from collections import defaultdict import os import json import csv import datasets _DESCRIPTION = """ A small-scale single lang. """ _CITATION = """ @inproceedings{wang-etal-2021-voxpopuli, title = "trevor", author = "diego", booktitle = "copy of voxpopuli", month = aug, year = "2023", publisher = "None", url = "", } """ _HOMEPAGE = "" _LICENSE = "None" _ASR_LANGUAGES = [ "en" ] _ASR_ACCENTED_LANGUAGES = [ "en_accented" ] _LANGUAGES = _ASR_LANGUAGES _BASE_DATA_DIR = "data/" _N_SHARDS_FILE = _BASE_DATA_DIR + "n_files.json" _AUDIO_ARCHIVE_PATH = _BASE_DATA_DIR + "{lang}/{split}/{split}_part_{n_shard}.tar.gz" _METADATA_PATH = _BASE_DATA_DIR + "{lang}/asr_{split}.tsv" class TrevorConfig(datasets.BuilderConfig): """BuilderConfig for Trevor.""" def __init__(self, name, languages="all", **kwargs): """ Args: name: `string` or `List[string]`: name of a config: either one of the supported languages or "multilang" for many languages. By default, "multilang" config includes all languages, including accented ones. To specify a custom set of languages, pass them to the `languages` parameter languages: `List[string]`: if config is "multilang" can be either "all" for all available languages, excluding accented ones (default), or a custom list of languages. **kwargs: keyword arguments forwarded to super. """ if name == "multilang": self.languages = _ASR_LANGUAGES if languages == "all" else languages name = "multilang" if languages == "all" else "_".join(languages) else: self.languages = [name] super().__init__(name=name, **kwargs) class Trevor(datasets.GeneratorBasedBuilder): """The Trevor dataset.""" VERSION = datasets.Version("1.3.0") # TODO: version BUILDER_CONFIGS = [ TrevorConfig( name=name, version=datasets.Version("1.3.0"), ) for name in _LANGUAGES + ["multilang"] ] DEFAULT_WRITER_BATCH_SIZE = 256 def _info(self): features = datasets.Features( { "audio_id": datasets.Value("string"), "language": datasets.ClassLabel(names=_LANGUAGES), "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) if self.config.name == "en_accented": splits = ["test"] else: splits = ["train", "dev", "test"] audio_urls = defaultdict(dict) for split in splits: for lang in self.config.languages: audio_urls[split][lang] = [ _AUDIO_ARCHIVE_PATH.format(lang=lang, split=split, n_shard=i) for i in range(n_shards[lang][split]) ] meta_urls = defaultdict(dict) for split in splits: for lang in self.config.languages: meta_urls[split][lang] = _METADATA_PATH.format(lang=lang, 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) if not dl_manager.is_streaming else { split: {lang: [None] * len(audio_paths[split][lang]) for lang in self.config.languages} for split in splits } ) if self.config.name == "en_accented": return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "audio_archives": { lang: [dl_manager.iter_archive(archive) for archive in lang_archives] for lang, lang_archives in audio_paths["test"].items() }, "local_extracted_archives_paths": local_extracted_audio_paths["test"], "metadata_paths": meta_paths["test"], } ), ] return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_archives": { lang: [dl_manager.iter_archive(archive) for archive in lang_archives] for lang, lang_archives in audio_paths["train"].items() }, "local_extracted_archives_paths": local_extracted_audio_paths["train"], "metadata_paths": meta_paths["train"], } ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "audio_archives": { lang: [dl_manager.iter_archive(archive) for archive in lang_archives] for lang, lang_archives in audio_paths["dev"].items() }, "local_extracted_archives_paths": local_extracted_audio_paths["dev"], "metadata_paths": meta_paths["dev"], } ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "audio_archives": { lang: [dl_manager.iter_archive(archive) for archive in lang_archives] for lang, lang_archives in audio_paths["test"].items() }, "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): assert len(metadata_paths) == len(audio_archives) == len(local_extracted_archives_paths) features = ["raw_text", "normalized_text", "speaker_id", "gender", "is_gold_transcript", "accent"] for lang in self.config.languages: assert len(audio_archives[lang]) == len(local_extracted_archives_paths[lang]) meta_path = metadata_paths[lang] print(f"Opening meta file {meta_path}") 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[lang], local_extracted_archives_paths[lang]): 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, "language": lang, **{feature: metadata[audio_id][feature] for feature in features}, "audio": {"path": path, "bytes": audio_file.read()}, }