Datasets:
Tasks:
Automatic Speech Recognition
Multilinguality:
multilingual
Size Categories:
100K<n<1M
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 10,089 Bytes
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# coding=utf-8
# Copyright 2022 The TensorFlow Datasets Authors and the HuggingFace Datasets 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.
# Lint as: python3
"""Multilingual Librispeech automatic speech recognition dataset."""
import os
import datasets
_CITATION = """\
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
"""
_DESCRIPTION = """\
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94)
to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research.
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages:
English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
"""
_URL = "http://www.openslr.org/94"
_DL_URL_FORMAT = "data/mls_{name}"
class MultilingualLibrispeechConfig(datasets.BuilderConfig):
"""BuilderConfig for MultilingualLibrispeech."""
def __init__(self, name, **kwargs):
"""
Args:
name: `string`, name of dataset config (=language)
**kwargs: keyword arguments forwarded to super.
"""
super(MultilingualLibrispeechConfig, self).__init__(
version=datasets.Version("2.1.0", ""), name=name, **kwargs
)
# relative path to full data inside a repo (for example `data/mls_german`)
self.data_root_url = _DL_URL_FORMAT.format(name=name)
class MultilingualLibrispeech(datasets.GeneratorBasedBuilder):
"""Multilingual Librispeech dataset."""
BUILDER_CONFIGS = [
MultilingualLibrispeechConfig(name="german", description="German LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="dutch", description="Dutch LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="french", description="French LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="spanish", description="Spanish LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="italian", description="Italian LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="portuguese", description="Portuguese LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="polish", description="Polish LibriSpeech dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.features.Audio(sampling_rate=16_000),
"text": datasets.Value("string"),
"speaker_id": datasets.Value("int64"),
"chapter_id": datasets.Value("int64"),
"id": datasets.Value("string"),
}
),
supervised_keys=("file", "text"),
homepage=_URL,
citation=_CITATION,
task_templates=None,
)
def _split_generators(self, dl_manager):
transcripts = dl_manager.download({
"train": self.config.data_root_url + "/train/transcripts.txt",
"dev": self.config.data_root_url + "/dev/transcripts.txt",
"test": self.config.data_root_url + "/test/transcripts.txt",
})
# Download handles.txt files containing ids for limited supervision train sets
limited_supervision_9h = dl_manager.download(
[self.config.data_root_url + "/train/limited_supervision/9hr/handles.txt"],
)
# in our case of 1 hour limited supervision ("train.1h") there are always 6 subfolders like:
# "limited_supervision/1h/0/handles.txt", "limited_supervision/1h/1/handles.txt", ...
limited_supervision_1h = dl_manager.download([
self.config.data_root_url + f"/train/limited_supervision/1hr/{i}/handles.txt" for i in range(6)
])
# each split contains many .tar.gz archives with its audio files
# audio_filenames.txt contains the names of these archives
audio_filenames_paths = dl_manager.download({
"train": self.config.data_root_url + "/train/audio_filenames.txt",
"dev": self.config.data_root_url + "/dev/audio_filenames.txt",
"test": self.config.data_root_url + "/test/audio_filenames.txt",
})
audio_archives = {}
for split in audio_filenames_paths:
with open(audio_filenames_paths[split], encoding="utf-8") as f:
audio_filenames = [line.strip() for line in f.readlines()]
audio_archives[split] = dl_manager.download([
self.config.data_root_url + "/" + split + "/audio/" + filename
for filename in audio_filenames
])
# (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
local_extracted_archives = dl_manager.extract(audio_archives) if not dl_manager.is_streaming else {}
train_splits = [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"transcript_path": transcripts["train"],
"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
"local_extracted_archive": local_extracted_archives.get("train"),
}
),
datasets.SplitGenerator(
name="train.9h",
gen_kwargs={
"transcript_path": transcripts["train"],
"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
"local_extracted_archive": local_extracted_archives.get("train"),
"limited_ids_paths": limited_supervision_9h,
},
),
datasets.SplitGenerator(
name="train.1h",
gen_kwargs={
"transcript_path": transcripts["train"],
"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
"local_extracted_archive": local_extracted_archives.get("train"),
"limited_ids_paths": limited_supervision_1h,
},
),
]
return train_splits + [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={
"transcript_path": transcripts["dev"],
"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["dev"]],
"local_extracted_archive": local_extracted_archives.get("dev"),
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={
"transcript_path": transcripts["test"],
"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["test"]],
"local_extracted_archive": local_extracted_archives.get("test"),
}
),
]
def _generate_examples(self, transcript_path, audio_archives, local_extracted_archive, limited_ids_paths=None):
"""Generate examples from a Multilingual LibriSpeech data dir."""
transcripts = dict()
with open(transcript_path, "r", encoding="utf-8") as file:
for line in file:
audio_id, transcript = line.strip().split("\t")
transcripts[audio_id] = transcript
limited_ids, limited_ids_archives_names = [], []
if limited_ids_paths:
for path in limited_ids_paths:
with open(path, "r", encoding="utf-8") as file:
limited_ids.extend([line.strip() for line in file.readlines()])
limited_ids = set(limited_ids)
for archive_idx, audio_archive in enumerate(audio_archives):
# TODO: check that archive doesn't contain needed ids
# if limited_ids and audio_archive not in limited_ids_archives_names:
# continue
for audio_filename, file in audio_archive:
speaker_id, chapter_id = audio_filename.split("_")[:2]
speaker_id, chapter_id = int(speaker_id), int(chapter_id)
audio_id = audio_filename.split(".flac")[0]
audio_transcript = transcripts[audio_id]
if limited_ids and audio_id not in limited_ids:
# this only can be true in limited supervision sets ("train.9h" and "train.1h")
continue
local_audio_file_path = os.path.join(
local_extracted_archive[archive_idx], audio_filename
) if local_extracted_archive else None
yield audio_filename, {
"file": local_audio_file_path,
"audio": {
"path": local_audio_file_path if local_audio_file_path else audio_filename,
"bytes": file.read()
},
"text": audio_transcript,
"speaker_id": speaker_id,
"chapter_id": chapter_id,
"id": audio_id
}
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