I’ve been building AudioCraft as a focused AI music tool for separating songs into useful stems.
The main workflow is simple:
- upload MP3, WAV, FLAC, or M4A
- separate the track into vocals, drums, bass, and other instruments
- preview the stems
- mute, solo, and adjust individual tracks
- create an instrumental version
- download individual stems or all stems together AI Stem Splitter https://audiocraft.app/ AudioCraft uses AI source separation rather than simple frequency filtering. It is useful for instrument practice, remixing, karaoke, arrangement analysis, and extracting vocals or backing tracks. The result page includes a synchronized stem mixer, so users can solo the bass, mute the vocals, or compare different parts of the arrangement without opening a DAW. Temporary Audio Processing Privacy: https://audiocraft.app/privacy Uploaded source audio is temporarily stored so the AI service can process it, then AudioCraft automatically attempts to delete the original file after processing finishes. Generated stem links are temporary, so users should download results they want to keep. Product Analytics and Feedback AudioCraft tracks product-level events such as uploads, successful separations, downloads, and failures, while avoiding file names, audio URLs, and media contents in analytics. Users can also rate the separation result as good or needing improvement, which should help improve future model and product decisions. Where I want to take AudioCraft next https://audiocraft.app/about The next direction is AI music transcription. The idea is to use stem separation first, then turn cleaner vocals, bass, or instrument stems into things like:
- MIDI
- sheet music
- melody transcription
- chord detection
- BPM and key analysis The longer-term goal is for AudioCraft to become a small AI music-analysis toolkit rather than just another vocal remover.
