Skip to content

Zhongyang-debug/Effective-Low-Cost-Time-Domain-Audio-Separation-Using-Globally-Attentive-Locally-Recurrent-Networks

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

22 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Effective-Low-Cost-Time-Domain-Audio-Separation-Using-Globally-Attentive-Locally-Recurrent-Networks

Effective-Low-Cost-Time-Domain-Audio-Separation-Using-Globally-Attentive-Locally-Recurrent-Networks

论文下载地址:https://arxiv.org/pdf/2101.05014v1.pdf

第一步,运行 perprocess.py 读取数据地址; ./config/train/train.json 设置参数;

第二步,训练模型,运行 train.py ;

第三步,测试分数,运行 evaluate.py;

第四步,分离语音,运行 separate.py。

About

Speech Separation

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages