| from datasets import Dataset, GeneratorBasedBuilder, Features | |
| import os | |
| import tarfile | |
| import librosa | |
| import datasets | |
| _LICENSE = "https://creativecommons.org/licenses/by/4.0/" | |
| _HOMEPAGE = "https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3126" | |
| _DATASET_URL = "https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3126/snemovna.tar.xz" | |
| _DESCRIPTION = "Large corpus of Czech parliament plenary sessions, originaly released 2019-11-29 by Kratochvíl Jonáš, Polák Peter and Bojar Ondřej\ | |
| The dataset consists of 444 hours of transcribed speech audio snippets 1 to 40 seconds long.\ | |
| Original dataset transcriptions were converted to true case from uppercase using spacy library." | |
| _CITATION = """\ | |
| @misc{11234/1-3126, | |
| title = {Large Corpus of Czech Parliament Plenary Hearings}, | |
| author = {Kratochv{\'{\i}}l, Jon{\'a}{\v s} and Pol{\'a}k, Peter and Bojar, Ond{\v r}ej}, | |
| url = {http://hdl.handle.net/11234/1-3126}, | |
| note = {{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University}, | |
| copyright = {Creative Commons - Attribution 4.0 International ({CC} {BY} 4.0)}, | |
| year = {2019} } """ | |
| class CzechParliamentPlenaryHearings(GeneratorBasedBuilder): | |
| def __init__(self, **kwargs): | |
| super().__init__(**kwargs) | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "audio": datasets.features.Audio(sampling_rate=16000), | |
| "transcription": datasets.Value("string") | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| license=_LICENSE | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(_DATASET_URL) | |
| data_dir = os.path.join(data_dir, 'ASR_DATA') | |
| splits = ("train", "dev", "test") | |
| split_names = { | |
| "train": datasets.Split.TRAIN, | |
| "dev": datasets.Split.VALIDATION, | |
| "test": datasets.Split.TEST, | |
| } | |
| split_generators = [] | |
| for split in splits: | |
| split_generators.append( | |
| name=split_names.get(split, split), | |
| gen_kwargs={'split': split, 'data_dir': data_dir} | |
| ) | |
| return split_generators | |
| def _generate_examples(self, split, data_dir): | |
| split_dir = os.path.join(data_dir, split) | |
| with os.scandir(split_dir) as it: | |
| for entry in it: | |
| if entry.is_dir(): | |
| folder_name = entry.name | |
| folder_path = os.path.join(split_dir, folder_name) | |
| with os.scandir(folder_path) as it2: | |
| for entry2 in it2: | |
| if entry2.is_file() and entry2.name.endswith('.wav'): | |
| audio_file = entry2.name | |
| audio_path = os.path.join(folder_path, audio_file) | |
| transcription_path = os.path.join( | |
| folder_path, audio_file + '.trn') | |
| transcription = open( | |
| transcription_path).read().strip() | |
| audio, sr = librosa.load(audio_path, sr=16000) | |
| yield f"{folder_name}/{audio_file}", { | |
| 'audio': audio, | |
| 'transcription': transcription, | |
| } | |