View File

@ -37,7 +37,11 @@ import torch
import torch.multiprocessing as mp
import torch.nn as nn
import torch.optim as optim
from asr_datamodule import LibriSpeechAsrDataModule
from asr_datamodule import (
LibriSpeechAsrDataModule,
LibriSpeechSharAsrDataModule,
add_dataloading_arguments,
)
from lhotse.cut import Cut
from lhotse.utils import fix_random_seed
from model import TdnnLstm
@ -112,6 +116,13 @@ def get_parser():
help="The seed for random generators intended for reproducibility",
)
parser.add_argument(
"--use-shar",
type=str2bool,
default=False,
help="Use Lhotse Shar data format for faster, sequential I/O. Requires running ./prepare_shar.sh first.",
)
return parser
@ -555,7 +566,10 @@ def run(rank, world_size, args):
optimizer.load_state_dict(checkpoints["optimizer"])
scheduler.load_state_dict(checkpoints["scheduler"])
librispeech = LibriSpeechAsrDataModule(args)
if args.use_shar:
librispeech = LibriSpeechSharAsrDataModule(args)
else:
librispeech = LibriSpeechAsrDataModule(args)
if params.full_libri:
train_cuts = librispeech.train_all_shuf_cuts()
@ -584,7 +598,10 @@ def run(rank, world_size, args):
for epoch in range(params.start_epoch, params.num_epochs):
fix_random_seed(params.seed + epoch)
train_dl.sampler.set_epoch(epoch)
try:
train_dl.sampler.set_epoch(epoch)
except Exception:
pass # with Lhotse Shar the sampler won't have a set_epoch attribute
if epoch > params.start_epoch:
logging.info(f"epoch {epoch}, lr: {scheduler.get_last_lr()[0]}")
@ -628,7 +645,7 @@ def run(rank, world_size, args):
def main():
parser = get_parser()
LibriSpeechAsrDataModule.add_arguments(parser)
add_dataloading_arguments(parser)
args = parser.parse_args()
world_size = args.world_size