Datasets:

MLCommons
/

ml_spoken_words

	# coding=utf-8
	# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.

	"""
	Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken
	words in 50 languages collectively spoken by over 5 billion people, for academic
	research and commercial applications in keyword spotting and spoken term search,
	licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords,
	totaling 23.4 million 1-second spoken examples (over 6,000 hours).
	"""


	import csv
	import os.path
	from functools import partial

	import datasets


	_CITATION = """\
	@inproceedings{mazumder2021multilingual,
	title={Multilingual Spoken Words Corpus},
	author={Mazumder, Mark and Chitlangia, Sharad and Banbury, Colby and Kang, Yiping and Ciro, Juan Manuel and Achorn, Keith and Galvez, Daniel and Sabini, Mark and Mattson, Peter and Kanter, David and others},
	booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
	year={2021}
	}
	"""

	_DESCRIPTION = """\
	Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken
	words in 50 languages collectively spoken by over 5 billion people, for academic
	research and commercial applications in keyword spotting and spoken term search,
	licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords,
	totaling 23.4 million 1-second spoken examples (over 6,000 hours). The dataset
	has many use cases, ranging from voice-enabled consumer devices to call center
	automation. This dataset is generated by applying forced alignment on crowd-sourced sentence-level
	audio to produce per-word timing estimates for extraction.
	All alignments are included in the dataset.
	"""

	_HOMEPAGE = "https://mlcommons.org/en/multilingual-spoken-words/"

	_LICENSE = "CC-BY 4.0."

	_VERSION = datasets.Version("1.0.0")

	_BASE_URL = "data/"
	_AUDIO_URL = _BASE_URL + "{format}/{lang}/{split}/audio/{n}.tar.gz"
	_N_FILES_URL = _BASE_URL + "{format}/{lang}/{split}/n_files.txt"
	_SPLITS_URL = _BASE_URL + "splits/{lang}/splits.tar.gz"

	_GENDERS = ["MALE", "FEMALE", "OTHER", "NAN"]

	_LANGUAGES = [
	"ar",
	"as",
	"br",
	"ca",
	"cnh",
	"cs",
	"cv",
	"cy",
	"de",
	"dv",
	"el",
	"en",
	"eo",
	"es",
	"et",
	"eu",
	"fa",
	"fr",
	"fy-NL",
	"ga-IE",
	"gn",
	"ha",
	"ia",
	"id",
	"it",
	"ka",
	"ky",
	"lt",
	"lv",
	"mn",
	"mt",
	"nl",
	"or",
	"pl",
	"pt",
	"rm-sursilv",
	"rm-vallader",
	"ro",
	"ru",
	"rw",
	"sah",
	"sk",
	"sl",
	"sv-SE",
	"ta",
	"tr",
	"tt",
	"uk",
	"vi",
	"zh-CN",
	]


	class MlSpokenWordsConfig(datasets.BuilderConfig):
	"""BuilderConfig for MlSpokenWords."""

	def __init__(self, args, languages, format="wav", *kwargs):
	"""BuilderConfig for MlSpokenWords.
	Args:
	languages (:obj:`Union[List[str], str]`): language or list of languages to load
	**kwargs: keyword arguments forwarded to super.
	"""
	super().__init__(
	*args,
	name="+".join(languages) + "_" + format if isinstance(languages, list) else languages + "_" + format,
	**kwargs,
	)
	self.languages = languages if isinstance(languages, list) else [languages]
	self.format = format


	class MlSpokenWords(datasets.GeneratorBasedBuilder):
	"""
	Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken
	words in 50 languages collectively spoken by over 5 billion people, for academic
	research and commercial applications in keyword spotting and spoken term search,
	licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords,
	totaling 23.4 million 1-second spoken examples (over 6,000 hours).
	"""

