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 high 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.
Want to try it out but don't want to install anything ? We have setup a Google Colab.
Ready to dig into it ? In a few lines you can install Spleeter using Conda and separate the vocal and accompaniment parts from an example audio file:
# install using conda
conda install -c conda-forge spleeter
# download an example audio file (if you don't have wget, use another tool for downloading)
wget https://github.com/deezer/spleeter/raw/master/audio_example.mp3
# separate the example audio into two components
spleeter separate -i audio_example.mp3 -p spleeter:2stems -o outputYou should get two separated audio files (vocals.wav and accompaniment.wav) in the output/audio_example folder.
For a detailed documentation, please check the repository wiki
The following set of commands will clone this repository, create a virtual environment provisioned with the dependencies and run the tests (will take a few minutes):
git clone https://github.com/Deezer/spleeter
python -m venv spleeterenv && source spleeterenv/bin/activate
pip install -r requirements.txt && pip install pytest pytest-xdist
make test- Deezer Research - Source Separation Engine Story - deezer.io blog post:
- Music Source Separation tool with pre-trained models / ISMIR2019 extended abstract
If you use Spleeter in your work, please cite:
@article{spleeter2020,
doi = {10.21105/joss.02154},
url = {https://doi.org/10.21105/joss.02154},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {50},
pages = {2154},
author = {Romain Hennequin and Anis Khlif and Felix Voituret and Manuel Moussallam},
title = {Spleeter: a fast and efficient music source separation tool with pre-trained models},
journal = {Journal of Open Source Software},
note = {Deezer Research}
}The code of Spleeter is MIT-licensed.
If you plan to use Spleeter on copyrighted material, make sure you get proper authorization from right owners beforehand.
As is commonly the case with open-source projects, there are multiple forks exposing spleeter through either a Guided User Interface (GUI) or a standalone free or paying website. Please note that we do not host, maintain or directly support any of these initiatives.
spleeter is a complex piece of software and although we continously try to improve and test it you may encounter unexpected issues running it. If that's the case please check the FAQ page first as well as the list of currently open issues
It appears that sometimes the shortcut command spleeter does not work properly on windows. This is a known issue that we will hopefully fix soon. In the meantime replace spleeter separate by python -m spleeter separate in command line and it should work.
If you would like to participate in the development of spleeter your are more than welcome to do so. Don't hesitate to throw us a pull request and we'll do our best to examine it quickly. Please check out our guidelines first.
This repository include a demo audio file audio_example.mp3 which is an excerpt
from Slow Motion Dream by Steven M Bryant (c) copyright 2011 Licensed under a Creative
Commons Attribution (3.0) license
Ft: CSoul,Alex Beroza & Robert Siekawitch