spleeter/README.md

<img src="https://github.com/deezer/spleeter/raw/master/images/spleeter_logo.png" height="80" />

[![Github actions](https://github.com/deezer/spleeter/workflows/pytest/badge.svg)](https://github.com/deezer/spleeter/actions) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/spleeter) [![PyPI version](https://badge.fury.io/py/spleeter.svg)](https://badge.fury.io/py/spleeter) [![Conda](https://img.shields.io/conda/vn/conda-forge/spleeter)](https://anaconda.org/conda-forge/spleeter) [![Docker Pulls](https://img.shields.io/docker/pulls/researchdeezer/spleeter)](https://hub.docker.com/r/researchdeezer/spleeter) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deezer/spleeter/blob/master/spleeter.ipynb) [![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/spleeter/community) [![status](https://joss.theoj.org/papers/259e5efe669945a343bad6eccb89018b/status.svg)](https://joss.theoj.org/papers/259e5efe669945a343bad6eccb89018b)

## About

**Spleeter** is [Deezer](https://www.deezer.com/) source separation library with pretrained models
written in [Python](https://www.python.org/) and uses [Tensorflow](https://tensorflow.org/). 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](https://github.com/deezer/spleeter/wiki/2.-Getting-started#using-2stems-model))
* Vocals / drums / bass / other separation ([4 stems](https://github.com/deezer/spleeter/wiki/2.-Getting-started#using-4stems-model))
* Vocals / drums / bass / piano / other separation ([5 stems](https://github.com/deezer/spleeter/wiki/2.-Getting-started#using-5stems-model))

2 stems and 4 stems models have [high performances](https://github.com/deezer/spleeter/wiki/Separation-Performances) on the [musdb](https://sigsep.github.io/datasets/musdb.html) 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](https://github.com/deezer/spleeter/wiki/2.-Getting-started#usage)
as well as directly in your own development pipeline as a [Python library](https://github.com/deezer/spleeter/wiki/4.-API-Reference#separator). It can be installed with [Conda](https://github.com/deezer/spleeter/wiki/1.-Installation#using-conda),
with [pip](https://github.com/deezer/spleeter/wiki/1.-Installation#using-pip) or be used with
[Docker](https://github.com/deezer/spleeter/wiki/2.-Getting-started#using-docker-image).

### Projects and Softwares using **Spleeter**

Since it's been released, 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.

That being said, many cool projects have been built on top of ours. Notably the porting to the *Ableton Live* ecosystem through the [Spleeter 4 Max](https://github.com/diracdeltas/spleeter4max#spleeter-for-max) project.

**Spleeter** pre-trained models have also been used by professionnal audio softwares. Here's a non-exhaustive list:

* [iZotope](https://www.izotope.com/en/shop/rx-8-standard.html) in its *Music Rebalance* feature within **RX 8**
* [SpectralLayers](https://new.steinberg.net/spectralayers/) in its *Unmix* feature in **SpectralLayers 7**
* [Acon Digital](https://acondigital.com/products/acoustica-audio-editor/) within **Acoustica 7**
* [VirtualDJ](https://www.virtualdj.com/stems/) in their stem isolation feature
* [Algoriddim](https://www.algoriddim.com/apps) in their **NeuralMix** and **djayPRO** app suite

## Quick start

Want to try it out but don't want to install anything ? We have set up a [Google Colab](https://colab.research.google.com/github/deezer/spleeter/blob/master/spleeter.ipynb).

Ready to dig into it ? In a few lines you can install **Spleeter** using [Conda](https://github.com/deezer/spleeter/wiki/1.-Installation#using-conda) and separate the vocal and accompaniment parts from an example audio file:

```bash
# 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 output
```

You 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](https://github.com/deezer/spleeter/wiki)

## Development and Testing

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):

```bash
git clone https://github.com/Deezer/spleeter && cd spleeter
python -m venv spleeterenv && source spleeterenv/bin/activate
pip install . && pip install pytest pytest-xdist
make test
```

## Reference

* Deezer Research - Source Separation Engine Story - deezer.io blog post:
  * [English version](https://deezer.io/releasing-spleeter-deezer-r-d-source-separation-engine-2b88985e797e)
  * [Japanese version](http://dzr.fm/splitterjp)
* [Music Source Separation tool with pre-trained models / ISMIR2019 extended abstract](http://archives.ismir.net/ismir2019/latebreaking/000036.pdf)

If you use **Spleeter** in your work, please cite:

```BibTeX
@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}
}
```

## License

The code of **Spleeter** is [MIT-licensed](LICENSE).

## Disclaimer

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

## Troubleshooting

**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](https://github.com/deezer/spleeter/wiki/5.-FAQ) first as well as the list of [currently open issues](https://github.com/deezer/spleeter/issues)

### Windows users

   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.

## Contributing

If you would like to participate in the development of **Spleeter** you 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](.github/CONTRIBUTING.md) first.

## 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