	VERSION = _VERSION
	BUILDER_CONFIGS = [
	MlSpokenWordsConfig(languages=[lang], format="wav", version=_VERSION) for lang in _LANGUAGES
	] + [
	MlSpokenWordsConfig(languages=[lang], format="opus", version=_VERSION) for lang in _LANGUAGES
	]
	BUILDER_CONFIG_CLASS = MlSpokenWordsConfig

	def _info(self):
	features = datasets.Features(
	{
	"file": datasets.Value("string"),
	"is_valid": datasets.Value("bool"),
	"language": datasets.ClassLabel(names=self.config.languages),
	"speaker_id": datasets.Value("string"),
	"gender": datasets.ClassLabel(names=_GENDERS),
	"keyword": datasets.Value("string"), # 340k unique keywords
	"audio": datasets.Audio(sampling_rate=48_000) if self.config.format == "opus" \
	else datasets.Audio(sampling_rate=16_000),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	splits_archive_path = [dl_manager.download(_SPLITS_URL.format(lang=lang)) for lang in self.config.languages]
	download_audio = partial(_download_audio_archives, dl_manager=dl_manager, format=self.config.format)
	download_extract_audio = partial(_download_extract_audio_archives, dl_manager=dl_manager, format=self.config.format)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages],
	"local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in
	self.config.languages] if not dl_manager.is_streaming else None,
	"splits_archives": [dl_manager.iter_archive(path) for path in splits_archive_path],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"audio_archives": [download_audio(split="dev", lang=lang) for lang in self.config.languages],
	"local_audio_archives_paths": [download_extract_audio(split="dev", lang=lang) for lang in
	self.config.languages] if not dl_manager.is_streaming else None,
	"splits_archives": [dl_manager.iter_archive(path) for path in splits_archive_path],
	"split": "dev",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"audio_archives": [download_audio(split="test", lang=lang) for lang in self.config.languages],
	"local_audio_archives_paths": [download_extract_audio(split="test", lang=lang) for lang in
	self.config.languages] if not dl_manager.is_streaming else None,
	"splits_archives": [dl_manager.iter_archive(path) for path in splits_archive_path],
	"split": "test",
	},
	),
	]

	def _generate_examples(self, audio_archives, local_audio_archives_paths, splits_archives, split):
	metadata = dict()
	for lang_idx, lang in enumerate(self.config.languages):
	for split_filename, split_file in splits_archives[lang_idx]:
	if split_filename.split(".csv")[0] == split:
	csv_reader = csv.reader([line.decode("utf-8") for line in split_file.readlines()], delimiter=",")
	for i, (link, word, is_valid, speaker, gender) in enumerate(csv_reader):
	if i == 0:
	continue
	audio_id, audio_ext = os.path.splitext("_".join(link.split("/")))
	metadata[audio_id] = {
	"keyword": word,
	"is_valid": is_valid,
	"speaker_id": speaker,
	"gender": gender if gender and gender != "NA" else "NAN", # some values are "NA"
	}

	for archive_idx, audio_archive in enumerate(audio_archives[lang_idx]):
	for audio_filename, audio_file in audio_archive:
	audio_id, audio_ext = os.path.splitext(audio_filename)
	path = os.path.join(local_audio_archives_paths[lang_idx][archive_idx], audio_filename) if local_audio_archives_paths else audio_filename
	yield audio_filename, {
	"file": path if local_audio_archives_paths else None,
	"language": lang,
	"audio": {"path": path, "bytes": audio_file.read()},
	**metadata[audio_id],
	}


	def _download_audio_archives_paths(dl_manager, lang, format, split):
	"""
	All audio files are stored in several .tar.gz archives with names like 0.tar.gz, 1.tar.gz, ...
	Number of archives stored in a separate .txt file (n_files.txt)

	Prepare all the audio archives for iterating over them and their audio files.
	"""

	n_files_url = _N_FILES_URL.format(lang=lang, format=format, split=split)
	n_files_path = dl_manager.download(n_files_url)

	with open(n_files_path, "r", encoding="utf-8") as file:
	n_files = int(file.read().strip()) # the file contains a number of archives

	archive_urls = [_AUDIO_URL.format(lang=lang, format=format, split=split, n=i) for i in range(n_files)]

	return dl_manager.download(archive_urls)


	# for default, non-streaming case
	def _download_extract_audio_archives(dl_manager, lang, format, split):
	archives_paths = _download_audio_archives_paths(dl_manager, lang, format, split)
	return [dl_manager.extract(archive_path) for archive_path in archives_paths]


	# for streaming case
	def _download_audio_archives(dl_manager, lang, format, split):
	archives_paths = _download_audio_archives_paths(dl_manager, lang, format, split)
	return [dl_manager.iter_archive(archive_path) for archive_path in archives_paths]