# 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. """ Common Voice Dataset""" import csv import os import json import datasets from datasets.utils.py_utils import size_str from tqdm import tqdm from .languages import LANGUAGES from .release_stats import STATS _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://huggingface.co/datasets/Seon25/common_voice_16_0_/tree/main" _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" # TODO: change "streaming" to "main" after merge! _BASE_URL = "https://huggingface.co/datasets/Seon25/common_voice_16_0_/tree/main" _AUDIO_URL = "audio/ha/{split}/ha_{split}_0.tar" _TRANSCRIPT_URL = "transcript/ha/transcript_ha_{split}.tsv" _N_SHARDS_URL = "n_shards.json" class CommonVoiceConfig(datasets.BuilderConfig): """BuilderConfig for CommonVoice.""" def __init__(self, name, version, **kwargs): self.language = kwargs.pop("language", None) self.release_date = kwargs.pop("release_date", None) self.num_clips = kwargs.pop("num_clips", None) self.num_speakers = kwargs.pop("num_speakers", None) self.validated_hr = kwargs.pop("validated_hr", None) self.total_hr = kwargs.pop("total_hr", None) self.size_bytes = kwargs.pop("size_bytes", None) self.size_human = size_str(self.size_bytes) description = ( f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. " f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data " f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. " f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}." ) super(CommonVoiceConfig, self).__init__( name=name, version=datasets.Version(version), description=description, **kwargs, ) class CommonVoice(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1000 BUILDER_CONFIGS = [ CommonVoiceConfig( name=lang, version=STATS["version"], language=LANGUAGES[lang], release_date=STATS["date"], num_clips=lang_stats["clips"], num_speakers=lang_stats["users"], validated_hr=float(lang_stats["validHrs"]) if lang_stats["validHrs"] else None, total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None, size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None, ) for lang, lang_stats in STATS["locales"].items() ] def _info(self): total_languages = len(STATS["locales"]) total_valid_hours = STATS["totalValidHrs"] description = ( "Common Voice is Mozilla's initiative to help teach machines how real people speak. " f"The dataset currently consists of {total_valid_hours} validated hours of speech " f" in {total_languages} languages, but more voices and languages are always added." ) 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): lang = self.config.name n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL) with open(n_shards_path, encoding="utf-8") as f: n_shards = json.load(f) audio_urls = {} splits = ("train", "dev", "test") for split in splits: audio_urls[split] = [ _AUDIO_URL.format(lang='ha', split=split, shard_idx=0) for i in range(n_shards['ha'][split]) ] archive_paths = dl_manager.download(audio_urls) local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {} meta_urls = {split: _TRANSCRIPT_URL.format(lang='ha', split=split) for split in splits} meta_paths = dl_manager.download_and_extract(meta_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(path) for path in archive_paths.get(split)], "meta_path": meta_paths[split], }, ), ) return split_generators def _generate_examples(self, local_extracted_archive_paths, archives, meta_path): data_fields = list(self._info().features.keys()) metadata = {} with open(meta_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