AI Stem Splitter

AI Stem Splitter is an audio editing tool. A free, no-signup browser tool that splits a finished song into up to six stems (vocals, drums, bass, piano, guitar, other) in roughly a minute. Upload a file, preview each track in the page, then download lossless stems for remixing, DJ sets, teaching or karaoke.

AI Stem Splitter is a browser-based audio separation service that takes a finished, mixed song and pulls it apart into individual instrument tracks. Upload an MP3, WAV, FLAC, M4A or OGG file (up to 50MB) and the underlying model returns up to six stems: vocals, drums, bass, piano, guitar and a catch-all other track. The site also positions the same engine as a vocal remover and isolator, so an instant instrumental or a clean acapella is a routine output rather than a separate product. The mechanics are straightforward. A deep-learning model reads the spectrogram of the uploaded mix, estimates a spectral mask for each instrument, and rebuilds the isolated audio from those masks. The project states the model was trained on large libraries of real multitrack recordings, which is how it distinguishes a human voice from a kick drum or a bass guitar across both frequency and time. There is nothing to configure: no stem count selector to fiddle with, no parameters, no plugin chain. Separation typically completes in about a minute. In practice the workflow is upload, wait, preview, download. Every separated track can be played in the browser before anything is exported, so you can audition the result and grab only the stems you need. Output is described as lossless at the original quality, which matters if the file is headed into a DAW, a sampler or a live set. Files are processed securely and deleted automatically after separation, with nothing persisting on the server. Producers and remixers use it to lift parts from reference tracks, DJs prep acapellas and instrumentals for sets and mashups, and educators isolate a single instrument to demonstrate arrangement. Singers get karaoke tracks or practice acapellas, beatmakers sample one clean element out of a dense mix, and podcast or video editors pull music away from dialogue. Against traditional plugin-and-EQ workflows it trades a modest amount of control for speed, no installation and no audio-engineering skill. The site is candid that results are an estimate: cleaner, higher-bitrate or lossless sources produce fewer artifacts than heavily compressed files.