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icefall/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
Daniel Povey d187ad8b73 Change max_frames from 0.2 to 0.15
2022-02-11 16:24:17 +08:00

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# 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
from functools import lru_cache
from pathlib import Path
from lhotse import CutSet, Fbank, FbankConfig, load_manifest
from lhotse.dataset import (
BucketingSampler,
CutConcatenate,
CutMix,
K2SpeechRecognitionDataset,
PrecomputedFeatures,
SingleCutSampler,
)
from lhotse.dataset.input_strategies import OnTheFlyFeatures
from torch.utils.data import DataLoader
from icefall.utils import str2bool
class LibriSpeechAsrDataModule:
"""
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(
"--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"),
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 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,
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(
"--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. ",
)
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"
)
transforms = []
if self.args.enable_musan:
logging.info("Enable MUSAN")
transforms.append(
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(
SpecAugment(
time_warp_factor=self.args.spec_aug_time_warp_factor,
num_frame_masks=10,
features_mask_size=27,
num_feature_masks=2,
frames_mask_size=100,
max_frames_mask_fraction=0.15,
p=0.9
)
)
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,
)
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,
)
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,
batch_size=None,
num_workers=self.args.num_workers,
persistent_workers=False,
)
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))
),
return_cuts=self.args.return_cuts,
)
else:
validate = K2SpeechRecognitionDataset(
cut_transforms=transforms,
return_cuts=self.args.return_cuts,
)
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,
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=self.args.return_cuts,
)
sampler = BucketingSampler(
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_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"
)
@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"
)
@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:
logging.info("About to get test-clean cuts")
return load_manifest(self.args.manifest_dir / "cuts_test-clean.json.gz")
@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")
import math
import random
import numpy as np
from typing import Optional, Dict
import torch
from lhotse import CutSet
class SpecAugment(torch.nn.Module):
"""
SpecAugment performs three augmentations:
- time warping of the feature matrix
- masking of ranges of features (frequency bands)
- masking of ranges of frames (time)
The current implementation works with batches, but processes each example separately
in a loop rather than simultaneously to achieve different augmentation parameters for
each example.
"""
def __init__(
self,
time_warp_factor: Optional[int] = 80,
num_feature_masks: int = 1,
features_mask_size: int = 13,
num_frame_masks: int = 1,
frames_mask_size: int = 70,
max_frames_mask_fraction: float = 0.2,
p=0.5,
):
"""
SpecAugment's constructor.
:param time_warp_factor: parameter for the time warping; larger values mean more warping.
Set to ``None``, or less than ``1``, to disable.
:param num_feature_masks: how many feature masks should be applied. Set to ``0`` to disable.
:param features_mask_size: the width of the feature mask (expressed in the number of masked feature bins).
This is the ``F`` parameter from the SpecAugment paper.
:param num_frame_masks: how many frame (temporal) masks should be applied. Set to ``0`` to disable.
:param frames_mask_size: the width of the frame (temporal) masks (expressed in the number of masked frames).
This is the ``T`` parameter from the SpecAugment paper.
:param max_frames_mask_fraction: limits the size of the frame (temporal) mask to this value times the length
of the utterance (or supervision segment).
This is the parameter denoted by ``p`` in the SpecAugment paper.
:param p: the probability of applying this transform.
It is different from ``p`` in the SpecAugment paper!
"""
super().__init__()
assert 0 <= p <= 1
assert num_feature_masks >= 0
assert num_frame_masks >= 0
assert features_mask_size > 0
assert frames_mask_size > 0
self.time_warp_factor = time_warp_factor
self.num_feature_masks = num_feature_masks
self.features_mask_size = features_mask_size
self.num_frame_masks = num_frame_masks
self.frames_mask_size = frames_mask_size
self.max_frames_mask_fraction = max_frames_mask_fraction
self.p = p
def forward(
self,
features: torch.Tensor,
supervision_segments: Optional[torch.IntTensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
"""
Computes SpecAugment for a batch of feature matrices.
Since the batch will usually already be padded, the user can optionally
provide a ``supervision_segments`` tensor that will be used to apply SpecAugment
only to selected areas of the input. The format of this input is described below.
:param features: a batch of feature matrices with shape ``(B, T, F)``.
:param supervision_segments: an int tensor of shape ``(S, 3)``. ``S`` is the number of
supervision segments that exist in ``features`` -- there may be either
less or more than the batch size.
