def cut_wave(wav_nam, save_file):
frame_size = 256
frame_shift = 128
sr = 16000
seg_point = bic_seg.multi_segmentation(wav_nam,
sr,
frame_size,
frame_shift,
save_file,
plot_seg=False,
save_seg=True,
cluster_method='bic')
print('The segmentation point for this audio file is listed (Unit: /s)',
seg_point)
def upload():
frame_size = 256
frame_shift = 128
sr = 16000
fname = request.files.get('file') # 获取上传的文件
merger_time = int(request.form.get('merger_time')) # 获取合并时间参数
if fname:
save_name = time.strftime('%Y%m%d%H%M%S') + "_" + fname.filename
save_path = r'aideo_file/upload_audio/' + save_name
# 保存原始音频
fname.save(save_path) # 保存文件到指定路径
file_name = save_name[:save_name.index(".")]
file_suffix = save_name[save_name.index("."):]
# 转换音频
conversion_path = audio_file_util.audio_conversion(save_path, file_name, file_suffix)
# 拆分音频
resolution_path = bic_seg.multi_segmentation(conversion_path, file_name, sr, frame_size, frame_shift,
plot_seg=False, save_seg=True,
cluster_method='bic')
# 合并音频
return_path = audio_file_util.audio_merge(resolution_path, file_name, merger_time)
return json.dumps(return_path)
from __future__ import print_function
import BIC.speech_segmentation as bic_seg
frame_size = 256
frame_shift = 128
sr = 16000
seg_point = bic_seg.multi_segmentation("test1.wav",
sr,
frame_size,
frame_shift,
plot_seg=True,
save_seg=True,
cluster_method='bic')
print('The segmentation point for this audio file is listed (Unit: /s)',
seg_point)
# -*- coding:UTF-8 -*-
from __future__ import print_function
import BIC.speech_segmentation as bic_seg
frame_size = 256
frame_shift = 128
sr = 16000
seg_point = bic_seg.multi_segmentation("duihua_sample.wav", sr, frame_size, frame_shift, plot_seg=False, save_seg=True,
cluster_method='bic')
print('The segmentation point for this audio file is listed (Unit: /s)', seg_point)