mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 01:52:41 +00:00
442 lines
15 KiB
Python
442 lines
15 KiB
Python
# Copyright 2021 Piotr Żelasko
|
|
#
|
|
# 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 logging
|
|
import re
|
|
from functools import lru_cache
|
|
from pathlib import Path
|
|
from typing import Any, Dict, Optional
|
|
|
|
import torch
|
|
from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
|
|
from lhotse.cut import Cut
|
|
from lhotse.dataset import (
|
|
CutConcatenate,
|
|
CutMix,
|
|
DynamicBucketingSampler,
|
|
K2SpeechRecognitionDataset,
|
|
PrecomputedFeatures,
|
|
SpecAugment,
|
|
)
|
|
from lhotse.dataset.input_strategies import OnTheFlyFeatures
|
|
from lhotse.utils import fix_random_seed
|
|
from torch.utils.data import DataLoader
|
|
from tqdm import tqdm
|
|
|
|
from icefall.utils import str2bool
|
|
|
|
|
|
class _SeedWorkers:
|
|
def __init__(self, seed: int):
|
|
self.seed = seed
|
|
|
|
def __call__(self, worker_id: int):
|
|
fix_random_seed(self.seed + worker_id)
|
|
|
|
|
|
class AmiAsrDataModule:
|
|
"""
|
|
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/valid/test cuts.",
|
|
)
|
|
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(
|
|
"--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(
|
|
"--max-duration",
|
|
type=int,
|
|
default=100.0,
|
|
help=(
|
|
"Maximum pooled recordings duration (seconds) in a "
|
|
"single batch. You can reduce it if it causes CUDA OOM."
|
|
),
|
|
)
|
|
group.add_argument(
|
|
"--max-cuts", type=int, default=None, help="Maximum cuts in a single batch."
|
|
)
|
|
group.add_argument(
|
|
"--num-buckets",
|
|
type=int,
|
|
default=50,
|
|
help=(
|
|
"The number of buckets for the BucketingSampler"
|
|
"(you might want to increase it for larger datasets)."
|
|
),
|
|
)
|
|
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(
|
|
"--num-workers",
|
|
type=int,
|
|
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,
|
|
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(
|
|
"--ihm-only",
|
|
type=str2bool,
|
|
default=False,
|
|
help="When enabled, only use IHM data for training.",
|
|
)
|
|
|
|
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.
|
|
"""
|
|
logging.info("About to get Musan cuts")
|
|
|
|
transforms = []
|
|
if self.args.enable_musan:
|
|
logging.info("Enable MUSAN")
|
|
cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
|
|
transforms.append(
|
|
CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
|
|
)
|
|
else:
|
|
logging.info("Disable MUSAN")
|
|
|
|
if self.args.concatenate_cuts:
|
|
logging.info(
|
|
"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(
|
|
SpecAugment(
|
|
time_warp_factor=self.args.spec_aug_time_warp_factor,
|
|
num_frame_masks=2,
|
|
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.on_the_fly_feats:
|
|
train = K2SpeechRecognitionDataset(
|
|
cut_transforms=transforms,
|
|
input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
|
|
input_transforms=input_transforms,
|
|
)
|
|
else:
|
|
train = K2SpeechRecognitionDataset(
|
|
cut_transforms=transforms,
|
|
input_transforms=input_transforms,
|
|
)
|
|
|
|
logging.info("Using DynamicBucketingSampler.")
|
|
train_sampler = DynamicBucketingSampler(
|
|
cuts_train,
|
|
max_duration=self.args.max_duration,
|
|
max_cuts=self.args.max_cuts,
|
|
shuffle=False,
|
|
num_buckets=self.args.num_buckets,
|
|
drop_last=True,
|
|
)
|
|
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)
|
|
|
|
# 'seed' is derived from the current random state, which will have
|
|
# previously been set in the main process.
|
|
seed = torch.randint(0, 100000, ()).item()
|
|
worker_init_fn = _SeedWorkers(seed)
|
|
|
|
train_dl = DataLoader(
|
|
train,
|
|
sampler=train_sampler,
|
|
batch_size=None,
|
|
num_workers=self.args.num_workers,
|
|
persistent_workers=False,
|
|
worker_init_fn=worker_init_fn,
|
|
)
|
|
|
|
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))),
|
|
)
|
|
else:
|
|
validate = K2SpeechRecognitionDataset(
|
|
cut_transforms=transforms,
|
|
)
|
|
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.on_the_fly_feats
|
|
else PrecomputedFeatures(),
|
|
return_cuts=True,
|
|
)
|
|
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
|
|
|
|
def remove_short_cuts(self, cut: Cut) -> bool:
|
|
"""
|
|
See: https://github.com/k2-fsa/icefall/issues/500
|
|
Basically, the zipformer model subsamples the input using the following formula:
|
|
num_out_frames = (num_in_frames - 7)//2
|
|
For num_out_frames to be at least 1, num_in_frames must be at least 9.
