| | |
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| | | 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 = [] |
|---|
| | | 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) |
|---|
| | | |
|---|
| | | return speech_list, speech_lengths_list |
|---|
