| | |
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| | | import numpy as np |
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| | | import torch |
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| | | import torchaudio |
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| | | import soundfile |
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| | | from torch.utils.data.dataset import IterableDataset |
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| | | from typeguard import check_argument_types |
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| | | import os.path |
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| | | |
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| | | from funasr.datasets.dataset import ESPnetDataset |
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| | |
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| | | bytes = f.read() |
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| | | return load_bytes(bytes) |
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| | | |
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| | | def load_wav(input): |
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| | | try: |
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| | | return torchaudio.load(input)[0].numpy() |
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| | | except: |
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| | | waveform, _ = soundfile.read(input, dtype='float32') |
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| | | if waveform.ndim == 2: |
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| | | waveform = waveform[:, 0] |
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| | | return np.expand_dims(waveform, axis=0) |
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| | | |
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| | | DATA_TYPES = { |
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| | | "sound": lambda x: torchaudio.load(x)[0].numpy(), |
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| | | "sound": load_wav, |
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| | | "pcm": load_pcm, |
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| | | "kaldi_ark": load_kaldi, |
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| | | "bytes": load_bytes, |
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| | |
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| | | int_dtype: str = "long", |
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| | | key_file: str = None, |
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| | | ): |
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| | | assert check_argument_types() |
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| | | if len(path_name_type_list) == 0: |
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| | | raise ValueError( |
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| | | '1 or more elements are required for "path_name_type_list"' |
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