feat(denoise): add self-hosted DeepFilterNet 3 ML noise-suppression model

Integrate DeepFilterNet 3 (deepfilternet3-noise-filter@1.2.1) as a new
client-side denoise model id 'deepfilternet', mirroring the DTLN pattern.

The npm package ships only an ESM whose AudioWorklet processor + wasm-bindgen
glue are inlined as a string (loaded via a Blob URL — no CDN for the worklet).
Its only runtime fetches are a single-threaded df_bg.wasm and an ONNX model
tarball, which previously loaded from an external CDN. We now VENDOR both
(build/denoise-vendor/deepfilternet/v2/...) and self-host them under
denoise/deepfilternet/, overriding the package's cdnUrl so nothing hits the
upstream CDN — keeping it self-hosted / Tauri-CSP safe.

The wasm is single-threaded (no SharedArrayBuffer / atomics / imported shared
memory), so it needs no COOP/COEP cross-origin isolation and runs fine in EC's
non-isolated iframe. Runs at 48 kHz fullband. Any init/runtime failure falls
back to the raw mic, like the other models.

- vite.config.js: copy ESM + vendored wasm/model into the EC denoise dir with a
  required-asset guard that aborts the build if any entry is missing.
- build/lotus-denoise.js: 'deepfilternet' branch — dynamic-import the ESM, build
  a DeepFilterNet3Core pointed at the self-hosted base, await init, return the
  worklet node; 48 kHz; raw-mic fail-safe preserved.
- denoisePipeline.ts: 'deepfilternet' branch for the in-app tester + sampleRate.
- settings.ts: add 'deepfilternet' to DenoiseModelId + getSettings whitelist.
- lotusDenoiseUtils.ts: add the comparison-chart row.
- General.tsx: add the "DeepFilterNet 3 (beta)" dropdown option.

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

src/app/features/settings/general/General.tsx

@@ -1358,6 +1358,7 @@ function Calls() {
                     { value: 'rnnoise', label: 'RNNoise' },
                     { value: 'speex', label: 'Speex (Legacy)' },
                     { value: 'dtln', label: 'DTLN (beta)' },
+                    { value: 'deepfilternet', label: 'DeepFilterNet 3 (beta)' },
                   ]}
                 />
               }

src/app/state/settings.ts

@@ -14,10 +14,12 @@ export type MessageSpacing = '0' | '100' | '200' | '300' | '400' | '500';
 //  - 'browser' : WebRTC built-in suppression (Element Call noiseSuppression param)
 //  - 'ml'      : client-side RNNoise ML suppression (Lotus denoise shim)
 export type NoiseSuppressionMode = 'off' | 'browser' | 'ml';
-// Self-hostable, build-bundled ML models. DeepFilterNet remains excluded — it
-// loads its runtime/models from external CDNs, which breaks the self-hosted /
-// Tauri-CSP strategy (see LOTUS_DENOISE_ENGINEERING_REVIEW.md).
-export type DenoiseModelId = 'rnnoise' | 'speex' | 'dtln';
+// Self-hostable, build-bundled ML models. DeepFilterNet 3 is included via
+// deepfilternet3-noise-filter with its df_bg.wasm + ONNX model VENDORED and
+// self-hosted (its cdnUrl is overridden), so it no longer depends on an external
+// CDN. Its wasm is single-threaded, so no COOP/COEP cross-origin isolation is
+// required (see LOTUS_DENOISE_ENGINEERING_REVIEW.md).
+export type DenoiseModelId = 'rnnoise' | 'speex' | 'dtln' | 'deepfilternet';
 export type ChatBackground =
   | 'none'
   | 'blueprint'
@@ -260,12 +262,13 @@ export const getSettings = (): Settings => {
             ? 'browser'
             : 'off'
           : (saved.callNoiseSuppression ?? defaultSettings.callNoiseSuppression),
-      // Coerce any retired/unknown persisted model (e.g. 'dtln', 'deepfilternet'
-      // from earlier beta builds) back to the default working model.
+      // Coerce any retired/unknown persisted model back to the default working
+      // model; only whitelisted ids pass through.
       callDenoiseModel:
         saved.callDenoiseModel === 'rnnoise' ||
         saved.callDenoiseModel === 'speex' ||
-        saved.callDenoiseModel === 'dtln'
+        saved.callDenoiseModel === 'dtln' ||
+        saved.callDenoiseModel === 'deepfilternet'
           ? saved.callDenoiseModel
           : defaultSettings.callDenoiseModel,
       composerToolbarButtons: {

