Creating a karaoke version of a song used to mean hunting for an official instrumental release or settling for a low-quality MIDI approximation. With our free stem separator, you can extract a radio-ready rhythm section from any uploaded audio file in seconds. Our AI splits the song into vocals, piano, guitar, bass, drums, and other instruments, giving you a clean backing track that retains all the warmth and detail of the original recording. Karaoke singers, vocal coaches, and music teachers rely on our tool daily for exactly this purpose. But the rhythm section is just the beginning — if you are a producers looking to create Pro Tools sessions or a music creators working on stem mixing, the individual separated audio files give you far more flexibility to rebuild and reimagine the original in Ardour. Amazing for mix engineers who need professional audio tools without professional costs. Our BPM & Key Finder tool analyses any uploaded audio file and returns the exact tempo in BPM and the musical key including major and minor scales — use it before working with your extracted stems. 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.

Some songs are defined entirely by their vocal performance, and "Ja Z - My 1st Song" is a perfect example. Extracting the acapella gives backing vocalists, bedroom producers, and game developers a high quality isolated vocal that stands completely on its own — no bleed, no backing music, just the pure performance. Use it in Cubase for lyric analysis and melody transcription, mashup creation and DJ performances, or as a reference and study track for vocal analysis and music education. Excellent for anyone who wants to experience and work with the vocal of this track in its purest form. Our BPM & Key Finder detects the exact tempo and musical key within seconds — a crucial step before importing stems into your project. 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.