mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 01:52:41 +00:00
latest snowfall gigaspeech script
This commit is contained in:
Guo Liyong
parent
343f99305f
commit
83b2705b44
@ -2,13 +2,14 @@
|
||||
# Apache 2.0
|
||||
import argparse
|
||||
import logging
|
||||
import warnings
|
||||
from functools import lru_cache
|
||||
from pathlib import Path
|
||||
from typing import List, Union
|
||||
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from lhotse import CutSet, Fbank, FbankConfig, load_manifest
|
||||
from lhotse import CutSet, KaldifeatFbank, FbankConfig, load_manifest
|
||||
from lhotse.dataset import (
|
||||
BucketingSampler,
|
||||
CutConcatenate,
|
||||
@ -24,11 +25,17 @@ from icefall.utils import str2bool
|
||||
from icefall.dataset.datamodule import DataModule
|
||||
|
||||
|
||||
def get_context_suffix(args):
|
||||
if args.giga_context_window is None or args.giga_context_window <= 0.0:
|
||||
ctx_suffix = ""
|
||||
def get_context_suffix(args, subparser=True):
|
||||
if subparser:
|
||||
if args.giga_context_window is None or args.giga_context_window <= 0.0:
|
||||
ctx_suffix = ""
|
||||
else:
|
||||
ctx_suffix = f"_{args.giga_context_direction}{args.giga_context_window}"
|
||||
else:
|
||||
ctx_suffix = f"_{args.giga_context_direction}{args.giga_context_window}"
|
||||
if args.context_window is None or args.context_window <= 0.0:
|
||||
ctx_suffix = ""
|
||||
else:
|
||||
ctx_suffix = f"_{args.context_direction}{args.context_window}"
|
||||
return ctx_suffix
|
||||
|
||||
|
||||
@ -36,13 +43,14 @@ class GigaSpeechAsrDataModule(DataModule):
|
||||
"""
|
||||
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.
|
||||
"""
|
||||
|
||||
@ -57,83 +65,103 @@ class GigaSpeechAsrDataModule(DataModule):
|
||||
parser = subparsers.add_parser(name='giga')
|
||||
super().add_arguments(parser)
|
||||
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.'
|
||||
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(
|
||||
'--feature-dir',
|
||||
"--feature-dir",
|
||||
dest="giga_feature_dir",
|
||||
type=Path,
|
||||
default=Path('exp/giga_data'),
|
||||
help='Path to directory with train/valid/test cuts.'
|
||||
help="Path to directory with train/valid/test cuts.",
|
||||
)
|
||||
group.add_argument(
|
||||
'--max-duration',
|
||||
"--max-duration",
|
||||
dest="giga_max_duration",
|
||||
type=int,
|
||||
default=500.0,
|
||||
help="Maximum pooled recordings duration (seconds) in a single batch.")
|
||||
help="Maximum pooled recordings duration (seconds) in a single batch.",
|
||||
)
|
||||
group.add_argument(
|
||||
'--bucketing-sampler',
|
||||
"--bucketing-sampler",
|
||||
dest="giga_bucketing_sampler",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help='When enabled, the batches will come from buckets of '
|
||||
'similar duration (saves padding frames).')
|
||||
help="When enabled, the batches will come from buckets of "
|
||||
"similar duration (saves padding frames).",
|
||||
)
|
||||
group.add_argument(
|
||||
'--num-buckets',
|
||||
"--num-buckets",
|
||||
dest="giga_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).')
|
||||
help="The number of buckets for the BucketingSampler"
|
||||
"(you might want to increase it for larger datasets).",
|
||||
)
|
||||
group.add_argument(
|
||||
'--concatenate-cuts',
|
||||
"--concatenate-cuts",
|
||||
dest="giga_concatenate_cuts",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help='When enabled, utterances (cuts) will be concatenated '
|
||||
'to minimize the amount of padding.')
|
||||
help="When enabled, utterances (cuts) will be concatenated "
|
||||
"to minimize the amount of padding.",
|
||||
)
|
||||
group.add_argument(
|
||||
'--duration-factor',
|
||||
"--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.')
