LALAL.AI
AI audio separation - extract vocals/instruments
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What is LALAL.AI?
LALAL.AI is an online audio separation tool powered by artificial intelligence that removes vocals, isolates instruments, and processes voice in music tracks, audio clips, and videos. Made by LALAL.AI, the platform uses transformer-based neural networks to analyze audio and split it into distinct stems—vocals, drums, bass, guitar, synth, strings, wind instruments, and more.
The platform offers several specialized products beyond basic vocal removal. Stem Splitter extracts individual instruments from mixed tracks for remixing and production work. Voice Cleaner removes background noise, vocal plosives, and mic rumble from recordings. Lead/Back Splitter separates lead and backing vocals with precision. Voice Changer modulates vocal characteristics including accent and tonality. Voice Cloner builds personalized AI voice models from user recordings for generating voiceovers and ad reads. Echo & Reverb Remover reduces room reflections in vocal recordings. The service processes files through its Andromeda engine, described as a sixth-generation model developed over six years of iterative training.
LALAL.AI supports common audio formats (MP3, OGG, WAV, FLAC, AIFF, AAC, M4A) and video formats (AVI, MP4, MKV, MOV, M4V). Users upload files through a web interface, preview separated outputs, and download individual stems. The tool accommodates casual users creating karaoke tracks and professionals preparing material for remixing, podcasting, and content production. Enterprise customers access bulk processing capabilities and custom API integrations for secure workflows. The platform operates on a pay-per-use pricing model with tiered subscription plans, plus refundable credits for unsuccessful processing.
Available through web apps and plugins across multiple platforms, LALAL.AI supports 15+ languages including English, Chinese, French, German, Japanese, Korean, and Spanish. Direct competitors include iZotope RX, Deezer Spleeter, and Adobe Audition's spectral editi