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| | | import torch |
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| | | from torch.nn.utils.rnn import pad_sequence |
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| | | def slice_padding_fbank(speech, speech_lengths, vad_segments): |
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| | | speech_list = [] |
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| | | speech_lengths_list = [] |
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| | | for i, segment in enumerate(vad_segments): |
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| | | |
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| | | bed_idx = int(segment[0][0]*16) |
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| | | end_idx = min(int(segment[0][1]*16), speech_lengths[0]) |
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| | | speech_i = speech[0, bed_idx: end_idx] |
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| | | speech_lengths_i = end_idx-bed_idx |
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| | | speech_list.append(speech_i) |
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| | | speech_lengths_list.append(speech_lengths_i) |
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| | | feats_pad = pad_sequence(speech_list, batch_first=True, padding_value=0.0) |
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| | | speech_lengths_pad = torch.Tensor(speech_lengths_list).int() |
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| | | return feats_pad, speech_lengths_pad |
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| | | |
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| | | speech_list = [] |
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| | | speech_lengths_list = [] |
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| | | for i, segment in enumerate(vad_segments): |
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| | | |
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| | | bed_idx = int(segment[0][0] * 16) |
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| | | end_idx = min(int(segment[0][1] * 16), speech_lengths[0]) |
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| | | speech_i = speech[0, bed_idx:end_idx] |
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| | | speech_lengths_i = end_idx - bed_idx |
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| | | speech_list.append(speech_i) |
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| | | speech_lengths_list.append(speech_lengths_i) |
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| | | feats_pad = pad_sequence(speech_list, batch_first=True, padding_value=0.0) |
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| | | speech_lengths_pad = torch.Tensor(speech_lengths_list).int() |
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| | | return feats_pad, speech_lengths_pad |
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| | | |
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| | | |
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| | | def slice_padding_audio_samples(speech, speech_lengths, vad_segments): |
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| | | speech_list = [] |
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| | | speech_lengths_list = [] |
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| | | for i, segment in enumerate(vad_segments): |
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| | | bed_idx = int(segment[0][0] * 16) |
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| | | end_idx = min(int(segment[0][1] * 16), speech_lengths) |
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| | | speech_i = speech[bed_idx:end_idx] |
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| | | speech_lengths_i = end_idx - bed_idx |
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| | | speech_list.append(speech_i) |
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| | | speech_lengths_list.append(speech_lengths_i) |
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| | | |
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| | | return speech_list, speech_lengths_list |
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| | | |
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| | | |
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| | | def merge_vad(vad_result, max_length=15000, min_length=0): |
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| | | new_result = [] |
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| | | if len(vad_result) <= 1: |
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| | | return vad_result |
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| | | time_step = [t[0] for t in vad_result] + [t[1] for t in vad_result] |
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| | | time_step = sorted(list(set(time_step))) |
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| | | if len(time_step) == 0: |
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| | | return [] |
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| | | bg = 0 |
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| | | for i in range(len(time_step) - 1): |
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| | | time = time_step[i] |
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| | | if time_step[i + 1] - bg < max_length: |
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| | | continue |
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| | | if time - bg > min_length: |
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| | | new_result.append([bg, time]) |
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| | | # if time - bg < max_length * 1.5: |
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| | | # new_result.append([bg, time]) |
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| | | # else: |
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| | | # split_num = int(time - bg) // max_length + 1 |
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| | | # spl_l = int(time - bg) // split_num |
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| | | # for j in range(split_num): |
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| | | # new_result.append([bg + j * spl_l, bg + (j + 1) * spl_l]) |
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| | | bg = time |
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| | | new_result.append([bg, time_step[-1]]) |
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| | | return new_result |
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