Metadata-Version: 2.1
Name: spleeter
Version: 1.5.0
Summary: 
        The Deezer source separation library with
        pretrained models based on tensorflow.
    
Home-page: https://github.com/deezer/spleeter
Author: Deezer Research
Author-email: research@deezer.com
License: MIT License
Description: <img src="https://github.com/deezer/spleeter/raw/master/images/spleeter_logo.png" height="80" />
        
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        ## About
        
        **Spleeter** is the [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 state of the art 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).
        
        ## Quick start 
        
        Want to try it out ? Just clone the repository and install a
        [Conda](https://github.com/deezer/spleeter/wiki/1.-Installation#using-conda)
        environment to start separating audio file as follows:
        
        ```bash
        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.
        
        ### 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 the above line and it should work.
        
        For a more detailed documentation, please check the [repository wiki](https://github.com/deezer/spleeter/wiki)
        
        Want to try it out but don't want to install anything ? we've setup a [Google Colab](https://colab.research.google.com/github/deezer/spleeter/blob/master/spleeter.ipynb)
        
        ## 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
        @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
        
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