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"""Speech Segment dataset. |
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""" |
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import os |
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from pathlib import Path |
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import datasets |
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import torchaudio |
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class SpeechSegmentConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Speech Segment. |
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For long audio files, segment them into smaller segments of fixed length. |
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For short audio files, return the whole audio file. |
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""" |
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def __init__(self, segment_length, **kwargs): |
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super(SpeechSegmentConfig, self).__init__(**kwargs) |
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self.segment_length = segment_length |
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class SpeechSegment(datasets.GeneratorBasedBuilder): |
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"""Speech Segment dataset.""" |
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BUILDER_CONFIGS = [ |
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SpeechSegmentConfig(name="all", segment_length=60.0,), |
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] |
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@property |
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def manual_download_instructions(self): |
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return ( |
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"Specify the data_dir as the path to the folder, will recursively search for .flac and .wav files. " |
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"`datasets.load_dataset('subatomicseer/speech_segment', data_dir='path/to/folder/folder_name')`" |
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) |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"file": datasets.Value("string"), |
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'sample_rate': datasets.Value('int64'), |
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'offset': datasets.Value('int64'), |
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'num_frames': datasets.Value('int64'), |
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} |
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) |
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return datasets.DatasetInfo( |
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features=features, |
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) |
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def _split_generators(self, dl_manager): |
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base_data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) |
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if not os.path.exists(base_data_dir): |
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raise FileNotFoundError( |
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f"{base_data_dir} does not exist. Manual download instructions: {self.manual_download_instructions}" |
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) |
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data_dirs = [str(p) for p in Path(base_data_dir).rglob('*') if p.suffix in ['.flac', '.wav']] |
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print(f"Found {len(data_dirs)} audio files in {base_data_dir}") |
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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={"data_dirs": data_dirs}, |
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), |
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] |
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def _generate_examples(self, data_dirs): |
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for key, path in enumerate(data_dirs): |
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path_split = path.split("/") |
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id_ = '/'.join(path_split[-4:]).replace(".flac", "") |
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audio_metadata = torchaudio.info(path) |
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segment_length = int(self.config.segment_length * audio_metadata.sample_rate) |
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total_length = audio_metadata.num_frames |
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if total_length <= segment_length: |
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yield id_, { |
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"id": id_, |
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"file": path, |
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'sample_rate': audio_metadata.sample_rate, |
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'offset': 0, |
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'num_frames': total_length, |
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} |
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else: |
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offsets = list(range(0, total_length, segment_length)) |
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if total_length - offsets[-1] < 1 * audio_metadata.sample_rate: |
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offsets.pop() |
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for segment_id, start in enumerate(offsets): |
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end = start + segment_length - 1 |
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if end > total_length: |
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end = total_length |
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yield f'{id_}_{segment_id}', { |
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"id": f'{id_}_{segment_id}', |
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"file": path, |
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'sample_rate': audio_metadata.sample_rate, |
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'offset': start, |
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'num_frames': end-start+1, |
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} |
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