import csv import os import datasets from tqdm import tqdm _DESCRIPTION = """\ Persian portion of the common voice 13 dataset, gathered and maintained by Hezar AI. """ _CITATION = """\ @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } """ _HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets" _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" _BASE_URL = "https://huggingface.co/datasets/hezarai/common-voice-13-fa/resolve/main/" _AUDIO_URL = _BASE_URL + "audio/{split}.zip" _TRANSCRIPT_URL = _BASE_URL + "transcripts/{split}.tsv" class CommonVoiceFaConfig(datasets.BuilderConfig): """BuilderConfig for CommonVoice.""" def __init__(self, **kwargs): super(CommonVoiceFaConfig, self).__init__(**kwargs) class CommonVoice(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1000 BUILDER_CONFIGS = [ CommonVoiceFaConfig( name="commonvoice-13-fa", version="1.0.0", description=_DESCRIPTION, ) ] def _info(self): features = datasets.Features( { "client_id": datasets.Value("string"), "path": datasets.Value("string"), "audio": datasets.features.Audio(sampling_rate=48_000), "sentence": datasets.Value("string"), "up_votes": datasets.Value("int64"), "down_votes": datasets.Value("int64"), "age": datasets.Value("string"), "gender": datasets.Value("string"), "accent": datasets.Value("string"), "locale": datasets.Value("string"), "segment": datasets.Value("string"), "variant": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, version=self.config.version, ) def _split_generators(self, dl_manager): splits = ("train", "dev", "test") audio_urls = {split: _AUDIO_URL.format(split=split) for split in splits} archive_paths = dl_manager.download(audio_urls) local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {} transcript_urls = {split: _TRANSCRIPT_URL.format(split=split) for split in splits} transcript_paths = dl_manager.download_and_extract(transcript_urls) split_generators = [] split_names = { "train": datasets.Split.TRAIN, "dev": datasets.Split.VALIDATION, "test": datasets.Split.TEST, } for split in splits: split_generators.append( datasets.SplitGenerator( name=split_names.get(split, split), gen_kwargs={ "local_extracted_archive_paths": local_extracted_archive_paths.get(split), "archives": [dl_manager.iter_archive(archive_paths.get(split))], "transcript_path": transcript_paths[split], }, ), ) return split_generators def _generate_examples(self, local_extracted_archive_paths, archives, transcript_path): data_fields = list(self._info().features.keys()) metadata = {} with open(transcript_path, encoding="utf-8") as f: reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) for row in tqdm(reader, desc="Reading metadata..."): if not row["path"].endswith(".mp3"): row["path"] += ".mp3" # accent -> accents in CV 8.0 if "accents" in row: row["accent"] = row["accents"] del row["accents"] # if data is incomplete, fill with empty values for field in data_fields: if field not in row: row[field] = "" metadata[row["path"]] = row for i, audio_archive in enumerate(archives): for path, file in audio_archive: _, filename = os.path.split(path) if filename in metadata: result = dict(metadata[filename]) # set the audio feature and the path to the extracted file path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path result["audio"] = {"path": path, "bytes": file.read()} result["path"] = path yield path, result