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.

Deezer, the French online music streaming service has announced that it is releasing Spleeter – an open-source library for sound source separation.

Splitter in it's basic functionality as a Deezer Spleeter web service with the standard 2 stem and 5 Stem models is 100% free and will remain free forever. No registration or email is required. We might add a few features here and there to make it fullfilling to you as the end-user. Ezstems is a website application that allows you to easily create audio stems from any audio file. The website converts audio files using Spleeter, which is a new artificial intelligence type of software. Deezer, the French online music streaming service has announced that it is releasing Spleeter – an open-source library for sound source separation. Sound source separation is an important task in signal processing and it has a large number of applications, for example in remixes, mixing, active listening, transcription, etc. Music/voice separation. Info: site splits the audio track into separate components: voice, music, drums, guitar, piano, etc. Examples of a track divided into two parts (voice and music) can be seen in the video below. You can also check results of separation on our demo-page. If playback doesn't begin shortly, try restarting your device.

Sound source separation is an important task in signal processing and it has a large number of applications, for example in remixes, mixing, active listening, transcription, etc. A large number of methods have been proposed in the past but still, sound separation remains a challenging task.

Splitter Deezer Online

According to Deezer’s blog post, their sound separation model Spleeter performs at least as good as the best proposed algorithms currently available. They decided to open-source the model together with a library also called Spleeter.

The library is written in Python and built on top of Tensorflow. It allows for easy training of source separation models and it contains and already pre-trained state-of-the-art sound separation model from Deezer. The library can work within a GPU accelerated environment and achieve 100x faster than real-time processing for sound source separation. Therefore, Spleeter can also be used to process large datasets.

Several different models based were included in the Spleeter library: “vocals (singing voice)/accompaniment separation (2 source), “vocals/drums/bass/other” separation (4-source) and “vocals/ drums/bass/piano/other”, 5-source separation. The 2-source and 4-source models achieve state-of-the-art performance on the musdb dataset.

Spleeter Website

More about Spleeter can be read in the official blog post or in the library’s documentation.