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"""Vystadial 2016 Czech automatic speech recognition dataset.""" |
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import os |
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import datasets |
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from datasets.tasks import AutomaticSpeechRecognition |
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_CITATION = """\ |
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@misc{11234/1-1740, |
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title = {Vystadial 2016 – Czech data}, |
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author = {Pl{\'a}tek, Ond{\v r}ej and Du{\v s}ek, Ond{\v r}ej and Jur{\v c}{\'{\i}}{\v c}ek, Filip}, |
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url = {http://hdl.handle.net/11234/1-1740}, |
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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-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)}, |
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year = {2016} } |
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""" |
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_DESCRIPTION = """\ |
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This is the Czech data collected during the `VYSTADIAL` project. It is an extension of the 'Vystadial 2013' Czech part data release. The dataset comprises of telephone conversations in Czech, developed for training acoustic models for automatic speech recognition in spoken dialogue systems. |
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""" |
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_URL = "https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1740" |
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_DL_URL = "https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-1740/data_voip_cs_2016.tar.gz" |
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class Vystadial2016ASRConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Vysadial 2016.""" |
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def __init__(self, **kwargs): |
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""" |
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Args: |
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data_dir: `string`, the path to the folder containing the files in the |
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downloaded .tar |
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citation: `string`, citation for the data set |
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url: `string`, url for information about the data set |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(Vystadial2016ASRConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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class Vystadial2016ASR(datasets.GeneratorBasedBuilder): |
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"""Vystadial 2016 dataset.""" |
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DEFAULT_WRITER_BATCH_SIZE = 256 |
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DEFAULT_CONFIG_NAME = "all" |
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BUILDER_CONFIGS = [ |
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Vystadial2016ASRConfig(name="all", description="All samples."), |
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] |
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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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"file": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"text": datasets.Value("string"), |
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} |
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), |
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supervised_keys=("file", "text"), |
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homepage=_URL, |
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citation=_CITATION, |
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task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")], |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download(_DL_URL) |
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local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {} |
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return [ datasets.SplitGenerator( |
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name="train", |
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gen_kwargs={ |
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"files": dl_manager.iter_archive(archive_path), |
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"local_extracted_archive": local_extracted_archive, |
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}, |
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), ] |
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def _generate_examples(self, files, local_extracted_archive): |
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"""Generate examples from a Vystadial2016 archive_path.""" |
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key = 0 |
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samples = {} |
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transcripts = {} |
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id_transcripts = b'' |
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id_samples = b'' |
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for path, f in files: |
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if path.endswith(".wav"): |
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id_ = path.split('/')[-1][:-4] |
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id_samples = bytes(a ^ b for a, b in itertools.zip_longest(id_.encode('utf-8'), id_samples, fillvalue=0)) |
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audio_data = f.read() |
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audio_file = f"{id_}.wav" |
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audio_file = ( |
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os.path.join(local_extracted_archive, audio_file) |
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if local_extracted_archive |
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else audio_file ) |
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samples[id_] = {'audio': audio_file, 'bytes': audio_data, 'file': path} |
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elif path.endswith(".trn"): |
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id_ = path.split('/')[-1][:-8] |
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id_transcripts = bytes(a ^ b for a, b in itertools.zip_longest(id_.encode('utf-8') , id_transcripts, fillvalue=0)) |
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lines = f.readlines() |
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if not lines: |
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continue |
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line = lines[0].decode("utf-8").strip() |
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transcripts[id_] = line |
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if (samples and len(samples) == len(transcripts) |
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and id_samples == id_transcripts): |
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for id_, sample in samples.items(): |
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audio = {"path": sample["audio"], "bytes": sample["bytes"]} |
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yield key, {'audio': audio, 'file': sample['audio'], 'text': transcripts[id_]} |
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key += 1 |
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samples = {} |
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transcripts = {} |
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