209 lines
13 KiB
Markdown
209 lines
13 KiB
Markdown
# Architecture
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> Companion to `CLAUDE.md` (product spec & tooling rules) and `Decisions.md` (why things
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> are the way they are). This file describes **how the system is structured**. Update it
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> whenever the structure changes.
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## 1. System overview
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Browser-first speech-to-text app. Whisper inference runs **in the browser by default**
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via [Transformers.js](https://github.com/huggingface/transformers)
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(`@huggingface/transformers`); no audio ever leaves the device for anonymous users.
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Logged-in users may optionally route inference to the server.
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```
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┌──────────────────────────── Browser ────────────────────────────┐
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│ │
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│ React UI (App shell) │
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│ ├─ ModelPicker (tiny → large, .en, quantized variants) │
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│ ├─ FileDropzone (audio/video upload) │
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│ ├─ LiveControls (microphone start/stop — D-016) │
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│ ├─ TranscriptView (streaming partial results) │
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│ └─ EngineToggle (device / server — logged-in only) │
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│ │ postMessage protocol │
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│ ▼ │
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│ Inference Web Worker │
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│ ├─ Transformers.js ASR pipeline (Whisper) │
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│ ├─ Device selection: WebNN → WebGPU → WASM fallback │
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│ └─ Model weights cached via browser Cache API │
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│ │
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│ Service Worker (PWA) │
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│ ├─ Precached app shell (offline-first) │
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│ └─ Works with zero network once installed + model cached │
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└──────────────────┬───────────────────────────────────────────────┘
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│ HTTPS (only when online / server engine chosen)
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▼
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┌──────────────────────────── Bun server ─────────────────────────┐
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│ Bun.serve() — routes + HTML imports (no vite, no express) │
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│ ├─ / → src/index.html (bundled React app) │
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│ ├─ /inference.worker.js → worker bundle (Bun.build at startup, │
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│ │ since Bun doesn't bundle `new Worker` graphs) │
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│ ├─ /api/auth/* → Passport.js (local strategy) + sessions │
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│ ├─ /api/transcribe → server engine (auth required) │
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│ └─ /manifest.webmanifest, /sw.js → PWA assets │
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│ bun:sqlite → users, sessions │
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│ Server inference: faster-whisper (CTranslate2) Python sidecar, │
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│ spawned & supervised by Bun (D-013) │
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└──────────────────────────────────────────────────────────────────┘
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```
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## 2. Runtime & tooling
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- **Runtime / bundler / test runner:** Bun only (`bun --hot src/index.ts` for dev,
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`bun build src/index.html` for production, `bun test`). No vite/webpack/jest.
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- **Frontend:** React 19, mounted from `src/index.html` via Bun HTML imports.
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- **Server:** `Bun.serve()` with the `routes` option. No express.
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- **DB:** `bun:sqlite`. No ORM planned initially.
