from collections import defaultdict import os import json import csv import datasets _NAME="samromur_milljon" _VERSION="1.0.0" _AUDIO_EXTENSIONS=".flac" _DESCRIPTION = """ Samrómur Milljón consists of approximately 1 million of speech recordings (967 hours) collected through the platform samromur.is; the transcripts accompanying these recordings were automatically verified using various ASR systems such as: Wav2Vec, Whisper and NeMo. """ _CITATION = """ @misc{menasamromurmilljon2023, title={Samrómur Milljón, Audio and Transcriptions}, author={Hernández Mena, Carlos Daniel and Guðnason, Jón}, publisher={Reykjavík University} year={2023}, url={https://huggingface.co/datasets/language-and-voice-lab/samromur_milljon}, } """ _HOMEPAGE = "https://huggingface.co/datasets/language-and-voice-lab/samromur_milljon" _LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/" _BASE_DATA_DIR = "corpus/" _METADATA_FEM_LT_18_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_fem_lt_18_yrs.tsv") _METADATA_FEM_18TO49_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_fem_18to49_yrs.tsv") _METADATA_FEM_GT_49_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_fem_gt_49_yrs.tsv") _METADATA_MALE_LT_18_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_male_lt_18_yrs.tsv") _METADATA_MALE_18TO49_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_male_18to49_yrs.tsv") _METADATA_MALE_GT_49_YRS = os.path.join(_BASE_DATA_DIR,"files","metadata_male_gt_49_yrs.tsv") _METADATA_OTHER = os.path.join(_BASE_DATA_DIR,"files","metadata_other.tsv") _TARS_FEM_LT_18_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_fem_lt_18_yrs.paths") _TARS_FEM_18TO49_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_fem_18to49_yrs.paths") _TARS_FEM_GT_49_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_fem_gt_49_yrs.paths") _TARS_MALE_LT_18_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_male_lt_18_yrs.paths") _TARS_MALE_18TO49_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_male_18to49_yrs.paths") _TARS_MALE_GT_49_YRS = os.path.join(_BASE_DATA_DIR,"files","tars_male_gt_49_yrs.paths") _TARS_OTHER = os.path.join(_BASE_DATA_DIR,"files","tars_other.paths") class SamromurMilljonConfig(datasets.BuilderConfig): """BuilderConfig for The Samrómur Milljón""" def __init__(self, name, **kwargs): name=_NAME super().__init__(name=name, **kwargs) class SamromurMilljon(datasets.GeneratorBasedBuilder): """Samrómur Milljón""" VERSION = datasets.Version(_VERSION) BUILDER_CONFIGS = [ SamromurMilljonConfig( 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"), "verified_with": datasets.Value("string"), "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_fem_lt_18_yrs=dl_manager.download_and_extract(_METADATA_FEM_LT_18_YRS) metadata_fem_18to49_yrs=dl_manager.download_and_extract(_METADATA_FEM_18TO49_YRS) metadata_fem_gt_49_yrs=dl_manager.download_and_extract(_METADATA_FEM_GT_49_YRS) metadata_male_lt_18_yrs=dl_manager.download_and_extract(_METADATA_MALE_LT_18_YRS) metadata_male_18to49_yrs=dl_manager.download_and_extract(_METADATA_MALE_18TO49_YRS) metadata_male_gt_49_yrs=dl_manager.download_and_extract(_METADATA_MALE_GT_49_YRS) metadata_other=dl_manager.download_and_extract(_METADATA_OTHER) tars_fem_lt_18_yrs=dl_manager.download_and_extract(_TARS_FEM_LT_18_YRS) tars_fem_18to49_yrs=dl_manager.download_and_extract(_TARS_FEM_18TO49_YRS) tars_fem_gt_49_yrs=dl_manager.download_and_extract(_TARS_FEM_GT_49_YRS) tars_male_lt_18_yrs=dl_manager.download_and_extract(_TARS_MALE_LT_18_YRS) tars_male_18to49_yrs=dl_manager.download_and_extract(_TARS_MALE_18TO49_YRS) tars_male_gt_49_yrs=dl_manager.download_and_extract(_TARS_MALE_GT_49_YRS) tars_other=dl_manager.download_and_extract(_TARS_OTHER) hash_tar_files=defaultdict(dict) with open(tars_fem_lt_18_yrs,'r') as f: hash_tar_files['fem_lt_18_yrs']=[path.replace('\n','') for path in f] with open(tars_fem_18to49_yrs,'r') as f: hash_tar_files['fem_18to49_yrs']=[path.replace('\n','') for path in f] with open(tars_fem_gt_49_yrs,'r') as f: hash_tar_files['fem_gt_49_yrs']=[path.replace('\n','') for path in f] with open(tars_male_lt_18_yrs,'r') as f: hash_tar_files['male_lt_18_yrs']=[path.replace('\n','') for path in f] with open(tars_male_18to49_yrs,'r') as f: hash_tar_files['male_18to49_yrs']=[path.replace('\n','') for path in f] with open(tars_male_gt_49_yrs,'r') as f: hash_tar_files['male_gt_49_yrs']=[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] hash_meta_paths={"fem_lt_18_yrs":metadata_fem_lt_18_yrs, "fem_18to49_yrs":metadata_fem_18to49_yrs, "fem_gt_49_yrs":metadata_fem_gt_49_yrs, "male_lt_18_yrs":metadata_male_lt_18_yrs, "male_18to49_yrs":metadata_male_18to49_yrs, "male_gt_49_yrs":metadata_male_gt_49_yrs, "other":metadata_other} audio_paths = dl_manager.download(hash_tar_files) splits=["fem_lt_18_yrs","fem_18to49_yrs","fem_gt_49_yrs","male_lt_18_yrs","male_18to49_yrs","male_gt_49_yrs","other"] 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="female_lt_18_yrs", gen_kwargs={ "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["fem_lt_18_yrs"]], "local_extracted_archives_paths": local_extracted_audio_paths["fem_lt_18_yrs"], "metadata_paths": hash_meta_paths["fem_lt_18_yrs"], } ), datasets.SplitGenerator( name="female_18to49_yrs", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["fem_18to49_yrs"]], "local_extracted_archives_paths": local_extracted_audio_paths["fem_18to49_yrs"], "metadata_paths": hash_meta_paths["fem_18to49_yrs"], } ), datasets.SplitGenerator( name="female_gt_49_yrs", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["fem_gt_49_yrs"]], "local_extracted_archives_paths": local_extracted_audio_paths["fem_gt_49_yrs"], "metadata_paths": hash_meta_paths["fem_gt_49_yrs"], } ), datasets.SplitGenerator( name="male_lt_18_yrs", gen_kwargs={ "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["male_lt_18_yrs"]], "local_extracted_archives_paths": local_extracted_audio_paths["male_lt_18_yrs"], "metadata_paths": hash_meta_paths["male_lt_18_yrs"], } ), datasets.SplitGenerator( name="male_18to49_yrs", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["male_18to49_yrs"]], "local_extracted_archives_paths": local_extracted_audio_paths["male_18to49_yrs"], "metadata_paths": hash_meta_paths["male_18to49_yrs"], } ), datasets.SplitGenerator( name="male_gt_49_yrs", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["male_gt_49_yrs"]], "local_extracted_archives_paths": local_extracted_audio_paths["male_gt_49_yrs"], "metadata_paths": hash_meta_paths["male_gt_49_yrs"], } ), 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"], } ), ] def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): features = ["speaker_id","gender","age","duration","verified_with","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()}, }