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import os |
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import tarfile |
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import soundfile as sf |
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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 MyAudioDataset(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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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(), |
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"transcription": datasets.Value("string"), |
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"audio_sampling_rate": datasets.Value("int32"), |
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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_and_extract(_DATASET_URL) |
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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={"directory": os.path.join( |
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archive_path, "ASR_DATA", "train")}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"directory": os.path.join( |
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archive_path, "ASR_DATA", "dev")}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"directory": os.path.join( |
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archive_path, "ASR_DATA", "test")}, |
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), |
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] |
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def _generate_examples(self, directory): |
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for root, _, files in os.walk(directory): |
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for filename in files: |
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if filename.endswith(".wav"): |
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audio_path = os.path.join(root, filename) |
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transcription_path = os.path.join(root, filename + ".trn") |
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audio, sampling_rate = sf.read(audio_path) |
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with open(transcription_path, "r", encoding="utf-8") as f: |
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transcription = f.read().strip() |
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yield f"{audio_path}-{transcription}", { |
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"audio": audio, |
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"transcription": transcription, |
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"audio_sampling_rate": sampling_rate, |
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} |
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