Upload czech_parliament_plenary_hearings.py

czech_parliament_plenary_hearings.py

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+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')
+        return [
+            self.SplitGenerator(name='train', gen_kwargs={'split': 'train', 'data_dir': data_dir}),
+            self.SplitGenerator(name='validation', gen_kwargs={'split': 'dev', 'data_dir': data_dir}),
+            self.SplitGenerator(name='test', gen_kwargs={'split': 'test', 'data_dir': data_dir})
+        ]
+
+    def _generate_examples(self, split, data_dir):
+        split_dir = os.path.join(data_dir, split)
+        for folder_name in os.listdir(split_dir):
+            folder_path = os.path.join(split_dir, folder_name)
+            if os.path.isdir(folder_path):
+                for audio_file in os.listdir(folder_path):
+                    if audio_file.endswith('.wav'):
+                        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,
+                        }