Split any track into ten clean stars.

LALAL.AI takes a song or a video and separates it into clean audio stems: vocal, instrumental, drums, bass, electric guitar, acoustic guitar, piano, synth, strings and wind instruments. You can extract one stem, extract several, or remove one and keep everything else. The result is studio-usable — mastering-grade for the strongest engines, and always cleaner than typical browser vocal removers.

Different material benefits from different models. Phoenix is the state-of-the-art vocal and instrumental extractor. Orion ensembles Phoenix with a second model for professional mastering work. Perseus is a transformer-based generalist with enhanced processing modes. Cassiopeia is kept as legacy for older material where Phoenix might be too aggressive on consonants. Andromeda is the 2025 quality benchmark. Lynx is the 2026 network optimised for voice cleaning speed. You can try different engines on the same track.

There is a limited free trial so you can hear the quality on your own material before paying. Full processing is paid — either through one-time credit packs starting around 15 USD, or ongoing plans. It is not an unlimited free tool. That is the honest trade-off for state-of-the-art quality, since running these neural networks at scale is not cheap.

Up to ten: vocal, instrumental, drums, bass, electric guitar, acoustic guitar, piano, synth, strings and wind instruments. Not every stem is present in every song — the split is only as clean as the source. On dense mixes with overlapping guitars and piano, a four-stem split (vocal, drums, bass, other) is usually more reliable than pushing for a full ten-stem separation.

Yes — LALAL.AI ships a VST plugin for DAW integration. You can also send audio out to the web app or use the REST API from custom scripts. The API is what production teams use to process catalogues at scale, with predictable rate limits and average latency around 0.3 to 0.5 times real-time on Phoenix — a four-minute song processes in roughly 60 to 120 seconds.

Yes. There is a dedicated lead-and-backing vocal split. It works best on tracks with clearly defined vocal layers — for example a lead line with harmonies stacked above and below. On tracks where the backing is a chorus effect or double-tracked lead, the split is less clean. Try both engines on a short section before committing to a full track.

The voice cleaner removes background music, plosives, mic rumble, hiss and clicks, and an echo/reverb removal tool handles boomy room recordings. It is a common workflow for podcast editors and video creators who have decent takes ruined by ambient noise. It is not a replacement for a good mic and treated room, but it rescues field recordings that would otherwise be unusable.

LALAL.AI leads on separation quality and stem count, and it is the tool that ships into commercial production pipelines via the API. Moises has a stronger practice-focused feature set (chord detection, pitch shift, click tracks). RipX offers deeper editing of the separated audio inside its own DAW. Ultimate Vocal Remover (UVR) is free and open source, which matters if you cannot pay. Pick the tool that matches your workflow, not the leaderboard.

LALAL.AI is a product of OmniSale GmbH, launched in 2020. The team has shipped a new neural network roughly every year — Rocknet, Cassiopeia, Phoenix, Orion, Perseus, Andromeda and the current Lynx. The pace of research is the reason quality kept improving beyond what most competitors match. It is not a hobby project — this is a specialist audio-AI company with a real engineering team and a documented API.