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fix option conflicts between libri and giga

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This commit is contained in:
Guo Liyong 2021-11-04 00:35:56 +08:00
parent 946d6ea00b
commit 343f99305f
2 changed files with 87 additions and 42 deletions
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101
egs/librispeech/ASR/conformer_ctc/gigaspeech_datamodule.py
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@ -25,10 +25,10 @@ from icefall.dataset.datamodule import DataModule
def get_context_suffix(args):
if args.context_window is None or args.context_window <= 0.0:
if args.giga_context_window is None or args.giga_context_window <= 0.0:
ctx_suffix = ""
else:
ctx_suffix = f"_{args.context_direction}{args.context_window}"
ctx_suffix = f"_{args.giga_context_direction}{args.giga_context_window}"
return ctx_suffix
@ -53,6 +53,8 @@ class GigaSpeechAsrDataModule(DataModule):
@classmethod
def add_arguments(cls, parser: argparse.ArgumentParser):
subparsers = parser.add_subparsers(help='seperate gigaspeech arguments from librispeech arguments')
parser = subparsers.add_parser(name='giga')
super().add_arguments(parser)
group = parser.add_argument_group(
title='ASR data related options',
@ -62,17 +64,20 @@ class GigaSpeechAsrDataModule(DataModule):
)
group.add_argument(
'--feature-dir',
dest="giga_feature_dir",
type=Path,
default=Path('exp/data'),
default=Path('exp/giga_data'),
help='Path to directory with train/valid/test cuts.'
)
group.add_argument(
'--max-duration',
dest="giga_max_duration",
type=int,
default=500.0,
help="Maximum pooled recordings duration (seconds) in a single batch.")
group.add_argument(
'--bucketing-sampler',
dest="giga_bucketing_sampler",
type=str2bool,
default=False,
help='When enabled, the batches will come from buckets of '
@ -81,28 +86,33 @@ class GigaSpeechAsrDataModule(DataModule):
'--num-buckets',
type=int,
default=30,
dest="giga_num_buckets",
help='The number of buckets for the BucketingSampler'
'(you might want to increase it for larger datasets).')
group.add_argument(
'--concatenate-cuts',
dest="giga_concatenate_cuts",
type=str2bool,
default=True,
help='When enabled, utterances (cuts) will be concatenated '
'to minimize the amount of padding.')
group.add_argument(
'--duration-factor',
dest="giga_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',
dest="giga_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',
dest="giga_on_the_fly_feats",
type=str2bool,
default=False,
help='When enabled, use on-the-fly cut mixing and feature extraction. '
@ -110,12 +120,14 @@ class GigaSpeechAsrDataModule(DataModule):
)
group.add_argument(
'--shuffle',
dest="giga_shuffle",
type=str2bool,
default=True,
help='When enabled (=default), the examples will be shuffled for each epoch.'
)
group.add_argument(
'--check-cuts',
dest="giga_check_cuts",
type=str2bool,
default=True,
help='When enabled (=default), we will iterate over the whole training cut set '
@ -126,12 +138,14 @@ class GigaSpeechAsrDataModule(DataModule):
# GigaSpeech specific arguments
group.add_argument(
"--subset",
dest="giga_subset",
type=str,
default="XS",
help="Select the GigaSpeech subset (XS|S|M|L|XL)",
)
group.add_argument(
"--context-window",
dest="giga_context_window",
type=float,
default=0.0,
help="Training cut duration in seconds. "
@ -140,6 +154,7 @@ class GigaSpeechAsrDataModule(DataModule):
)
group.add_argument(
"--context-direction",
dest="giga_context_direction",
type=str,
default="center",
help="If context-window is 0, does nothing. "
@ -148,6 +163,7 @@ class GigaSpeechAsrDataModule(DataModule):
)
group.add_argument(
'--use-context-for-test',
dest="giga_use_context_for_text",
type=str2bool,
default=False,
help='Should we read cuts with acoustic context or without it. '
@ -155,24 +171,25 @@ class GigaSpeechAsrDataModule(DataModule):
)
group.add_argument(
'--small-dev',
dest="giga_small_dev",
type=str2bool,
default=False,
help='Should we use only 1000 utterances for dev (speeds up training)'
)
def validate_args(self):
if self.args.subset in ['L', 'XL']:
if self.args.giga_subset in ['L', 'XL']:
assert (
self.args.shuffle == False
self.args.giga_shuffle == False
), "For GigaSpeech L/XL, you must use --shuffle 0 to avoid eagerly reading pyarrow manifests."