|
|
"""
|
|
return cut.duration >= 0.09
|
|
|
|
@lru_cache()
|
|
def train_cuts(self, sp: Optional[Any] = None) -> CutSet:
|
|
logging.info("About to get AMI train cuts")
|
|
|
|
def _remove_short_and_long_utt(c: Cut):
|
|
if c.duration < 0.2 or c.duration > 25.0:
|
|
return False
|
|
|
|
# In pruned RNN-T, we require that T >= S
|
|
# where T is the number of feature frames after subsampling
|
|
# and S is the number of tokens in the utterance
|
|
|
|
# In ./zipformer.py, the conv module uses the following expression
|
|
# for subsampling
|
|
T = ((c.num_frames - 7) // 2 + 1) // 2
|
|
tokens = sp.encode(c.supervisions[0].text, out_type=str)
|
|
return T >= len(tokens)
|
|
|
|
if self.args.ihm_only:
|
|
cuts_train = load_manifest_lazy(
|
|
self.args.manifest_dir / "cuts_train_ihm.jsonl.gz"
|
|
)
|
|
else:
|
|
cuts_train = load_manifest_lazy(
|
|
self.args.manifest_dir / "cuts_train_all.jsonl.gz"
|
|
)
|
|
|
|
return cuts_train.filter(_remove_short_and_long_utt)
|
|
|
|
@lru_cache()
|
|
def dev_ihm_cuts(self) -> CutSet:
|
|
logging.info("About to get AMI IHM dev cuts")
|
|
cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_ihm.jsonl.gz")
|
|
return cs.filter(self.remove_short_cuts)
|
|
|
|
@lru_cache()
|
|
def dev_sdm_cuts(self) -> CutSet:
|
|
logging.info("About to get AMI SDM dev cuts")
|
|
cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_sdm.jsonl.gz")
|
|
return cs.filter(self.remove_short_cuts)
|
|
|
|
@lru_cache()
|
|
def dev_mdm_cuts(self) -> CutSet:
|
|
logging.info("About to get AMI MDM dev cuts")
|
|
cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_mdm.jsonl.gz")
|
|
return cs.filter(self.remove_short_cuts)
|
|
|
|
@lru_cache()
|
|
def dev_gss_cuts(self) -> CutSet:
|
|
if not (self.args.manifest_dir / "cuts_dev_gss.jsonl.gz").exists():
|
|
logging.info("No GSS dev cuts found")
|
|
return None
|
|
logging.info("About to get AMI GSS-enhanced dev cuts")
|
|
cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_gss.jsonl.gz")
|
|
return cs.filter(self.remove_short_cuts)
|
|
|
|
@lru_cache()
|
|
def test_ihm_cuts(self) -> CutSet:
|
|
logging.info("About to get AMI IHM test cuts")
|
|
cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_ihm.jsonl.gz")
|
|
return cs.filter(self.remove_short_cuts)
|
|
|
|
@lru_cache()
|
|
def test_sdm_cuts(self) -> CutSet:
|
|
logging.info("About to get AMI SDM test cuts")
|
|
cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_sdm.jsonl.gz")
|
|
return cs.filter(self.remove_short_cuts)
|
|
|
|
@lru_cache()
|
|
def test_mdm_cuts(self) -> CutSet:
|
|
logging.info("About to get AMI MDM test cuts")
|
|
cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_mdm.jsonl.gz")
|
|
return cs.filter(self.remove_short_cuts)
|
|
|
|
@lru_cache()
|
|
def test_gss_cuts(self) -> CutSet:
|
|
if not (self.args.manifest_dir / "cuts_test_gss.jsonl.gz").exists():
|
|
logging.info("No GSS test cuts found")
|
|
return None
|
|
logging.info("About to get AMI GSS-enhanced test cuts")
|
|
cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_gss.jsonl.gz")
|
|
return cs.filter(self.remove_short_cuts)
|