|
||||
help="Determines the maximum duration of a concatenated cut "
|
||||
"relative to the duration of the longest cut in a batch.",
|
||||
)
|
||||
group.add_argument(
|
||||
'--gap',
|
||||
"--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.')
|
||||
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',
|
||||
"--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. '
|
||||
'Will drop existing precomputed feature manifests if available.'
|
||||
help="When enabled, use on-the-fly cut mixing and feature extraction. "
|
||||
"Will drop existing precomputed feature manifests if available.",
|
||||
)
|
||||
group.add_argument(
|
||||
'--shuffle',
|
||||
"--shuffle",
|
||||
dest="giga_shuffle",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help='When enabled (=default), the examples will be shuffled for each epoch.'
|
||||
)
|
||||
help="When enabled (=default), the examples will be shuffled for each epoch.",
|
||||
)
|
||||
group.add_argument(
|
||||
'--check-cuts',
|
||||
dest="giga_check_cuts",
|
||||
"--return-cuts",
|
||||
dest="giga_return_cuts",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help='When enabled (=default), we will iterate over the whole training cut set '
|
||||
'to validate it. It should be disabled when using Apache Arrow manifests '
|
||||
'to avoid an excessive starting time of the script with datasets>1000h.'
|
||||
)
|
||||
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",
|
||||
dest="giga_num_workers",
|
||||
type=int,
|
||||
default=4,
|
||||
help="The number of training dataloader workers that collect the batches.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--num-workers-inner",
|
||||
dest="giga_num_workers_inner",
|
||||
type=int,
|
||||
default=16,
|
||||
help="The number of sub-workers (replicated for each of training dataloader"
|
||||
" workers) that parallelize the I/O to collect each batch.",
|
||||
)
|
||||
|
||||
# GigaSpeech specific arguments
|
||||
group.add_argument(
|
||||
@ -162,35 +190,36 @@ class GigaSpeechAsrDataModule(DataModule):
|
||||
"to seek for extra acoustic context. Available values: (left|right|center|random).",
|
||||
)
|
||||
group.add_argument(
|
||||
'--use-context-for-test',
|
||||
"--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. '
|
||||
'(note: for now, they may contain duplicated segments)'
|
||||
help="Should we read cuts with acoustic context or without it. "
|
||||
"(note: for now, they may contain duplicated segments)",
|
||||
)
|
||||
group.add_argument(
|
||||
'--small-dev',
|
||||
"--small-dev",
|
||||
dest="giga_small_dev",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help='Should we use only 1000 utterances for dev (speeds up training)'
|
||||
help="Should we use only 1000 utterances for dev (speeds up training)",
|
||||
)
|
||||
|
||||
def validate_args(self):
|
||||
if self.args.giga_subset in ['L', 'XL']:
|
||||
if self.args.giga_subset in ["L", "XL"]:
|
||||
assert (
|
||||
self.args.giga_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.giga_check_cuts == False
|
||||
), "For GigaSpeech L/XL, you must use --check-cuts 0 to avoid eagerly reading pyarrow manifests."
|
||||
assert (
|
||||
self.args.giga_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.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."
|
||||
# compute_and_store_features_batch is efficient for L/XL subsets.
|
||||
# if not self.args.giga_on_the_fly_feats:
|
||||
# warnings.warn(
|
||||
# "For GigaSpeech L/XL, we advise to set --on-the-fly-feats 1,"
|
||||
# " as we do not pre-compute them by default. If you pre-computed them,"
|
||||
# " ignore this warning."
|
||||
# )
|
||||
|
||||
def train_dataloaders(self) -> DataLoader:
|
||||
self.validate_args()
|
||||
@ -200,27 +229,26 @@ class GigaSpeechAsrDataModule(DataModule):
|
||||
self.consumed_cuts = 0
|
||||
|
||||
logging.info("About to get Musan cuts")
|
||||
cuts_musan = load_manifest(self.args.giga_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.giga_concatenate_cuts:
|
||||
logging.info(f'Using cut concatenation with duration factor '
|
||||
f'{self.args.giga_duration_factor} and gap {self.args.giga_gap}.')
