from collections import defaultdict import os import json import csv import datasets _NAME="annotated_catalan_common_voice_v17" _VERSION="1.0.0" _AUDIO_EXTENSIONS=".mp3" _DESCRIPTION = """ This version of the Catalan sentences of the Common Voice corpus v17 includes metadata (gender and accent) for 263 speakers annotated by a team of experts. """ _CITATION = """ @misc{armentanoannotated2024, title={Annotated Catalan Common Voice v17}, author={Armentano, Carme}, publisher={Barcelona Supercomputing Center} year={2024}, url={https://huggingface.co/datasets/projecte-aina/annotated_catalan_common_voice_v17}, } """ _HOMEPAGE = "https://huggingface.co/datasets/projecte-aina/annotated_catalan_common_voice_v17" _LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/" _BASE_DATA_DIR = "corpus/" _METADATA_DEV = os.path.join(_BASE_DATA_DIR,"files","annotated_dev.tsv") _METADATA_INVALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_invalidated.tsv") _METADATA_OTHER = os.path.join(_BASE_DATA_DIR,"files","annotated_other.tsv") _METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files","annotated_test.tsv") _METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files","annotated_train.tsv") _METADATA_VALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_validated.tsv") _TARS_DEV = os.path.join(_BASE_DATA_DIR,"files","annotated_dev.paths") _TARS_INVALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_invalidated.paths") _TARS_OTHER = os.path.join(_BASE_DATA_DIR,"files","annotated_other.paths") _TARS_TEST = os.path.join(_BASE_DATA_DIR,"files","annotated_test.paths") _TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","annotated_train.paths") _TARS_VALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_validated.paths") class AnnotatedCatalanCommonVoicev17Config(datasets.BuilderConfig): """BuilderConfig for The Annotated Catalan Common Voice v17""" def __init__(self, name, **kwargs): name=_NAME super().__init__(name=name, **kwargs) class AnnotatedCatalanCommonVoicev17(datasets.GeneratorBasedBuilder): """Annotated Catalan Common Voice v17""" VERSION = datasets.Version(_VERSION) BUILDER_CONFIGS = [ AnnotatedCatalanCommonVoicev17Config( name=_NAME, version=datasets.Version(_VERSION), ) ] def _info(self): features = datasets.Features( { "audio": datasets.Audio(sampling_rate=16000), "client_id": datasets.Value("string"), "path": datasets.Value("string"), "sentence_id": datasets.Value("string"), "sentence": datasets.Value("string"), "sentence_domain": datasets.Value("string"), "up_votes": datasets.Value("int32"), "down_votes": datasets.Value("int32"), "age": datasets.Value("string"), "gender": datasets.Value("string"), "accents": datasets.Value("string"), "variant": datasets.Value("string"), "locale": datasets.Value("string"), "segment": datasets.Value("string"), "mean quality": datasets.Value("string"), "stdev quality": datasets.Value("string"), "annotated_accent": datasets.Value("string"), "annotated_accent_agreement": datasets.Value("string"), "annotated_gender": datasets.Value("string"), "annotated_gender_agreement": datasets.Value("string"), "propagated_gender": datasets.Value("string"), "propagated_accents": datasets.Value("string"), "propagated_accents_norm": datasets.Value("string"), "variant_norm": datasets.Value("string"), "assigned_accent": datasets.Value("string"), "assigned_gender": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): metadata_dev=dl_manager.download_and_extract(_METADATA_DEV) metadata_invalidated=dl_manager.download_and_extract(_METADATA_INVALIDATED) metadata_other=dl_manager.download_and_extract(_METADATA_OTHER) metadata_test=dl_manager.download_and_extract(_METADATA_TEST) metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN) metadata_validated=dl_manager.download_and_extract(_METADATA_VALIDATED) tars_dev=dl_manager.download_and_extract(_TARS_DEV) tars_invalidated=dl_manager.download_and_extract(_TARS_INVALIDATED) tars_other=dl_manager.download_and_extract(_TARS_OTHER) tars_test=dl_manager.download_and_extract(_TARS_TEST) tars_train=dl_manager.download_and_extract(_TARS_TRAIN) tars_validated=dl_manager.download_and_extract(_TARS_VALIDATED) hash_tar_files=defaultdict(dict) with open(tars_dev,'r') as f: hash_tar_files['validation']=[path.replace('\n','') for path in f] with open(tars_invalidated,'r') as f: hash_tar_files['invalidated']=[path.replace('\n','') for path in f] with open(tars_other,'r') as f: hash_tar_files['other']=[path.replace('\n','') for path in f] with open(tars_test,'r') as f: hash_tar_files['test']=[path.replace('\n','') for path in f] with open(tars_train,'r') as f: hash_tar_files['train']=[path.replace('\n','') for path in f] with open(tars_validated,'r') as f: hash_tar_files['validated']=[path.replace('\n','') for path in f] hash_meta_paths={"validation":metadata_dev, "invalidated":metadata_invalidated, "other":metadata_other, "test":metadata_test, "train":metadata_train, "validated":metadata_validated} audio_paths = dl_manager.download(hash_tar_files) splits=["validation","invalidated","other","test","train","validated"] local_extracted_audio_paths = ( dl_manager.extract(audio_paths) if not dl_manager.is_streaming else { split:[None] * len(audio_paths[split]) for split in splits } ) return [ datasets.SplitGenerator( name="validation", gen_kwargs={ "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["validation"]], "local_extracted_archives_paths": local_extracted_audio_paths["validation"], "metadata_paths": hash_meta_paths["validation"], } ), datasets.SplitGenerator( name="invalidated", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["invalidated"]], "local_extracted_archives_paths": local_extracted_audio_paths["invalidated"], "metadata_paths": hash_meta_paths["invalidated"], } ), datasets.SplitGenerator( name="other", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["other"]], "local_extracted_archives_paths": local_extracted_audio_paths["other"], "metadata_paths": hash_meta_paths["other"], } ), datasets.SplitGenerator( name="test", gen_kwargs={ "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["test"]], "local_extracted_archives_paths": local_extracted_audio_paths["test"], "metadata_paths": hash_meta_paths["test"], } ), datasets.SplitGenerator( name="train", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["train"]], "local_extracted_archives_paths": local_extracted_audio_paths["train"], "metadata_paths": hash_meta_paths["train"], } ), datasets.SplitGenerator( name="validated", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["validated"]], "local_extracted_archives_paths": local_extracted_audio_paths["validated"], "metadata_paths": hash_meta_paths["validated"], } ), ] def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): features = ["client_id","path","sentence_id","sentence","sentence_domain","up_votes", "down_votes","age","gender","accents","variant","locale","segment", "mean quality","stdev quality","annotated_accent","annotated_accent_agreement", "annotated_gender","annotated_gender_agreement","propagated_gender", "propagated_accents","propagated_accents_norm","variant_norm","assigned_accent", "assigned_gender"] with open(metadata_paths) as f: metadata = {x["path"]: x for x in csv.DictReader(f, delimiter="\t")} for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths): for audio_filename, audio_file in audio_archive: audio_id =os.path.splitext(os.path.basename(audio_filename))[0] audio_id=audio_id+_AUDIO_EXTENSIONS path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename try: yield audio_id, { "path": audio_id, **{feature: metadata[audio_id][feature] for feature in features}, "audio": {"path": path, "bytes": audio_file.read()}, } except: continue