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Working datamodule

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This commit is contained in:
Piotr Żelasko 2022-01-15 00:59:26 +00:00
parent 80b83a2a36
commit ccab93e8e2
3 changed files with 105 additions and 209 deletions
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16
egs/fisher_swbd/ASR/conformer_ctc/train.py
View File

@ -27,7 +27,7 @@ import k2
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from asr_datamodule import LibriSpeechAsrDataModule
from asr_datamodule import AsrDataModule
from conformer import Conformer
from lhotse.utils import fix_random_seed
from torch import Tensor
@ -620,17 +620,13 @@ def run(rank, world_size, args):
if checkpoints:
optimizer.load_state_dict(checkpoints["optimizer"])
librispeech = LibriSpeechAsrDataModule(args)
datamodule = AsrDataModule(args)
train_cuts = librispeech.train_clean_100_cuts()
if params.full_libri:
train_cuts += librispeech.train_clean_360_cuts()
train_cuts += librispeech.train_other_500_cuts()
train_dl = librispeech.train_dataloaders(train_cuts)
train_cuts = datamodule.train_cuts()
train_dl = datamodule.train_dataloaders(train_cuts)
valid_cuts = librispeech.dev_clean_cuts()
valid_cuts += librispeech.dev_other_cuts()
valid_dl = librispeech.valid_dataloaders(valid_cuts)
valid_cuts = datamodule.dev_cuts()
valid_dl = datamodule.valid_dataloaders(valid_cuts)
scan_pessimistic_batches_for_oom(
model=model,

4
egs/fisher_swbd/ASR/prepare.sh
View File

@ -92,7 +92,7 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
# We assume that you have downloaded the LibriSpeech corpus
# to $dl_dir/LibriSpeech
mkdir -p data/manifests/fisher
lhotse prepare fisher-english $dl_dir data/manifests/fisher
lhotse prepare fisher-english --absolute-paths 1 $dl_dir data/manifests/fisher
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
@ -100,7 +100,7 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
# We assume that you have downloaded the LibriSpeech corpus
# to $dl_dir/LibriSpeech
mkdir -p data/manifests/swbd
lhotse prepare switchboard --omit-silence $dl_dir/LDC97S62 data/manifests/swbd
lhotse prepare switchboard --absolute-paths 1 --omit-silence $dl_dir/LDC97S62 data/manifests/swbd
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then

