Spleeter is Deezer source separation library with pretrained models written in Python and uses Tensorflow. 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)
Vocals / drums / bass / other separation (4 stems)
Vocals / drums / bass / piano / other separation (5 stems)
2 stems and 4 stems models have high performances on the musdb 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 as well as directly in your own development pipeline as a Python library. It can be installed with pip or be used with Docker.
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 project.
Spleeter pre-trained models have also been used by professionnal audio softwares. Here's a non-exhaustive list:
iZotope in its Music Rebalance feature within RX 8
SpectralLayers in its Unmix feature in SpectralLayers 7
Acon Digital within Acoustica 7
VirtualDJ in their stem isolation feature
Algoriddim in their NeuralMix and djayPRO app suite