fix(calls): wire DTLN ML denoise correctly via @workadventure JS API

The prior DTLN attempt (89a2321d) broke the build (missing dep, wrong
`cinny/` asset paths) and typecheck (`'dtln'` not in DenoiseModelId), and was
wired against an API the package doesn't expose. @workadventure/noise-
suppression is not a flat AudioWorklet — it's a self-contained ES module whose
processor name is `workadventure-noise-suppression` and which resolves its own
LiteRT WASM + TFLite models via import.meta.url. Driving it by hand-rolled
addModule + processorOptions cannot work.

- Re-add @workadventure/noise-suppression@0.0.4 (package.json + lockfile).
- vite: copy the package's whole dist/ tree intact to
  denoise/workadventure/ (preserving assets/ + vendor/litert) so import.meta
  resolution works at runtime; fail the build if the entry module is missing.
- shim: for the DTLN model, dynamic-import denoise/workadventure/audio-worklet
  .js and use createNoiseSuppressionAudioWorklet(ctx, { bypassUntilReady })
  to build the node; RNNoise/Speex keep their direct flat-worklet path. Async
  init errors are logged + reported and fall back to the raw mic.
- Restore 'dtln' in DenoiseModelId (+ settings coercion), the model chart, and
  the settings dropdown, labelled "(beta)".

DTLN builds and is fully self-hosted, but its in-call audio is UNVERIFIED in
this environment — needs a real-call test. DeepFilterNet stays excluded (CDN
asset loading, incompatible with self-hosting / Tauri CSP).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

src/app/utils/lotusDenoiseUtils.ts

@@ -31,6 +31,15 @@ export const DENOISE_MODELS: DenoiseModel[] = [
     transients: 'Poor',
     voiceQuality: 'Moderate',
   },
+  {
+    id: 'dtln',
+    name: 'DTLN (beta)',
+    description: 'Deep-learning model (TFLite). Stronger on transient noise; higher CPU.',
+    cpuUsage: '10-20%',
+    binarySize: '~4 MB',
+    transients: 'Excellent',
+    voiceQuality: 'High',
+  },
 ];
 
 export const isMLDenoiseSupported = (): boolean => {