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""" Common Voice Dataset""" |
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import csv |
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import os |
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import json |
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import datasets |
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from datasets.utils.py_utils import size_str |
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from tqdm import tqdm |
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from .languages import LANGUAGES |
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from .release_stats import STATS |
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_CITATION = """\ |
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@inproceedings{commonvoice:2020, |
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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.}, |
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title = {Common Voice: A Massively-Multilingual Speech Corpus}, |
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booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, |
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pages = {4211--4215}, |
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year = 2020 |
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} |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/Seon25/common_voice_16_0_/tree/main" |
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_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" |
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_BASE_URL = "https://huggingface.co/datasets/Seon25/common_voice_16_0_/tree/main" |
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_AUDIO_URL = "https://huggingface.co/datasets/Seon25/common_voice_16_0_/blob/audio/{lang}/{split}/{lang}_{split}_0.tar" |
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_TRANSCRIPT_URL = "https://huggingface.co/datasets/Seon25/common_voice_16_0_/blob/main/transcript/{lang}/transcript_{lang}_{split}.tsv" |
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_N_SHARDS_URL = "n_shards.json" |
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class CommonVoiceConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CommonVoice.""" |
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def __init__(self, name, version, **kwargs): |
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self.language = kwargs.pop("language", None) |
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self.release_date = kwargs.pop("release_date", None) |
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self.num_clips = kwargs.pop("num_clips", None) |
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self.num_speakers = kwargs.pop("num_speakers", None) |
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self.validated_hr = kwargs.pop("validated_hr", None) |
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self.total_hr = kwargs.pop("total_hr", None) |
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self.size_bytes = kwargs.pop("size_bytes", None) |
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self.size_human = size_str(self.size_bytes) |
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description = ( |
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f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. " |
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f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data " |
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f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. " |
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f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}." |
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) |
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super(CommonVoiceConfig, self).__init__( |
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name=name, |
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version=datasets.Version(version), |
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description=description, |
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**kwargs, |
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) |
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class CommonVoice(datasets.GeneratorBasedBuilder): |
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DEFAULT_WRITER_BATCH_SIZE = 1000 |
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BUILDER_CONFIGS = [ |
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CommonVoiceConfig( |
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name=lang, |
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version=STATS["version"], |
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language=LANGUAGES[lang], |
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release_date=STATS["date"], |
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num_clips=lang_stats["clips"], |
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num_speakers=lang_stats["users"], |
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validated_hr=float(lang_stats["validHrs"]) if lang_stats["validHrs"] else None, |
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total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None, |
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size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None, |
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) |
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for lang, lang_stats in STATS["locales"].items() |
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] |
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def _info(self): |
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total_languages = len(STATS["locales"]) |
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total_valid_hours = STATS["totalValidHrs"] |
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description = ( |
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"Common Voice is Mozilla's initiative to help teach machines how real people speak. " |
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f"The dataset currently consists of {total_valid_hours} validated hours of speech " |
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f" in {total_languages} languages, but more voices and languages are always added." |
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) |
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features = datasets.Features( |
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{ |
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"client_id": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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"audio": datasets.features.Audio(sampling_rate=48_000), |
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"sentence": datasets.Value("string"), |
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"up_votes": datasets.Value("int64"), |
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"down_votes": datasets.Value("int64"), |
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"age": datasets.Value("string"), |
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"gender": datasets.Value("string"), |
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"accent": datasets.Value("string"), |
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"locale": datasets.Value("string"), |
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"segment": datasets.Value("string"), |
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"variant": datasets.Value("string"), |
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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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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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version=self.config.version, |
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) |
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def _split_generators(self, dl_manager): |
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lang = self.config.name |
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n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL) |
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with open(n_shards_path, encoding="utf-8") as f: |
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n_shards = json.load(f) |
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audio_urls = {} |
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splits = ("train", "dev", "test") |
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for split in splits: |
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audio_urls[split] = [ |
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_AUDIO_URL.format(lang='ha', split=split, shard_idx=0) for i in range(n_shards['ha'][split]) |
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] |
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archive_paths = dl_manager.download(audio_urls) |
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local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {} |
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meta_urls = {split: _TRANSCRIPT_URL.format(lang='ha', split=split) for split in splits} |
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meta_paths = dl_manager.download_and_extract(meta_urls) |
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split_generators = [] |
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split_names = { |
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"train": datasets.Split.TRAIN, |
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"dev": datasets.Split.VALIDATION, |
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"test": datasets.Split.TEST, |
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} |
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for split in splits: |
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split_generators.append( |
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datasets.SplitGenerator( |
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name=split_names.get(split, split), |
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gen_kwargs={ |
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"local_extracted_archive_paths": local_extracted_archive_paths.get(split), |
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"archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)], |
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"meta_path": meta_paths[split], |
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}, |
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), |
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) |
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return split_generators |
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def _generate_examples(self, local_extracted_archive_paths, archives, meta_path): |
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data_fields = list(self._info().features.keys()) |
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metadata = {} |
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with open(meta_path, encoding="utf-8") as f: |
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for row in tqdm(reader, desc="Reading metadata..."): |
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if not row["path"].endswith(".mp3"): |
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row["path"] += ".mp3" |
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if "accents" in row: |
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row["accent"] = row["accents"] |
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del row["accents"] |
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for field in data_fields: |
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if field not in row: |
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row[field] = "" |
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metadata[row["path"]] = row |
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for i, audio_archive in enumerate(archives): |
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for path, file in audio_archive: |
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_, filename = os.path.split(path) |
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if filename in metadata: |
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result = dict(metadata[filename]) |
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path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path |
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result["audio"] = {"path": path, "bytes": file.read()} |
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result["path"] = path |
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yield path, result |