Producers know that finding the right vocal sample or reference track can completely define the direction of a new production. Our free AI stem separator gives home studio owners and music students access to clean isolated lead vocals from any uploaded audio file, opening up a world of creative sampling and interpolation possibilities. Upload this track in M4A and receive six separated components: vocals, piano, guitar, bass, drums, and other instruments, all at clean quality and available immediately. Chop the lead vocals into individual phrases, pitch-shift it to match the key of your project, or layer multiple vocal extractions to create rich, harmonically complex textures. The drum and bass stems provide equally powerful raw material for groove-based production. Import your samples into Cakewalk and start building. Brilliant for game developers who create without boundaries and want tools to match. Our BPM & Key Finder tool detects the exact tempo and musical key of any uploaded audio file within seconds — a crucial step before importing stems into your project. Tribute bands and cover artists use extracted instrumental stems to create backing tracks far more accurate and natural-sounding than MIDI-based or re-recorded alternatives.

The acapella of "Ismet Peja - Ti Ne Zemren Time" is a pristine isolated vocal resource that band members use for singing competitions and talent showcases and sound designers apply to wedding and event performance preparation. Extracted with our free AI tool, the vocal stem preserves the full depth and character of the original performance while completely removing all backing instrumentation. Import it into Studio One and use it however your creative project demands — remix, mashup, karaoke, vocal practice, or sampling. Ideal for karaoke singers who want high-quality vocal isolation without a subscription or studio session. Our free BPM & Key Finder detects the root note, scale, and exact tempo of any audio file — use it before importing your extracted audio into your DAW. Stem separation technology uses deep learning models trained on thousands of hours of professionally recorded music to accurately isolate each instrument from a mixed audio track.