from collections import defaultdict import os import json import csv import datasets _NAME="tedx_spanish" _VERSION="1.0.0" _DESCRIPTION = """ The TEDX SPANISH CORPUS is a dataset created from TEDx talks in Spanish and it aims to be used in the Automatic Speech Recognition (ASR) Task. """ _CITATION = """ @misc{carlosmenatedxspanish2019, title={TEDX SPANISH CORPUS: Audio and Transcripts in Spanish in a CIEMPIESS Corpus style, taken from the TEDx Talks.}, author={Hernandez Mena, Carlos Daniel}, year={2019}, url={https://huggingface.co/ciempiess/tedx_spanish}, } """ _HOMEPAGE = "https://huggingface.co/ciempiess/tedx_spanish" _LICENSE = "CC-BY-NC-ND-4.0, See https://creativecommons.org/licenses/by-nc-nd/4.0/" _BASE_DATA_DIR = "corpus/" _METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "metadata_train.tsv") _TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "tars_train.paths") class TedxSpanishConfig(datasets.BuilderConfig): """BuilderConfig for TEDX SPANISH CORPUS""" def __init__(self, name, **kwargs): name=_NAME super().__init__(name=name, **kwargs) class TedxSpanish(datasets.GeneratorBasedBuilder): """TEDX SPANISH CORPUS""" VERSION = datasets.Version(_VERSION) BUILDER_CONFIGS = [ TedxSpanishConfig( name=_NAME, version=datasets.Version(_VERSION), ) ] def _info(self): features = datasets.Features( { "audio_id": datasets.Value("string"), "audio": datasets.Audio(sampling_rate=16000), "speaker_id": datasets.Value("string"), "gender": datasets.Value("string"), "duration": datasets.Value("float32"), "normalized_text": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN) tars_train=dl_manager.download_and_extract(_TARS_TRAIN) hash_tar_files=defaultdict(dict) with open(tars_train,'r') as f: hash_tar_files['train']=[path.replace('\n','') for path in f] hash_meta_paths={"train":metadata_train} audio_paths = dl_manager.download(hash_tar_files) splits=["train"] 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=datasets.Split.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"], } ), ] def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): features = ["speaker_id","gender","duration","normalized_text"] with open(metadata_paths) as f: metadata = {x["audio_id"]: 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] path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename yield audio_id, { "audio_id": audio_id, **{feature: metadata[audio_id][feature] for feature in features}, "audio": {"path": path, "bytes": audio_file.read()}, }