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104 lines
6.5 KiB
Markdown
104 lines
6.5 KiB
Markdown
<img src="https://github.com/deezer/spleeter/raw/master/images/spleeter_logo.png" height="80" />
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[](https://circleci.com/gh/deezer/spleeter/tree/master)  [](https://badge.fury.io/py/spleeter) [](https://anaconda.org/conda-forge/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)
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## About
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**Spleeter** is the [Deezer](https://www.deezer.com/) source separation library with pretrained models
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written in [Python](https://www.python.org/) and uses [Tensorflow](https://tensorflow.org/). It makes it easy
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to train source separation model (assuming you have a dataset of isolated sources), and provides
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already trained state of the art model for performing various flavour of separation :
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* Vocals (singing voice) / accompaniment separation ([2 stems](https://github.com/deezer/spleeter/wiki/2.-Getting-started#using-2stems-model))
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* Vocals / drums / bass / other separation ([4 stems](https://github.com/deezer/spleeter/wiki/2.-Getting-started#using-4stems-model))
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* Vocals / drums / bass / piano / other separation ([5 stems](https://github.com/deezer/spleeter/wiki/2.-Getting-started#using-5stems-model))
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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.
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We designed **Spleeter** so you can use it straight from [command line](https://github.com/deezer/spleeter/wiki/2.-Getting-started#usage)
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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),
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with [pip](https://github.com/deezer/spleeter/wiki/1.-Installation#using-pip) or be used with
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[Docker](https://github.com/deezer/spleeter/wiki/2.-Getting-started#using-docker-image).
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## Quick start
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Want to try it out but don't want to install anything ? We have setup a [Google Colab](https://colab.research.google.com/github/deezer/spleeter/blob/master/spleeter.ipynb).
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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:
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```bash
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# install using conda
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conda install -c conda-forge spleeter
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# download an example audio file (if you don't have wget, use another tool for downloading)
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wget https://github.com/deezer/spleeter/raw/master/audio_example.mp3
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# separate the example audio into two components
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spleeter separate -i audio_example.mp3 -p spleeter:2stems -o output
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```
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You should get two separated audio files (`vocals.wav` and `accompaniment.wav`) in the `output/audio_example` folder.
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For a detailed documentation, please check the [repository wiki](https://github.com/deezer/spleeter/wiki)
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## Development and testing
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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):
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```bash
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git clone https://github.com/Deezer/spleeter
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python -m venv spleeterenv && source spleeterenv/bin/activate
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pip install -r requirements.txt && pip install pytest pytest-xdist
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make test
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```
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## Reference
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* Deezer Research - Source Separation Engine Story - deezer.io blog post:
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* [English version](https://deezer.io/releasing-spleeter-deezer-r-d-source-separation-engine-2b88985e797e)
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* [Japanese version](http://dzr.fm/splitterjp)
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* [Music Source Separation tool with pre-trained models / ISMIR2019 extended abstract](http://archives.ismir.net/ismir2019/latebreaking/000036.pdf)
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If you use **Spleeter** in your work, please cite:
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```BibTeX
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@misc{spleeter2019,
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title={Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models},
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author={Romain Hennequin and Anis Khlif and Felix Voituret and Manuel Moussallam},
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howpublished={Late-Breaking/Demo ISMIR 2019},
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month={November},
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note={Deezer Research},
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year={2019}
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}
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```
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## License
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The code of **Spleeter** is [MIT-licensed](LICENSE).
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## Disclaimer
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If you plan to use Spleeter on copyrighted material, make sure you get proper authorization from right owners beforehand.
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### Forks and related projects
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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.
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## Troubleshooting
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**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)
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### Windows users
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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.
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## Contributing
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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](.github/CONTRIBUTING.md) first.
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## Note
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This repository include a demo audio file `audio_example.mp3` which is an excerpt
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from Slow Motion Dream by Steven M Bryant (c) copyright 2011 Licensed under a Creative
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Commons Attribution (3.0) [license](http://dig.ccmixter.org/files/stevieb357/34740)
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Ft: CSoul,Alex Beroza & Robert Siekawitch
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