element-call/src/lotus/lotusDenoiseProcessor.ts

/*
Copyright 2026 Lotus Guild

SPDX-License-Identifier: AGPL-3.0-only OR LicenseRef-Element-Commercial
Please see LICENSE in the repository root for full details.
*/

import {
  type AudioProcessorOptions,
  type Track,
  type TrackProcessor,
} from "livekit-client";
import { logger } from "matrix-js-sdk/lib/logger";

export type LotusDenoiseModel = "rnnoise" | "speex" | "dtln" | "deepfilternet";

export interface LotusDenoiseConfig {
  model: LotusDenoiseModel;
  /** Base URL the worklet scripts/wasm/ESM are served from (e.g. "./denoise/"). */
  assetBase: string;
  gate: boolean;
  gateThreshold: number;
  /**
   * Attenuation floor as a dry/wet mix: the fraction (0..~0.3) of the ORIGINAL
   * mic blended back under the denoised signal so full suppression never fully
   * collapses the noise floor — this is what kills the "underwater"/pumping
   * artifact. 0.15 ≈ a -16 dB floor. 0 = full suppression (no floor).
   */
  floor: number;
}

// 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",
    script: "speexWorklet.js",
    wasm: "speex.wasm",
  },
};
// The sapphi gate worklet registers under "noise-gate" (hyphenated).
const GATE = {
  name: "@sapphi-red/web-noise-suppressor/noise-gate",
  script: "noiseGateWorklet.js",
};

// 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 doesn't re-download.
const wasmCache = new Map<string, Promise<ArrayBuffer>>();
async function fetchWasmUncached(url: string): Promise<ArrayBuffer> {
  const r = await fetch(url);
  if (!r.ok) throw new Error(`denoise wasm ${url} -> ${r.status}`);
  return r.arrayBuffer();
}
async function fetchWasm(url: string): Promise<ArrayBuffer> {
  let p = wasmCache.get(url);
  if (!p) {
    p = fetchWasmUncached(url);
    // Never cache a REJECTED fetch: a transient failure (e.g. a blip during a
    // reconnect) must not permanently disable denoise for the whole session.
    // Evict on failure so the next restart/device-switch retries.
    void p.catch(() => wasmCache.delete(url));
    wasmCache.set(url, p);
  }
  return p;
}

/**
 * Resume an AudioContext, but never block indefinitely. A suspended context can
 * only resume after a user gesture; the denoise action can arrive via host
 * postMessage (no gesture), so `resume()` may stay pending forever. Since this
 * runs inside LiveKit's per-track change lock, a hung resume() would deadlock
 * every later mute/unmute/device-switch. Race it against a timeout and proceed
 * either way — the processor's `statechange` watcher resumes it once a gesture
 * lands, and a still-suspended context degrades to (temporary) silence that the
 * watcher heals, not a hang.
 */
// [lotus] 500 ms, not 3 s: this still runs under LiveKit's trackChangeLock (#7),
// and the statechange watcher heals a still-suspended context later anyway.
async function resumeCtx(ctx: AudioContext, timeoutMs = 500): Promise<void> {
  await Promise.race([
    ctx.resume().catch(() => undefined),
    new Promise<void>((resolve) => setTimeout(resolve, timeoutMs)),
  ]);
}

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: AudioNode[];
  disposes: (() => void)[];
  track: MediaStreamTrack;
}

// [lotus #26] Minimal local contracts for the two dynamically-imported ESM
// helpers (not bundled here — see the CONTRACT note above). Their exports are
// asserted at runtime so an asset bump that renames/removes one fails loudly
// (and flows into the rnnoise fallback in lotusDenoise.ts) instead of as a
// vague TypeError deep inside `init()`.
interface DtlnModule {
  createNoiseSuppressionAudioWorklet: (
    ctx: AudioContext,
    opts: { bypassUntilReady: boolean },
  ) => Promise<MlNode>;
}
interface DfnCore {
  initialize: () => Promise<void>;
  createAudioWorkletNode: (ctx: AudioContext) => Promise<AudioNode>;
  destroy: () => void;
}
interface DfnModule {
  DeepFilterNet3Core: new (opts: {
    sampleRate: number;
    noiseReductionLevel: number;
    assetConfig: { cdnUrl: string };
  }) => DfnCore;
}

