from collections import defaultdict import os import json import csv import datasets _NAME="parlament_parla_v3_asr_a" _VERSION="1.0.0" _DESCRIPTION = """ This is the third version of the ParlamentParla speech corpus for Catalan: a collection of speech recordings with transcriptions intended for Automatic Speech Recognition (ASR) applications. """ _CITATION = """ @misc{bscib32024, title={ParlamentParla v3 - Speech Corpus of Catalan Parliamentary Sessions}, author={Baybars, Kulebi}, publisher={Barcelona Supercomputing Center}, year={2024}, url={https://huggingface.co/datasets/projecte-aina/parlament_parla_v3_asr_a}, } """ _HOMEPAGE = "https://huggingface.co/datasets/projecte-aina/parlament_parla_v3_asr_a" _LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/deed.es" _BASE_DATA_DIR = "corpus/" _METADATA_CLEAN_TRAIN_SHORT = os.path.join(_BASE_DATA_DIR,"files","clean_train_parlament_short.csv") _METADATA_CLEAN_TEST_SHORT = os.path.join(_BASE_DATA_DIR,"files", "clean_test_parlament_short.csv") _METADATA_CLEAN_DEV_SHORT = os.path.join(_BASE_DATA_DIR,"files", "clean_dev_parlament_short.csv") _METADATA_OTHER_TRAIN_SHORT = os.path.join(_BASE_DATA_DIR,"files","other_train_parlament_short.csv") _METADATA_OTHER_TEST_SHORT = os.path.join(_BASE_DATA_DIR,"files", "other_test_parlament_short.csv") _METADATA_OTHER_DEV_SHORT = os.path.join(_BASE_DATA_DIR,"files", "other_dev_parlament_short.csv") _TARS_CLEAN_TRAIN_SHORT = os.path.join(_BASE_DATA_DIR,"files","tars_clean_train_short.paths") _TARS_CLEAN_TEST_SHORT = os.path.join(_BASE_DATA_DIR,"files", "tars_clean_test_short.paths") _TARS_CLEAN_DEV_SHORT = os.path.join(_BASE_DATA_DIR,"files", "tars_clean_dev_short.paths") _TARS_OTHER_TRAIN_SHORT = os.path.join(_BASE_DATA_DIR,"files","tars_other_train_short.paths") _TARS_OTHER_TEST_SHORT = os.path.join(_BASE_DATA_DIR,"files", "tars_other_test_short.paths") _TARS_OTHER_DEV_SHORT = os.path.join(_BASE_DATA_DIR,"files", "tars_other_dev_short.paths") _METADATA_CLEAN_TRAIN_LONG = os.path.join(_BASE_DATA_DIR,"files","clean_train_parlament_long.csv") _METADATA_CLEAN_TEST_LONG = os.path.join(_BASE_DATA_DIR,"files", "clean_test_parlament_long.csv") _METADATA_CLEAN_DEV_LONG = os.path.join(_BASE_DATA_DIR,"files", "clean_dev_parlament_long.csv") _METADATA_OTHER_TRAIN_LONG = os.path.join(_BASE_DATA_DIR,"files","other_train_parlament_long.csv") _METADATA_OTHER_TEST_LONG = os.path.join(_BASE_DATA_DIR,"files", "other_test_parlament_long.csv") _METADATA_OTHER_DEV_LONG = os.path.join(_BASE_DATA_DIR,"files", "other_dev_parlament_long.csv") _TARS_CLEAN_TRAIN_LONG = os.path.join(_BASE_DATA_DIR,"files","tars_clean_train_long.paths") _TARS_CLEAN_TEST_LONG = os.path.join(_BASE_DATA_DIR,"files", "tars_clean_test_long.paths") _TARS_CLEAN_DEV_LONG = os.path.join(_BASE_DATA_DIR,"files", "tars_clean_dev_long.paths") _TARS_OTHER_TRAIN_LONG = os.path.join(_BASE_DATA_DIR,"files","tars_other_train_long.paths") _TARS_OTHER_TEST_LONG = os.path.join(_BASE_DATA_DIR,"files", "tars_other_test_long.paths") _TARS_OTHER_DEV_LONG = os.path.join(_BASE_DATA_DIR,"files", "tars_other_dev_long.paths") class ParlamentASRConfig(datasets.BuilderConfig): """BuilderConfig for Parlament ASR""" def __init__(self, name, **kwargs): name=_NAME super().__init__(name=name, **kwargs) class ParlamentASR(datasets.GeneratorBasedBuilder): """Parlament ASR""" VERSION = datasets.Version(_VERSION) BUILDER_CONFIGS = [ ParlamentASRConfig( name=_NAME, version=datasets.Version(_VERSION), ) ] def _info(self): features = datasets.Features( { "identifier": datasets.Value("string"), "audio": datasets.Audio(sampling_rate=16000), "segment_path": datasets.Value("string"), "text": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): metadata_clean_train_short=dl_manager.download_and_extract(_METADATA_CLEAN_TRAIN_SHORT) metadata_clean_test_short=dl_manager.download_and_extract(_METADATA_CLEAN_TEST_SHORT) metadata_clean_dev_short=dl_manager.download_and_extract(_METADATA_CLEAN_DEV_SHORT) metadata_other_train_short=dl_manager.download_and_extract(_METADATA_OTHER_TRAIN_SHORT) metadata_other_test_short=dl_manager.download_and_extract(_METADATA_OTHER_TEST_SHORT) metadata_other_dev_short=dl_manager.download_and_extract(_METADATA_OTHER_DEV_SHORT) tars_clean_train_short=dl_manager.download_and_extract(_TARS_CLEAN_TRAIN_SHORT) tars_clean_test_short=dl_manager.download_and_extract(_TARS_CLEAN_TEST_SHORT) tars_clean_dev_short=dl_manager.download_and_extract(_TARS_CLEAN_DEV_SHORT) tars_other_train_short=dl_manager.download_and_extract(_TARS_OTHER_TRAIN_SHORT) tars_other_test_short=dl_manager.download_and_extract(_TARS_OTHER_TEST_SHORT) tars_other_dev_short=dl_manager.download_and_extract(_TARS_OTHER_DEV_SHORT) metadata_clean_train_long=dl_manager.download_and_extract(_METADATA_CLEAN_TRAIN_LONG) metadata_clean_test_long=dl_manager.download_and_extract(_METADATA_CLEAN_TEST_LONG) metadata_clean_dev_long=dl_manager.download_and_extract(_METADATA_CLEAN_DEV_LONG) metadata_other_train_long=dl_manager.download_and_extract(_METADATA_OTHER_TRAIN_LONG) metadata_other_test_long=dl_manager.download_and_extract(_METADATA_OTHER_TEST_LONG) metadata_other_dev_long=dl_manager.download_and_extract(_METADATA_OTHER_DEV_LONG) tars_clean_train_long=dl_manager.download_and_extract(_TARS_CLEAN_TRAIN_LONG) tars_clean_test_long=dl_manager.download_and_extract(_TARS_CLEAN_TEST_LONG) tars_clean_dev_long=dl_manager.download_and_extract(_TARS_CLEAN_DEV_LONG) tars_other_train_long=dl_manager.download_and_extract(_TARS_OTHER_TRAIN_LONG) tars_other_test_long=dl_manager.download_and_extract(_TARS_OTHER_TEST_LONG) tars_other_dev_long=dl_manager.download_and_extract(_TARS_OTHER_DEV_LONG) hash_tar_files=defaultdict(dict) with open(tars_clean_train_short,'r') as f: hash_tar_files['clean_train_short']=[path.replace('\n','') for path in f] with open(tars_clean_test_short,'r') as f: hash_tar_files['clean_test_short']=[path.replace('\n','') for path in f] with open(tars_clean_dev_short,'r') as f: hash_tar_files['clean_dev_short']=[path.replace('\n','') for path in f] with open(tars_other_train_short,'r') as f: hash_tar_files['other_train_short']=[path.replace('\n','') for path in f] with open(tars_other_test_short,'r') as f: hash_tar_files['other_test_short']=[path.replace('\n','') for path in f] with open(tars_other_dev_short,'r') as f: hash_tar_files['other_dev_short']=[path.replace('\n','') for path in f] with open(tars_clean_train_long,'r') as f: hash_tar_files['clean_train_long']=[path.replace('\n','') for path in f] with open(tars_clean_test_long,'r') as f: hash_tar_files['clean_test_long']=[path.replace('\n','') for path in f] with open(tars_clean_dev_long,'r') as f: hash_tar_files['clean_dev_long']=[path.replace('\n','') for path in f] with open(tars_other_train_long,'r') as f: hash_tar_files['other_train_long']=[path.replace('\n','') for path in f] with open(tars_other_test_long,'r') as f: hash_tar_files['other_test_long']=[path.replace('\n','') for path in f] with open(tars_other_dev_long,'r') as f: hash_tar_files['other_dev_long']=[path.replace('\n','') for path in f] hash_meta_paths={"clean_train_short":metadata_clean_train_short, "clean_test_short":metadata_clean_test_short, "clean_dev_short":metadata_clean_dev_short, "other_train_short":metadata_other_train_short, "other_test_short":metadata_other_test_short, "other_dev_short":metadata_other_dev_short, "clean_train_long":metadata_clean_train_long, "clean_test_long":metadata_clean_test_long, "clean_dev_long":metadata_clean_dev_long, "other_train_long":metadata_other_train_long, "other_test_long":metadata_other_test_long, "other_dev_long":metadata_other_dev_long} audio_paths = dl_manager.download(hash_tar_files) splits=["clean_train_short","clean_test_short","clean_dev_short","other_train_short","other_test_short","other_dev_short","clean_train_long","clean_test_long","clean_dev_long","other_train_long","other_test_long","other_dev_long"] 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="clean_train_short", gen_kwargs={ "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["clean_train_short"]], "local_extracted_archives_paths": local_extracted_audio_paths["clean_train_short"], "metadata_paths": hash_meta_paths["clean_train_short"], } ), datasets.SplitGenerator( name="clean_test_short", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["clean_test_short"]], "local_extracted_archives_paths": local_extracted_audio_paths["clean_test_short"], "metadata_paths": hash_meta_paths["clean_test_short"], } ), datasets.SplitGenerator( name="clean_dev_short", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["clean_dev_short"]], "local_extracted_archives_paths": local_extracted_audio_paths["clean_dev_short"], "metadata_paths": hash_meta_paths["clean_dev_short"], } ), datasets.SplitGenerator( name="other_train_short", gen_kwargs={ "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["other_train_short"]], "local_extracted_archives_paths": local_extracted_audio_paths["other_train_short"], "metadata_paths": hash_meta_paths["other_train_short"], } ), datasets.SplitGenerator( name="other_test_short", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["other_test_short"]], "local_extracted_archives_paths": local_extracted_audio_paths["other_test_short"], "metadata_paths": hash_meta_paths["other_test_short"], } ), datasets.SplitGenerator( name="other_dev_short", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["other_dev_short"]], "local_extracted_archives_paths": local_extracted_audio_paths["other_dev_short"], "metadata_paths": hash_meta_paths["other_dev_short"], } ), datasets.SplitGenerator( name="clean_train_long", gen_kwargs={ "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["clean_train_long"]], "local_extracted_archives_paths": local_extracted_audio_paths["clean_train_long"], "metadata_paths": hash_meta_paths["clean_train_long"], } ), datasets.SplitGenerator( name="clean_test_long", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["clean_test_long"]], "local_extracted_archives_paths": local_extracted_audio_paths["clean_test_long"], "metadata_paths": hash_meta_paths["clean_test_long"], } ), datasets.SplitGenerator( name="clean_dev_long", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["clean_dev_long"]], "local_extracted_archives_paths": local_extracted_audio_paths["clean_dev_long"], "metadata_paths": hash_meta_paths["clean_dev_long"], } ), datasets.SplitGenerator( name="other_train_long", gen_kwargs={ "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["other_train_long"]], "local_extracted_archives_paths": local_extracted_audio_paths["other_train_long"], "metadata_paths": hash_meta_paths["other_train_long"], } ), datasets.SplitGenerator( name="other_test_long", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["other_test_long"]], "local_extracted_archives_paths": local_extracted_audio_paths["other_test_long"], "metadata_paths": hash_meta_paths["other_test_long"], } ), datasets.SplitGenerator( name="other_dev_long", gen_kwargs={ "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["other_dev_long"]], "local_extracted_archives_paths": local_extracted_audio_paths["other_dev_long"], "metadata_paths": hash_meta_paths["other_dev_long"], } ), ] def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): features = ["segment_path","text"] with open(metadata_paths) as f: metadata = {x["identifier"]: x for x in csv.DictReader(f, delimiter=",")} 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, { "identifier": audio_id, **{feature: metadata[audio_id][feature] for feature in features}, "audio": {"path": path, "bytes": audio_file.read()}, }