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A10G
Running
on
A10G
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 | |