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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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| | | import librosa |
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| | | import torchaudio.compliance.kaldi as kaldi |
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| | | try: |
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| | | waveform, audio_sr = torchaudio.load(wav_file) |
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| | | except: |
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| | | waveform, audio_sr = soundfile.read(wav_file, dtype='float32') |
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| | | waveform, audio_sr = librosa.load(wav_file, dtype='float32') |
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| | | if waveform.ndim == 2: |
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| | | waveform = waveform[:, 0] |
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| | | waveform = torch.tensor(np.expand_dims(waveform, axis=0)) |
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| | | try: |
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| | | waveform, sampling_rate = torchaudio.load(wav_path) |
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| | | except: |
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| | | waveform, sampling_rate = soundfile.read(wav_path) |
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| | | waveform, sampling_rate = librosa.load(wav_path) |
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| | | waveform = torch.tensor(np.expand_dims(waveform, axis=0)) |
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| | | speech_length = (waveform.shape[1] / sampling_rate) * 1000. |
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| | | n_frames = (waveform.shape[1] * 1000.0) / (sampling_rate * frontend_conf["frame_shift"] * frontend_conf["lfr_n"]) |
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