Released on october 29th 2019, the Spleeter (https://github.com/deezer/spleeter) github repository received more than 5000 stars on its first week online and numerous positive feedbacks as well as press coverage. This talk will explain how we went from research code to this fairly easy to use open Python library, that integrates pre-trained models for inference and re-training.

While not a broadly known topic, the problem of source separation has interested a large community of music signal researchers for a couple of decades now. It starts from a simple observation: music recordings are usually a mix of several individual instrument tracks (lead vocal, drums, bass, piano etc.). The task of music source separation is: given a mix can we recover these separate tracks (sometimes called stems)? This has many potential applications: think remixes, upmixing, active listening, educational purposes, but also pre-processing for other tasks such as transcription.

The current state-of-the-art systems start to give convincing results on very wide catalogs of tracks, but the possibility of training such models remains largely bound by training data availability. In the case of copyrighted material like music, getting access to enough data is a pain point, and a source of inequality between research teams. Beside, an essential feature of good scientific research is that it must be reproducible by others. For these reasons and to even the playing field, we decided to not only release the code, but also our models pretrained on a carefully crafter in-house dataset.

Specific topics on which our presentation will dwell on are:- technical aspects of the models architecture and training- software design, and how to leverage tensorflow’s API in a user facing python library- how to package and version a code that leverages pre-trained models and that can be run on different architectures: CPU and GPU.- licensing and legal concerns- what we learned along the way- legacy

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Recently, the research team at Deezer released a free and open source software as well as trained models to perform multi-source separation of music, with state-of-the-art accuracy. In this presentation we come back on our journey to open sourcing the Spleeter library, from doing the ground research, training the models, to releasing them.

Pick and play any track ad-free, plus download your favourites and listen offline. Stream across all your devices. Then £9.99/month. No commitment, cancel anytime. All the benefits of Deezer Premium at a discounted annual price. 1-year commitment. 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. 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.

Deezer Spleeter Online

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. Vivaldi portrait. Web scraping using python and beautifulsoup 10. A large number of methods have been proposed in the past but still, sound separation remains a challenging task.

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.

Spleeter Audio

Spleeter Website

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.

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