lotus(#1): implement all 4 denoise models; fix gate name + rates
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Faithful port of cinny's proven pipeline into the TrackProcessor, closing
protocol gap F3 (host offers rnnoise/speex/dtln/deepfilternet; only the
first two existed in-source).

- Fix real bug: gate worklet registers as "noise-gate" (hyphenated), not
  "noiseGate" — the gated path would have failed to construct the node.
- Per-model sample rate: DTLN runs at 16kHz, others 48kHz (worklets don't
  resample); verify the context actually got the rate, else fall back.
- resume() a suspended context (host postMessage isn't a gesture).
- DTLN via dynamic-imported @workadventure helper (bypassUntilReady);
  DeepFilterNet via dynamic-imported ESM + DeepFilterNet3Core pointed at
  the self-hosted base. Same-origin base (kept from the C1 fix) makes these
  dynamic imports safe.
- Prefer SIMD rnnoise.wasm with non-SIMD fallback; cache wasm per URL.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Lotus CI
2026-06-30 00:28:10 -04:00
co-authored by Claude Opus 4.8
parent 29592fbb18
commit 78350a21f4
2 changed files with 167 additions and 90 deletions
+7 -10
View File
@@ -53,16 +53,13 @@ function safeAssetBase(raw: string | null): string {
export function startLotusDenoise(vm: CallViewModel): () => void {
if (lotusParam("lotusDenoise") !== "ml") return () => undefined;
const requestedModel = lotusParam("lotusModel");
// Only RNNoise/Speex are implemented in-source so far. Don't silently
// degrade an unsupported model (e.g. the host's dtln/deepfilternet) to
// rnnoise — log it so the mismatch is visible.
let model: LotusDenoiseConfig["model"] = "rnnoise";
if (requestedModel === "speex") model = "speex";
else if (requestedModel && requestedModel !== "rnnoise")
logger.warn(
`[lotus] denoise model "${requestedModel}" not implemented in-source; using rnnoise`,
);
const requested = lotusParam("lotusModel");
const model: LotusDenoiseConfig["model"] =
requested === "speex" ||
requested === "dtln" ||
requested === "deepfilternet"
? requested
: "rnnoise";
const rawThreshold = lotusParam("lotusGateThreshold");
const config: LotusDenoiseConfig = {
+160 -80
View File
@@ -12,35 +12,37 @@ import {
} from "livekit-client";
import { logger } from "matrix-js-sdk/lib/logger";
export type LotusDenoiseModel = "rnnoise" | "speex";
export type LotusDenoiseModel =
| "rnnoise"
| "speex"
| "dtln"
| "deepfilternet";
export interface LotusDenoiseConfig {
model: LotusDenoiseModel;
/** Base URL the worklet scripts + wasm are served from (e.g. "./denoise/"). */
/** Base URL the worklet scripts/wasm/ESM are served from (e.g. "./denoise/"). */
assetBase: string;
gate: boolean;
gateThreshold: number;
}
// Flat sapphi worklets: each registers a processor under these names when its
// script module is added. Same assets the Lotus host already ships under
// public/element-call/denoise/, so no new EC dependency is needed.
//
// ⚠️ CONTRACT: this is an undeclared, cross-repo asset dependency. The embedded
// fork build does NOT bundle these — they are copied in by cinny's
// vite.config.js `lotusDenoise()` plugin from `@sapphi-red/web-noise-suppressor`.
// The worklet/wasm version must match what this processor expects (sapphi
// rnnoise/speex, 48kHz). If the fork ever wants to own them, add
// `@sapphi-red/web-noise-suppressor` as a dep + a copy step here. Until then,
// an integration smoke-check should assert GET .../denoise/rnnoise.wasm == 200.
const PROCESSORS: Record<
LotusDenoiseModel,
{ name: string; script: string; wasm: string }
// Flat sapphi worklets (RNNoise/Speex): each registers a processor under `name`
// when its `script` module is added; we feed it the fetched wasm binary.
