from collections import defaultdict import os import json import csv import datasets _DESCRIPTION = """ A large-scale speech corpus for representation learning, semi-supervised learning and interpretation. """ _CITATION = """ @inproceedings{} """ _HOMEPAGE = "" _LICENSE = "" _ASR_LANGUAGES = [ "hy" ] _ASR_ACCENTED_LANGUAGES = [ "" ] _LANGUAGES = _ASR_LANGUAGES + _ASR_ACCENTED_LANGUAGES _BASE_DATA_DIR = "data/" _N_SHARDS_FILE = _BASE_DATA_DIR + "n_files.json" _AUDIO_ARCHIVE_PATH = _BASE_DATA_DIR + "{split}/{split}_dataset.tar.gz" _METADATA_PATH = _BASE_DATA_DIR + "{split}.tsv" class Hyvoxpopuli(datasets.GeneratorBasedBuilder): """The VoxPopuli dataset.""" VERSION = datasets.Version("1.1.0") # TODO: version DEFAULT_WRITER_BATCH_SIZE = 256 def _info(self): features = datasets.Features( { "audio_id": datasets.Value("string"), "audio": datasets.Audio(sampling_rate=16_000), "raw_text": datasets.Value("string"), "normalized_text": datasets.Value("string"), "gender": datasets.Value("string"), # TODO: ClassVar? "speaker_id": datasets.Value("string"), "is_gold_transcript": datasets.Value("bool"), "accent": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE) with open(n_shards_path) as f: n_shards = json.load(f) splits = ["train", "dev", "test"] audio_urls = defaultdict(dict) for split in splits: audio_urls[split] = [_AUDIO_ARCHIVE_PATH.format(split=split)] meta_urls = defaultdict(dict) for split in splits: meta_urls[split] = _METADATA_PATH.format(split=split) # dl_manager.download_config.num_proc = len(urls) meta_paths = dl_manager.download_and_extract(meta_urls) audio_paths = dl_manager.download(audio_urls) local_extracted_audio_paths = ( dl_manager.extract(audio_paths) ) 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": 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": 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": meta_paths["test"], } ), ] def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): features = ["raw_text", "normalized_text", "speaker_id", "gender", "is_gold_transcript", "accent"] meta_path = metadata_paths with open(meta_path) as f: metadata = {x["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(".wav")[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()}, }