The second dimension encoder three kinds of information:
the sequence index of the corresponding feature matrix in `features`,
the start frame index, and the number of frames for each segment.
:return: an augmented tensor of shape ``(B, T, F)``.
"""
assert len(features.shape) == 3, (
"SpecAugment only supports batches of " "single-channel feature matrices."
)
features = features.clone()
if supervision_segments is None:
# No supervisions - apply spec augment to full feature matrices.
for sequence_idx in range(features.size(0)):
features[sequence_idx] = self._forward_single(features[sequence_idx])
else:
# Supervisions provided - we will apply time warping only on the supervised areas.
for sequence_idx, start_frame, num_frames in supervision_segments:
end_frame = start_frame + num_frames
features[sequence_idx, start_frame:end_frame] = self._forward_single(
features[sequence_idx, start_frame:end_frame], warp=True, mask=False
)
# ... and then time-mask the full feature matrices. Note that in this mode,
# it might happen that masks are applied to different sequences/examples
# than the time warping.
for sequence_idx in range(features.size(0)):
features[sequence_idx] = self._forward_single(
features[sequence_idx], warp=False, mask=True
)
return features
def _forward_single(
self, features: torch.Tensor, warp: bool = True, mask: bool = True
) -> torch.Tensor:
"""
Apply SpecAugment to a single feature matrix of shape (T, F).
"""
if random.random() > self.p:
# Randomly choose whether this transform is applied
return features
if warp:
if self.time_warp_factor is not None and self.time_warp_factor >= 1:
features = time_warp(features, factor=self.time_warp_factor)
if mask:
from torchaudio.functional import mask_along_axis
mean = features.mean()
for _ in range(self.num_feature_masks):
features = mask_along_axis(
features.unsqueeze(0),
mask_param=self.features_mask_size,
mask_value=mean,
axis=2,
).squeeze(0)
_max_tot_mask_frames = self.max_frames_mask_fraction * features.size(0)
num_frame_masks = min(self.num_frame_masks, math.ceil(_max_tot_mask_frames / self.frames_mask_size))
max_mask_frames = min(self.frames_mask_size, _max_tot_mask_frames // num_frame_masks)
for _ in range(num_frame_masks):
features = mask_along_axis(
features.unsqueeze(0),
mask_param=max_mask_frames,
mask_value=mean,
axis=1,
).squeeze(0)
return features
def state_dict(self) -> Dict:
return dict(
time_warp_factor=self.time_warp_factor,
num_feature_masks=self.num_feature_masks,
features_mask_size=self.features_mask_size,
num_frame_masks=self.num_frame_masks,
frames_mask_size=self.frames_mask_size,
max_frames_mask_fraction=self.max_frames_mask_fraction,
p=self.p,
)
def load_state_dict(self, state_dict: Dict):
self.time_warp_factor = state_dict.get(
"time_warp_factor", self.time_warp_factor
)
self.num_feature_masks = state_dict.get(
"num_feature_masks", self.num_feature_masks
)
self.features_mask_size = state_dict.get(
"features_mask_size", self.features_mask_size
)
self.num_frame_masks = state_dict.get("num_frame_masks", self.num_frame_masks)
self.frames_mask_size = state_dict.get(
"frames_mask_size", self.frames_mask_size
)
self.max_frames_mask_fraction = state_dict.get(
"max_frames_mask_fraction", self.max_frames_mask_fraction
)
self.p = state_dict.get("p", self.p)
def time_warp(features: torch.Tensor, factor: int) -> torch.Tensor:
"""
Time warping as described in the SpecAugment paper.
Implementation based on Espresso:
https://github.com/freewym/espresso/blob/master/espresso/tools/specaug_interpolate.py#L51
:param features: input tensor of shape ``(T, F)``
:param factor: time warping parameter.
:return: a warped tensor of shape ``(T, F)``
"""
t = features.size(0)
if t - factor <= factor + 1:
return features
center = np.random.randint(factor + 1, t - factor)
warped = np.random.randint(center - factor, center + factor + 1)
if warped == center:
return features
features = features.unsqueeze(0).unsqueeze(0)
left = torch.nn.functional.interpolate(
features[:, :, :center, :],
size=(warped, features.size(3)),
mode="bicubic",
align_corners=False,
)
right = torch.nn.functional.interpolate(
features[:, :, center:, :],
size=(t - warped, features.size(3)),
mode="bicubic",
align_corners=False,
)
return torch.cat((left, right), dim=2).squeeze(0).squeeze(0)
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