FluidVad

Voice activity detection for npm — Silero VAD (v6) compiled to WebAssembly. Model bundled, zero config, no runtime downloads. Works in the browser, Node, and Electron (both processes) on macOS and Windows.

• No native modules — pure wasm. Nothing to electron-rebuild, no per-arch prebuilds, no onnxruntime peer dependency, no extra binaries to sign.
• Model embedded — the 1.3 MB Silero v6 16 kHz graph ships inside the .wasm (5.3 MB raw, 2.3 MB gzipped total). npm install and go; nothing fetched at runtime, fully offline.
• Streaming + offline — SpeechStart/SpeechEnd events with hysteresis, or whole-buffer segmentation. ~150× real-time; a 32 ms frame costs well under a millisecond.

npm i @fluidinference/fluidvad

Microphone (browser / Electron renderer)

import { MicVad } from "@fluidinference/fluidvad/mic";

const mic = new MicVad({
  onSpeechStart: (t) => console.log("speech started", t),
  onSpeechEnd: (audio, start, end) => {
    // audio: Float32Array, 16 kHz mono, whole utterance incl. pre-roll
    console.log(`utterance ${start.toFixed(2)}s → ${end.toFixed(2)}s`);
  },
});
await mic.start();

Buffers (Node / Electron main / browser)

import { createVad } from "@fluidinference/fluidvad";

const vad = await createVad({ threshold: 0.5 });

// streaming: push any chunk size, get boundary events
const events = vad.push(samples); // Float32Array, 16 kHz mono
// [{ isStart: true, sampleIndex: 15872, timeSeconds: 0.99 }, ...]

// offline: segment a whole buffer
const segments = vad.segment(samples);
// [{ startTime: 0.9, endTime: 4.21 }, ...]

Input is always 16 kHz mono f32 in [-1, 1]. The model consumes 512-sample frames (32 ms); push buffers partial frames internally.

Electron

Runnable example in examples/electron (mic UI + headless smoke mode, CI-tested on macOS and Windows).

• Main / preload (Node env): createVad() works as-is; the wasm is read from disk (asar-transparent).
• Renderer with contextIsolation: the renderer cannot fetch() file:// URLs, so hand the wasm bytes over from the preload:

// preload.cjs
const wasmPath = require.resolve("@fluidinference/fluidvad/dist/fluidvad_bg.wasm");
contextBridge.exposeInMainWorld("fluidvad", { wasmBytes: new Uint8Array(fs.readFileSync(wasmPath)) });

// renderer
const mic = new MicVad({ load: { wasm: window.fluidvad.wasmBytes }, onSpeechEnd: ... });

• CSP: add 'wasm-unsafe-eval' to script-src (compiles wasm without enabling JS eval).
• macOS mic: call systemPreferences.askForMediaAccess("microphone") from main and set NSMicrophoneUsageDescription when packaging.

Configuration

| Option | Default | Meaning | |---|---|---| | threshold | 0.5 | entry threshold (frame is speech at ≥) | | negativeThreshold | threshold - 0.15 | exit threshold (hysteresis) | | minSpeechDuration | 0.15 s | drop shorter speech runs | | minSilenceDuration | 0.75 s | silence needed to close a segment | | maxSpeechDuration | 14 s | force-split longer segments at the best silence | | speechPadding | 0.1 s | padding around each segment |

Development

The wasm is built from a Rust core (src/) using tract for CPU inference — no onnxruntime anywhere.

Upstream Silero ONNX contains If nodes whose branches disagree on rank — onnxruntime broadcasts through it, strict runtimes cannot. We bake sr = 16000 as a constant, fix the input shapes, and constant-fold with onnxruntime's basic optimizer, which eliminates every If (scripts/prepare_model.py, bit-exact with upstream). The result is pre-compiled to NNEF (examples/export_nnef.rs) so the shipped wasm only carries tract's lightweight loader. Per-frame parity vs onnxruntime is asserted in tests (tests/model_parity.rs); the hysteresis / segmentation state machines (adapted from FluidAudio) are unit-tested with synthetic probability sequences.

cargo test --release              # core + parity tests
./scripts/build_npm.sh            # build the npm package into npm/
python3 scripts/prepare_model.py  # regenerate model artifacts (needs onnx, onnxruntime)
cd examples/electron && npm i && FLUIDVAD_SMOKE=1 npx electron .   # headless check

License

MIT. The bundled Silero VAD model is © Silero Team, MIT-licensed (SILERO_LICENSE, snakers4/silero-vad).