|
||||
logging.info(
|
||||
f"Using cut concatenation with duration factor "
|
||||
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.giga_duration_factor,
|
||||
gap=self.args.giga_gap
|
||||
duration_factor=self.args.giga_duration_factor, gap=self.args.giga_gap
|
||||
)
|
||||
] + transforms
|
||||
|
||||
train = K2SpeechRecognitionDataset(
|
||||
# cuts_train,
|
||||
cut_transforms=transforms,
|
||||
return_cuts=True,
|
||||
# check_inputs=self.args.giga_check_cuts,
|
||||
return_cuts=self.args.giga_return_cuts,
|
||||
)
|
||||
|
||||
if self.args.giga_on_the_fly_feats:
|
||||
@ -231,75 +259,71 @@ class GigaSpeechAsrDataModule(DataModule):
|
||||
# # 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
|
||||
train = K2SpeechRecognitionDataset(
|
||||
cuts=cuts_train,
|
||||
cut_transforms=transforms,
|
||||
input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)), num_workers=20),
|
||||
return_cuts=True,
|
||||
# check_inputs=self.args.giga_check_cuts,
|
||||
input_strategy=OnTheFlyFeatures(
|
||||
KaldifeatFbank(FbankConfig(num_mel_bins=80)),
|
||||
num_workers=self.args.giga_num_workers_inner,
|
||||
),
|
||||
return_cuts=self.args.giga_return_cuts,
|
||||
)
|
||||
|
||||
if self.args.giga_bucketing_sampler:
|
||||
logging.info('Using BucketingSampler.')
|
||||
logging.info("Using BucketingSampler.")
|
||||
train_sampler = BucketingSampler(
|
||||
cuts_train,
|
||||
max_duration=self.args.giga_max_duration,
|
||||
shuffle=self.args.giga_shuffle,
|
||||
num_buckets=self.args.giga_num_buckets
|
||||
num_buckets=self.args.giga_num_buckets,
|
||||
)
|
||||
else:
|
||||
logging.info('Using SingleCutSampler.')
|
||||
logging.info("Using SingleCutSampler.")
|
||||
train_sampler = SingleCutSampler(
|
||||
cuts_train,
|
||||
max_duration=self.args.giga_max_duration,
|
||||
shuffle=self.args.giga_shuffle,
|
||||
)
|
||||
logging.info("About to create train dataloader")
|
||||
#train_dl = DataLoader(
|
||||
# train_dl = DataLoader(
|
||||
# train,
|
||||
# sampler=train_sampler,
|
||||
# batch_size=None,
|
||||
# num_workers=16,
|
||||
# persistent_workers=True,
|
||||
#)
|
||||
# )
|
||||
train_dl = LhotseDataLoader(
|
||||
train,
|
||||
sampler=train_sampler,
|
||||
num_workers=3,
|
||||
num_workers=self.args.giga_num_workers,
|
||||
prefetch_factor=5,
|
||||
)
|
||||
return train_dl
|
||||
|
||||
def inexhaustible_train_dataloaders(self):
|
||||
return self
|
||||
|
||||
def valid_dataloaders(self) -> DataLoader:
|
||||
self.validate_args()
|
||||
logging.info("About to get dev cuts")
|
||||
cuts_valid = self.valid_cuts()
|
||||
|
||||
transforms = [ ]
|
||||
transforms = []
|
||||
if self.args.giga_concatenate_cuts:
|
||||
transforms = [ CutConcatenate(
|
||||
duration_factor=self.args.giga_duration_factor,
|
||||
gap=self.args.giga_gap)
|
||||
] + transforms
|
||||
|
||||
transforms = [
|
||||
CutConcatenate(
|
||||
duration_factor=self.args.giga_duration_factor, gap=self.args.giga_gap
|
||||
)
|
||||
] + transforms
|
||||
|
||||
logging.info("About to create dev dataset")
