mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
340c55838b
* 添加富文本解码约束 * special token * bug fix * fix * 增加unk score的参数 * emobaned * kwargs2cfg * merge_vad bug fix --------- Co-authored-by: 常材 <gaochangfeng.gcf@alibaba-inc.com>
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
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 = []
|
|
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
|
|
|
|
|
|
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 |