assert (
self.args.check_cuts == False
self.args.giga_check_cuts == False
), "For GigaSpeech L/XL, you must use --check-cuts 0 to avoid eagerly reading pyarrow manifests."
assert (
self.args.bucketing_sampler == False
self.args.giga_bucketing_sampler == False
), "For GigaSpeech L/XL, you must use --bucketing-sampler 0 to avoid eagerly reading pyarrow manifests."
assert (
self.args.on_the_fly_feats == True
self.args.giga_on_the_fly_feats == True
), "For GigaSpeech L/XL, you must use --on-the-fly-feats 1 as we do not pre-compute them by default."
def train_dataloaders(self) -> DataLoader:
@ -183,19 +200,19 @@ class GigaSpeechAsrDataModule(DataModule):
self.consumed_cuts = 0
logging.info("About to get Musan cuts")
cuts_musan = load_manifest(self.args.feature_dir / 'cuts_musan.json.gz')
cuts_musan = load_manifest(self.args.giga_feature_dir / 'cuts_musan.json.gz')
logging.info("About to create train dataset")
transforms = [CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20))]
if self.args.concatenate_cuts:
if self.args.giga_concatenate_cuts:
logging.info(f'Using cut concatenation with duration factor '
f'{self.args.duration_factor} and gap {self.args.gap}.')
f'{self.args.giga_duration_factor} and gap {self.args.giga_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
duration_factor=self.args.giga_duration_factor,
gap=self.args.giga_gap
)
] + transforms
@ -203,10 +220,10 @@ class GigaSpeechAsrDataModule(DataModule):
# cuts_train,
cut_transforms=transforms,
return_cuts=True,
# check_inputs=self.args.check_cuts,
# check_inputs=self.args.giga_check_cuts,
)
if self.args.on_the_fly_feats:
if self.args.giga_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.
@ -218,23 +235,23 @@ class GigaSpeechAsrDataModule(DataModule):
cut_transforms=transforms,
input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)), num_workers=20),
return_cuts=True,
# check_inputs=self.args.check_cuts,
# check_inputs=self.args.giga_check_cuts,
)
if self.args.bucketing_sampler:
if self.args.giga_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
max_duration=self.args.giga_max_duration,
shuffle=self.args.giga_shuffle,
num_buckets=self.args.giga_num_buckets
)
else:
logging.info('Using SingleCutSampler.')
train_sampler = SingleCutSampler(
cuts_train,
max_duration=self.args.max_duration,
shuffle=self.args.shuffle,
max_duration=self.args.giga_max_duration,
shuffle=self.args.giga_shuffle,
)
logging.info("About to create train dataloader")
#train_dl = DataLoader(
@ -261,32 +278,32 @@ class GigaSpeechAsrDataModule(DataModule):
cuts_valid = self.valid_cuts()
transforms = [ ]
if self.args.concatenate_cuts:
if self.args.giga_concatenate_cuts:
transforms = [ CutConcatenate(
duration_factor=self.args.duration_factor,
gap=self.args.gap)
duration_factor=self.args.giga_duration_factor,
gap=self.args.giga_gap)
] + transforms
logging.info("About to create dev dataset")
if self.args.on_the_fly_feats:
if self.args.giga_on_the_fly_feats:
validate = K2SpeechRecognitionDataset(
cuts_valid,
cut_transforms=transforms,
input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)), num_workers=8),
return_cuts=True,
check_inputs=self.args.check_cuts,
check_inputs=self.args.giga_check_cuts,
)
else:
validate = K2SpeechRecognitionDataset(
# cuts_valid,
cut_transforms=transforms,
return_cuts=True,
# check_inputs=self.args.check_cuts,
# check_inputs=self.args.giga_check_cuts,
)
valid_sampler = SingleCutSampler(
cuts_valid,
max_duration=self.args.max_duration,
max_duration=self.args.giga_max_duration,
shuffle=False,
)
logging.info("About to create dev dataloader")
@ -318,13 +335,13 @@ class GigaSpeechAsrDataModule(DataModule):
cuts_test,
input_strategy=(
OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)), num_workers=8)
if self.args.on_the_fly_feats
if self.args.giga_on_the_fly_feats
else PrecomputedFeatures()
),
return_cuts=True,
check_inputs=self.args.check_cuts,
check_inputs=self.args.giga_check_cuts,
)
sampler = SingleCutSampler(cuts_test, max_duration=self.args.max_duration)
sampler = SingleCutSampler(cuts_test, max_duration=self.args.giga_max_duration)
logging.debug("About to create test dataloader")
#test_dl = DataLoader(test, batch_size=None, sampler=sampler, num_workers=1)
test_dl = LhotseDataLoader(test, sampler=sampler, num_workers=2)
@ -339,32 +356,32 @@ class GigaSpeechAsrDataModule(DataModule):
def train_cuts(self) -> CutSet:
logging.info("About to get train cuts")
