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update this pr
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Mingshuang Luo
parent
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commit
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@ -1,594 +1 @@
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# Copyright 2021 Piotr Żelasko
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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from functools import lru_cache
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from pathlib import Path
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from lhotse import CutSet, Fbank, FbankConfig, load_manifest
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from lhotse.dataset import (
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BucketingSampler,
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CutConcatenate,
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CutMix,
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K2SpeechRecognitionDataset,
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PrecomputedFeatures,
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SingleCutSampler,
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)
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from lhotse.dataset.input_strategies import OnTheFlyFeatures
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from torch.utils.data import DataLoader
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from icefall.utils import str2bool
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class LibriSpeechAsrDataModule:
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"""
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DataModule for k2 ASR experiments.
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It assumes there is always one train and valid dataloader,
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but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
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and test-other).
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It contains all the common data pipeline modules used in ASR
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experiments, e.g.:
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- dynamic batch size,
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- bucketing samplers,
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- cut concatenation,
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- augmentation,
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- on-the-fly feature extraction
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This class should be derived for specific corpora used in ASR tasks.
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"""
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def __init__(self, args: argparse.Namespace):
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self.args = args
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@classmethod
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def add_arguments(cls, parser: argparse.ArgumentParser):
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group = parser.add_argument_group(
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title="ASR data related options",
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description="These options are used for the preparation of "
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"PyTorch DataLoaders from Lhotse CutSet's -- they control the "
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"effective batch sizes, sampling strategies, applied data "
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"augmentations, etc.",
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)
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group.add_argument(
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"--full-libri",
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type=str2bool,
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default=True,
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help="When enabled, use 960h LibriSpeech. "
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"Otherwise, use 100h subset.",
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)
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group.add_argument(
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"--manifest-dir",
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type=Path,
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default=Path("data/fbank"),
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help="Path to directory with train/valid/test cuts.",
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)
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group.add_argument(
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"--max-duration",
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type=int,
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default=200.0,
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help="Maximum pooled recordings duration (seconds) in a "
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"single batch. You can reduce it if it causes CUDA OOM.",
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)
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group.add_argument(
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"--bucketing-sampler",
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type=str2bool,
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default=True,
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help="When enabled, the batches will come from buckets of "
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"similar duration (saves padding frames).",
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)
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group.add_argument(
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"--num-buckets",
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type=int,
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default=30,
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help="The number of buckets for the BucketingSampler"
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"(you might want to increase it for larger datasets).",
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)
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group.add_argument(
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"--concatenate-cuts",
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type=str2bool,
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default=False,
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help="When enabled, utterances (cuts) will be concatenated "
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"to minimize the amount of padding.",
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)
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group.add_argument(
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"--duration-factor",
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type=float,
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default=1.0,
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help="Determines the maximum duration of a concatenated cut "
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"relative to the duration of the longest cut in a batch.",
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)
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group.add_argument(
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"--gap",
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type=float,
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default=1.0,
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help="The amount of padding (in seconds) inserted between "
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"concatenated cuts. This padding is filled with noise when "
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"noise augmentation is used.",
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)
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group.add_argument(
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"--on-the-fly-feats",
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type=str2bool,
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default=False,
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help="When enabled, use on-the-fly cut mixing and feature "
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"extraction. Will drop existing precomputed feature manifests "
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"if available.",
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)
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group.add_argument(
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"--shuffle",
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type=str2bool,
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default=True,
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help="When enabled (=default), the examples will be "
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"shuffled for each epoch.",
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)
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group.add_argument(
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"--return-cuts",
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type=str2bool,
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default=True,
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help="When enabled, each batch will have the "
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"field: batch['supervisions']['cut'] with the cuts that "
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"were used to construct it.",
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)
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group.add_argument(
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"--num-workers",
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type=int,
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default=2,
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help="The number of training dataloader workers that "
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"collect the batches.",
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)
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group.add_argument(
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"--enable-spec-aug",
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type=str2bool,
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default=True,
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help="When enabled, use SpecAugment for training dataset.",
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)
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group.add_argument(
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"--spec-aug-time-warp-factor",
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type=int,
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default=80,
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help="Used only when --enable-spec-aug is True. "
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"It specifies the factor for time warping in SpecAugment. "
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"Larger values mean more warping. "
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"A value less than 1 means to disable time warp.",
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)
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group.add_argument(
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"--enable-musan",
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type=str2bool,
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default=True,
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help="When enabled, select noise from MUSAN and mix it"
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"with training dataset. ",
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)
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def train_dataloaders(self, cuts_train: CutSet) -> DataLoader:
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logging.info("About to get Musan cuts")
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cuts_musan = load_manifest(
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self.args.manifest_dir / "cuts_musan.json.gz"
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)
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transforms = []
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if self.args.enable_musan:
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logging.info("Enable MUSAN")
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transforms.append(
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CutMix(
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cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
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)
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)
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else:
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logging.info("Disable MUSAN")
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if self.args.concatenate_cuts:
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logging.info(
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f"Using cut concatenation with duration factor "
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f"{self.args.duration_factor} and gap {self.args.gap}."