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## 3. Directory layout (target)
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```
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src/
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index.ts # Bun.serve entry — routes, static, API
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index.html # HTML entry, imports frontend.tsx
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frontend.tsx # React root
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App.tsx # top-level UI composition
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components/ # ModelPicker, TranscriptionOptions, FileDropzone, LiveControls, TranscriptView, EngineToggle, AuthForms
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lib/
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audio.ts # decode + resample to 16 kHz mono Float32Array (Web Audio API)
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live.ts # live-mode segment loop: RMS gate, LiveSegmenter, LiveTranscriber (pure, D-016)
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mic.ts # microphone capture: AudioWorklet chunker (Blob URL) + fallback (D-016)
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models.ts # model catalog: ids, sizes, dtype defaults per device, languages
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modelCache.ts # "downloaded?" per model via Cache Storage (D-005; reused by M3 offline indicator)
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engine.ts # engine abstraction: BrowserEngine | ServerEngine
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worker/
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inference.worker.ts # Transformers.js pipeline, WebNN/WebGPU/WASM selection
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protocol.ts # typed postMessage messages (shared with UI)
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pwa/
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sw.ts # service worker (app-shell precache, /ort runtime cache)
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precache.ts # placeholder-injection helpers shared by build + dev serve
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register.ts # SW registration + update-toast flow
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manifest.webmanifest
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icons/ # generated by scripts/generate-icons.ts (not checked in)
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server/
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auth.ts # Passport.js wiring + session middleware adapter
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db.ts # bun:sqlite schema + queries
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transcribe.ts # server-side ASR endpoint (bridges the sidecar, M5)
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server-py/ # faster-whisper sidecar (Python, D-013; M5)
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tests/ # bun test
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```
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(Current state: M0–M3 built — browser transcription with the curated model
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catalog, WebNN→WebGPU→WASM auto-selection (D-017), model switching,
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language/task options,
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and the installable offline PWA (`pwa/`, `scripts/build.ts` production build,
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`dist/` serving in `src/index.ts`; browser-level offline behavior pending the
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manual run in `tests/offline.md`). M4 accounts are built (`server/db.ts`,
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`server/auth.ts`, `/api/auth/*`, `AuthForms`). M7 live microphone
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transcription is built (`lib/live.ts`, `lib/mic.ts`, `LiveControls`, D-016;
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in-browser run pending per `tests/live.md`). M5's server side is built —
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`server-py/sidecar.py`, `server/transcribe.ts` (supervisor +
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`/api/transcribe` + `/status`), `lib/engine.ts` (both engines, D-010), and
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the catalog mapping in `lib/models.ts` — with graceful degradation when
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Python/faster-whisper is absent; the `EngineToggle` component and App wiring
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are the remaining M5 work; see `WorkBreakdown.md`.)
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## 4. Key flows
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### 4.1 Browser transcription (anonymous, default)
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1. User picks a model in `ModelPicker` (curated catalog from `lib/models.ts`, with
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size estimates and a Cache-API-backed "downloaded" badge). Selection alone
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downloads nothing (D-020): the UI posts `{type:"load", modelId, device}` when a
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browser-engine transcription starts (file drop / live start) or when the user
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clicks the explicit **Download** button on the selected model; server-engine
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runs never post a load. `device` is a preference
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(`"auto" | "webnn" | "webgpu" | "wasm"`, default `"auto"`). An explicit `dtype`
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override is expressible in the message but normally omitted.
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2. Worker resolves the device (D-004, D-017): for `"auto"` it probes
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`navigator.ml` (WebNN) first, then `navigator.gpu?.requestAdapter()` (WebGPU),
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and picks in priority order WebNN → WebGPU → WASM; if pipeline creation on the
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picked device throws, it retries down the same chain (WASM always last). An
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explicit `"webnn"`/`"webgpu"` preference falls back straight to WASM. It then
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resolves the catalog's dtype default for the device actually used and creates
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the Transformers.js `automatic-speech-recognition` pipeline.
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Download progress events are forwarded to the UI; the terminal
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`{status:"ready", device}` message reports the backend actually selected, which
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the UI surfaces. Loading a different model/device disposes the old pipeline
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(`pipeline.dispose()`) before creating the new one.
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3. Weights are fetched from the Hugging Face Hub **once**; Transformers.js stores them
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in the browser Cache API, so subsequent loads (including offline) hit cache
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(`lib/modelCache.ts` reads that cache for the downloaded badges).
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4. User drops an audio/video file. Main thread decodes it with
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`AudioContext.decodeAudioData`, downmixes to mono, resamples to 16 kHz,
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transfers the `Float32Array` to the worker. For multilingual models the
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`transcribe` message also carries `language` (or auto-detect) and `task`
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(transcribe | translate).
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5. Worker runs the pipeline (chunked, with timestamps), streaming partial results
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back via `{type:"partial"}` messages; final result as `{type:"result"}`.
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### 4.1b Live microphone transcription (anonymous, browser engine — D-016)
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1. `LiveControls` starts a session: `getUserMedia` → `AudioContext` at the
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device's native rate → AudioWorklet chunker (module from a Blob URL;
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`ScriptProcessorNode` fallback) posts ~100 ms Float32 frames; the main
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thread resamples each to 16 kHz (`lib/mic.ts`).
