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)