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>

build/lotus-denoise.js

@@ -55,8 +55,11 @@
       wasm: 'speex.wasm',
     },
     dtln: {
-      name: 'noise-suppression-audio-worklet',
-      script: 'dtlnWorklet.js',
+      // @workadventure/noise-suppression is a self-contained ES module that
+      // resolves its own AudioWorklet processor + LiteRT WASM + TFLite models
+      // via import.meta.url. We dynamic-import this helper and let it build the
+      // node, rather than addModule-ing a flat worklet ourselves.
+      helper: 'workadventure/audio-worklet.js',
     },
     gate: {
       name: '@sapphi-red/web-noise-suppressor/noise-gate',
@@ -117,9 +120,10 @@
           } catch (e) {}
           return Promise.reject(new Error('SampleRate mismatch: ' + ctx.sampleRate));
         }
-        // Load required modules
-        var scripts = [PROCESSORS[MODEL].script];
-        if (MODEL === 'dtln') scripts.push('dtlnProcessor.js');
+        // Load worklet modules. DTLN registers its own processor via the
+        // dynamic-imported helper (see buildMlNode), so it needs nothing here.
+        var scripts = [];
+        if (MODEL === 'rnnoise' || MODEL === 'speex') scripts.push(PROCESSORS[MODEL].script);
         if (USE_GATE) scripts.push(PROCESSORS.gate.script);
 
         return Promise.all(
@@ -143,6 +147,36 @@
 
   var hasNotifiedActive = false;
 
+  // Build the ML denoise AudioWorkletNode. RNNoise/Speex are flat sapphi
+  // worklets we instantiate directly with the fetched WASM binary. DTLN comes
+  // from @workadventure's self-contained helper, which we dynamic-import; it
+  // resolves its own processor + LiteRT WASM + TFLite models internally and
+  // returns the node. Resolves to { node, ready, dispose }.
+  function buildMlNode(ctx, wasmBinary) {
+    if (MODEL === 'dtln') {
+      return import(ASSET_BASE + PROCESSORS.dtln.helper).then(function (mod) {
+        // bypassUntilReady: pass raw audio through until the model is loaded so
+        // the call never has a silent/missing track during init.
+        return mod.createNoiseSuppressionAudioWorklet(ctx, { bypassUntilReady: true });
+      });
+    }
+    var node = new AudioWorkletNode(ctx, PROCESSORS[MODEL].name, {
+      channelCount: 1,
+      numberOfInputs: 1,
+      numberOfOutputs: 1,
+      processorOptions: { maxChannels: 1, wasmBinary: wasmBinary },
+    });
+    return Promise.resolve({
+      node: node,
+      ready: Promise.resolve(),
+      dispose: function () {
+        try {
+          node.port.postMessage('destroy');
+        } catch (e) {}
+      },
+    });
+  }
+
   function processStream(stream) {
     var audioTracks = stream.getAudioTracks();
     if (audioTracks.length === 0) return Promise.resolve(stream);
@@ -171,78 +205,72 @@
         }
 
         // 2. ML Processor
-        var mlOptions = {
-          channelCount: 1,
-          numberOfInputs: 1,
-          numberOfOutputs: 1,
-          processorOptions: { maxChannels: 1 },
-        };
+        return buildMlNode(ctx, wasmBinary).then(function (ml) {
+          var mlNode = ml.node;
+          head.connect(mlNode);
+          mlNode.connect(dest);
 
-        if (MODEL === 'rnnoise' || MODEL === 'speex') {
-          mlOptions.processorOptions.wasmBinary = wasmBinary;
-        } else if (MODEL === 'dtln') {
-          mlOptions.processorOptions = {
-            wasmBinary: wasmBinary,
-            model1: ASSET_BASE + 'model_1.tflite',
-            model2: ASSET_BASE + 'model_2.tflite',
-          };
-        }
+          // Surface async init failures (e.g. DTLN model load) without blocking
+          // the track handoff — audio flows via bypassUntilReady meanwhile.
+          if (ml.ready && typeof ml.ready.then === 'function') {
+            ml.ready.catch(function (err) {
+              var m = err instanceof Error ? err.message : String(err);
+              console.error('[lotus-denoise] ' + MODEL + ' init failed:', m);
+            });
+          }
 
-        var mlNode = new AudioWorkletNode(ctx, PROCESSORS[MODEL].name, mlOptions);
-        head.connect(mlNode);
-        mlNode.connect(dest);
+          var origTrack = audioTracks[0];
+          var processedTrack = dest.stream.getAudioTracks()[0];
 
-        var origTrack = audioTracks[0];
-        var processedTrack = dest.stream.getAudioTracks()[0];
+          var torndown = false;
+          function cleanup() {
+            if (torndown) return;
+            torndown = true;
+            try {
+              ml.dispose();
+            } catch (e) {}
+            try {
+              source.disconnect();
+              mlNode.disconnect();
+            } catch (e) {}
+            try {
+              origTrack.stop();
+            } catch (e) {}
+          }
 
-        var torndown = false;
-        function cleanup() {
-          if (torndown) return;
-          torndown = true;
-          try {
-            mlNode.port.postMessage('destroy');
-          } catch (e) {}
-          try {
-            source.disconnect();
-            mlNode.disconnect();
-          } catch (e) {}
-          try {
-            origTrack.stop();
-          } catch (e) {}
-        }
-
-        var rawStop = processedTrack.stop.bind(processedTrack);
-        processedTrack.stop = function () {
-          cleanup();
-          rawStop();
-        };
-        origTrack.addEventListener('ended', function () {
-          try {
+          var rawStop = processedTrack.stop.bind(processedTrack);
+          processedTrack.stop = function () {
+            cleanup();
             rawStop();
-          } catch (e) {}
-          cleanup();
-        });
+          };
+          origTrack.addEventListener('ended', function () {
+            try {
+              rawStop();
+            } catch (e) {}
+            cleanup();
+          });
 
-        if (!hasNotifiedActive) {
-          hasNotifiedActive = true;
-          window.parent.postMessage(
-            {
-              type: 'lotus-denoise-status',
-              active: true,
-              model: MODEL,
-              nativeNS: USE_NATIVE_NS,
-              gate: USE_GATE,
-            },
-            '*',
-          );
-        }
+          if (!hasNotifiedActive) {
+            hasNotifiedActive = true;
+            window.parent.postMessage(
+              {
+                type: 'lotus-denoise-status',
+                active: true,
+                model: MODEL,
+                nativeNS: USE_NATIVE_NS,
+                gate: USE_GATE,
+              },
+              '*',
+            );
+          }
 
-        var out = new MediaStream();
-        out.addTrack(processedTrack);
-        stream.getVideoTracks().forEach(function (t) {
-          out.addTrack(t);
+          var out = new MediaStream();
+          out.addTrack(processedTrack);
+          stream.getVideoTracks().forEach(function (t) {
+            out.addTrack(t);
+          });
+          return out;
         });
-        return out;
       })
       .catch(function (e) {
         var msg = e instanceof Error ? e.message : String(e);