|
||||
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.giga_check_cuts,
|
||||
input_strategy=OnTheFlyFeatures(
|
||||
KaldifeatFbank(FbankConfig(num_mel_bins=80)), num_workers=8
|
||||
),
|
||||
return_cuts=self.args.giga_return_cuts,
|
||||
)
|
||||
else:
|
||||
validate = K2SpeechRecognitionDataset(
|
||||
# cuts_valid,
|
||||
cut_transforms=transforms,
|
||||
return_cuts=True,
|
||||
# check_inputs=self.args.giga_check_cuts,
|
||||
return_cuts=self.args.giga_return_cuts,
|
||||
)
|
||||
valid_sampler = SingleCutSampler(
|
||||
cuts_valid,
|
||||
@ -307,13 +331,13 @@ class GigaSpeechAsrDataModule(DataModule):
|
||||
shuffle=False,
|
||||
)
|
||||
logging.info("About to create dev dataloader")
|
||||
#valid_dl = DataLoader(
|
||||
# valid_dl = DataLoader(
|
||||
# validate,
|
||||
# sampler=valid_sampler,
|
||||
# batch_size=None,
|
||||
# num_workers=8,
|
||||
# persistent_workers=True,
|
||||
#)
|
||||
# )
|
||||
valid_dl = LhotseDataLoader(
|
||||
validate,
|
||||
sampler=valid_sampler,
|
||||
@ -332,18 +356,16 @@ class GigaSpeechAsrDataModule(DataModule):
|
||||
for cuts_test in cuts:
|
||||
logging.debug("About to create test dataset")
|
||||
test = K2SpeechRecognitionDataset(
|
||||
cuts_test,
|
||||
input_strategy=(
|
||||
OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)), num_workers=8)
|
||||
OnTheFlyFeatures(KaldifeatFbank(FbankConfig(num_mel_bins=80)), num_workers=8)
|
||||
if self.args.giga_on_the_fly_feats
|
||||
else PrecomputedFeatures()
|
||||
),
|
||||
return_cuts=True,
|
||||
check_inputs=self.args.giga_check_cuts,
|
||||
return_cuts=self.args.giga_return_cuts,
|
||||
)
|
||||
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 = DataLoader(test, batch_size=None, sampler=sampler, num_workers=1)
|
||||
test_dl = LhotseDataLoader(test, sampler=sampler, num_workers=2)
|
||||
test_loaders.append(test_dl)
|
||||
|
||||
@ -355,18 +377,30 @@ class GigaSpeechAsrDataModule(DataModule):
|
||||
@lru_cache()
|
||||
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.giga_subset not in ['L', 'XL'] else 'arrow'
|
||||
cuts_train = CutSet.from_file(
|
||||
self.args.giga_feature_dir
|
||||
/ f"gigaspeech_cuts_{self.args.giga_subset}{get_context_suffix(self.args)}.{cuts_path_ext}"
|
||||
path = (
|
||||
self.args.giga_feature_dir
|
||||
/ f"gigaspeech_cuts_{self.args.giga_subset}{get_context_suffix(self.args)}.jsonl.gz"
|
||||
)
|
||||
if self.args.giga_subset in ["L", "XL"]:
|
||||
# "L" and "XL" partitions are large enough that we have to read their manifests lazily;
|
||||
# The "CutSet" holds a file handle and reads the items sequentially on-the-fly to avoid
|
||||
# wasting memory and time pre-reading everything. Some operations on "CutSet" won't work,
|
||||
# e.g. shuffling (or they would have read everything into memory in the process).
|
||||
# We expect that the manifests read lazily are pre-shuffled, otherwise you might experience
|
||||
# issues with convergence.
|
||||
cuts_train = CutSet.from_jsonl_lazy(path)
|
||||
else:
