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>
Two issues found from real testing of the in-app tester:
1. Raw ≈ RNNoise ≈ Speex sounded identical in Record & compare because the clip
was captured with browser noise suppression ON (the user's native-NS
setting), so "Raw" was already cleaned and the models had nothing left to
remove. Record & compare now captures fully raw audio (noiseSuppression /
AGC / echoCancellation off) so each model's effect on real noise is audible.
(Friends still heard differences in calls — the models work; the test was
feeding them pre-cleaned audio.)
2. DTLN was robotic/choppy/quiet because @workadventure/noise-suppression
targets 16 kHz (AUDIO_CONFIG.sampleRate) and does NOT resample internally,
while we ran it at 48 kHz. Run DTLN's whole graph in a 16 kHz context:
- denoisePipeline: add sampleRateFor(model) (16k for dtln, 48k otherwise);
tester live-monitor + playback contexts use it (bufferSource resamples the
48k clip down for DTLN).
- shim (build/lotus-denoise.js): SAMPLE_RATE is now model-aware, so DTLN is
correct in real calls too (it was previously broken at 48 kHz). The 16 kHz
processed track is still published to LiveKit (WebRTC/Opus resamples).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The previous "Test Microphone" meter only showed a raw 0-100% level bar — it
never ran the gate or any model, and its scale wasn't dBFS, so it couldn't tell
you which threshold to pick or let you hear the models solo. Replace it with a
real tester that reuses the shipped worklets (/public/element-call/denoise/) in
a main-app AudioContext, mirroring the call pipeline (source -> gate -> model).
- denoisePipeline.ts: shared loader for the RNNoise/Speex flat worklets and the
DTLN @workadventure helper, the noise gate, and a dBFS RMS meter helper.
- DenoiseTester.tsx:
- Live monitor: hear yourself through the selected model (+gate) in real time
(headphones) with In/Out dBFS meters and a threshold marker on the In meter
so the gate value is meaningful to calibrate.
- Record & compare: capture a short clip, then A/B the same audio Raw vs
RNNoise vs Speex vs DTLN.
- Wire it into the ML settings block; remove the old raw-only MicMeter. Use real
TDS tokens (--accent-*, --border-color, --bg-card) instead of the invented
--lt-* names + hardcoded hex the old meter used.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
jared