Initial commit from private spleeter

README.md

@@ -1,6 +1,64 @@
-# spleeter
+<img src="https://github.com/deezer/spleeter/raw/master/images/spleeter_logo.png" height="80" />
 
-<img src=images/spleeter_logo.png height=100>
+[![PyPI version](https://badge.fury.io/py/spleeter.svg)](https://badge.fury.io/py/spleeter) ![Conda](https://img.shields.io/conda/dn/conda-forge/spleeter)
 
+## About
 
-spleeter will be made available soon!
+**Spleeter** is the [Deezer](https://www.deezer.com/) source separation library with pretrained models
+written in [Python](https://www.python.org/) and uses [Tensorflow](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. It 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 it 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)
+
+**Spleeter** 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 env create -f spleeter/conda/spleeter-cpu.yaml
+$ conda activate spleeter-cpu
+$ 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](https://github.com/deezer/spleeter/wiki)
+
+## 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},
+  year={2019}
+}
+```
+
+## License
+The code of **Spleeter** is MIT-licensed.
+
+## 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