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)
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# Cut concatenation should be the first transform in the list,
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# so that if we e.g. mix noise in, it will fill the gaps between
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# different utterances.
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transforms = [
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CutConcatenate(
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duration_factor=self.args.duration_factor, gap=self.args.gap
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)
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] + transforms
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input_transforms = []
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if self.args.enable_spec_aug:
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logging.info("Enable SpecAugment")
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logging.info(
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f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
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)
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input_transforms.append(
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SpecAugment(
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time_warp_factor=self.args.spec_aug_time_warp_factor,
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features_mask_size=27,
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num_feature_masks=2,
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frames_mask_size=100,
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)
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)
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else:
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logging.info("Disable SpecAugment")
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logging.info("About to create train dataset")
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train = K2SpeechRecognitionDataset(
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cut_transforms=transforms,
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input_transforms=input_transforms,
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return_cuts=self.args.return_cuts,
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)
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if self.args.on_the_fly_feats:
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# NOTE: the PerturbSpeed transform should be added only if we
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# remove it from data prep stage.
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# Add on-the-fly speed perturbation; since originally it would
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# have increased epoch size by 3, we will apply prob 2/3 and use
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# 3x more epochs.
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# Speed perturbation probably should come first before
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# concatenation, but in principle the transforms order doesn't have
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# to be strict (e.g. could be randomized)
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# transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa
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# Drop feats to be on the safe side.
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train = K2SpeechRecognitionDataset(
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cut_transforms=transforms,
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input_strategy=OnTheFlyFeatures(
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Fbank(FbankConfig(num_mel_bins=80))
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),
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input_transforms=input_transforms,
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return_cuts=self.args.return_cuts,
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)
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if self.args.bucketing_sampler:
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logging.info("Using BucketingSampler.")
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train_sampler = BucketingSampler(
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cuts_train,
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max_duration=self.args.max_duration,
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shuffle=self.args.shuffle,
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num_buckets=self.args.num_buckets,
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bucket_method="equal_duration",
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drop_last=True,
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)
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else:
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logging.info("Using SingleCutSampler.")
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train_sampler = SingleCutSampler(
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cuts_train,
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max_duration=self.args.max_duration,
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shuffle=self.args.shuffle,
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)
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logging.info("About to create train dataloader")
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train_dl = DataLoader(
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train,
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sampler=train_sampler,
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batch_size=None,
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num_workers=self.args.num_workers,
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persistent_workers=False,
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)
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return train_dl
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def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
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transforms = []
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if self.args.concatenate_cuts:
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transforms = [
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CutConcatenate(
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duration_factor=self.args.duration_factor, gap=self.args.gap
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)
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] + transforms
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logging.info("About to create dev dataset")
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if self.args.on_the_fly_feats:
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validate = K2SpeechRecognitionDataset(
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cut_transforms=transforms,
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input_strategy=OnTheFlyFeatures(
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Fbank(FbankConfig(num_mel_bins=80))
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),
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return_cuts=self.args.return_cuts,
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)
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else:
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validate = K2SpeechRecognitionDataset(
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cut_transforms=transforms,
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return_cuts=self.args.return_cuts,
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)
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valid_sampler = BucketingSampler(
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cuts_valid,
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max_duration=self.args.max_duration,
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shuffle=False,
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)
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logging.info("About to create dev dataloader")
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valid_dl = DataLoader(
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validate,
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sampler=valid_sampler,
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batch_size=None,
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num_workers=2,
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persistent_workers=False,
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)
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return valid_dl
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def test_dataloaders(self, cuts: CutSet) -> DataLoader:
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logging.debug("About to create test dataset")
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test = K2SpeechRecognitionDataset(
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input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
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if self.args.on_the_fly_feats
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else PrecomputedFeatures(),
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return_cuts=self.args.return_cuts,
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)
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sampler = BucketingSampler(
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cuts, max_duration=self.args.max_duration, shuffle=False
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)
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logging.debug("About to create test dataloader")
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test_dl = DataLoader(
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test,
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batch_size=None,
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sampler=sampler,
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num_workers=self.args.num_workers,
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)
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return test_dl
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@lru_cache()
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def train_clean_100_cuts(self) -> CutSet:
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logging.info("About to get train-clean-100 cuts")
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return load_manifest(
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self.args.manifest_dir / "cuts_train-clean-100.json.gz"
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)
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@lru_cache()
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def train_clean_360_cuts(self) -> CutSet:
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logging.info("About to get train-clean-360 cuts")
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return load_manifest(
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self.args.manifest_dir / "cuts_train-clean-360.json.gz"
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)
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@lru_cache()
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def train_other_500_cuts(self) -> CutSet:
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logging.info("About to get train-other-500 cuts")
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return load_manifest(
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self.args.manifest_dir / "cuts_train-other-500.json.gz"
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)
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@lru_cache()
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def dev_clean_cuts(self) -> CutSet:
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logging.info("About to get dev-clean cuts")
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return load_manifest(self.args.manifest_dir / "cuts_dev-clean.json.gz")
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@lru_cache()
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def dev_other_cuts(self) -> CutSet:
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logging.info("About to get dev-other cuts")
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return load_manifest(self.args.manifest_dir / "cuts_dev-other.json.gz")
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@lru_cache()
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def test_clean_cuts(self) -> CutSet:
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logging.info("About to get test-clean cuts")
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return load_manifest(self.args.manifest_dir / "cuts_test-clean.json.gz")
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@lru_cache()
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def test_other_cuts(self) -> CutSet:
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logging.info("About to get test-other cuts")
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return load_manifest(self.args.manifest_dir / "cuts_test-other.json.gz")
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import math
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import random
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import numpy as np
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from typing import Optional, Dict
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import torch
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from lhotse import CutSet
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class SpecAugment(torch.nn.Module):
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"""
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SpecAugment performs three augmentations:
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- time warping of the feature matrix
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- masking of ranges of features (frequency bands)
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- masking of ranges of frames (time)
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The current implementation works with batches, but processes each example separately
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in a loop rather than simultaneously to achieve different augmentation parameters for
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each example.