// ⚠️ CONTRACT: these assets are NOT bundled by the fork build — cinny's
// vite.config.js `lotusDenoise()` plugin copies them from
// `@sapphi-red/web-noise-suppressor` / `@workadventure/noise-suppression` /
// `deepfilternet3-noise-filter` into public/element-call/denoise/. The
// worklet/wasm/ESM versions must match what this processor expects. An
// integration smoke-check should assert GET .../denoise/rnnoise.wasm == 200.
const FLAT: Record<
"rnnoise" | "speex",
{ name: string; script: string; wasm: string; simdWasm?: string }
> = {
rnnoise: {
name: "@sapphi-red/web-noise-suppressor/rnnoise",
script: "rnnoiseWorklet.js",
wasm: "rnnoise.wasm",
simdWasm: "rnnoise_simd.wasm",
},
speex: {
name: "@sapphi-red/web-noise-suppressor/speex",
@@ -48,21 +50,25 @@ const PROCESSORS: Record<
wasm: "speex.wasm",
},
};
const GATE_NAME = "@sapphi-red/web-noise-suppressor/noiseGate";
const GATE_SCRIPT = "noiseGateWorklet.js";
// The sapphi gate worklet registers under "noise-gate" (hyphenated).
const GATE = {
name: "@sapphi-red/web-noise-suppressor/noise-gate",
script: "noiseGateWorklet.js",
};
// The sapphi rnnoise/speex worklets are 48kHz-fullband and don't resample, so
// the graph MUST run at 48kHz regardless of the hardware default.
const SAMPLE_RATE = 48_000;
// DTLN (@workadventure) targets 16kHz and doesn't resample; RNNoise/Speex and
// DeepFilterNet are 48kHz fullband. The worklets don't resample, so the whole
// graph must run at the model's native rate.
const sampleRateFor = (model: LotusDenoiseModel): number =>
model === "dtln" ? 16_000 : 48_000;
// Cache fetched wasm per URL so a reconnect/device-switch (which rebuilds the
// graph) doesn't re-download it.
// Cache fetched wasm per URL so a reconnect/device-switch doesn't re-download.
const wasmCache = new Map<string, Promise<ArrayBuffer>>();
function loadWasm(url: string): Promise<ArrayBuffer> {
function fetchWasm(url: string): Promise<ArrayBuffer> {
let p = wasmCache.get(url);
if (!p) {
p = fetch(url).then((r) => {
if (!r.ok) throw new Error(`denoise wasm ${r.status}`);
if (!r.ok) throw new Error(`denoise wasm ${url} -> ${r.status}`);
return r.arrayBuffer();
});
wasmCache.set(url, p);
@@ -70,27 +76,44 @@ function loadWasm(url: string): Promise<ArrayBuffer> {
return p;
}
function supportsSimd(): boolean {
try {
// Minimal SIMD module (v128) — validates only where SIMD is supported.
return WebAssembly.validate(
new Uint8Array([
0, 97, 115, 109, 1, 0, 0, 0, 1, 5, 1, 96, 0, 1, 123, 3, 2, 1, 0, 10,
10, 1, 8, 0, 65, 0, 253, 15, 253, 98, 11,
]),
);
} catch {
return false;
}
}
interface MlNode {
node: AudioNode;
dispose?: () => void;
}
interface Graph {
source: MediaStreamAudioSourceNode;
nodes: AudioWorkletNode[];
nodes: AudioNode[];
disposes: (() => void)[];
track: MediaStreamTrack;
}
/**
* A LiveKit audio TrackProcessor that runs Lotus ML noise suppression
* (RNNoise/Speex) on the local microphone track, as a first-class stage in
* Element Call's publish pipeline.
* (RNNoise / Speex / DTLN / DeepFilterNet) on the local microphone track, as a
* first-class stage in Element Call's publish pipeline — replacing the host's
* `getUserMedia` monkeypatch.
