# 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 urllib import datasets import requests from datasets.utils.py_utils import size_str from huggingface_hub import HfApi, HfFolder 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/" _API_URL = "https://commonvoice.mozilla.org/api/v1" 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_CONFIG_NAME = "en" DEFAULT_CONFIG_NAME = "bn" 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"]), total_hr=float(lang_stats["totalHrs"]), size_bytes=int(lang_stats["size"]), ) 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"), } ) return datasets.DatasetInfo( description=description, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, version=self.config.version, # task_templates=[ # AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="sentence") # ], ) def _get_bundle_url(self, locale, url_template): # path = encodeURIComponent(path) path = url_template.replace("{locale}", locale) path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") # use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 # response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() response = requests.get(f"{_API_URL}/bucket/dataset/{path}", timeout=10.0).json() return response["url"] def _log_download(self, locale, bundle_version, auth_token): if isinstance(auth_token, bool): auth_token = HfFolder().get_token() whoami = HfApi().whoami(auth_token) email = whoami["email"] if "email" in whoami else "" payload = {"email": email, "locale": locale, "dataset": bundle_version} requests.post(f"{_API_URL}/{locale}/downloaders", json=payload).json() def _split_generators(self, dl_manager): """Returns SplitGenerators.""" hf_auth_token = dl_manager.download_config.use_auth_token if hf_auth_token is None: raise ConnectionError( "Please set use_auth_token=True or use_auth_token='' to download this dataset" ) bundle_url_template = STATS["bundleURLTemplate"] bundle_version = bundle_url_template.split("/")[0] dl_manager.download_config.ignore_url_params = True self._log_download(self.config.name, bundle_version, hf_auth_token) archive_path = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None if self.config.version < datasets.Version("5.0.0"): path_to_data = "" else: path_to_data = "/".join([bundle_version, self.config.name]) path_to_clips = "/".join([path_to_data, "clips"]) if path_to_data else "clips" #we provide our custom csvs with the huggingface repo so, path_to_tsvs = "/" + "bengali_ai_tsv" + "/" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "local_extracted_archive": local_extracted_archive, "archive_iterator": dl_manager.iter_archive(archive_path), #"metadata_filepath": "/".join([path_to_data, "train.tsv"]) if path_to_data else "train.tsv", "metadata_filepath": "/".join([path_to_tsvs, "train.tsv"]), "path_to_clips": path_to_clips, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "local_extracted_archive": local_extracted_archive, "archive_iterator": dl_manager.iter_archive(archive_path), #"metadata_filepath": "/".join([path_to_data, "test.tsv"]) if path_to_data else "test.tsv", "metadata_filepath": "/".join([path_to_tsvs, "test.tsv"]), "path_to_clips": path_to_clips, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "local_extracted_archive": local_extracted_archive, "archive_iterator": dl_manager.iter_archive(archive_path), #"metadata_filepath": "/".join([path_to_data, "dev.tsv"]) if path_to_data else "dev.tsv", "metadata_filepath": "/".join([path_to_tsvs, "dev.tsv"]), "path_to_clips": path_to_clips, }, ), ] def _generate_examples( self, local_extracted_archive, archive_iterator, metadata_filepath, path_to_clips, ): """Yields examples.""" data_fields = list(self._info().features.keys()) metadata = {} metadata_found = False for path, f in archive_iterator: if path == metadata_filepath: metadata_found = True lines = (line.decode("utf-8") for line in f) reader = csv.DictReader(lines, delimiter="\t", quoting=csv.QUOTE_NONE) for row in reader: # set absolute path for mp3 audio file if not row["path"].endswith(".mp3"): row["path"] += ".mp3" row["path"] = os.path.join(path_to_clips, row["path"]) # 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 elif path.startswith(path_to_clips): assert metadata_found, "Found audio clips before the metadata TSV file." if not metadata: break if path in metadata: result = metadata[path] # set the audio feature and the path to the extracted file path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path result["audio"] = {"path": path, "bytes": f.read()} # set path to None if the audio file doesn't exist locally (i.e. in streaming mode) result["path"] = path if local_extracted_archive else None yield path, result