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cleaned-up version of recipe
This commit is contained in:
Kinan Martin
parent
a4be3cb3db
commit
cf8e9a8a1c
@ -21,366 +21,230 @@ 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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from typing import Any, Dict, List, Optional
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from typing import Any, Dict, List, Optional, Union
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from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
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from lhotse import CutSet, Fbank, FbankConfig
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from lhotse.dataset import (
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CutConcatenate,
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CutMix,
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DynamicBucketingSampler,
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K2SpeechRecognitionDataset,
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PrecomputedFeatures,
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SimpleCutSampler,
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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 lhotse.utils import is_module_available
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from torch.utils.data import DataLoader
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from icefall.utils import str2bool
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class MLSEnglishHFAsrDataModule:
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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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DataModule for MLS English ASR experiments using HuggingFace dataset.
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Handles dataset loading and provides train/valid/test dataloaders with
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on-the-fly feature extraction.
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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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self.dataset = None
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# self._validate_args()
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# def _validate_args(self) -> None:
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# """Validate configuration arguments."""
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# if self.args.on_the_fly_feats is False:
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# raise ValueError("This recipe requires on-the-fly feature extraction")
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@classmethod
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def add_arguments(cls, parser: argparse.ArgumentParser):
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def add_arguments(cls, parser: argparse.ArgumentParser) -> 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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description="Options for data loading and processing",
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)
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# Dataset configuration
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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/manifests"),
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help="Path to directory with train/dev/test cuts.",
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"--dataset-path",
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type=str,
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default="parler-tts/mls_eng",
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help="Path to HuggingFace MLS English dataset (name or local path)",
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)
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# Sampling and batching
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group.add_argument(
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"--max-duration",
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type=int,
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type=float,
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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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help="Maximum batch duration in seconds",
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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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help="Whether to use bucketing sampler",
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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 DynamicBucketingSampler"
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"(you might want to increase it for larger datasets).",
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help="Number of buckets for DynamicBucketingSampler",
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)
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# Data augmentation
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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=True, # must be true without lhotse feature prep
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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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"--enable-spec-aug",
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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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help="Whether to enable SpecAugment",
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)
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group.add_argument(
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"--drop-last",
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type=str2bool,
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default=True,
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help="Whether to drop last batch. Used by sampler.",
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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="Time warp factor for SpecAugment",
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)
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# Dataloader configuration
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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="Number of workers for data loading",
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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=False,
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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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help="Whether to return cuts in batch",
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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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"--drop-last",
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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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help="Whether to drop last incomplete batch",
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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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return parser
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group.add_argument(
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"--enable-musan",
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type=str2bool,
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default=False,
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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 load_dataset(self, dataset_path: Optional[str] = None) -> None:
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"""Load the HuggingFace dataset."""
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dataset_path = dataset_path or self.args.dataset_path
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logging.info(f"Loading MLS English dataset from: {dataset_path}")
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def load_hf_dataset(
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self, mls_eng_hf_dataset_path: str = "parler-tts/mls_eng",
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):
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"""
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Method to load HF dataset with datasets.load_dataset
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and save it in this DataModule.
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Intended usage inside a training script:
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```
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mls_english_corpus = MLSEnglishHFAsrDataModule(args)
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mls_english_corpus.load_hf_dataset("parler-tts/mls_eng")
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train_cuts = mls_english_corpus.train_cuts()
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train_dataloader = mls_english_corpus.train_dataloaders(
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train_cuts, sampler_state_dict=sampler_state_dict
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)
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...
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for epoch in range(...):
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train_one_epoch(
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...,
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train_dl=train_dl,
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...,
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)
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```
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"""
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if not is_module_available("datasets"):
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try:
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from datasets import load_dataset
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self.dataset = load_dataset(dataset_path)
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logging.info("Dataset loaded successfully")
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except ImportError:
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raise ImportError(
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"To process the MLS English HF corpus, please install optional dependency: pip install datasets"
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"Please install datasets package: pip install datasets"
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)
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from datasets import load_dataset
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self.dataset = load_dataset(mls_eng_hf_dataset_path) #, split="test")
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def train_dataloaders(
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self, cuts_train: CutSet, sampler_state_dict: Optional[Dict[str, Any]] = None
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) -> DataLoader:
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"""
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Args:
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cuts_train:
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CutSet for training.
