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import datasets |
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class AudioDataset(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=datasets.Features( |
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{ |
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"file_name": datasets.Value("string"), |
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"transcription": datasets.Value("string"), |
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"speaker_id": datasets.Value("int64"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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} |
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), |
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) |
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def _split_generators(self, dl_manager): |
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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={"filepath": "train_metadata.csv", "audio_dir": "audio"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": "test_metadata.csv", "audio_dir": "audio"}, |
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), |
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] |
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def _generate_examples(self, filepath, audio_dir): |
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with open(filepath, "r", encoding="utf-8") as f: |
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for id_, line in enumerate(f): |
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if id_ == 0: |
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continue |
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parts = line.strip().split(",") |
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file_name = parts[0] |
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transcription = parts[1] |
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speaker_id = int(parts[2]) |
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yield id_, { |
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"file_name": file_name, |
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"transcription": transcription, |
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"speaker_id": speaker_id, |
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"audio": f"{audio_dir}/{file_name.split('/')[-1]}", |
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} |