Lalal.ai vs MusicLM

LALAL.AI is an audio stem splitter that separates vocals, drums, bass, guitar, piano, and other parts from songs and video files in seconds. Upload an MP3, FLAC, MKV, or MP4, pick what to extract, and download isolated tracks for karaoke, remixing, or podcast cleanup. The service runs in the browser plus desktop, iOS, and Android apps.

Generic vocal removers output one instrumental file and call it done. LALAL.AI splits individual instruments, separates lead from backing vocals, and adds voice cleaning, echo removal, and voice cloning in the same account. Pro subscribers also get API access, a VST plugin for local DAW processing, and batch uploads for up to 20 files at once.

Producers use it to pull acapellas, DJs prep mashup stems, and video editors strip background music from clips. The free Starter plan includes 10 processing minutes and previews, while Lite and Pro plans add fast-queue minutes, full downloads, and higher upload limits up to 2GB per file.

MusicLM is a Google Research audio generation model that creates music from text captions at 24 kHz. The public examples page hosts sample clips for prompts like arcade soundtracks, reggaeton-EDM fusions, and relaxing jazz, plus longer story-mode generations that shift styles across timed segments. Google released the MusicCaps dataset of 5,500 music-text pairs alongside the paper.

Unlike consumer apps that ship one prompt box, MusicLM was published as a research demo with pre-generated samples rather than a login product. Its headline trick is joint text-and-melody conditioning: you can hum or whistle a tune and have the model re-render it in a new genre described in text. Story mode chains multiple captions so the music evolves across sections.

MusicLM matters as the research foundation behind Google's later Lyria music models, but this page is for listening to published examples, not creating new tracks interactively. Researchers and musicians study it for long-form consistency, painting-to-music conditioning, and melody transfer results documented in the 2023 paper.