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""" LibriVox-Indonesia Dataset""" |
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import csv |
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import os |
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import datasets |
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from datasets.utils.py_utils import size_str |
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from .languages import LANGUAGES |
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from .release_stats import STATS |
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_CITATION = """\ |
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""" |
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_HOMEPAGE = "https://huggingface.co/indonesian-nlp/librivox-indonesia" |
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_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" |
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_DATA_URL = "https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/resolve/main/data" |
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class LibriVoxIndonesiaConfig(datasets.BuilderConfig): |
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"""BuilderConfig for LibriVoxIndonesia.""" |
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def __init__(self, name, version, **kwargs): |
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self.language = kwargs.pop("language", None) |
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self.release_date = kwargs.pop("release_date", None) |
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self.num_clips = kwargs.pop("num_clips", None) |
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self.num_speakers = kwargs.pop("num_speakers", None) |
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self.total_hr = kwargs.pop("total_hr", None) |
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self.size_bytes = kwargs.pop("size_bytes", None) |
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self.size_human = size_str(self.size_bytes) |
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description = ( |
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f"LibriVox-Indonesia speech to text dataset in {self.language} released on {self.release_date}. " |
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f"The dataset comprises {self.total_hr} hours of transcribed speech data" |
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) |
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super(LibriVoxIndonesiaConfig, self).__init__( |
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name=name, |
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version=datasets.Version(version), |
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description=description, |
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**kwargs, |
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) |
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class LibriVoxIndonesia(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG_NAME = "_all_" |
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BUILDER_CONFIGS = [ |
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LibriVoxIndonesiaConfig( |
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name=lang, |
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version=STATS["version"], |
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language=LANGUAGES[lang], |
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release_date=STATS["date"], |
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num_clips=lang_stats["clips"], |
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num_speakers=lang_stats["users"], |
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total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None, |
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size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None, |
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) |
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for lang, lang_stats in STATS["locales"].items() |
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] |
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def _info(self): |
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total_languages = len(STATS["locales"]) |
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total_hours = self.config.total_hr |
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description = ( |
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"LibriVox-Indonesia is a speech dataset generated from LibriVox with only languages from Indonesia." |
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f"The dataset currently consists of {total_hours} hours of speech " |
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f"in {total_languages} languages, but more voices and languages are always added." |
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) |
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features = datasets.Features( |
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{ |
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"path": datasets.Value("string"), |
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"language": datasets.Value("string"), |
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"reader": datasets.Value("string"), |
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"sentence": datasets.Value("string"), |
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"audio": datasets.features.Audio(sampling_rate=44100) |
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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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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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version=self.config.version, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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audio_path = {} |
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local_extracted_archive = {} |
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metadata_path = {} |
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split_type = {"train": datasets.Split.TRAIN, "test": datasets.Split.TEST} |
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for split in split_type: |
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audio_path[split] = dl_manager.download(f"{_DATA_URL}/audio_{split}.tgz") |
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local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None |
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metadata_path[split] = dl_manager.download_and_extract(f"{_DATA_URL}/metadata_{split}.csv.gz") |
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path_to_clips = "librivox-indonesia" |
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return [ |
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datasets.SplitGenerator( |
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name=split_type[split], |
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gen_kwargs={ |
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"local_extracted_archive": local_extracted_archive[split], |
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"audio_files": dl_manager.iter_archive(audio_path[split]), |
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"metadata_path": dl_manager.download_and_extract(metadata_path[split]), |
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"path_to_clips": path_to_clips, |
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}, |
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) for split in split_type |
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] |
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def _generate_examples( |
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self, |
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local_extracted_archive, |
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audio_files, |
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metadata_path, |
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path_to_clips, |
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): |
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"""Yields examples.""" |
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data_fields = list(self._info().features.keys()) |
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metadata = {} |
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with open(metadata_path, "r", encoding="utf-8") as f: |
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reader = csv.DictReader(f) |
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for row in reader: |
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if self.config.name == "_all_" or self.config.name == row["language"]: |
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row["path"] = os.path.join(path_to_clips, row["path"]) |
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for field in data_fields: |
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if field not in row: |
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row[field] = "" |
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metadata[row["path"]] = row |
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id_ = 0 |
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for path, f in audio_files: |
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if path in metadata: |
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result = dict(metadata[path]) |
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path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path |
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result["audio"] = {"path": path, "bytes": f.read()} |
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result["path"] = path |
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yield id_, result |
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id_ += 1 |
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