Spleeter: a fast and efficient music source separation tool with pre-trained models
Romain Hennequin, Anis Khlif, Félix Voituret, Manuel Moussallam · The Journal of Open Source Software · 2020
We present and release a new tool for music source separation with pre-trained models called Spleeter.Spleeter was designed with ease of use, separation performance, and speed in mind.Spleeter is based on Tensorflow (Abadi, 2015) and makes it possible to:• split music audio files into several stems with a single command line using pre-trained models.A music audio file can be separated into 2 stems (vocals and accompaniments), 4 stems (vocals, drums, bass, and other) or 5 stems (vocals, drums, bass, piano and other).• train source separation models or fine-tune pre-trained ones with Tensorflow (provided you have a dataset of isolated sources).The performance of the pre-trained models are very close to the published state-of-the-art and is one of the best performing 4 stems separation model on the common musdb18 benchmark (Rafii, Liutkus, Stöter, Mimilakis, & Bittner, 2017) to be publicly released.Spleeter is also very fast as it can separate a mix audio file into 4 stems 100 times faster than real-time (we note, though, that the model cannot be applied in real-time as it needs buffering) on a single Graphics Processing Unit (GPU) using the pre-trained 4-stems model.