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."""

	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
	}