# Note: for L and XL subsets, we are expecting that the training manifest is stored using pyarrow and pre-shuffled.
cuts_path_ext = 'jsonl.gz' if self.args.subset not in ['L', 'XL'] else 'arrow'
cuts_path_ext = 'jsonl.gz' if self.args.giga_subset not in ['L', 'XL'] else 'arrow'
cuts_train = CutSet.from_file(
self.args.feature_dir
/ f"gigaspeech_cuts_{self.args.subset}{get_context_suffix(self.args)}.{cuts_path_ext}"
self.args.giga_feature_dir
/ f"gigaspeech_cuts_{self.args.giga_subset}{get_context_suffix(self.args)}.{cuts_path_ext}"
)
return cuts_train
@lru_cache()
def valid_cuts(self) -> CutSet:
if self.args.use_context_for_test:
path = self.args.feature_dir / f"gigaspeech_cuts_DEV{get_context_suffix(self.args)}.jsonl.gz"
if self.args.giga_use_context_for_test:
path = self.args.giga_feature_dir / f"gigaspeech_cuts_DEV{get_context_suffix(self.args)}.jsonl.gz"
else:
path = self.args.feature_dir / f"gigaspeech_cuts_DEV.jsonl.gz"
path = self.args.giga_feature_dir / f"gigaspeech_cuts_DEV.jsonl.gz"
logging.info(f"About to get valid cuts from {path}")
cuts_valid = load_manifest(path)
if self.args.small_dev:
if self.args.giga_small_dev:
return cuts_valid.subset(first=1000)
else:
return cuts_valid
@lru_cache()
def test_cuts(self) -> CutSet:
if self.args.use_context_for_test:
path = self.args.feature_dir / f"gigaspeech_cuts_TEST{get_context_suffix(self.args)}.jsonl.gz"
if self.args.giga_use_context_for_test:
path = self.args.giga_feature_dir / f"gigaspeech_cuts_TEST{get_context_suffix(self.args)}.jsonl.gz"
else:
path = self.args.feature_dir / f"gigaspeech_cuts_TEST.jsonl.gz"
path = self.args.giga_feature_dir / f"gigaspeech_cuts_TEST.jsonl.gz"
logging.info(f"About to get test cuts from {path}")
cuts_test = load_manifest(path)
return cuts_test

28
egs/librispeech/ASR/example_giga_dataloader.py Normal file
View File

@ -0,0 +1,28 @@
import argparse
import json
from pathlib import Path
from gigaspeech_datamodule import GigaSpeechAsrDataModule
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
group = parser.add_argument_group(title='libri related options')
group.add_argument(
'--max-duration',
type=int,
default=500.0,
help="Maximum pooled recordings duration (seconds) in a single batch.")
return parser
if __name__ == '__main__':
parser = get_parser()
GigaSpeechAsrDataModule.add_arguments(parser)
args = parser.parse_args()
gigaspeech = GigaSpeechAsrDataModule(args)
train_dl = gigaspeech.inexhaustible_train_dataloaders()
for idx, batch in enumerate(train_dl):
print(batch["inputs"].shape)
print(len(batch["supervisions"]["text"]))
print(batch["supervisions"]["text"][0:2])