Spleeter Deezer Online
Deezer, the French online music streaming service has announced that it is releasing Spleeter – an open-source library for sound source separation.
- Conda-forge / packages / spleeter 1.5.3. 9 The Deezer source separation library with pretrained models based on tensorflow. Conda Files; Labels; Badges; License: MIT; 48837 total downloads Last upload: 9 months and 21 days ago Installers. Info: This package contains files in non-standard.
- 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.
Spleeter Stems
Spleeter is the Deezer source separation library with pretrained models written in Python and using Tensorflow. It makes it easy to train music source separation models (assuming you have a dataset of isolated sources), and provides already trained state of the art models for performing various flavours of separation. The models available are. 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.
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.
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.
Deezer Spleeter Online
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.
Spleeter Audio
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.