# Show HN: Classify mechanical faults using Contrastive Language-Audio Pretraining > Source: > Published: 2026-07-01 16:57:20+00:00 `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 samemethod reaches 0.93 AUROC on clean engine audio. See[docs/DEFENSE.md]. Two pages visualize the first two stages of the pipeline: [Isolating the engine audio](https://adamsohn.com/separate/)— an interactive look at the`clean()` cascade pulling a short mechanical span out of noisy YouTube audio (speech, music, road noise).[CLAP, visualized](https://adamsohn.com/clap/)— how the frozen CLAP model turns those spans into the 512-d embedding the linear heads classify. Measured out-of-sample, leakage-safe (by-video grouped CV over 1,031 video groups; permutation **p = 0.0005**). These are honest numbers, not a leaderboard. | Capability | Result | vs. chance | |---|---|---| | Is something wrong? (fault/normal) | AUROC 0.79 [0.76, 0.83] | 0.50 | | Where in the car? (6 zones) | right zone in top-3 ≈ 75% | 2× | | Which part? (12+ families) | right part in top-3 ≈ 45–65% | 3–4× | | Knows when it doesn't know | calibrated (ECE ≈ 0.04), returns `UNCERTAIN` | — | Full details, and the one head we *demoted* for failing out-of-sample (knock), are in [docs/MODEL_CARD.md](/adam-s/car-diagnosis/blob/main/docs/MODEL_CARD.md). A fresh clone is immediately usable. A small pre-trained model ships in `models/` , and a synthetic demo clip is bundled, so nothing needs to be downloaded or scraped. ``` git clone && cd car-diagnosis uv venv && source .venv/bin/activate uv pip install -e ".[scrape,web,dev,viz]" # Python 3.11 cardiag doctor # preflight: what's installed cardiag train --fixtures # a working model offline in ~2s (no scrape, no 2 GB download) cardiag diagnose # verdict + where-in-the-car + ranked parts cardiag serve --model models # live web app: drop a clip / paste a link, "explain why" ``` Verify the whole thing end-to-end in an isolated worktree: `bash scripts/clone_verify.sh` . ``` audio ──► clean() cascade ──► CLAP embedding ──► linear heads ──► Diagnosis (isolate spans) (frozen, 512-d) (fault/region/ (calibrated, part/knock) UNCERTAIN-aware) ``` There is **one segmentation path**. Scraped clips, your own recordings (`cardiag ingest` , any length), and uploads at inference all flow through the same `clean()` cascade that isolates short mechanical spans. Spans over ~10 s are split into windows so CLAP never silently truncates them. Training and serving share one embedding contract, so there is no train/serve skew. ``` cardiag diagnose clip.wav # full model: verdict + region + ranked parts cardiag triage clip.wav # calibrated engine-vs-running-gear cardiag clean clip.wav # isolate the mechanical sound (no model needed) cardiag inspect clip.wav -o r.html # SEE/HEAR the pipeline: spans, spectrograms, scores cardiag ingest ./my_audio --kind fault --cause wheel_bearing # bring your own audio cardiag scrape youtube|tiktok # build a corpus (Reddit is deprecated — too noisy) cardiag train # train on your corpus ``` Add `--json` to any inference command for machine-readable output. [docs/DEFENSE.md](/adam-s/car-diagnosis/blob/main/docs/DEFENSE.md)— the honest case that a deliberately crude method earns a real triage result.[docs/MODEL_CARD.md](/adam-s/car-diagnosis/blob/main/docs/MODEL_CARD.md)— per-head metrics, intended use, limitations.[docs/architecture.md](/adam-s/car-diagnosis/blob/main/docs/architecture.md)— pipeline diagrams.[docs/scraping-guide.md](/adam-s/car-diagnosis/blob/main/docs/scraping-guide.md)— start-to-finish corpus building. Valid for social-style / targeted-upload audio (YouTube, TikTok, or a phone clip a user records deliberately). It is **not** a safety-critical or standalone diagnostic. It is a triage assistant that narrows where to look and is honest about its uncertainty. Model files are joblib artifacts: load only ones you trust. License: see [LICENSE](/adam-s/car-diagnosis/blob/main/LICENSE).