GoKawiilcardiag — diagnose a car fault from its sound
cardiag is an end-to-end audio-ML pipeline. It scrapes fault-sound clips from YouTube/TikTok, cleans the audio (isolating the mechanical sound from speech, music, and noise), embeds it with a frozen CLAP model, and trains small linear heads to triage the fault. It is exposed as a CLI and a live web app.
cardiag-demo.mp4
This is a proof of concept, and honest about what that means. Diagnosing a car fault from a phone recording is genuinely hard, so cardiag is built as a calibrated triage aid rather than a diagnoser: it tells you whether something sounds wrong, roughly where in the car it is, and a ranked shortlist of likely parts. When the audio won't support a call, it says "uncertain" instead of bluffing.
The real contribution is the cleaning + honest-training recipe, which is reusable on other audio datasets. The modest accuracy here reflects how hard the problem is from crude phone audio (we hit the literature ceiling); the same method reaches 0.93 AUROC on clean engine audio. See docs/DEFENSE.md.
Interactive demos
Two pages visualize the first two stages of the pipeline:
Isolating the engine audio — an interactive look at the clean() cascade pulling a short mechanical span out of noisy YouTube audio (speech, music, road noise).
cascade pulling a short mechanical span out of noisy YouTube audio (speech, music, road noise). CLAP, visualized — how the frozen CLAP model turns those spans into the 512-d embedding the linear heads classify.
What it actually achieves
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