4 Commits

Include renames

Author	SHA1	Message	Date
nathan	0656e6ecb0	removed eslint unused ignore lines, config handles ignoring it now	2026-08-02 20:55:42 -04:00
jaredandClaude Opus 4.8	04b56ffacd	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>	2026-06-17 19:57:08 -04:00
jaredandClaude Opus 4.8	abb7f743b8	fix(calls): DTLN at 16kHz + raw-capture A/B; explains weak/robotic results ... 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>	2026-06-17 17:27:15 -04:00
jaredandClaude Opus 4.8	14cfa021c5	feat(calls): in-app denoise tester to audition models + calibrate gate ... 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>	2026-06-16 17:53:57 -04:00