Datasets:
multilingual_librispeech

	# coding=utf-8
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	# you may not use this file except in compliance with the License.
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	# http://www.apache.org/licenses/LICENSE-2.0
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	# 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