Skip to main content

The Deezer source separation library with pretrained models based on tensorflow.

Project description

CircleCI PyPI - Python Version PyPI version Conda Docker Pulls Open In Colab Gitter chat

About

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.

Quick start

Want to try it out ? Just clone the repository and install a Conda environment to start separating audio file as follows:

git clone https://github.com/Deezer/spleeter
conda install -c conda-forge spleeter
spleeter separate -i spleeter/audio_example.mp3 -p spleeter:2stems -o output

You should get two separated audio files (vocals.wav and accompaniment.wav) in the output/audio_example folder.

For a more detailed documentation, please check the repository wiki

Want to try it out but don't want to install anything ? we've setup a Google Colab

Reference

If you use Spleeter in your work, please cite:

@misc{spleeter2019,
  title={Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models},
  author={Romain Hennequin and Anis Khlif and Felix Voituret and Manuel Moussallam},
  howpublished={Late-Breaking/Demo ISMIR 2019},
  month={November},
  note={Deezer Research},
  year={2019}
}

License

The code of Spleeter is MIT-licensed.

Disclaimer

If you plan to use Spleeter on copyrighted material, make sure you get proper authorization from right owners beforehand.

Note

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. http://dig.ccmixter.org/files/stevieb357/34740 Ft: CSoul,Alex Beroza & Robert Siekawitch

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for spleeter, version 1.4.8
Filename, size File type Python version Upload date Hashes
Filename, size spleeter-1.4.8.tar.gz (34.8 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page