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from datasets import Dataset, GeneratorBasedBuilder, Features |
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import os |
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import tarfile |
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import librosa |
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import datasets |
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_LICENSE = "https://creativecommons.org/licenses/by/4.0/" |
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_HOMEPAGE = "https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3126" |
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_DATASET_URL = "https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3126/snemovna.tar.xz" |
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_DESCRIPTION = "Large corpus of Czech parliament plenary sessions, originaly released 2019-11-29 by Kratochvíl Jonáš, Polák Peter and Bojar Ondřej\ |
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The dataset consists of 444 hours of transcribed speech audio snippets 1 to 40 seconds long.\ |
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Original dataset transcriptions were converted to true case from uppercase using spacy library." |
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_CITATION = """\ |
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@misc{11234/1-3126, |
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title = {Large Corpus of Czech Parliament Plenary Hearings}, |
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author = {Kratochv{\'{\i}}l, Jon{\'a}{\v s} and Pol{\'a}k, Peter and Bojar, Ond{\v r}ej}, |
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url = {http://hdl.handle.net/11234/1-3126}, |
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note = {{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University}, |
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copyright = {Creative Commons - Attribution 4.0 International ({CC} {BY} 4.0)}, |
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year = {2019} } """ |
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class CzechParliamentPlenaryHearings(GeneratorBasedBuilder): |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"audio": datasets.features.Audio(sampling_rate=16000), |
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"transcription": datasets.Value("string") |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download(_DATASET_URL) |
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data_dir = os.path.join(archive_path, 'ASR_DATA') |
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splits = ("train", "dev", "test") |
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split_names = { |
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"train": datasets.Split.TRAIN, |
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"dev": datasets.Split.VALIDATION, |
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"test": datasets.Split.TEST, |
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} |
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split_generators = [] |
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for split in splits: |
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split_dir = os.path.join(data_dir, split) |
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split_generators.append( |
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datasets.SplitGenerator( |
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name=split_names.get(split, split), |
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gen_kwargs={'archive_path': archive_path, 'split_dir': split_dir}) |
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) |
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return split_generators |
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def _generate_examples(self, split_dir, archive_path): |
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for folder_name in os.listdir(split_dir): |
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folder_path = os.path.join(split_dir, folder_name) |
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if os.path.isdir(folder_path): |
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for file_name in os.listdir(folder_path): |
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file_path = os.path.join(folder_path, file_name) |
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if file_name.endswith('.trn'): |
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with open(file_path, 'r') as f: |
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transcription = f.read().strip() |
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elif file_name.endswith('.wav'): |
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with self._dl_manager.iter_archive(archive_path, filter_func=lambda x: x.path == file_path) as files: |
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audio_file = next(files) |
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audio_path = os.path.join( |
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folder_name, audio_file.path.split('/')[-1]) |
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audio, sr = librosa.load(audio_file, sr=16000) |
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yield f'{folder_name}/{audio_file.path.split("/")[-1]}', { |
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'audio': { |
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'path': audio_path, 'bytes': audio |
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}, |
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'transcription': transcription, |
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} |
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