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| | | import numpy as np |
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| | | import torch.distributed as dist |
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| | | import torchaudio |
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| | | import soundfile |
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| | | def filter_wav_text(data_dir, dataset): |
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| | | def wav2num_frame(wav_path, frontend_conf): |
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| | | waveform, sampling_rate = torchaudio.load(wav_path) |
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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 = np.expand_dims(waveform, axis=0) |
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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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| | | feature_dim = frontend_conf["n_mels"] * frontend_conf["lfr_m"] |
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| | | return n_frames, feature_dim |
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| | | for i in range(nj): |
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| | | path = "" |
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| | | for file_name in file_names: |
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| | | path = path + os.path.join(split_path, str(i + 1), file_name) |
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| | | path = path + " " + os.path.join(split_path, str(i + 1), file_name) |
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| | | f_data.write(path + "\n") |
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