Datasets:
Tasks:
Automatic Speech Recognition
Languages:
Uzbek
| # 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://commonvoice.mozilla.org/en/datasets" | |
| # _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" | |
| # TODO: change "streaming" to "main" after merge! | |
| _BASE_URL = "https://huggingface.co/datasets/Bobur/example/tree/main" | |
| _AUDIO_URL = _BASE_URL + "audio/{lang}/{split}/{lang}_{split}_{shard_idx}.tar" | |
| _TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}.tsv" | |
| _N_SHARDS_URL = _BASE_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", "other", "invalidated") | |
| for split in splits: | |
| audio_urls[split] = [ | |
| _AUDIO_URL.format(lang=lang, split=split, shard_idx=i) for i in range(n_shards[lang][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=lang, 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 |