Swan measures the biological physics of speech to tell a human voice from a synthetic one. It never needs retraining for the next voice-cloning tool.
Swan reads the acoustic behavior that only biological phonation produces, then integrates it into a stable verdict.
Jitter, spectral flatness, cross-band divergence, phase dispersion, and onset and offset transition morphology, frame by frame.
A leaky-bucket integrator accumulates risk over time, so one noisy frame cannot flip the verdict.
A forensic-style determination: human, synthetic, or inconclusive, with a stated behavioral driver.
A JSON report plus readable dashboard and audit reports for every interaction.
Ships with no API keys. You bring your own. The engine is local; the only optional external call is LLM adjudication, off by default.
# clone, install, run git clone https://github.com/sriramnatrajhen/humsana-swan.git cd humsana-swan python3 -m venv venv && source venv/bin/activate pip install -r requirements.txt cp .env.example .env # set your own keys python run_bioauth.py
Swan assesses whether a voice is consistent with human phonation. It does not confirm who is speaking.