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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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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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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return speech_list, speech_lengths_list
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def merge_vad(vad_result, max_length=15000):
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new_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 < 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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