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sampler_state_dict:
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The state dict for the training sampler.
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"""
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except Exception as e:
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raise RuntimeError(f"Failed to load dataset: {e}")
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def _create_dataset(self, cuts: CutSet, is_train: bool = False) -> K2SpeechRecognitionDataset:
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"""Create appropriate dataset with transforms."""
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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(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
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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=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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)
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)
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else:
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logging.info("Disable SpecAugment")
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if is_train and self.args.enable_spec_aug:
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input_transforms.append(self._create_spec_augment())
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logging.info("About to create train dataset")
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train = K2SpeechRecognitionDataset(
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return K2SpeechRecognitionDataset(
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cut_transforms=transforms,
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input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
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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(Fbank(FbankConfig(num_mel_bins=80))),
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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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def _create_spec_augment(self) -> SpecAugment:
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"""Create SpecAugment transform based on config."""
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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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return SpecAugment(
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time_warp_factor=self.args.spec_aug_time_warp_factor,
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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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)
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def _create_sampler(self, cuts: CutSet, shuffle: bool) -> Union[DynamicBucketingSampler, SimpleCutSampler]:
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"""Create appropriate sampler based on config."""
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if self.args.bucketing_sampler:
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logging.info("Using DynamicBucketingSampler.")
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train_sampler = DynamicBucketingSampler(
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cuts_train,
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return DynamicBucketingSampler(
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cuts,
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max_duration=self.args.max_duration,
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shuffle=self.args.shuffle,
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shuffle=shuffle,
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num_buckets=self.args.num_buckets,
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drop_last=self.args.drop_last,
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)
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else:
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logging.info("Using SimpleCutSampler.")
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train_sampler = SimpleCutSampler(
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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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return SimpleCutSampler(
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cuts,
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max_duration=self.args.max_duration,
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shuffle=shuffle,
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)
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if sampler_state_dict is not None:
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logging.info("Loading sampler state dict")
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train_sampler.load_state_dict(sampler_state_dict)
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def train_dataloader(self, sampler_state_dict: Optional[Dict[str, Any]] = None) -> DataLoader:
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"""Create train dataloader."""
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cuts = self.train_cuts()
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dataset = self._create_dataset(cuts, is_train=True)
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sampler = self._create_sampler(cuts, shuffle=True)
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train_dl = DataLoader(
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train,
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sampler=train_sampler,
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if sampler_state_dict:
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sampler.load_state_dict(sampler_state_dict)
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return DataLoader(
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dataset,
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sampler=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(Fbank(FbankConfig(num_mel_bins=80))),
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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 = DynamicBucketingSampler(
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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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def valid_dataloader(self) -> DataLoader:
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"""Create validation dataloader."""
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cuts = self.valid_cuts()
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return DataLoader(
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self._create_dataset(cuts),
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sampler=self._create_sampler(cuts, shuffle=False),
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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.info("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 = DynamicBucketingSampler(
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cuts,
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max_duration=self.args.max_duration,
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shuffle=False,
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)
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test_dl = DataLoader(
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test,
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def test_dataloader(self) -> DataLoader:
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"""Create test dataloader."""