|
||||
# For other subsets, just read everything into memory.
|
||||
cuts_train = CutSet.from_file(path)
|
||||
return cuts_train
|
||||
|
||||
@lru_cache()
|
||||
def valid_cuts(self) -> CutSet:
|
||||
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"
|
||||
path = (
|
||||
self.args.giga_feature_dir
|
||||
/ f"gigaspeech_cuts_DEV{get_context_suffix(self.args)}.jsonl.gz"
|
||||
)
|
||||
else:
|
||||
path = self.args.giga_feature_dir / f"gigaspeech_cuts_DEV.jsonl.gz"
|
||||
logging.info(f"About to get valid cuts from {path}")
|
||||
@ -379,7 +413,10 @@ class GigaSpeechAsrDataModule(DataModule):
|
||||
@lru_cache()
|
||||
def test_cuts(self) -> CutSet:
|
||||
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"
|
||||
path = (
|
||||
self.args.giga_feature_dir
|
||||
/ f"gigaspeech_cuts_TEST{get_context_suffix(self.args)}.jsonl.gz"
|
||||
)
|
||||
else:
|
||||
path = self.args.giga_feature_dir / f"gigaspeech_cuts_TEST.jsonl.gz"
|
||||
logging.info(f"About to get test cuts from {path}")
|
||||
|
||||
@ -22,7 +22,6 @@ from lhotse import (
|
||||
combine,
|
||||
)
|
||||
from lhotse.recipes import prepare_gigaspeech, prepare_musan
|
||||
from lhotse.utils import is_module_available
|
||||
from icefall.utils import str2bool
|
||||
|
||||
# Torch's multithreaded behavior needs to be disabled or it wastes a lot of CPU and
|
||||
@ -81,7 +80,7 @@ def get_parser():
|
||||
parser.add_argument(
|
||||
"--num-jobs",
|
||||
type=int,
|
||||
default=min(5, os.cpu_count()),
|
||||
default=min(15, os.cpu_count()),
|
||||
help="Number of parallel jobs.",
|
||||
)
|
||||
parser.add_argument(
|
||||
@ -115,6 +114,19 @@ def get_parser():
|
||||
"might currently consume excessive memory and time -- use on-the-fly feature "
|
||||
"extraction in the training script instead.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num-workers",
|
||||
type=int,
|
||||
default=4,
|
||||
help="Number of workers for compute_and_store_features_batch.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--batch-duration",
|
||||
type=float,
|
||||
default=600.0,
|
||||
help="The maximum number of audio seconds in a batch"
|
||||
"for compute_and_store_features_batch.",
|
||||
)
|
||||
return parser
|
||||
|
||||
|
||||
@ -139,12 +151,6 @@ def has_no_oov(
|
||||
def main():
|
||||
args = get_parser().parse_args()
|
||||
dataset_parts = [args.subset, "DEV", "TEST"]
|
||||
if args.subset in ["L", "XL"]:
|
||||
assert is_module_available("pyarrow"), (
|
||||
"Running the GigaSpeech recipe for L and XL splits "
|
||||
"currently requires installing optional dependencies: "
|
||||
"'pip install pyarrow pandas'."
|
||||
)
|
||||
|
||||
print("Parts we will prepare: ", dataset_parts)
|
||||
|
||||
@ -159,7 +165,7 @@ def main():
|
||||
Path("/root/fangjun/data/musan"),
|
||||
)
|
||||
|
||||
output_dir = Path("exp/data")
|
||||
output_dir = Path("exp/giga_data")
|
||||
print("GigaSpeech manifest preparation:")
|
||||
gigaspeech_manifests = prepare_gigaspeech(
|
||||
corpus_dir=corpus_dir,
|
||||
@ -174,21 +180,19 @@ def main():
|
||||
corpus_dir=musan_dir, output_dir=output_dir, parts=("music", "speech", "noise")
|
||||
)
|
||||
|
||||
ctx_suffix = get_context_suffix(args)
|
||||
ctx_suffix = get_context_suffix(args, subparser=False)
|
||||
|
||||
print("Feature extraction:")
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=80))
|
||||
with get_executor() as ex: # Initialize the executor only once.