/** Throw a clear error if a dynamic-import module lacks an expected export. */
export function assertModuleExport<T>(
  mod: unknown,
  name: string,
  url: string,
): T {
  const exp = (mod as Record<string, unknown> | null | undefined)?.[name];
  if (typeof exp !== "function")
    throw new Error(
      `denoise: ${url} does not export ${name} (got ${typeof exp}) — asset/version mismatch`,
    );
  return mod as T;
}

async function loadDfnCore(config: LotusDenoiseConfig): Promise<DfnCore> {
  const base = config.assetBase;
  const url = `${base}deepfilternet/index.esm.js`;
  const dfnBase = new URL(`${base}deepfilternet`, window.location.href).href;
  const mod = assertModuleExport<DfnModule>(
    await import(/* @vite-ignore */ url),
    "DeepFilterNet3Core",
    url,
  );
  const core = new mod.DeepFilterNet3Core({
    sampleRate: 48_000,
    // 60, not 80: full-strength suppression is the main source of the
    // "over-processed" character; a lower level keeps voice natural while
    // the dry/wet floor handles the noise tail.
    noiseReductionLevel: 60,
    assetConfig: { cdnUrl: dfnBase },
  });
  await core.initialize();
  return core;
}

async function loadDtlnModule(config: LotusDenoiseConfig): Promise<DtlnModule> {
  const url = `${config.assetBase}workadventure/audio-worklet.js`;
  return assertModuleExport<DtlnModule>(
    await import(/* @vite-ignore */ url),
    "createNoiseSuppressionAudioWorklet",
    url,
  );
}

/** Which wasm file a flat model uses (SIMD build when supported). */
function flatWasmFiles(model: "rnnoise" | "speex"): {
  primary: string;
  fallback?: string;
} {
  const flat = FLAT[model];
  const useSimd = model === "rnnoise" && !!flat.simdWasm && supportsSimd();
  return useSimd
    ? { primary: flat.simdWasm!, fallback: flat.wasm }
    : { primary: flat.wasm };
}

// [lotus #24] Force every node in the graph to a single, explicitly-downmixed
// channel. Without `channelCountMode: "explicit"` the default ("max") IGNORES
// `channelCount`, so a stereo capture device would feed 2 channels into a
// worklet configured with `maxChannels: 1` and sum a stereo dry copy against a
// mono wet one at the destination.
const MONO: AudioNodeOptions = {
  channelCount: 1,
  channelCountMode: "explicit",
  channelInterpretation: "speakers",
};

// [lotus #25] Algorithmic latency of each model in samples at its native rate,
// used to delay the DRY copy of the floor mix so it lines up with the wet path
// (otherwise the sum comb-filters — a hollow/phasey colouration on voice).
//  - rnnoise: 480-sample (10 ms @ 48 kHz) frames; the sapphi worklet buffers
//    128-sample quanta up to one frame, so the wet path lags by one frame.
//  - speex: the sapphi speex worklet uses the same 480-sample framing.
//  - dtln: 512-sample block / 128 hop @ 16 kHz (~32 ms) per the DTLN paper —
//    best-known, unmeasured (the floor is not mixed for dtln, see buildGraph).
//  - deepfilternet: 480-sample hop + 2-frame lookahead @ 48 kHz (~30 ms) per
//    DeepFilterNet3 — best-known, unmeasured (floor not mixed for dfn either).
const DRY_DELAY_SAMPLES: Record<LotusDenoiseModel, number> = {
  rnnoise: 480,
  speex: 480,
  dtln: 512,
  deepfilternet: 1440,
};

/**
 * Create the model-rate context and register the flat/gate worklet modules.
 * Closes the context (and rethrows) on any failure so nothing half-built leaks.
 */
async function createModelContext(
  config: LotusDenoiseConfig,
): Promise<AudioContext> {
  const rate = sampleRateFor(config.model);
  const ctx = new AudioContext({ sampleRate: rate });
  try {
    if (ctx.sampleRate !== rate)
      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 (config.model === "rnnoise" || config.model === "speex")
      await ctx.audioWorklet.addModule(
        config.assetBase + FLAT[config.model].script,
      );
    if (config.gate)
      await ctx.audioWorklet.addModule(config.assetBase + GATE.script);
    return ctx;
  } catch (e) {
    await ctx.close().catch(() => undefined);
    throw e;
  }
}