*
* This replaces the host's `getUserMedia` monkeypatch: because it's a real
* LiveKit processor, EC re-applies it on every (re)publish/restart, so denoise
* survives EC's own mid-call reconnect — the root cause of the A7 "mic dead
* after reconnect" bug.
*
* It owns a dedicated 48kHz AudioContext (the worklets require it, and LiveKit
* does NOT pass an audioContext to restart()), reused across restarts and
* closed on destroy. restart() never throws and never leaves a stopped track
* on the sender: on failure it degrades to the raw (un-denoised) track rather
* than silence.
* Because it's a real LiveKit processor, EC re-applies it on every
* (re)publish/restart, so denoise survives EC's mid-call reconnect — the root
* cause of A7. It owns a dedicated AudioContext at the model's required sample
* rate (LiveKit does NOT pass an audioContext to restart()), reused across
* restarts and closed on destroy. restart() never throws and never leaves a
* stopped track on the sender: on failure it degrades to the RAW mic track
* rather than silence.
*/
export class LotusDenoiseProcessor
implements TrackProcessor<Track.Kind.Audio, AudioProcessorOptions>
@@ -104,18 +127,14 @@ export class LotusDenoiseProcessor
public constructor(private readonly config: LotusDenoiseConfig) {}
public async init(_opts: AudioProcessorOptions): Promise<void> {
// Own context at the required rate — do NOT use opts.audioContext (it's the
// room's hardware-rate context, and is undefined on restart()).
this.ctx = new AudioContext({ sampleRate: SAMPLE_RATE });
const graph = await this.buildGraph(_opts.track);
this.graph = graph;
this.processedTrack = graph.track;
await this.ensureContext();
this.graph = await this.buildGraph(_opts.track);
this.processedTrack = this.graph.track;
}
public async restart(opts: AudioProcessorOptions): Promise<void> {
if (!this.ctx || this.ctx.state === "closed")
this.ctx = new AudioContext({ sampleRate: SAMPLE_RATE });
try {
await this.ensureContext();
const next = await this.buildGraph(opts.track);
this.disposeGraph(this.graph);
this.graph = next;
@@ -138,22 +157,45 @@ export class LotusDenoiseProcessor
this.ctx = undefined;
}
/** Create (once) the model-rate context + register the flat worklet modules. */
private async ensureContext(): Promise<void> {
const rate = sampleRateFor(this.config.model);
if (this.ctx && this.ctx.state !== "closed" && this.ctx.sampleRate === rate) {
if (this.ctx.state === "suspended") await this.ctx.resume();
return;
}
if (this.ctx && this.ctx.state !== "closed")
await this.ctx.close().catch(() => undefined);
const ctx = new AudioContext({ sampleRate: rate });
if (ctx.sampleRate !== rate) {
await ctx.close().catch(() => undefined);
throw new Error(`denoise: got ${ctx.sampleRate}Hz, need ${rate}Hz`);
}
// Flat models register via addModule here; DTLN/DeepFilterNet bring their
// own processor via the dynamic-imported helper (see buildMlNode).
if (this.config.model === "rnnoise" || this.config.model === "speex")
await ctx.audioWorklet.addModule(
this.config.assetBase + FLAT[this.config.model].script,
);
if (this.config.gate)
await ctx.audioWorklet.addModule(this.config.assetBase + GATE.script);
// The action arrives via host postMessage, not a gesture in this iframe, so
// the context can start suspended.
