Datasets:

facebook
/

multilingual_librispeech

	# 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."""


	from functools import partial
	import os

	import datasets
	from datasets.streaming import xopen


	_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_dir = _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):
	download_kwargs = {
	"dl_manager": dl_manager,
	"root_dir": self.config.data_root_dir
	}
	download_transcript = partial(
	download_extract_transcript, **download_kwargs
	)
	download_audio_non_streaming = partial(
	download_extract_audio_archives, **download_kwargs
	)
	download_audio_streaming = partial(
	download_audio_archives, **download_kwargs
	)
	download_limited_ids = partial(
	download_extract_limited_ids, **download_kwargs
	)

	train_kwargs = {
	"transcript_path": download_transcript(split="train"),
	"audio_archives": download_audio_streaming(split="train"),
	"local_audio_archives_paths": download_audio_non_streaming(split="train")
	if not dl_manager.is_streaming else None
	}

	train_splits = [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN, gen_kwargs=train_kwargs
	),
	datasets.SplitGenerator(
	name="train.9h",
	gen_kwargs={
	**train_kwargs,
	"limited_ids_paths": download_limited_ids(sub_folder="limited_supervision/9hr"),
	},
	),
	datasets.SplitGenerator(
	name="train.1h",
	gen_kwargs={
	**train_kwargs,
	"limited_ids_paths": download_limited_ids(sub_folder="limited_supervision/1hr"),
	},
	),
	]

	return train_splits + [
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION, gen_kwargs={
	"transcript_path": download_transcript(split="dev"),
	"audio_archives": download_audio_streaming(split="dev"),
	"local_audio_archives_paths": download_audio_non_streaming(split="dev")
	if not dl_manager.is_streaming else None
	}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST, gen_kwargs={
	"transcript_path": download_transcript(split="test"),
	"audio_archives": download_audio_streaming(split="test"),
	"local_audio_archives_paths": download_audio_non_streaming(split="test")
	if not dl_manager.is_streaming else None
	}
	),
	]

	def _generate_examples(self, transcript_path, audio_archives, local_audio_archives_paths, 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

	path = os.path.join(local_audio_archives_paths[archive_idx], audio_filename)\
	if local_audio_archives_paths else audio_filename
	yield audio_filename, {
	"file": path if local_audio_archives_paths else None,
	"audio": {"path": path, "bytes": file.read()},
	"text": audio_transcript,
	"speaker_id": speaker_id,
	"chapter_id": chapter_id,
	"id": audio_id
	}


	def download_extract_limited_ids(dl_manager, root_dir, sub_folder):
	"""Download handles.txt files containing ids for limited supervision train sets. """

	sub_path = os.path.join(root_dir, "train", sub_folder)

	if sub_folder.endswith("9hr"):
	limited_ids_paths = [os.path.join(sub_path, "handles.txt")]
	else: # => sub_folder.endswith("1hr")
	# in 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_ids_paths = [os.path.join(sub_path, str(i), "handles.txt") for i in range(6)]

	limited_ids_paths = dl_manager.download(limited_ids_paths)

	return limited_ids_paths


	def download_extract_transcript(dl_manager, root_dir, split):
	"""
	Download file with audio transcriptions.

	Return:
	path (str): path to locally extracted `transcripts.txt` file
	"""
	transcript_path = os.path.join(root_dir, split, "transcripts.txt")
	return dl_manager.download(transcript_path)


	def download_audio_archive_paths(dl_manager, root_dir, split):
	# each split contains many .tar.gz archives with its audio files
	# audio_filenames.txt contains the names of these archives
	split_dir = os.path.join(root_dir, split)
	audio_filenames_path = dl_manager.download(os.path.join(split_dir, "audio_filenames.txt"))

	with open(audio_filenames_path, "r", encoding="utf-8") as file:
	audio_filenames = [line.strip() for line in file.readlines()]

	return dl_manager.download([os.path.join(split_dir, "audio", filename) for filename in audio_filenames])


	# for non-streaming case
	def download_extract_audio_archives(dl_manager, root_dir, split):
	"""
	Download and extract audio archives locally.

	Return:
	archive_paths (List `str`): paths to locally extracted archives
	"""
	archive_paths = download_audio_archive_paths(dl_manager, root_dir, split)
	return [dl_manager.extract(archive_path) for archive_path in archive_paths]


	# for streaming case
	def download_audio_archives(dl_manager, root_dir, split):
	"""Prepare archives with audio files for iterating over them.

	Return:
	audio_archives (List `Generator`): list of generators to iterate over files in each audio archive.
	"""
	archive_paths = download_audio_archive_paths(dl_manager, root_dir, split)
	return [dl_manager.iter_archive(archive_path) for archive_path in archive_paths]