File size: 10,103 Bytes
7155eaf 8db4a65 8f6c4ad 7155eaf 2f1bff9 7155eaf f3bf4e9 7155eaf 2f1bff9 7155eaf 2f1bff9 7155eaf 2f1bff9 7155eaf 2f1bff9 92be018 7155eaf 2f1bff9 92be018 7155eaf 2f1bff9 7155eaf 2f1bff9 7155eaf 2f1bff9 7155eaf 7374369 7155eaf 8db4a65 7155eaf 2f1bff9 7155eaf 2f1bff9 7155eaf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
# 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": tuple(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": tuple(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
}
|