if (ctx.state === "suspended") await ctx.resume();
this.ctx = ctx;
}
private async buildGraph(track: MediaStreamTrack): Promise<Graph> {
const ctx = this.ctx!;
const base = this.config.assetBase;
const proc = PROCESSORS[this.config.model];
await ctx.audioWorklet.addModule(base + proc.script);
if (this.config.gate) await ctx.audioWorklet.addModule(base + GATE_SCRIPT);
const wasmBinary = await loadWasm(base + proc.wasm);
const source = ctx.createMediaStreamSource(new MediaStream([track]));
const dest = ctx.createMediaStreamDestination();
const nodes: AudioWorkletNode[] = [];
const nodes: AudioNode[] = [];
const disposes: (() => void)[] = [];
let head: AudioNode = source;
if (this.config.gate) {
const gate = new AudioWorkletNode(ctx, GATE_NAME, {
const gate = new AudioWorkletNode(ctx, GATE.name, {
processorOptions: {
openThreshold: this.config.gateThreshold,
closeThreshold: this.config.gateThreshold - 5,
@@ -166,39 +208,77 @@ export class LotusDenoiseProcessor
nodes.push(gate);
}
const ml = new AudioWorkletNode(ctx, proc.name, {
const ml = await this.buildMlNode(ctx);
head.connect(ml.node);
ml.node.connect(dest);
nodes.push(ml.node);
if (ml.dispose) disposes.push(ml.dispose);
logger.info(`[lotus] denoise processor active (${this.config.model})`);
return { source, nodes, disposes, track: dest.stream.getAudioTracks()[0] };
}
private async buildMlNode(ctx: AudioContext): Promise<MlNode> {
const base = this.config.assetBase;
const model = this.config.model;
if (model === "dtln") {
// Self-contained ESM that resolves its own processor + LiteRT wasm +
// TFLite models. bypassUntilReady passes raw audio until the model loads.
const mod = await import(/* @vite-ignore */ `${base}workadventure/audio-worklet.js`);
return (await mod.createNoiseSuppressionAudioWorklet(ctx, {
bypassUntilReady: true,
})) as MlNode;
}
if (model === "deepfilternet") {
const dfnBase = new URL(`${base}deepfilternet`, window.location.href).href;
const mod = await import(/* @vite-ignore */ `${base}deepfilternet/index.esm.js`);
const core = new mod.DeepFilterNet3Core({
sampleRate: 48_000,
noiseReductionLevel: 80,
assetConfig: { cdnUrl: dfnBase },
});
await core.initialize();
const node = (await core.createAudioWorkletNode(ctx)) as AudioNode;
return { node, dispose: () => void safeCall(() => core.destroy()) };
}
// Flat sapphi worklet (rnnoise/speex).
const flat = FLAT[model];
const useSimd = model === "rnnoise" && !!flat.simdWasm && supportsSimd();
const wasmFile = useSimd ? flat.simdWasm! : flat.wasm;
let wasmBinary: ArrayBuffer;
try {
wasmBinary = await fetchWasm(base + wasmFile);
} catch (e) {
if (useSimd) {
wasmCache.delete(base + wasmFile);
wasmBinary = await fetchWasm(base + flat.wasm); // fall back to non-SIMD
} else throw e;
}
const node = new AudioWorkletNode(ctx, flat.name, {
channelCount: 1,
numberOfInputs: 1,
numberOfOutputs: 1,
processorOptions: { maxChannels: 1, wasmBinary: wasmBinary.slice(0) },
processorOptions: { maxChannels: 1, wasmBinary },
});
head.connect(ml);
ml.connect(dest);
nodes.push(ml);
logger.info(`[lotus] denoise processor active (${this.config.model})`);
return { source, nodes, track: dest.stream.getAudioTracks()[0] };
return { node, dispose: () => void safeCall(() => node.port.postMessage("destroy")) };
}
private disposeGraph(graph: Graph | undefined): void {
if (!graph) return;
for (const node of graph.nodes) {
try {
node.port.postMessage("destroy");
} catch {
/* ignore */
}
try {
node.disconnect();
} catch {
/* ignore */
}
}
try {
graph.source.disconnect();
} catch {
/* ignore */
}
for (const dispose of graph.disposes) safeCall(dispose);
for (const node of graph.nodes) safeCall(() => node.disconnect());
safeCall(() => graph.source.disconnect());
graph.track.stop();
}
}
function safeCall(fn: () => void): void {
try {
fn();
} catch {
/* ignore */
}
}