// [lotus #7] Everything heavy that `init()` needs but that does NOT depend on
// the mic track: the AudioContext + worklet modules, the flat wasm binary, and
// (DFN) the fully-initialised model core. `LocalAudioTrack.setProcessor()`
// holds LiveKit's `trackChangeLock` while awaiting `init()`, so every
// mute/unmute/device-switch queues behind it — prepare these as soon as the
// flag is seen (before any track exists) so `init()` only wires them up.
interface PreparedAssets {
  ctx: AudioContext;
  dfnCore?: DfnCore;
}
const preparedAssets = new Map<string, Promise<PreparedAssets>>();
const preparedKey = (c: LotusDenoiseConfig): string =>
  `${c.model}|${c.gate ? 1 : 0}|${c.assetBase}`;

async function prepareUncached(
  config: LotusDenoiseConfig,
): Promise<PreparedAssets> {
  const ctx = await createModelContext(config);
  try {
    let dfnCore: DfnCore | undefined;
    if (config.model === "rnnoise" || config.model === "speex") {
      const { primary, fallback } = flatWasmFiles(config.model);
      // Warm the wasm cache; a SIMD miss is fine — buildMlNode falls back.
      await fetchWasm(config.assetBase + primary).catch(async () =>
        fallback ? fetchWasm(config.assetBase + fallback) : undefined,
      );
    } else if (config.model === "dtln") {
      await loadDtlnModule(config); // warms the browser's module map
    } else {
      dfnCore = await loadDfnCore(config);
    }
    return { ctx, dfnCore };
  } catch (e) {
    await ctx.close().catch(() => undefined);
    throw e;
  }
}

/**
 * Prefetch/prepare the assets for `config` (idempotent per model). Never
 * rejects: a failed prepare is evicted so `init()` simply loads inline and
 * surfaces the real error there.
 */
export async function prepareDenoiseAssets(
  config: LotusDenoiseConfig,
): Promise<void> {
  const key = preparedKey(config);
  let p = preparedAssets.get(key);
  if (!p) {
    p = prepareUncached(config);
    void p.catch((e) => {
      if (preparedAssets.get(key) === p) preparedAssets.delete(key);
      logger.warn(`[lotus] denoise prepare failed (${config.model})`, e);
    });
    preparedAssets.set(key, p);
  }
  await p.catch(() => undefined);
}

/** Take (one-shot) the prepared assets for `config`, if any were prepared. */
function claimPreparedAssets(
  config: LotusDenoiseConfig,
): Promise<PreparedAssets> | undefined {
  const key = preparedKey(config);
  const p = preparedAssets.get(key);
  if (p) preparedAssets.delete(key);
  return p;
}

/** Close any prepared-but-unclaimed contexts (call on feature teardown). */
export async function releasePreparedDenoiseAssets(): Promise<void> {
  const all = [...preparedAssets.values()];
  preparedAssets.clear();
  await Promise.all(
    all.map(async (p) =>
      p
        .then(async (a) => {
          safeCall(() => a.dfnCore?.destroy());
          if (a.ctx.state !== "closed") await a.ctx.close();
        })
        .catch(() => undefined),
    ),
  );
}

/**
 * A LiveKit audio TrackProcessor that runs Lotus ML noise suppression
 * (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.
 *
 * 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
> {
  public readonly name = "lotus-denoise";
  public processedTrack?: MediaStreamTrack;

  private ctx?: AudioContext;
  private graph?: Graph;
  private ctxStateHandler?: () => void;
  private preparedDfnCore?: DfnCore;
  // [lotus #9] True while the mic is muted: we suspend our own context so the
  // worklet stops running inference on silence, and the statechange watcher
  // must not "heal" that intentional suspension.
  private micMuted = false;

  public constructor(private readonly config: LotusDenoiseConfig) {}

  /**
   * [lotus #9] Mirror the mic's mute state onto the owned context. EC uses
   * `stopMicTrackOnMute: false`, so a muted mic keeps producing (silent) frames
   * and the ML worklet would otherwise keep running full inference for the
   * whole time the user is muted.
   */
  public setMicMuted(muted: boolean): void {
    this.micMuted = muted;
    const ctx = this.ctx;
    if (!ctx || ctx.state === "closed") return;
    if (muted) {
      if (ctx.state === "running") void ctx.suspend().catch(() => undefined);
    } else if (ctx.state === "suspended" && this.graph) {
      void ctx.resume().catch(() => undefined);
    }
  }

  public async init(_opts: AudioProcessorOptions): Promise<void> {
    try {
      await this.ensureContext();
      this.graph = await this.buildGraph(_opts.track);
      this.processedTrack = this.graph.track;
    } catch (e) {
      // Don't orphan the owned context if graph construction fails (browsers
      // cap live AudioContexts, so repeated failed inits could exhaust them).
      // The caller degrades to the raw mic; we just release our resources.
      const core = this.preparedDfnCore;
      this.preparedDfnCore = undefined;
      if (core) safeCall(() => core.destroy());
      await this.closeContext();
      throw e;
    }
  }

