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Change for asr_datamodule.py (#241)
* change for asr_datamodule.py * fix style check * do a fix
This commit is contained in:
Mingshuang Luo
GitHub
parent
bb7f6ed6b7
commit
d0d806560f
@ -1,4 +1,5 @@
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# Copyright 2021 Piotr Żelasko
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# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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@ -16,6 +17,7 @@
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import argparse
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import inspect
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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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@ -210,10 +212,20 @@ class AishellAsrDataModule:
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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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# Set the value of num_frame_masks according to Lhotse's version.
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# In different Lhotse's versions, the default of num_frame_masks is
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# different.
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num_frame_masks = 10
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num_frame_masks_parameter = inspect.signature(
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SpecAugment.__init__
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).parameters["num_frame_masks"]
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if num_frame_masks_parameter.default == 1:
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num_frame_masks = 2
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logging.info(f"Num frame mask: {num_frame_masks}")
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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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num_frame_masks=2,
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num_frame_masks=num_frame_masks,
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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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@ -1,5 +1,6 @@
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# Copyright 2021 Piotr Żelasko
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# 2022 Xiaomi Corp. (authors: Fangjun Kuang)
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# 2022 Xiaomi Corp. (authors: Fangjun Kuang
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# Mingshuang Luo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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@ -16,6 +17,7 @@
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# limitations under the License.
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import argparse
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import inspect
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import logging
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from pathlib import Path
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from typing import Optional
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@ -180,10 +182,20 @@ class AsrDataModule:
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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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# Set the value of num_frame_masks according to Lhotse's version.
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# In different Lhotse's versions, the default of num_frame_masks is
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# different.
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num_frame_masks = 10
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num_frame_masks_parameter = inspect.signature(
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SpecAugment.__init__
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).parameters["num_frame_masks"]
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if num_frame_masks_parameter.default == 1:
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num_frame_masks = 2
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logging.info(f"Num frame mask: {num_frame_masks}")
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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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num_frame_masks=2,
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num_frame_masks=num_frame_masks,
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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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@ -1,356 +0,0 @@
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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 typing import List, Union
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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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SpecAugment,
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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.dataset.datamodule import DataModule
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from icefall.utils import str2bool
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class LibriSpeechAsrDataModule(DataModule):
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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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@classmethod
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def add_arguments(cls, parser: argparse.ArgumentParser):
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super().add_arguments(parser)
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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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"--feature-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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def train_dataloaders(self) -> DataLoader:
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logging.info("About to get train cuts")
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cuts_train = self.train_cuts()
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logging.info("About to get Musan cuts")
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cuts_musan = load_manifest(self.args.feature_dir / "cuts_musan.json.gz")
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logging.info("About to create train dataset")
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transforms = [
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CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
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]
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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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SpecAugment(
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num_frame_masks=2,
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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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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) -> DataLoader:
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logging.info("About to get dev cuts")
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cuts_valid = self.valid_cuts()
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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 = SingleCutSampler(
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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) -> Union[DataLoader, List[DataLoader]]:
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cuts = self.test_cuts()
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is_list = isinstance(cuts, list)
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test_loaders = []
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if not is_list:
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cuts = [cuts]
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for cuts_test in cuts:
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logging.debug("About to create test dataset")
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test = K2SpeechRecognitionDataset(
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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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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 = SingleCutSampler(
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cuts_test, max_duration=self.args.max_duration
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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, batch_size=None, sampler=sampler, num_workers=1
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)
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test_loaders.append(test_dl)
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if is_list:
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return test_loaders
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else:
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return test_loaders[0]
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@lru_cache()
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def train_cuts(self) -> CutSet:
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logging.info("About to get train cuts")
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cuts_train = load_manifest(
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self.args.feature_dir / "cuts_train-clean-100.json.gz"
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)
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if self.args.full_libri:
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cuts_train = (
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cuts_train
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+ load_manifest(
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self.args.feature_dir / "cuts_train-clean-360.json.gz"
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)
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+ load_manifest(
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self.args.feature_dir / "cuts_train-other-500.json.gz"
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)
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)
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return cuts_train
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@lru_cache()
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def valid_cuts(self) -> CutSet:
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logging.info("About to get dev cuts")
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cuts_valid = load_manifest(
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self.args.feature_dir / "cuts_dev-clean.json.gz"
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) + load_manifest(self.args.feature_dir / "cuts_dev-other.json.gz")
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return cuts_valid
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@lru_cache()
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def test_cuts(self) -> List[CutSet]:
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test_sets = ["test-clean", "test-other"]
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cuts = []
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for test_set in test_sets:
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logging.debug("About to get test cuts")
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cuts.append(
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load_manifest(
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self.args.feature_dir / f"cuts_{test_set}.json.gz"
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)
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)
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return cuts
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1
egs/librispeech/ASR/conformer_mmi/asr_datamodule.py
Symbolic link
1
egs/librispeech/ASR/conformer_mmi/asr_datamodule.py
Symbolic link
@ -0,0 +1 @@
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../conformer_ctc/asr_datamodule.py
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@ -1,4 +1,5 @@
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# Copyright 2021 Piotr Żelasko
|
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# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo)
|
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#
|
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# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
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@ -16,6 +17,7 @@
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|
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import argparse
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import inspect
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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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@ -216,10 +218,20 @@ class LibriSpeechAsrDataModule:
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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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# Set the value of num_frame_masks according to Lhotse's version.
