Better tests and more user feedback while doing transcription; WIP.

Architecture.md

@@ -24,7 +24,7 @@ Logged-in users may optionally route inference to the server.
 │          ▼                                                       │
 │  Inference Web Worker                                            │
 │   ├─ Transformers.js ASR pipeline (Whisper)                      │
-│   ├─ Device selection: WebGPU → WASM fallback                    │
+│   ├─ Device selection: WebNN → WebGPU → WASM fallback            │
 │   └─ Model weights cached via browser Cache API                  │
 │                                                                  │
 │  Service Worker (PWA)                                            │
@@ -72,7 +72,7 @@ src/
     modelCache.ts          # "downloaded?" per model via Cache Storage (D-005; reused by M3 offline indicator)
     engine.ts              # engine abstraction: BrowserEngine | ServerEngine
   worker/
-    inference.worker.ts    # Transformers.js pipeline, WebGPU/WASM selection
+    inference.worker.ts    # Transformers.js pipeline, WebNN/WebGPU/WASM selection
     protocol.ts            # typed postMessage messages (shared with UI)
   pwa/
     sw.ts                  # service worker (app-shell precache, /ort runtime cache)
@@ -89,7 +89,8 @@ tests/                     # bun test
 ```
 
 (Current state: M0–M3 built — browser transcription with the curated model
-catalog, WebGPU→WASM auto-selection, model switching, language/task options,
+catalog, WebNN→WebGPU→WASM auto-selection (D-017), model switching,
+language/task options,
 and the installable offline PWA (`pwa/`, `scripts/build.ts` production build,
 `dist/` serving in `src/index.ts`; browser-level offline behavior pending the
 manual run in `tests/offline.md`). M4 accounts are built (`server/db.ts`,
@@ -108,12 +109,15 @@ are the remaining M5 work; see `WorkBreakdown.md`.)
 1. User picks a model in `ModelPicker` (curated catalog from `lib/models.ts`, with
    size estimates and a Cache-API-backed "downloaded" badge); UI posts
    `{type:"load", modelId, device}` where `device` is a preference
-   (`"auto" | "webgpu" | "wasm"`, default `"auto"`). An explicit `dtype` override
-   is expressible in the message but normally omitted.
-2. Worker resolves the device: `navigator.gpu?.requestAdapter()` → `"webgpu"` if an
-   adapter exists (and pipeline creation succeeds — otherwise it retries on WASM),
-   else `"wasm"` (D-004). It then resolves the catalog's dtype default for that
-   device and creates the Transformers.js `automatic-speech-recognition` pipeline.
+   (`"auto" | "webnn" | "webgpu" | "wasm"`, default `"auto"`). An explicit `dtype`
+   override is expressible in the message but normally omitted.
+2. Worker resolves the device (D-004, D-017): for `"auto"` it probes
+   `navigator.ml` (WebNN) first, then `navigator.gpu?.requestAdapter()` (WebGPU),
+   and picks in priority order WebNN → WebGPU → WASM; if pipeline creation on the
+   picked device throws, it retries down the same chain (WASM always last). An
+   explicit `"webnn"`/`"webgpu"` preference falls back straight to WASM. It then
+   resolves the catalog's dtype default for the device actually used and creates
+   the Transformers.js `automatic-speech-recognition` pipeline.
    Download progress events are forwarded to the UI; the terminal
    `{status:"ready", device}` message reports the backend actually selected, which
    the UI surfaces. Loading a different model/device disposes the old pipeline