  public async restart(opts: AudioProcessorOptions): Promise<void> {
    try {
      await this.ensureContext();
      const next = await this.buildGraph(opts.track);
      this.disposeGraph(this.graph);
      this.graph = next;
      this.processedTrack = next.track;
    } catch (e) {
      // Never go silent on the A7/device-switch path: fall back to raw audio.
      // [lotus] IMPORTANT: never assign `opts.track` (LiveKit-owned) here.
      // LiveKit's internalStopProcessor() does `processor.processedTrack?.stop()`
      // then re-publishes `_mediaStreamTrack` — the SAME object if we set it as
      // processedTrack — which kills the live mic on the next stopProcessor()/
      // teardown. Leaving processedTrack undefined makes LiveKit fall through to
      // its own `_mediaStreamTrack` instead.
      logger.warn("[lotus] denoise restart failed; using raw mic", e);
      this.disposeGraph(this.graph);
      this.graph = undefined;
      this.processedTrack = undefined;
    }
  }

  public async destroy(): Promise<void> {
    this.disposeGraph(this.graph);
    this.graph = undefined;
    this.processedTrack = undefined;
    const core = this.preparedDfnCore;
    this.preparedDfnCore = undefined;
    if (core) safeCall(() => core.destroy());
    await this.closeContext();
  }

  /** Remove the state watcher and close the owned context, if any. */
  private async closeContext(): Promise<void> {
    const ctx = this.ctx;
    if (!ctx) return;
    if (this.ctxStateHandler) {
      ctx.removeEventListener("statechange", this.ctxStateHandler);
      this.ctxStateHandler = undefined;
    }
    this.ctx = undefined;
    if (ctx.state !== "closed") await ctx.close().catch(() => undefined);
  }

  /** Adopt the prepared context (or create one) + install the state watcher. */
  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" && !this.micMuted)
        await resumeCtx(this.ctx);
      return;
    }
    await this.closeContext();

    // [lotus #7] Prefer the context/modules/model prepared before
    // setProcessor() was called; only load inline if nothing was prepared
    // (e.g. the rnnoise fallback path, or a second processor after a
    // republish).
    const claimed = await claimPreparedAssets(this.config)?.catch(
      () => undefined,
    );
    let ctx: AudioContext;
    if (claimed && claimed.ctx.state !== "closed") {
      ctx = claimed.ctx;
      this.preparedDfnCore = claimed.dfnCore;
    } else {
      ctx = await createModelContext(this.config);
    }
    try {
      // Auto-resume if the OS/browser suspends the context mid-call (mobile
      // backgrounding, audio interruption): the dest node otherwise emits
      // silence with no recovery. Only resume while a graph is live and the
      // suspension isn't our own mute suspension (#9).
      const onStateChange = (): void => {
        if (ctx.state === "suspended" && this.graph && !this.micMuted)
          void ctx.resume().catch(() => undefined);
      };
      ctx.addEventListener("statechange", onStateChange);
      // The action can arrive via host postMessage, not a gesture in this
      // iframe, so the context can start suspended — resume without hanging.
      if (ctx.state === "suspended" && !this.micMuted) await resumeCtx(ctx);
      // Attached while already muted (#9): don't let a prepared, running
      // context burn inference until the first unmute.
      else if (ctx.state === "running" && this.micMuted)
        await ctx.suspend().catch(() => undefined);

      this.ctx = ctx;
      this.ctxStateHandler = onStateChange;
    } catch (e) {
      // Don't leak a half-initialised context on any failure path.
      await ctx.close().catch(() => undefined);
      throw e;
    }
  }

  private async buildGraph(track: MediaStreamTrack): Promise<Graph> {
    const ctx = this.ctx!;
    const source = ctx.createMediaStreamSource(new MediaStream([track]));
    const dest = new MediaStreamAudioDestinationNode(ctx, MONO);
    const nodes: AudioNode[] = [];
    const disposes: (() => void)[] = [];

    try {
      // Wet (denoised) path: source → ml → [gate] → wetGain.
      const ml = await this.buildMlNode(ctx);
      source.connect(ml.node);
      nodes.push(ml.node);
      if (ml.dispose) disposes.push(ml.dispose);
      let wetHead: AudioNode = ml.node;

