spleeter 使用机器学习来分离人声和伴奏，效果相当好。甚至可以分离出各种乐器的声音
- GitHub: https://github.com/deezer/spleeter
- colab: https://colab.research.google.com/github/deezer/spleeter/blob/master/spleeter.ipynb
简介
Spleeter is the Deezer source separation library with pretrained models written in Python and uses Tensorflow. It makes it easy to train source separation model (assuming you have a dataset of isolated sources), and provides already trained state of the art model for performing various flavour of separation :
Vocals (singing voice) / accompaniment separation (2 stems) Vocals / drums / bass / other separation (4 stems) Vocals / drums / bass / piano / other separation (5 stems) 2 stems and 4 stems models have state of the art performances on the musdb dataset. Spleeter is also very fast as it can perform separation of audio files to 4 stems 100x faster than real-time when run on a GPU.
We designed Spleeter so you can use it straight from command line as well as directly in your own development pipeline as a Python library. It can be installed with Conda, with pip or be used with Docker.
安装
参见 https://github.com/deezer/spleeter/wiki/1.-Installation
使用
分离两个音轨：
1
spleeter separate -i audio_example.mp3 -o audio_output -p spleeter:2stems
详情参见 https://github.com/deezer/spleeter/wiki/2.-Getting-started
链接
下面总结了本文中使用的所有链接：