|
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# In different Lhotse's versions, the default of num_frame_masks is
|
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# different.
|
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num_frame_masks = 10
|
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num_frame_masks_parameter = inspect.signature(
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SpecAugment.__init__
|
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).parameters["num_frame_masks"]
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if num_frame_masks_parameter.default == 1:
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num_frame_masks = 2
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logging.info(f"Num frame mask: {num_frame_masks}")
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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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num_frame_masks=2,
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num_frame_masks=num_frame_masks,
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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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@ -1,5 +1,6 @@
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# Copyright 2021 Piotr Żelasko
|
||||
# 2022 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
# 2022 Xiaomi Corp. (authors: Fangjun Kuang
|
||||
# Mingshuang Luo)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
@ -16,6 +17,7 @@
|
||||
# limitations under the License.
|
||||
|
||||
import argparse
|
||||
import inspect
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
@ -180,10 +182,20 @@ class AsrDataModule:
|
||||
logging.info(
|
||||
f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
|
||||
)
|
||||
# Set the value of num_frame_masks according to Lhotse's version.
|
||||
# In different Lhotse's versions, the default of num_frame_masks is
|
||||
# different.
|
||||
num_frame_masks = 10
|
||||
num_frame_masks_parameter = inspect.signature(
|
||||
SpecAugment.__init__
|
||||
).parameters["num_frame_masks"]
|
||||
if num_frame_masks_parameter.default == 1:
|
||||
num_frame_masks = 2
|
||||
logging.info(f"Num frame mask: {num_frame_masks}")
|
||||
input_transforms.append(
|
||||
SpecAugment(
|
||||
time_warp_factor=self.args.spec_aug_time_warp_factor,
|
||||
num_frame_masks=2,
|
||||
num_frame_masks=num_frame_masks,
|
||||
features_mask_size=27,
|
||||
num_feature_masks=2,
|
||||
frames_mask_size=100,
|
||||
|
||||
@ -1,330 +0,0 @@
|
||||
# Copyright 2021 Piotr Żelasko
|
||||
# 2021 Xiaomi Corp. (authors: Mingshuang Luo)
|
||||
#
|
||||
# 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 typing import List, Union
|
||||
|
||||
from lhotse import CutSet, Fbank, FbankConfig, load_manifest
|
||||
from lhotse.dataset import (
|
||||
BucketingSampler,
|
||||
CutConcatenate,
|
||||
CutMix,
|
||||
K2SpeechRecognitionDataset,
|
||||
PrecomputedFeatures,
|
||||
SingleCutSampler,
|
||||
SpecAugment,
|
||||
)
|
||||
from lhotse.dataset.input_strategies import OnTheFlyFeatures
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from icefall.dataset.datamodule import DataModule
|
||||
from icefall.utils import str2bool
|
||||
|
||||
|
||||
class TimitAsrDataModule(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.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def add_arguments(cls, parser: argparse.ArgumentParser):
|
||||
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.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--feature-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.",
|
||||
)
|
||||
|
||||
