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""" |
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Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken |
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words in 50 languages collectively spoken by over 5 billion people, for academic |
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research and commercial applications in keyword spotting and spoken term search, |
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licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords, |
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totaling 23.4 million 1-second spoken examples (over 6,000 hours). |
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""" |
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import csv |
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from functools import partial |
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import datasets |
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_CITATION = """\ |
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@inproceedings{mazumder2021multilingual, |
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title={Multilingual Spoken Words Corpus}, |
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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}, |
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booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, |
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year={2021} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken |
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words in 50 languages collectively spoken by over 5 billion people, for academic |
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research and commercial applications in keyword spotting and spoken term search, |
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licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords, |
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totaling 23.4 million 1-second spoken examples (over 6,000 hours). The dataset |
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has many use cases, ranging from voice-enabled consumer devices to call center |
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automation. This dataset is generated by applying forced alignment on crowd-sourced sentence-level |
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audio to produce per-word timing estimates for extraction. |
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All alignments are included in the dataset. |
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""" |
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_HOMEPAGE = "https://mlcommons.org/en/multilingual-spoken-words/" |
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_LICENSE = "CC-BY 4.0." |
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_VERSION = datasets.Version("1.0.0") |
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_BASE_URL = "data/{lang}/" |
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_AUDIO_URL = _BASE_URL + "{split}/audio/{n}.tar.gz" |
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_SPLITS_URL = _BASE_URL + "splits.tar.gz" |
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_N_FILES_URL = _BASE_URL + "{split}/n_files.txt" |
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_GENDERS = ["MALE", "FEMALE", "OTHER", "NAN"] |
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_LANGUAGES = [ |
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"ar", |
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"as", |
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"br", |
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"ca", |
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"cnh", |
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"cs", |
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"cv", |
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"cy", |
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"de", |
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"dv", |
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"el", |
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"en", |
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"eo", |
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"es", |
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"et", |
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"eu", |
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"fa", |
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"fr", |
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"fy-NL", |
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"ga-IE", |
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"gn", |
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"ha", |
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"ia", |
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"id", |
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"it", |
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"ka", |
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"ky", |
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"lt", |
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"lv", |
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"mn", |
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"mt", |
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"nl", |
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"or", |
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"pl", |
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"pt", |
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"rm-sursilv", |
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"rm-vallader", |
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"ro", |
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"ru", |
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"rw", |
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"sah", |
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"sk", |
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"sl", |
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"sv-SE", |
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"ta", |
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"tr", |
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"tt", |
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"uk", |
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"vi", |
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"zh-CN", |
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] |
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class MlSpokenWordsConfig(datasets.BuilderConfig): |
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"""BuilderConfig for MlSpokenWords.""" |
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def __init__(self, *args, languages, **kwargs): |
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"""BuilderConfig for MlSpokenWords. |
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Args: |
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languages (:obj:`Union[List[str], str]`): language or list of languages to load |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__( |
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*args, |
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name="+".join(languages) if isinstance(languages, list) else languages, |
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**kwargs, |
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) |
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self.languages = languages if isinstance(languages, list) else [languages] |
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class MlSpokenWords(datasets.GeneratorBasedBuilder): |
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""" |
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Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken |
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words in 50 languages collectively spoken by over 5 billion people, for academic |
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research and commercial applications in keyword spotting and spoken term search, |
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licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords, |
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totaling 23.4 million 1-second spoken examples (over 6,000 hours). |
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""" |
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VERSION = _VERSION |
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BUILDER_CONFIGS = [MlSpokenWordsConfig(languages=[lang], version=_VERSION) for lang in _LANGUAGES] |
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BUILDER_CONFIG_CLASS = MlSpokenWordsConfig |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"file": datasets.Value("string"), |
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"is_valid": datasets.Value("bool"), |
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"language": datasets.ClassLabel(names=self.config.languages), |
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"speaker_id": datasets.Value("string"), |
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"gender": datasets.ClassLabel(names=_GENDERS), |
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"keyword": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=48_000), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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splits_archive_path = [dl_manager.download(_SPLITS_URL.format(lang=lang)) for lang in self.config.languages] |
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download_audio = partial(_download_audio_archives, dl_manager=dl_manager) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages], |
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"splits_archives": [dl_manager.iter_archive(path) for path in splits_archive_path], |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"audio_archives": [download_audio(split="dev", lang=lang) for lang in self.config.languages], |
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"splits_archives": [dl_manager.iter_archive(path) for path in splits_archive_path], |
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"split": "dev", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"audio_archives": [download_audio(split="test", lang=lang) for lang in self.config.languages], |
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"splits_archives": [dl_manager.iter_archive(path) for path in splits_archive_path], |
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"split": "test", |
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}, |
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), |
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] |
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def _generate_examples(self, audio_archives, splits_archives, split): |
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metadata = dict() |
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for lang_idx, lang in enumerate(self.config.languages): |
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for split_filename, split_file in splits_archives[lang_idx]: |
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if split_filename.split(".csv")[0] == split: |
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csv_reader = csv.reader([line.decode("utf-8") for line in split_file.readlines()], delimiter=",") |
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for i, (link, word, is_valid, speaker, gender) in enumerate(csv_reader): |
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if i == 0: |
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continue |
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audio_filename = "_".join(link.split("/")) |
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metadata[audio_filename] = { |
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"keyword": word, |
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"is_valid": is_valid, |
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"speaker_id": speaker, |
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"gender": gender if gender and gender != "NA" else "NAN", |
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} |
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for audio_archive in audio_archives[lang_idx]: |
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for audio_filename, audio_file in audio_archive: |
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yield audio_filename, { |
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"file": audio_filename, |
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"language": lang, |
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"audio": {"path": audio_filename, "bytes": audio_file.read()}, |
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**metadata[audio_filename], |
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} |
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def _download_audio_archives(dl_manager, lang, split): |
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""" |
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All audio files are stored in several .tar.gz archives with names like 0.tar.gz, 1.tar.gz, ... |
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Number of archives stored in a separate .txt file (n_files.txt) |
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Prepare all the audio archives for iterating over them and their audio files. |
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""" |
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n_files_url = _N_FILES_URL.format(lang=lang, split=split) |
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n_files_path = dl_manager.download(n_files_url) |
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with open(n_files_path, "r", encoding="utf-8") as file: |
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n_files = int(file.read().strip()) |
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archive_urls = [_AUDIO_URL.format(lang=lang, split=split, n=i) for i in range(n_files)] |
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archive_paths = dl_manager.download(archive_urls) |
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return [dl_manager.iter_archive(archive_path) for archive_path in archive_paths] |
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