whisper_web/tests/server-engine.md

Manual verification: server engine (M5, D-013)

The dev environment has no Python package manager and no browser, so — like tests/offline.md and tests/live.md — the real-inference path needs a manual run on a machine with Python ≥ 3.9. Everything protocol- and HTTP-level is already covered by bun test (tests/transcribe.test.ts exercises the real Bun.spawn supervisor against tests/fixtures/fake_sidecar.py, plus the degradation paths against the real server-py/sidecar.py).

1. Install the sidecar dependencies

python3 -m pip install -r server-py/requirements.txt

2. Opt-in automated pass (no browser needed)

FW_INTEGRATION=1 bun test tests/transcribe.test.ts

Runs the real sidecar end to end (spawn → ready handshake → tiny.en load → transcribe → done), then the first 60 s of tests/audio_files/en_alien.opus (needs ffmpeg) in both decoding modes (D-WS5a): file (faster-whisper's BatchedInferencePipeline + Silero VAD, what POST /api/transcribe uses) and live (sequential, what server-live passes use). First run downloads ~75 MB of weights from the Hub. Point SIDECAR_PYTHON at a virtualenv's python if faster-whisper isn't installed for python3 itself.

Without faster-whisper at all, bun test tests/transcribe.test.ts still runs the real server-py/sidecar.py against a stub (tests/fixtures/stub_fw) to check the mode routing, the one-model cache and cancel between segments.

• The real faster-whisper sidecar describe block passes.

3. In-browser run (both engines, same audio)

1. bun run dev, open the app, register/sign in.
2. GET /api/transcribe/status (with the session cookie) returns {"available": true} — e.g. check the Network tab.
3. Pick Whisper Tiny (English); transcribe a short clean English clip (e.g. the JFK "ask not…" sample) on device. Note the transcript.
4. Switch the engine toggle to Server (visible only while signed in; requires the M5 UI wiring — until that lands, drive it manually: curl -X POST --data-binary @audio.f32 -b "sid=…" \ "http://localhost:3000/api/transcribe?model=onnx-community/whisper-tiny.en" where audio.f32 is raw float32 mono 16 kHz PCM).
5. Transcribe the same clip through the server engine.

• Segments stream in as JSON lines (not one blob at the end). With the batched pipeline (D-WS5a) they arrive in bursts — one burst per 8 speech chunks (≈ up to 4 min of audio) — with blank heartbeat lines every 5 s in between; on a clip under ~30 s of speech, everything arrives as a single segment at the end.
• Cancelling a long server transcription (abort / page refresh) stops the sidecar within one burst; the next request then starts normally.
• Both transcripts agree in content (minor punctuation/casing drift between Transformers.js and faster-whisper is expected).
• Signed out, the toggle is gone and POST /api/transcribe answers 401.
• Second request on the same model is visibly faster (no reload — warm-up strategy).
• AUTH_ENABLED=false bun run dev: /api/transcribe* answers 404 and the anonymous browser flow is unaffected.

When this passes, check off the M5 integration item in WorkBreakdown.md.