from collections import defaultdict import os import json import csv import datasets _NAME="masri_test" _VERSION="1.0.0" _AUDIO_EXTENSIONS=".flac" _DESCRIPTION = """ The MASRI-TEST CORPUS was created out of YouTube videos belonging to the channel of the University of Malta. It has a length of 1 hour and it is gender balanced, as it has the same number of male and female speakers. """ _CITATION = """ @misc{carlosmenamasritest2020, title={MASRI-TEST CORPUS: Audio and Transcriptions in Maltese extracted from the YouTube channel of the University of Malta.}, author={Hernandez Mena, Carlos Daniel and Brincat, Ayrton Didier and Gatt, Albert and DeMarco, Andrea and Borg, Claudia and van der Plas, Lonneke and Meza Ruiz, Iván Vladimir}, journal={MASRI Project, Malta}, year={2020}, url={https://www.um.edu.mt/projects/masri/}, } """ _HOMEPAGE = "https://www.um.edu.mt/projects/masri/" _LICENSE = "CC-BY-4.0. The copyright remains with the original owners of the video. See https://creativecommons.org/licenses/by/4.0/" _BASE_DATA_DIR = "corpus/" _METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv") _TARS_TEST = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths") class MasriTestConfig(datasets.BuilderConfig): """BuilderConfig for MASRI-TEST Corpus""" def __init__(self, name, **kwargs): name=_NAME super().__init__(name=name, **kwargs) class MasriTest(datasets.GeneratorBasedBuilder): """MASRI-TEST Corpus""" VERSION = datasets.Version(_VERSION) BUILDER_CONFIGS = [ MasriTestConfig( 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_test=dl_manager.download_and_extract(_METADATA_TEST) tars_test=dl_manager.download_and_extract(_TARS_TEST) hash_tar_files=defaultdict(dict) with open(tars_test,'r') as f: hash_tar_files['test']=[path.replace('\n','') for path in f] hash_meta_paths={"test":metadata_test} audio_paths = dl_manager.download(hash_tar_files) splits=["test"] 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.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"], } ), ] 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 = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[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()}, }