Datasets:
Sub-tasks:
speaker-identification
Multilinguality:
multilingual
Size Categories:
100K<n<1M
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
File size: 6,603 Bytes
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# coding=utf-8
# Copyright 2021 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 glob
import os
import warnings
import datasets
from datasets.tasks import AutomaticSpeechRecognition
_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 = """\
Multilingual LibriSpeech (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 = "https://dl.fbaipublicfiles.com/mls/mls_{}.tar.gz"
class MultilingualLibrispeechConfig(datasets.BuilderConfig):
"""BuilderConfig for MultilingualLibrispeech."""
def __init__(self, name, **kwargs):
"""
Args:
name: `string`, name of dataset config
**kwargs: keyword arguments forwarded to super.
"""
super(MultilingualLibrispeechConfig, self).__init__(
version=datasets.Version("2.1.0", ""), name=name, data_dir=_DL_URL_FORMAT.format(name), **kwargs
)
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):
warnings.warn(
"""
This version of the Multilingual Librispeech dataset doesn't support streaming and is deprecated.
You can download the latest one with
>>> load_dataset(\"facebook/multilingual_librispeech\", \"polish\")
"""
)
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=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download_and_extract(self.config.data_dir)
data_path = os.path.join(archive_path, "mls_" + self.config.name)
train_splits = [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"data_dir": os.path.join(data_path, "train")}
),
datasets.SplitGenerator(
name="train.9h",
gen_kwargs={"data_dir": os.path.join(data_path, "train"), "sub_folder": "limited_supervision/9hr"},
),
datasets.SplitGenerator(
name="train.1h",
gen_kwargs={"data_dir": os.path.join(data_path, "train"), "sub_folder": "limited_supervision/1hr"},
),
]
return train_splits + [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"data_dir": os.path.join(data_path, "dev")}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"data_dir": os.path.join(data_path, "test")}
),
]
def _generate_examples(self, data_dir, sub_folder=""):
"""Generate examples from a Multilingual LibriSpeech data dir."""
transcript_path = os.path.join(data_dir, "transcripts.txt")
key = 0
all_ids = None
if sub_folder != "":
sub_path = os.path.join(data_dir, sub_folder)
all_ids_paths = glob.glob(sub_path + "/*/*.txt") + glob.glob(sub_path + "/*.txt")
all_ids = []
for path in all_ids_paths:
with open(path, "r", encoding="utf-8") as f:
all_ids += [line.strip() for line in f.readlines()]
all_ids = set(all_ids)
with open(transcript_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
id_, transcript = line.split("\t")
if all_ids is not None and id_ not in all_ids:
# this only holds true for train.9h and train.1h
continue
audio_file = f"{id_}.flac"
speaker_id, chapter_id = [int(el) for el in id_.split("_")[:2]]
yield key, {
"id": id_,
"speaker_id": speaker_id,
"chapter_id": chapter_id,
"file": os.path.join(data_dir, "audio", str(speaker_id), str(chapter_id), audio_file),
"audio": os.path.join(data_dir, "audio", str(speaker_id), str(chapter_id), audio_file),
"text": transcript,
}
key += 1
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