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from collections import defaultdict |
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import os |
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import json |
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import csv |
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import datasets |
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_DESCRIPTION = """ |
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A large-scale speech corpus for representation learning, semi-supervised learning and interpretation. |
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""" |
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_CITATION = """ |
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@inproceedings{} |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "" |
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_ASR_LANGUAGES = [ |
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"hy" |
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] |
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_ASR_ACCENTED_LANGUAGES = [ |
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"" |
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] |
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_LANGUAGES = _ASR_LANGUAGES + _ASR_ACCENTED_LANGUAGES |
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_BASE_DATA_DIR = "data/" |
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_N_SHARDS_FILE = _BASE_DATA_DIR + "n_files.json" |
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_AUDIO_ARCHIVE_PATH = _BASE_DATA_DIR + "{split}/{split}_dataset.tar.gz" |
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_METADATA_PATH = _BASE_DATA_DIR + "{split}.tsv" |
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class Hyvoxpopuli(datasets.GeneratorBasedBuilder): |
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"""The VoxPopuli dataset.""" |
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VERSION = datasets.Version("1.1.0") |
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DEFAULT_WRITER_BATCH_SIZE = 256 |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"audio_id": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"raw_text": datasets.Value("string"), |
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"normalized_text": datasets.Value("string"), |
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"gender": datasets.Value("string"), |
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"speaker_id": datasets.Value("string"), |
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"is_gold_transcript": datasets.Value("bool"), |
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"accent": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE) |
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with open(n_shards_path) as f: |
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n_shards = json.load(f) |
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splits = ["train", "dev", "test"] |
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audio_urls = defaultdict(dict) |
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for split in splits: |
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audio_urls[split] = [_AUDIO_ARCHIVE_PATH.format(split=split)] |
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meta_urls = defaultdict(dict) |
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for split in splits: |
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meta_urls[split] = _METADATA_PATH.format(split=split) |
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meta_paths = dl_manager.download_and_extract(meta_urls) |
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audio_paths = dl_manager.download(audio_urls) |
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local_extracted_audio_paths = ( |
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dl_manager.extract(audio_paths) |
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) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["train"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["train"], |
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"metadata_paths": meta_paths["train"], |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["dev"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["dev"], |
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"metadata_paths": meta_paths["dev"], |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["test"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["test"], |
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"metadata_paths": meta_paths["test"], |
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} |
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), |
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] |
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def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): |
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features = ["raw_text", "normalized_text", "speaker_id", "gender", "is_gold_transcript", "accent"] |
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meta_path = metadata_paths |
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with open(meta_path) as f: |
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metadata = {x["id"]: x for x in csv.DictReader(f, delimiter="\t")} |
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for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths): |
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for audio_filename, audio_file in audio_archive: |
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audio_id = audio_filename.split(os.sep)[-1].split(".wav")[0] |
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path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename |
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yield audio_id, { |
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"audio_id": audio_id, |
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**{feature: metadata[audio_id][feature] for feature in features}, |
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"audio": {"path": path, "bytes": audio_file.read()}, |
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} |
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