from collections import defaultdict import os import json import csv import datasets _NAME="samromur_asr" _VERSION="1.0.0" _AUDIO_EXTENSIONS=".flac" _DESCRIPTION = """ Samrómur Icelandic Speech 1.0. """ _CITATION = """ @misc{mollbergsamromur2022, title={Samrómur Icelandic Speech 1.0}, ldc_catalog_no={LDC2022S05}, DOI={https://doi.org/10.35111/thx3-f170}, author={Mollberg, David Erik and Jónsson, Ólafur Helgi and Þorsteinsdóttir, Sunneva and Guðmundsdóttir, Jóhanna Vigdís and Steingrimsson, Steinthor and Magnusdottir, Eydis Huld and Fong, Judy Y. and Borsky, Michal and Guðnason, Jón}, publisher={Reykjavík University} journal={Linguistic Data Consortium, Philadelphia}, year={2022}, url={https://catalog.ldc.upenn.edu/LDC2022S05}, } """ _HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2022S05/" _LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/" _BASE_DATA_DIR = "corpus/" _METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files","metadata_train.tsv") _METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv") _METADATA_DEV = os.path.join(_BASE_DATA_DIR,"files", "metadata_dev.tsv") _TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths") _TARS_TEST = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths") _TARS_DEV = os.path.join(_BASE_DATA_DIR,"files", "tars_dev.paths") class SamromurAsrConfig(datasets.BuilderConfig): """BuilderConfig for The Samrómur Corpus""" def __init__(self, name, **kwargs): name=_NAME super().__init__(name=name, **kwargs) class SamromurAsr(datasets.GeneratorBasedBuilder): """Samrómur Icelandic Speech 1.0""" VERSION = datasets.Version(_VERSION) BUILDER_CONFIGS = [ SamromurAsrConfig( 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"), "age": 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) metadata_test=dl_manager.download_and_extract(_METADATA_TEST) metadata_dev=dl_manager.download_and_extract(_METADATA_DEV) tars_train=dl_manager.download_and_extract(_TARS_TRAIN) tars_test=dl_manager.download_and_extract(_TARS_TEST) tars_dev=dl_manager.download_and_extract(_TARS_DEV) hash_tar_files=defaultdict(dict) with open(tars_train,'r') as f: hash_tar_files['train']=[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_dev,'r') as f: hash_tar_files['dev']=[path.replace('\n','') for path in f] hash_meta_paths={"train":metadata_train,"test":metadata_test,"dev":metadata_dev} audio_paths = dl_manager.download(hash_tar_files) splits=["train","dev","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.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=datasets.Split.VALIDATION, gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["dev"]], "local_extracted_archives_paths": local_extracted_audio_paths["dev"], "metadata_paths": hash_meta_paths["dev"], } ), 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","age","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] 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()}, }