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- <img src="https://github.com/deezer/spleeter/raw/master/images/spleeter_logo.png" height="80" />
-
- [](https://github.com/deezer/spleeter/actions)  [](https://badge.fury.io/py/spleeter) [](https://anaconda.org/deezer-research/spleeter) [](https://hub.docker.com/r/researchdeezer/spleeter) [](https://colab.research.google.com/github/deezer/spleeter/blob/master/spleeter.ipynb) [](https://gitter.im/spleeter/community) [](https://joss.theoj.org/papers/259e5efe669945a343bad6eccb89018b)
-
- > :warning: [Spleeter 2.1.0](https://pypi.org/project/spleeter/) release introduces some breaking changes, including new CLI option naming for input, and the drop
- > of dedicated GPU package. Please read [CHANGELOG](CHANGELOG.md) for more details.
-
- ## 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 [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
-
- 🆕 **Spleeter** is a baseline in the ongoing [Music Demixing Challenge](https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021)!
-
- ## 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** and separate the vocal and accompaniment parts from an example audio file.
- You need first to install `ffmpeg` and `libsndfile`. It can be done on most platform using [Conda](https://github.com/deezer/spleeter/wiki/1.-Installation#using-conda):
-
- ```bash
- # install dependencies using conda
- conda install -c conda-forge ffmpeg libsndfile
- # install spleeter with pip
- pip install 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 -p spleeter:2stems -o output audio_example.mp3
- ```
-
- > :warning: Note that we no longer recommend using `conda` for installing spleeter.
-
- > ⚠️ There are known issues with Apple M1 chips, mostly due to TensorFlow compatibility. Until these are fixed, you can use [this workaround](https://github.com/deezer/spleeter/issues/607#issuecomment-828352392)
-
- 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/1.-Installation)
-
- ## Development and Testing
-
- This project is managed using [Poetry](https://python-poetry.org/docs/basic-usage/), to run test suite you
- can execute the following set of commands:
-
- ```bash
- # Clone spleeter repository
- git clone https://github.com/Deezer/spleeter && cd spleeter
- # Install poetry
- pip install poetry
- # Install spleeter dependencies
- poetry install
- # Run unit test suite
- poetry run pytest tests/
- ```
-
- ## 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
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