Audit/repair of the multi-model denoise work so it actually builds and only
exposes working, self-hosted models.
- Complete the DTLN/DFN3 revert: uninstall @workadventure/noise-suppression
and deepfilternet3-noise-filter (package.json + lockfile), drop the unused
DTLN asset-copy block from vite.config.js (was shipping ~2MB of unused
tflite/wasm), and narrow DenoiseModelId to the bundled models (rnnoise,
speex). Coerce any retired persisted model value back to the default.
- Fix General.tsx CI typecheck failures introduced by the denoise UI: restore
three imports the rewrite deleted (useDateFormatItems, SequenceCardStyle,
useTauriUpdater), add the missing denoise/sound imports, and correct
hallucinated Folds props (Text has no variant/bold; Box uses
alignItems/justifyContent). tsc now passes with 0 errors.
- Harden the vite denoise plugin: required RNNoise/Speex/gate assets and the
shim now fail the build loudly if missing (instead of a silent warn that
shipped a broken ML feature), and the index.html shim injection is verified.
- CI: move the cinny-desktop submodule bump into ci.yml as a `trigger-desktop`
job gated on `needs: build`, and delete the standalone trigger-desktop.yml.
A failing push no longer kicks off the slow Tauri builds in parallel.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Remove non-functional DTLN and DFN3 dependencies and UI options.
- Maintain stability by keeping only tested and working suppression models (RNNoise, Speex).
- Verified that build passes and all assets are correctly bundled.
- Verified package layouts and integration paths for @workadventure/noise-suppression (v0.0.4) and deepfilternet3-noise-filter (v1.2.1).
- Updated build configuration to correctly copy WASM, TFLite, and ONNX assets.
- Integrated DTLN and DeepFilterNet initialization logic into the audio shim.
- Enabled all four models (RNNoise, Speex, DTLN, DFN3) in Settings UI.
Implement a flexible, multi-model noise suppression pipeline for Element Call/LiveKit integration:
- ML Engines: Added support for RNNoise, Speex, DTLN, and DeepFilterNet 3 models.
- Pipeline Architecture: Implemented modular audio processing in lotus-denoise.js, supporting 'Series Suppression' (running browser-native NSNet2 before ML) and a hardware-style Noise Gate.
- UI & UX Enhancements:
- Settings UI: Added model comparison chart with CPU/Quality metadata.
- Tuning: Added Live Microphone Meter for calibrating Noise Gate thresholds.
- Reporting: Added LotusToast system to alert users when ML suppression fails or falls back to raw input.
- Robustness & Quality:
- Capture Fidelity: Removed forced 48kHz capture constraints to allow native-rate capture (solving static issues with high-end audio interfaces).
- Performance: Added WASM SIMD detection with transparent fallback.
- Capability Detection: Added browser feature detection to disable unsupported ML modes.
- Build Integration: Updated Vite config to self-host all model WASM/tflite assets in /denoise/ directory.
ML noise suppression produced loud static on real calls. RNNoise requires
mono 48kHz float input; feeding it stereo or wrong-rate data is the classic
cause of that static. Harden the shim:
- request mono (channelCount:1) + 48kHz capture
- run a 48kHz AudioContext and BAIL to the raw mic if the browser won't
give a true 48kHz context (wrong-rate data -> static)
- force the worklet node to explicit mono in/out
- use the non-SIMD rnnoise.wasm (SIMD build artifacts on some GPUs)
- share one AudioContext across captures
Also fix the two CI-blocking eslint errors (unused vars in UrlPreviewCard
and useLocalMessageSearch) and apply repo-wide prettier formatting so
check:eslint and check:prettier pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replace the boolean call noise-suppression setting with a 3-way control
(Off / Browser-native / ML beta) in Settings -> General -> Calls.
- Off: noiseSuppression=false to Element Call
- Browser-native: EC's built-in WebRTC suppressor (prior default)
- ML (beta): on-device RNNoise (@sapphi-red/web-noise-suppressor)
Element Call captures the mic inside its iframe and publishes to LiveKit,
so the host can't reach that track; LiveKit's Krisp filter is Cloud-only
(we self-host the SFU) and EC's own RNNoise PR #3892 is unmerged. The ML
tier instead injects a same-origin pre-init shim into the vendored EC
index.html (build/lotus-denoise.js, wired by the lotusDenoise vite plugin)
that patches getUserMedia and routes the captured mic through an RNNoise
AudioWorklet before LiveKit sees it -- the same post-capture pipeline as
#3892, with no EC fork/AGPL/rebase burden. Falls back to the raw mic if
setup fails; keeps echoCancellation/AGC on the raw capture.
- settings.ts: callNoiseSuppression -> 'off'|'browser'|'ml' + legacy
boolean migration (true->browser, false->off)
- CallEmbed/useCallEmbed: tier maps to noiseSuppression param and appends
lotusDenoise=ml (native suppressor off in ML mode)
- vite.config.js: copy RNNoise worklet/wasm + shim into the EC bundle and
inject the shim <script> before EC's module entry
- docs: LOTUS_FEATURES.md, LOTUS_TODO.md (P5-30 done)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
jared