SplitFire
SplitFire is a native AI audio separator that splits any song into vocals, bass, drums, and other stems entirely on the user's device. The application runs offline with no upload or account required, using the HT-Demucs model accelerated by GPU (Metal on Apple Silicon) to deliver clean separation in under two seconds per split. Users simply pick a track in MP3, WAV, FLAC, or M4A format and keep the separated stems locally.
- Music Technology
- Artificial Intelligence
- Creator / Creative Economy
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Musicians, producers, and content creators often need isolated audio stems for remixing, sampling, or karaoke, but most AI separation tools require uploading files to cloud servers, raising privacy concerns, latency, and dependence on internet connectivity. Desktop software that runs locally often demands complex setup or suffers from poor separation quality, limiting accessibility for non-technical users.
Solution
SplitFire provides a native AI audio separator that splits any song into four stems—vocals, bass, drums, and other—entirely on the user's device. The application runs offline with no upload or account required, ensuring audio files never leave the machine. It uses the HT-Demucs model, accelerated by GPU (Metal on Apple Silicon), to deliver clean, studio-quality separation in under two seconds per split. Users simply pick a track in MP3, WAV, FLAC, or M4A format, run the model, and keep the separated stems locally.
Target Audience
Primary users are musicians, audio producers, podcasters, video editors, and content creators who need isolated stems for remixing, sampling, karaoke, or transcription without compromising privacy or requiring cloud connectivity.
Features
- Four-stem separation (vocals, bass, drums, other) using the HT-Demucs deep-learning model
- Full on-device processing with no cloud upload, no account creation, and complete offline functionality
- GPU acceleration via Metal on Apple Silicon for fast performance—under two seconds per split
- Support for MP3, WAV, FLAC, and M4A audio file formats
- Simple three-step workflow: pick a track, run the model, and save stems locally