Get the track you need in just a few seconds.
What Spleeter offers
Spleeter is a solution for recreating multitracks from a flat audio file, whether for a song, podcast, movie, game, etc. Our model extracts voice and instruments from music or any audio source and separates them from the rest of the file.
Work on large catalogs you own automatically.
Use the existing file, no need to re-record.
How we use it at Deezer
At Deezer, we use Spleeter to separate vocal and instrumental tracks and analyze them in order to enrich our metadata.
The tool also enables us to improve our recommendation engines and will open opportunities to build playlists around specific attributes, like a particular piano quality, for example.
Perform audio restoration or remastering
Split the elements in a track for studio work or to restore them one by one.
Remaster old songs and old audio tracks from movies or TV shows, even without the original multitracks.
Generate karaoke or music learning tracks
Create learning or singing experiences by separating and removing the voice or instrument track from any song.
Generate multitracks for DJ mixes
Use Spleeter to rework a track or assemble a bespoke mashup. As a DJ or mixing app, benefit from the source separation features you need.
Simplify subtitling activities
Use Spleeter to extract the voice track on any audio file (e.g. a file from a movie or TV show) and use it to then facilitate subtitle or lyrics creation via speech-to-text technology and our automated synchronization solution (AnSync).
Create bespoke audio-centric experiences
Extract elements from audio tracks in order to use them in your creations, whether you’re looking for insights on a drum rhythm to accompany gameplay or piano music to match a lighting ambiance.
Spleeter Pro - coming soon
We are now developing Spleeter Pro, a faster, more efficient and accurate version, that will be available as an API.
You can already try our open source version below and reach out to learn more about Spleeter Pro.
Spleeter Free - open source
Our source separation library uses Tensorflow and comes with pre-trained models written in Python. You can use it straight from command line as well as directly via your own development pipeline as a Python library. It can be installed with Pip or used with Docker.
The library 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.