src/app/utils/denoisePipeline.ts

@@ -14,10 +14,10 @@ import { DenoiseModelId } from '../state/settings';
 const BASE = `${import.meta.env.BASE_URL.replace(/\/+$/, '')}/public/element-call/denoise/`;
 
 /**
- * Required AudioContext sample rate per model. RNNoise/Speex (sapphi) assume
- * 48 kHz. DTLN (@workadventure) targets 16 kHz and does NOT resample internally
- * — running it at 48 kHz produces robotic/choppy/quiet output, so its whole
- * graph must run in a 16 kHz context.
+ * Required AudioContext sample rate per model. RNNoise/Speex (sapphi) and
+ * DeepFilterNet 3 are 48 kHz. DTLN (@workadventure) targets 16 kHz and does NOT
+ * resample internally — running it at 48 kHz produces robotic/choppy/quiet
+ * output, so its whole graph must run in a 16 kHz context.
  */
 export const sampleRateFor = (model: DenoiseModelId): number => (model === 'dtln' ? 16000 : 48000);
 
@@ -75,6 +75,32 @@ export async function buildModelNode(
     const handle = await mod.createNoiseSuppressionAudioWorklet(ctx, { bypassUntilReady: true });
     return { node: handle.node, dispose: () => handle.dispose() };
   }
+  if (model === 'deepfilternet') {
+    // deepfilternet3-noise-filter ESM: inlines its worklet/wasm-bindgen glue and
+    // fetches only df_bg.wasm + the ONNX model, which we self-host under
+    // deepfilternet/v2/... Override its cdnUrl to our absolute base so nothing
+    // hits the upstream CDN. DeepFilterNet3Core builds the worklet node directly.
+    const dfnBase = new URL(`${BASE}deepfilternet`, window.location.href).href;
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    const mod: any = await import(/* @vite-ignore */ `${BASE}deepfilternet/index.esm.js`);
+    const core = new mod.DeepFilterNet3Core({
+      sampleRate: sampleRateFor(model),
+      noiseReductionLevel: 80,
+      assetConfig: { cdnUrl: dfnBase },
+    });
+    await core.initialize();
+    const node: AudioWorkletNode = await core.createAudioWorkletNode(ctx);
+    return {
+      node,
+      dispose: () => {
+        try {
+          core.destroy();
+        } catch {
+          /* noop */
+        }
+      },
+    };
+  }
   const cfg = SAPPHI[model];
   const [, wasmBinary] = await Promise.all([addModuleOnce(ctx, cfg.script), fetchWasm(cfg.wasm)]);
   const node = new AudioWorkletNode(ctx, cfg.proc, {

src/app/utils/lotusDenoiseUtils.ts

@@ -40,6 +40,15 @@ export const DENOISE_MODELS: DenoiseModel[] = [
     transients: 'Excellent',
     voiceQuality: 'High',
   },
+  {
+    id: 'deepfilternet',
+    name: 'DeepFilterNet 3 (beta)',
+    description: 'Studio-grade deep-learning model (48 kHz, ONNX). Best quality; highest CPU.',
+    cpuUsage: '25-50%',
+    binarySize: '~18 MB',
+    transients: 'Excellent',
+    voiceQuality: 'Very High',
+  },
 ];
 
 export const isMLDenoiseSupported = (): boolean => {