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