def train_dataloaders(self) -> DataLoader:
|
||||
logging.info("About to get train cuts")
|
||||
cuts_train = self.train_cuts()
|
||||
|
||||
logging.info("About to get Musan cuts")
|
||||
cuts_musan = load_manifest(self.args.feature_dir / "cuts_musan.json.gz")
|
||||
|
||||
logging.info("About to create train dataset")
|
||||
transforms = [CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20))]
|
||||
if self.args.concatenate_cuts:
|
||||
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 = [
|
||||
SpecAugment(
|
||||
num_frame_masks=2,
|
||||
features_mask_size=27,
|
||||
num_feature_masks=2,
|
||||
frames_mask_size=100,
|
||||
)
|
||||
]
|
||||
|
||||
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) -> DataLoader:
|
||||
logging.info("About to get dev cuts")
|
||||
cuts_valid = self.valid_cuts()
|
||||
|
||||
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 = SingleCutSampler(
|
||||
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) -> Union[DataLoader, List[DataLoader]]:
|
||||
cuts = self.test_cuts()
|
||||
is_list = isinstance(cuts, list)
|
||||
test_loaders = []
|
||||
if not is_list:
|
||||
cuts = [cuts]
|
||||
|
||||
for cuts_test in cuts:
|
||||
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 = SingleCutSampler(
|
||||
cuts_test, max_duration=self.args.max_duration
|
||||
)
|
||||
logging.debug("About to create test dataloader")
|
||||
test_dl = DataLoader(
|
||||
test, batch_size=None, sampler=sampler, num_workers=1
|
||||
)
|
||||
test_loaders.append(test_dl)
|
||||
|
||||
if is_list:
|
||||
return test_loaders
|
||||
else:
|
||||
return test_loaders[0]
|
||||
|
||||
@lru_cache()
|
||||
def train_cuts(self) -> CutSet:
|
||||
logging.info("About to get train cuts")
|
||||
cuts_train = load_manifest(self.args.feature_dir / "cuts_TRAIN.json.gz")
|
||||
|
||||
return cuts_train
|
||||
|
||||
@lru_cache()
|
||||
def valid_cuts(self) -> CutSet:
|
||||
logging.info("About to get dev cuts")
|
||||
cuts_valid = load_manifest(self.args.feature_dir / "cuts_DEV.json.gz")
|
||||
|
||||
return cuts_valid
|
||||
|
||||
@lru_cache()
|
||||
def test_cuts(self) -> CutSet:
|
||||
logging.debug("About to get test cuts")
|
||||
cuts_test = load_manifest(self.args.feature_dir / "cuts_TEST.json.gz")
|
||||
|
||||
return cuts_test
|
||||
1
egs/timit/ASR/tdnn_ligru_ctc/asr_datamodule.py
Symbolic link
1
egs/timit/ASR/tdnn_ligru_ctc/asr_datamodule.py
Symbolic link
@ -0,0 +1 @@
|
||||
../tdnn_lstm_ctc/asr_datamodule.py
|
||||
@ -1,5 +1,5 @@
|
||||
# Copyright 2021 Piotr Żelasko
|
||||
# 2021 Xiaomi Corp. (authors: Mingshuang Luo)
|
||||
# 2022 Xiaomi Corporation (Author: Mingshuang Luo)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
@ -17,6 +17,7 @@
|
||||
|
||||
|
||||
import argparse
|
||||
import inspect
|
||||
import logging
|
||||
from functools import lru_cache
|
||||
from pathlib import Path
|
||||
@ -171,9 +172,19 @@ class TimitAsrDataModule(DataModule):
|
||||
)
|
||||
] + transforms
|
||||
|
||||
# Set the value of num_frame_masks according to Lhotse's version.
|
||||
# In different Lhotse's versions, the default of num_frame_masks is
|
||||
# different.
|
||||
num_frame_masks = 10
|
||||
num_frame_masks_parameter = inspect.signature(
|
||||
SpecAugment.__init__
|
||||
).parameters["num_frame_masks"]
|
||||
if num_frame_masks_parameter.default == 1:
|
||||
num_frame_masks = 2
|
||||
logging.info(f"Num frame mask: {num_frame_masks}")
|
||||
input_transforms = [
|
||||
SpecAugment(
|
||||
num_frame_masks=2,
|
||||
num_frame_masks=num_frame_masks,
|
||||
features_mask_size=27,
|
||||
num_feature_masks=2,
|
||||
frames_mask_size=100,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user