|
||||
for partition, manifests in gigaspeech_manifests.items():
|
||||
# For L and XL partition we are going to store the manifest using pyarrow.
|
||||
cuts_path_ext = "jsonl.gz" if partition not in ["L", "XL"] else "arrow"
|
||||
raw_cuts_path = output_dir / f"gigaspeech_cuts_{partition}_raw.jsonl.gz"
|
||||
cuts_path = (
|
||||
output_dir / f"gigaspeech_cuts_{partition}{ctx_suffix}.{cuts_path_ext}"
|
||||
output_dir / f"gigaspeech_cuts_{partition}{ctx_suffix}.jsonl.gz"
|
||||
)
|
||||
|
||||
if raw_cuts_path.is_file():
|
||||
print(f"{partition} already exists - skipping checking transcript.")
|
||||
print(f"{partition} already exists - skipping feature extraction.")
|
||||
else:
|
||||
# Note this step makes the recipe different than LibriSpeech:
|
||||
# We must filter out some utterances and remove punctuation to be consistent with Kaldi.
|
||||
@ -217,7 +221,7 @@ def main():
|
||||
|
||||
if cuts_path.is_file():
|
||||
print(
|
||||
f"{partition} already exists - skipping cutting into sub-segments and feature extraction."
|
||||
f"{partition} already exists - skipping cutting into sub-segments."
|
||||
)
|
||||
else:
|
||||
try:
|
||||
@ -241,7 +245,7 @@ def main():
|
||||
context_direction=args.context_direction,
|
||||
)
|
||||
if partition in ["L", "XL"]:
|
||||
# Before storing manifests in the arrow format, we want to pre-shuffle them,
|
||||
# Before storing manifests in, we want to pre-shuffle them,
|
||||
# as the sampler won't be able to do it later in an efficient manner.
|
||||
cut_set = cut_set.shuffle()
|
||||
|
||||
@ -252,14 +256,21 @@ def main():
|
||||
# data augmentation and feature computation for long recordings yet.
|
||||
# Therefore, we sacrifice some storage for the ability to precompute
|
||||
# features on shorter chunks, without memory blow-ups.
|
||||
cut_set = cut_set.compute_and_store_features(
|
||||
# cut_set = cut_set.compute_and_store_features(
|
||||
# extractor=extractor,
|
||||
# storage_path=f"{output_dir}/feats_gigaspeech_{partition}",
|
||||
# # when an executor is specified, make more partitions
|
||||
# num_jobs=args.num_jobs if ex is None else 80,
|
||||
# executor=ex,
|
||||
# )
|
||||
cut_set = cut_set.compute_and_store_features_batch(
|
||||
extractor=extractor,
|
||||
storage_path=f"{output_dir}/feats_gigaspeech_{partition}",
|
||||
# when an executor is specified, make more partitions
|
||||
num_jobs=args.num_jobs if ex is None else 80,
|
||||
executor=ex,
|
||||
batch_duration=args.batch_duration,
|
||||
num_workers=args.num_workers,
|
||||
)
|
||||
|
||||
|
||||
cut_set.to_file(cuts_path)
|
||||
|
||||
# Remove cut_set so the next iteration can correctly infer whether it needs to
|
||||
@ -278,13 +289,19 @@ def main():
|
||||
)
|
||||
.cut_into_windows(10.0)
|
||||
.filter(lambda c: c.duration > 5)
|
||||
.compute_and_store_features(
|
||||
.compute_and_store_features_batch(
|
||||
extractor=extractor,
|
||||
storage_path=f"{output_dir}/feats_musan",
|
||||
num_jobs=args.num_jobs if ex is None else 80,
|
||||
executor=ex,
|
||||
storage_type=LilcomHdf5Writer,
|
||||
batch_duration=args.batch_duration,
|
||||
num_workers=args.num_workers,
|
||||
)
|
||||
# .compute_and_store_features(
|
||||
# extractor=extractor,
|
||||
# storage_path=f"{output_dir}/feats_musan",
|
||||
# num_jobs=args.num_jobs if ex is None else 80,
|
||||
# executor=ex,
|
||||
# storage_type=LilcomHdf5Writer,
|
||||
# )
|
||||
)
|
||||
musan_cuts.to_file(musan_cuts_path)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user