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Running
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A10G
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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
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