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| | | import kaldiio |
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| | | import numpy as np |
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| | | import librosa |
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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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| | | 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 = 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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| | | sample_name, feature_path = line.strip().split() |
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| | | feature = kaldiio.load_mat(feature_path) |
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| | | n_frames, feature_dim = feature.shape |
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| | | write_flag = True |
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| | | if n_frames > 0 and length_min > 0: |
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| | | write_flag = n_frames >= length_min |
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| | | if n_frames > 0 and length_max > 0: |
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