      // Gate AFTER the ML model, not before: gating the raw noisy signal fed
      // hard-zeroed frames into the model (discontinuities it must fight) and
      // made the threshold operate on pre-denoise levels. Gate the residual.
      if (this.config.gate) {
        const gate = new AudioWorkletNode(ctx, GATE.name, {
          ...MONO,
          processorOptions: {
            openThreshold: this.config.gateThreshold,
            closeThreshold: this.config.gateThreshold - 5,
            holdMs: 150,
            maxChannels: 1,
          },
        });
        wetHead.connect(gate);
        wetHead = gate;
        nodes.push(gate);
      }

      // Only mix a dry floor for the flat models (RNNoise/Speex), whose
      // framing latency is known exactly (DRY_DELAY_SAMPLES); the DTLN/DFN
      // figures are best-known estimates, so for those we rely on the model's
      // own level (e.g. DFN noiseReductionLevel) instead. RNNoise is also where
      // the "robotic/underwater" reports come from, so this targets it.
      const lowLatency =
        this.config.model === "rnnoise" || this.config.model === "speex";
      const floor = lowLatency
        ? Math.min(0.5, Math.max(0, this.config.floor))
        : 0;
      if (floor > 0) {
        // Dry/wet mix: blend a small amount of the ORIGINAL mic under the
        // denoised signal so suppression can't fully collapse the noise floor
        // (kills the "underwater"/pumping artifact). During speech (denoised ≈
        // original) the two sum back to ~unity; in noise-only gaps the output
        // floors at `floor` × original instead of digital silence.
        const wetGain = new GainNode(ctx, { ...MONO, gain: 1 - floor });
        wetHead.connect(wetGain);
        wetGain.connect(dest);
        nodes.push(wetGain);

        // [lotus #25] Delay the dry copy by the model's algorithmic latency so
        // it sums in phase with the (framed, hence delayed) wet path instead
        // of comb-filtering against it.
        const delaySec = DRY_DELAY_SAMPLES[this.config.model] / ctx.sampleRate;
        const dryDelay = new DelayNode(ctx, {
          ...MONO,
          maxDelayTime: Math.max(delaySec, 1 / ctx.sampleRate),
          delayTime: delaySec,
        });
        const dryGain = new GainNode(ctx, { ...MONO, gain: floor });
        source.connect(dryDelay);
        dryDelay.connect(dryGain);
        dryGain.connect(dest);
        nodes.push(dryDelay, dryGain);
      } else {
        wetHead.connect(dest);
      }

      logger.info(
        `[lotus] denoise processor active (${this.config.model}, floor=${floor})`,
      );
      return {
        source,
        nodes,
        disposes,
        track: dest.stream.getAudioTracks()[0],
      };
    } catch (e) {
      // A node constructor / model load can throw mid-build; clean up the
      // partially-built graph so it doesn't leak (init/restart still fall back
      // to the raw mic on the rejection).
      this.disposeGraph({
        source,
        nodes,
        disposes,
        track: dest.stream.getAudioTracks()[0],
      });
      throw e;
    }
  }

  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 loadDtlnModule(this.config);
      return await mod.createNoiseSuppressionAudioWorklet(ctx, {
        bypassUntilReady: true,
      });
    }

    if (model === "deepfilternet") {
      // [lotus #7] Use the core initialised by prepareDenoiseAssets() if we
      // have one (first graph); later rebuilds (restart) load a fresh core.
      const prepared = this.preparedDfnCore;
      this.preparedDfnCore = undefined;
      const core = prepared ?? (await loadDfnCore(this.config));
      const node = await core.createAudioWorkletNode(ctx);
      return { node, dispose: () => safeCall(() => core.destroy()) };
    }

    // Flat sapphi worklet (rnnoise/speex).
    const flat = FLAT[model];
    const { primary, fallback } = flatWasmFiles(model);
    let wasmBinary: ArrayBuffer;
    try {
      wasmBinary = await fetchWasm(base + primary);
    } catch (e) {
      if (fallback) {
        wasmCache.delete(base + primary);
        wasmBinary = await fetchWasm(base + fallback); // fall back to non-SIMD
      } else throw e;
    }
    const node = new AudioWorkletNode(ctx, flat.name, {
      ...MONO,
      numberOfInputs: 1,
      numberOfOutputs: 1,
      processorOptions: { maxChannels: 1, wasmBinary },
    });
    return {
      node,
      dispose: () => safeCall(() => node.port.postMessage("destroy")),
    };
  }

  private disposeGraph(graph: Graph | undefined): void {
    if (!graph) return;
    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 */
  }
}