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cuts = self.test_cuts()
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return DataLoader(
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self._create_dataset(cuts),
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sampler=self._create_sampler(cuts, shuffle=False),
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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_cuts(self) -> CutSet:
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logging.info("About to get train cuts")
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cutset = CutSet.from_huggingface_dataset(self.dataset["train"], text_key="transcript")
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return cutset
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return CutSet.from_huggingface_dataset(
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self.dataset["train"],
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text_key="transcript"
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)
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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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cutset = CutSet.from_huggingface_dataset(self.dataset["dev"], text_key="transcript")
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return cutset
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return CutSet.from_huggingface_dataset(
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||||
self.dataset["dev"],
|
||||
text_key="transcript"
|
||||
)
|
||||
|
||||
@lru_cache()
|
||||
def test_cuts(self) -> List[CutSet]:
|
||||
logging.info("About to get test cuts")
|
||||
cutset = CutSet.from_huggingface_dataset(self.dataset["test"], text_key="transcript")
|
||||
return cutset
|
||||
def test_cuts(self) -> CutSet:
|
||||
return CutSet.from_huggingface_dataset(
|
||||
self.dataset["test"],
|
||||
text_key="transcript"
|
||||
)
|
||||
@ -19,59 +19,71 @@
|
||||
import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from lhotse import CutSet
|
||||
from asr_datamodule import MLSEnglishHFAsrDataModule
|
||||
|
||||
from tqdm import tqdm
|
||||
|
||||
def get_args():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Generate transcripts for BPE training from MLS English dataset",
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
)
|
||||
|
||||
# parser.add_argument(
|
||||
# "train_cut", metavar="train-cut", type=Path, help="Path to the train cut"
|
||||
# )
|
||||
|
||||
parser.add_argument(
|
||||
"--dataset-path",
|
||||
type=str,
|
||||
default="parler-tts/mls_eng",
|
||||
help="Path to HuggingFace MLS English dataset (name or local path)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=Path,
|
||||
default=Path("data/lang"),
|
||||
help=(
|
||||
"Name of lang dir. "
|
||||
"If not set, this will default to data/lang"
|
||||
),
|
||||
help="Directory to store output transcripts",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--split",
|
||||
type=str,
|
||||
default="train",
|
||||
help="Dataset split to use for generating transcripts (train/dev/test)",
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
def generate_transcript_from_cuts(cuts: CutSet, output_file: Path) -> None:
|
||||
"""Generate transcript text file from Lhotse CutSet."""
|
||||
with open(output_file, "w") as f:
|
||||
for cut in tqdm(cuts, desc="Processing cuts"):
|
||||
for sup in cut.supervisions:
|
||||
f.write(f"{sup.text}\n")
|
||||
|
||||
def main():
|
||||
args = get_args()
|
||||
logging.basicConfig(
|
||||
format=("%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"),
|
||||
format="%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s",
|
||||
level=logging.INFO,
|
||||
)
|
||||
|
||||
args.lang_dir.mkdir(parents=True, exist_ok=True)
|
||||
output_file = args.lang_dir / "transcript.txt"
|
||||
|
||||
mls_english_corpus = MLSEnglishHFAsrDataModule(args)
|
||||
mls_english_corpus.load_hf_dataset("/root/datasets/parler-tts--mls_eng")
|
||||
|
||||
train_cuts = mls_english_corpus.train_cuts()
|
||||
|
||||
logging.info(f"Creating transcript from MLS English train cut.")
|
||||
|
||||
def generate_text(train_cuts):
|
||||
for cut in tqdm(train_cuts):
|
||||
for sup in cut.supervisions:
|
||||
yield sup.text + "\n"
|
||||
|
||||
with open(args.lang_dir / "transcript.txt", "w") as file:
|
||||
file.writelines(generate_text(train_cuts))
|
||||
|
||||
logging.info("Done.")
|
||||
logging.info(f"Loading {args.split} split from dataset: {args.dataset_path}")
|
||||
try:
|
||||
cuts = CutSet.from_huggingface_dataset(
|
||||
args.dataset_path,
|
||||
split=args.split,
|
||||
text_key="transcript"
|
||||
)
|
||||
except Exception as e:
|
||||
logging.error(f"Failed to load dataset: {e}")
|
||||
raise
|
||||
|
||||
logging.info(f"Generating transcript to {output_file}")
|
||||
generate_transcript_from_cuts(cuts, output_file)
|
||||
logging.info("Transcript generation completed")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
main()
|
||||
@ -1,5 +1,9 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# Prepare script for MLS English ASR recipe in icefall
|
||||
# This recipe uses on-the-fly feature extraction, so it skips manifest
|
||||
# and feature generation steps used in other recipes.
|
||||
|
||||
# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
|
||||
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
|
||||
|
||||
@ -9,118 +13,50 @@ nj=15
|
||||
stage=-1
|
||||
stop_stage=100
|
||||
|
||||
# vocab_sizes=(500 1000 2000)
|
||||
vocab_sizes=(2000)