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2. `LiveTranscriber` (`lib/live.ts`, pure) feeds frames through an RMS energy
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gate: segments start at speech onset (short pre-roll), commit on trailing
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silence or at ~25 s; pure silence is never transcribed.
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3. The current segment is re-transcribed through the normal inference worker
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on a rolling basis (throttled, single-flight, latest-snapshot-wins) as
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*tentative* text; a committed segment gets one final pass and freezes.
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When inference runs slower than speech, queued committed segments are
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batched into merged final passes (one Whisper window each, D-024) so the
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backlog stays bounded. `requestId` on the protocol messages correlates
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responses.
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4. Stopping flushes the segmenter, drains pending final passes (merged, with
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a "N remaining segments" countdown in the UI), and yields a normal
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transcript (text + absolute-timestamped chunks) in `TranscriptView`.
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Everything runs on-device; works offline like the file flow. Server-engine
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live input is a recorded future want (WorkBreakdown M7, D-016).
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### 4.2 Offline (PWA)
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- Service worker (`pwa/sw.ts`, hand-rolled — D-007) precaches the built app shell
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(hashed JS/CSS, `/`, worker bundle, manifest, icons) on install into
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`whisper-shell-<version>`, cache-first thereafter; unknown navigations fall back
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to the cached shell. `scripts/build.ts` injects the precache list + a
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content-hash version into `dist/sw.js` via `pwa/precache.ts`; the dev server
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injects an empty list ("dev"), making the dev shell network-first so HMR works.
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- onnxruntime's `.wasm`/`.mjs` binaries are served from our origin at `/ort/*`
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(never the jsdelivr CDN default) and runtime-cached cache-first into
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`whisper-runtime-<version>` — they are required for offline inference but too
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large to precache eagerly.
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- Model weights live in the Cache API (`transformers-cache`) via Transformers.js —
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independent of the SW, which never touches that cache (D-005). `/api/*` is
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network-only.
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- Updates: `pwa/register.ts` surfaces a waiting SW as a toast; accepting posts
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`SKIP_WAITING`, activation drops old `whisper-*` caches, and the page reloads.
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- Result: with the app installed and at least one model previously loaded, the full
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transcription flow works with no network and with our server down (manual
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verification procedure: `tests/offline.md`).
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### 4.3 Server engine (logged-in only)
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1. `EngineToggle` is visible only when a session exists.
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2. `ServerEngine.transcribe()` uploads the decoded (or original) audio to
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`POST /api/transcribe` with the chosen model id.
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3. Server inference runs on **faster-whisper** (CTranslate2) in a Python sidecar
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(`server-py/`) that Bun spawns and supervises (D-013; faster-whisper has no
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browser/WASM build, so it is server-only — the browser stays on Transformers.js).
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`src/server/transcribe.ts` bridges the sidecar's segment stream to the streamed
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JSON-lines HTTP response (D-011). The shared catalog maps model ids to
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`Systran/faster-whisper-*` CTranslate2 conversions. If the sidecar is absent,
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the server engine reports unavailable; the browser/anonymous flow is unaffected.
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4. Both engines implement the same `Engine` interface in `src/lib/engine.ts`, so the
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UI is engine-agnostic.
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### 4.4 Auth
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- Passport.js **local strategy** behind a thin adapter that bridges Bun's
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`Request`/`Response` to Passport's connect-style API (see Decisions.md D-008).
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- Sessions in SQLite; session id in an HttpOnly cookie.
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- Anonymous users are never blocked from browser inference — auth only unlocks the
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server engine.
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## 5. Invariants
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- **Privacy:** browser-engine audio and transcripts never touch the network.
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- **Anonymous-first:** every feature except the server engine works logged out.
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- **Offline-first:** app shell + previously-downloaded models must work offline.
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- **One browser inference codebase:** Transformers.js in the browser; the server
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engine runs faster-whisper (D-013). The model catalog stays shared across both.
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- **Worker isolation:** inference never runs on the main thread.
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