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"""
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def __init__(
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self,
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time_warp_factor: Optional[int] = 80,
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num_feature_masks: int = 1,
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features_mask_size: int = 13,
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num_frame_masks: int = 10,
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frames_mask_size: int = 70,
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max_frames_mask_fraction: float = 0.4,
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p=0.5,
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):
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"""
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SpecAugment's constructor.
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:param time_warp_factor: parameter for the time warping; larger values mean more warping.
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Set to ``None``, or less than ``1``, to disable.
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:param num_feature_masks: how many feature masks should be applied. Set to ``0`` to disable.
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:param features_mask_size: the width of the feature mask (expressed in the number of masked feature bins).
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This is the ``F`` parameter from the SpecAugment paper.
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:param num_frame_masks: how many frame (temporal) masks should be applied. Set to ``0`` to disable.
|
||||
This is the maximum (it's also constrained by max_frames_mask_fraction).
|
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: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). It is the total masked fraction including all masked regions.
|
||||
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!
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||||
"""
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||||
super().__init__()
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assert 0 <= p <= 1
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||||
assert num_feature_masks >= 0
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||||
assert num_frame_masks >= 0
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||||
assert features_mask_size > 0
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assert frames_mask_size > 0
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self.time_warp_factor = time_warp_factor
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self.num_feature_masks = num_feature_masks
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||||
self.features_mask_size = features_mask_size
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||||
self.num_frame_masks = num_frame_masks
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||||
self.frames_mask_size = frames_mask_size
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||||
self.max_frames_mask_fraction = max_frames_mask_fraction
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self.p = p
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||||
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||||
def forward(
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||||
self,
|
||||
features: torch.Tensor,
|
||||
supervision_segments: Optional[torch.IntTensor] = None,
|
||||
*args,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Computes SpecAugment for a batch of feature matrices.
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||||
|
||||
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)``.
|
||||
"""
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||||
assert len(features.shape) == 3, (
|
||||
"SpecAugment only supports batches of " "single-channel feature matrices."
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||||
)
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||||
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)
|
||||
../tdnn_lstm_ctc/asr_datamodule.py
|
||||
@ -28,7 +28,6 @@ from lhotse.dataset import (
|
||||
K2SpeechRecognitionDataset,
|
||||
PrecomputedFeatures,
|
||||
SingleCutSampler,
|
||||
SpecAugment,
|
||||
)
|
||||
from lhotse.dataset.input_strategies import OnTheFlyFeatures
|
||||
from torch.utils.data import DataLoader
|
||||
@ -179,6 +178,20 @@ class LibriSpeechAsrDataModule:
|
||||
"with training dataset. ",
|
||||
)
|
||||
|
||||
group.add_argument(
|
||||
"--max-mask-fraction",
|
||||
type=float,
|
||||
default=0.4,
|
||||
help="the max mask fraction for specaugment.",
|
||||
)
|
||||
|
||||
group.add_argument(
|
||||
"--p",
|
||||
type=float,
|
||||
default=0.5,
|
||||
help="the probility for specaugment.",
|
||||
)
|
||||
|
||||
def train_dataloaders(self, cuts_train: CutSet) -> DataLoader:
|
||||
logging.info("About to get Musan cuts")
|
||||
cuts_musan = load_manifest(
|
||||
@ -219,10 +232,12 @@ class LibriSpeechAsrDataModule:
|
||||
input_transforms.append(
|
||||
SpecAugment(
|
||||
time_warp_factor=self.args.spec_aug_time_warp_factor,
|
||||
num_frame_masks=2,
|
||||
num_frame_masks=10,
|
||||
features_mask_size=27,
|
||||
num_feature_masks=2,
|
||||
frames_mask_size=100,
|
||||
max_frames_mask_fraction=self.args.max_mask_fraction,
|
||||
p=self.args.p,
|
||||
)
|
||||
)
|
||||
else:
|
||||
@ -383,3 +398,213 @@ class LibriSpeechAsrDataModule:
|
||||
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)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user