|
||||
|
||||
|
||||
# We assume dl_dir (download dir) contains the following
|
||||
# directories and files. If not, they will be downloaded
|
||||
# by this script automatically.
|
||||
#
|
||||
# - $dl_dir/ReazonSpeech
|
||||
# You can find FLAC files in this directory.
|
||||
# You can download them from https://huggingface.co/datasets/reazon-research/reazonspeech
|
||||
#
|
||||
# - $dl_dir/dataset.json
|
||||
# The metadata of the ReazonSpeech dataset.
|
||||
# Configuration for BPE tokenizer
|
||||
vocab_sizes=(2000) # You can add more sizes like (500 1000 2000) for comparison
|
||||
|
||||
# Directory where dataset will be downloaded
|
||||
dl_dir=$PWD/download
|
||||
|
||||
. shared/parse_options.sh || exit 1
|
||||
|
||||
# All files generated by this script are saved in "data".
|
||||
# You can safely remove "data" and rerun this script to regenerate it.
|
||||
mkdir -p data
|
||||
|
||||
log() {
|
||||
# This function is from espnet
|
||||
local fname=${BASH_SOURCE[1]##*/}
|
||||
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
|
||||
}
|
||||
|
||||
log "Running prepare.sh"
|
||||
|
||||
log "dl_dir: $dl_dir"
|
||||
log "Starting MLS English data preparation"
|
||||
|
||||
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
|
||||
log "Stage 0: Download data"
|
||||
|
||||
# If you have pre-downloaded it to /path/to/mls_eng,
|
||||
# you can create a symlink
|
||||
#
|
||||
# ln -sfv /path/to/mls_eng $dl_dir/mls_eng
|
||||
#
|
||||
log "Stage 0: Download MLS English dataset"
|
||||
if [ ! -d $dl_dir/mls_english ]; then
|
||||
git clone https://huggingface.co/datasets/parler-tts/mls_eng $dl_dir/mls_eng
|
||||
if ! git clone https://huggingface.co/datasets/parler-tts/mls_eng $dl_dir/mls_english; then
|
||||
log "Failed to download MLS English dataset"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
## Not necessary to create manifest or pre-compute fbank for on-the-fly feature computation ##
|
||||
|
||||
# if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
|
||||
# log "Stage 1: Prepare MLS English manifest"
|
||||
# # We assume that you have downloaded the ReazonSpeech corpus
|
||||
# # to $dl_dir/ReazonSpeech
|
||||
# mkdir -p data/manifests
|
||||
# if [ ! -e data/manifests/.reazonspeech.done ]; then
|
||||
# lhotse prepare reazonspeech -j $nj $dl_dir/ReazonSpeech data/manifests
|
||||
# touch data/manifests/.reazonspeech.done
|
||||
# fi
|
||||
# fi
|
||||
|
||||
# if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
|
||||
# log "Stage 2: Compute ReazonSpeech fbank"
|
||||
# if [ ! -e data/manifests/.reazonspeech-validated.done ]; then
|
||||
# python local/compute_fbank_reazonspeech.py --manifest-dir data/manifests
|
||||
# python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_train.jsonl.gz
|
||||
# python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_dev.jsonl.gz
|
||||
# python local/validate_manifest.py --manifest data/manifests/reazonspeech_cuts_test.jsonl.gz
|
||||
# touch data/manifests/.reazonspeech-validated.done
|
||||
# fi
|
||||
# fi
|
||||
|
||||
###############################################################################################
|
||||
|
||||
# if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
|
||||
# log "Stage 3: Prepare ReazonSpeech lang_char"
|
||||
# python local/prepare_lang_char.py data/manifests/reazonspeech_cuts_train.jsonl.gz
|
||||
# fi
|
||||
|
||||
# if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
|
||||
# log "Stage 4: Show manifest statistics"
|
||||
# python local/display_manifest_statistics.py --manifest-dir data/manifests > data/manifests/manifest_statistics.txt
|
||||
# cat data/manifests/manifest_statistics.txt
|
||||
# fi
|
||||
|
||||
mkdir -p data/lang
|
||||
|
||||
lang_dir=data/lang