294
egs/fisher_swbd/ASR/tdnn_lstm_ctc/asr_datamodule.py
View File

@ -20,14 +20,16 @@ import logging
from functools import lru_cache
from pathlib import Path
from lhotse import CutSet, Fbank, FbankConfig, load_manifest
from tqdm import tqdm
from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
from lhotse.dataset import (
BucketingSampler,
CutConcatenate,
CutMix,
DynamicBucketingSampler,
K2SpeechRecognitionDataset,
PerturbSpeed,
PrecomputedFeatures,
SingleCutSampler,
SpecAugment,
)
from lhotse.dataset.input_strategies import OnTheFlyFeatures
@ -36,7 +38,12 @@ from torch.utils.data import DataLoader
from icefall.utils import str2bool
class LibriSpeechAsrDataModule:
class Resample16kHz:
def __call__(self, cuts: CutSet) -> CutSet:
return cuts.resample(16000).with_recording_path_prefix('download')
class AsrDataModule:
"""
DataModule for k2 ASR experiments.
It assumes there is always one train and valid dataloader,
@ -66,17 +73,10 @@ class LibriSpeechAsrDataModule:
"effective batch sizes, sampling strategies, applied data "
"augmentations, etc.",
)
group.add_argument(
"--full-libri",
type=str2bool,
default=True,
help="When enabled, use 960h LibriSpeech. "
"Otherwise, use 100h subset.",
)
group.add_argument(
"--manifest-dir",
type=Path,
default=Path("data/fbank"),
default=Path("data/manifests"),
help="Path to directory with train/valid/test cuts.",
)
group.add_argument(
@ -86,13 +86,6 @@ class LibriSpeechAsrDataModule:
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,
@ -100,32 +93,10 @@ class LibriSpeechAsrDataModule:
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,
default=True,
help="When enabled, use on-the-fly cut mixing and feature "
"extraction. Will drop existing precomputed feature manifests "
"if available.",
@ -137,30 +108,15 @@ class LibriSpeechAsrDataModule:
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,
default=8,
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,
@ -171,52 +127,28 @@ class LibriSpeechAsrDataModule:
"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. ",
)
def train_dataloaders(self, cuts_train: CutSet) -> DataLoader:
logging.info("About to get Musan cuts")
cuts_musan = load_manifest(
self.args.manifest_dir / "cuts_musan.json.gz"
self.args.manifest_dir / "musan_cuts.jsonl.gz"
)
transforms = []
if self.args.enable_musan:
logging.info("Enable MUSAN")
transforms.append(
input_strategy = PrecomputedFeatures()
if self.args.on_the_fly_feats:
input_strategy = OnTheFlyFeatures(
Fbank(FbankConfig(num_mel_bins=80, sampling_rate=16000)),
)
train = K2SpeechRecognitionDataset(
input_strategy=input_strategy,
cut_transforms=[
PerturbSpeed(factors=[0.9, 1.1], p=2 / 3, preserve_id=True),
Resample16kHz(),
CutMix(
cuts=cuts_musan, prob=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}"
)
input_transforms.append(
),
],
input_transforms=[
SpecAugment(
time_warp_factor=self.args.spec_aug_time_warp_factor,
num_frame_masks=2,
@ -224,56 +156,19 @@ class LibriSpeechAsrDataModule:
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,
],
return_cuts=True,
)
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,
)
train_sampler = DynamicBucketingSampler(
cuts_train,
max_duration=self.args.max_duration,
shuffle=self.args.shuffle,
num_buckets=self.args.num_buckets,
drop_last=True,
)
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,
@ -285,39 +180,34 @@ class LibriSpeechAsrDataModule:
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")
input_strategy = PrecomputedFeatures()
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,
input_strategy = OnTheFlyFeatures(
Fbank(FbankConfig(num_mel_bins=80, sampling_rate=16000)),
)
validate = K2SpeechRecognitionDataset(
return_cuts=True,
input_strategy=input_strategy,
cut_transforms=[
Resample16kHz(),
],
)
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,
num_workers=self.args.num_workers,
persistent_workers=False,
)
@ -325,11 +215,19 @@ class LibriSpeechAsrDataModule:
def test_dataloaders(self, cuts: CutSet) -> DataLoader:
logging.debug("About to create test dataset")
input_strategy = PrecomputedFeatures()
if self.args.on_the_fly_feats:
input_strategy = OnTheFlyFeatures(
Fbank(FbankConfig(num_mel_bins=80, sampling_rate=16000)),
)
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,
return_cuts=True,
input_strategy=input_strategy,
cut_transforms=[
Resample16kHz(),
],
)
sampler = BucketingSampler(
cuts, max_duration=self.args.max_duration, shuffle=False
@ -344,42 +242,44 @@ class LibriSpeechAsrDataModule:
return test_dl
@lru_cache()
def train_clean_100_cuts(self) -> CutSet:
logging.info("About to get train-clean-100 cuts")
return load_manifest(
self.args.manifest_dir / "cuts_train-clean-100.json.gz"
def train_cuts(self) -> CutSet:
logging.info("About to get train Fisher + SWBD cuts")
return load_manifest_lazy(
self.args.manifest_dir
/ "train_utterances_fisher-swbd_cuts.jsonl.gz"
)
@lru_cache()
def train_clean_360_cuts(self) -> CutSet:
logging.info("About to get train-clean-360 cuts")
return load_manifest(
self.args.manifest_dir / "cuts_train-clean-360.json.gz"
def dev_cuts(self) -> CutSet:
logging.info("About to get dev Fisher + SWBD cuts")
return load_manifest_lazy(
self.args.manifest_dir / "dev_utterances_fisher-swbd_cuts.jsonl.gz"
)
@lru_cache()
def train_other_500_cuts(self) -> CutSet:
logging.info("About to get train-other-500 cuts")
return load_manifest(
self.args.manifest_dir / "cuts_train-other-500.json.gz"
)
@lru_cache()
def dev_clean_cuts(self) -> CutSet:
logging.info("About to get dev-clean cuts")
return load_manifest(self.args.manifest_dir / "cuts_dev-clean.json.gz")
@lru_cache()
def dev_other_cuts(self) -> CutSet:
logging.info("About to get dev-other cuts")
return load_manifest(self.args.manifest_dir / "cuts_dev-other.json.gz")
@lru_cache()
def test_clean_cuts(self) -> CutSet:
def test_cuts(self) -> CutSet:
logging.info("About to get test-clean cuts")
return load_manifest(self.args.manifest_dir / "cuts_test-clean.json.gz")
raise NotImplemented
@lru_cache()
def test_other_cuts(self) -> CutSet:
logging.info("About to get test-other cuts")
return load_manifest(self.args.manifest_dir / "cuts_test-other.json.gz")
def test():
parser = argparse.ArgumentParser()
AsrDataModule.add_arguments(parser)
args = parser.parse_args()
adm = AsrDataModule(args)
cuts = adm.train_cuts()
dl = adm.train_dataloaders(cuts)
for i, batch in tqdm(enumerate(dl)):
if i == 100:
break
cuts = adm.dev_cuts()
dl = adm.valid_dataloaders(cuts)
for i, batch in tqdm(enumerate(dl)):
if i == 100:
break
if __name__ == '__main__':
test()