|
||||
|
||||
log "lang_dir: $lang_dir"
|
||||
|
||||
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
|
||||
log "Stage 1: Prepare BPE based lang"
|
||||
|
||||
log "Stage 1: Prepare BPE tokenizer"
|
||||
|
||||
if [ ! -f $lang_dir/transcript.txt ]; then
|
||||
log "Generate transcript for BPE training"
|
||||
|
||||
log "Generating transcripts for BPE training"
|
||||
./local/utils/generate_transcript.py --lang-dir $lang_dir
|
||||
# files=$(
|
||||
# find "$dl_dir/LibriSpeech/train-clean-100" -name "*.trans.txt"
|
||||
# find "$dl_dir/LibriSpeech/train-clean-360" -name "*.trans.txt"
|
||||
# find "$dl_dir/LibriSpeech/train-other-500" -name "*.trans.txt"
|
||||
# )
|
||||
# for f in ${files[@]}; do
|
||||
# cat $f | cut -d " " -f 2-
|
||||
# done > $lang_dir/transcript_words.txt
|
||||
fi
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
log "Train BPE model with vocab_size: $vocab_size"
|
||||
log "Training BPE model with vocab_size=${vocab_size}"
|
||||
bpe_dir=data/lang/bpe_${vocab_size}
|
||||
mkdir -p $bpe_dir
|
||||
|
||||
|
||||
if [ ! -f $bpe_dir/bpe.model ]; then
|
||||
./local/train_bpe_model.py \
|
||||
--lang-dir $bpe_dir \
|
||||
@ -128,4 +64,6 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
|
||||
--transcript $lang_dir/transcript.txt
|
||||
fi
|
||||
done
|
||||
fi
|
||||
fi
|
||||
|
||||
log "MLS English data preparation completed successfully"
|
||||
@ -1 +1 @@
|
||||
local/utils/asr_datamodule.py
|
||||
../local/utils/asr_datamodule.py
|
||||
@ -1043,13 +1043,13 @@ def main():
|
||||
# we need cut ids to display recognition results.
|
||||
args.return_cuts = True
|
||||
mls_english_corpus = MLSEnglishHFAsrDataModule(args)
|
||||
mls_english_corpus.load_hf_dataset("/root/datasets/parler-tts--mls_eng")
|
||||
mls_english_corpus.load_dataset(args.dataset_path)
|
||||
|
||||
# dev_cuts = mls_english_corpus.dev_cuts()
|
||||
test_cuts = mls_english_corpus.test_cuts()
|
||||
# # dev_cuts = mls_english_corpus.dev_cuts()
|
||||
# test_cuts = mls_english_corpus.test_cuts()
|
||||
|
||||
# dev_dl = mls_english_corpus.test_dataloaders(dev_cuts)
|
||||
test_dl = mls_english_corpus.test_dataloaders(test_cuts)
|
||||
# dev_dl = mls_english_corpus.test_dataloader()
|
||||
test_dl = mls_english_corpus.test_dataloader()
|
||||
|
||||
test_sets = ["test"]
|
||||
test_dls = [test_dl]
|
||||
|
||||
@ -1215,9 +1215,9 @@ def run(rank, world_size, args):
|
||||
return True
|
||||
|
||||
mls_english_corpus = MLSEnglishHFAsrDataModule(args)
|
||||
mls_english_corpus.load_hf_dataset("/root/datasets/parler-tts--mls_eng")
|
||||
mls_english_corpus.load_dataset(args.dataset_path)
|
||||
|
||||
train_cuts = mls_english_corpus.train_cuts()
|
||||
# train_cuts = mls_english_corpus.train_cuts()
|
||||
|
||||
# train_cuts = train_cuts.filter(remove_short_and_long_utt)
|
||||
|
||||
@ -1228,12 +1228,17 @@ def run(rank, world_size, args):
|
||||
else:
|
||||
sampler_state_dict = None
|
||||
|
||||
train_dl = mls_english_corpus.train_dataloaders(
|
||||
train_cuts, sampler_state_dict=sampler_state_dict
|
||||
# train_dl = mls_english_corpus.train_dataloaders(
|
||||
# train_cuts, sampler_state_dict=sampler_state_dict
|
||||
# )
|
||||
train_dl = mls_english_corpus.train_dataloader(
|
||||
sampler_state_dict=sampler_state_dict
|
||||
)
|
||||
|
||||
valid_cuts = mls_english_corpus.valid_cuts()
|
||||
valid_dl = mls_english_corpus.valid_dataloaders(valid_cuts)
|
||||
# valid_cuts = mls_english_corpus.valid_cuts()
|
||||
# valid_dl = mls_english_corpus.valid_dataloader(valid_cuts)
|
||||
valid_dl = mls_english_corpus.valid_dataloader()
|
||||
|
||||
|
||||
if not params.print_diagnostics:
|
||||
scan_pessimistic_batches_for_oom(
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user