TEN VAD

OverviewCopy Anchor Link

TenVADAnalyzer is a VAD analyzer backed by TEN VAD, a small voice activity detector from the TEN framework. It goes wherever a VAD analyzer goes (the user aggregator's vadAnalyzer, VADProcessor), in place of Silero. This is pipedog only: upstream Pipecat has no TEN VAD.

InstallationCopy Anchor Link

bun add @pipedog/audio-ten-vad

import { loadTenVadModule, TenVADAnalyzer } from "@pipedog/audio-ten-vad";

Then get TEN's WebAssembly build from TEN's repository: lib/Web/ten_vad.js (the Emscripten loader, whose default export is createVADModule) and lib/Web/ten_vad.wasm. Copy both into your app, for example as ten-vad/.

RuntimesCopy Anchor Link

Generated from pipecat.runtimes and pipecat.compat in packages/audio-ten-vad/package.json: ✅ means the compat smoke test passes on that runtime.

The table covers pipedog's analyzer, which the compat test runs against a stand-in module. Whether TEN's ten_vad.js loads on a runtime is up to TEN's build (it targets browsers and web workers).

UsageCopy Anchor Link

loadTenVadModule() calls TEN's factory with the Emscripten options for where the .wasm comes from. Load it once per process and pass the promise to every analyzer: each one creates its own TEN VAD instance in the shared module.

On Bun, Node and Deno, read the .wasm bytes:

// import createVADModule from "./ten-vad/ten_vad.js"; // TEN's lib/Web/ten_vad.js

// Load TEN's module once and share it: each analyzer creates its own TEN VAD instance in it.
const tenVad = loadTenVadModule({
  factory: createVADModule,
  wasmBinary: await readFile("./ten-vad/ten_vad.wasm"),
});

const vad = new TenVADAnalyzer({ module: tenVad, sampleRate: 16000 });

In browsers, TEN's loader can fetch ten_vad.wasm from next to ten_vad.js:

// import createVADModule from "/ten-vad/ten_vad.js";

// TEN's glue fetches ten_vad.wasm from next to ten_vad.js (or pass `wasmBinary`).
const vad = new TenVADAnalyzer({ module: loadTenVadModule({ factory: createVADModule }) });

On Cloudflare Workers, which can't compile WebAssembly from bytes, import the .wasm file as a module (Wrangler's default rule for .wasm gives a WebAssembly.Module) and pass it as wasmModule. It goes through Emscripten's instantiateWasm hook:

// import createVADModule from "./ten-vad/ten_vad.js";
// import tenVadWasm from "./ten-vad/ten_vad.wasm"; // Wrangler gives a WebAssembly.Module

const tenVad = loadTenVadModule({ factory: createVADModule, wasmModule: tenVadWasm });
const vad = new TenVADAnalyzer({ module: tenVad });

Then use the analyzer like Silero:

const context = new LLMContext();
const vad = new TenVADAnalyzer({
  module: tenVad,
  hopSize: 160, // 10 ms frames (default: 256, 16 ms)
  // TEN's speech probability is the confidence: VADParams decides, as for Silero.
  params: new VADParams({ confidence: 0.7, startSecs: 0.2, stopSecs: 0.2 }),
});
const aggregators = new LLMContextAggregatorPair(context, {
  userParams: new LLMUserAggregatorParams({ vadAnalyzer: vad }),
});

TEN VAD is created once the module has loaded: await vad.ready() waits for it, otherwise the first analyzeAudio does. After that every frame is scored synchronously. cleanup() destroys the instance and frees its memory.

16 kHz onlyCopy Anchor Link

TEN VAD takes 16 kHz mono 16-bit audio, so the analyzer throws for any other sample rate. Run the transport's input at 16 kHz (audioInSampleRate: 16000), or resample before the VAD.

ConfigurationCopy Anchor Link

new TenVADAnalyzer(options):

loadTenVadModule(options):

Pass wasmBinary or wasmModule, or neither to let TEN's loader find the file.

TEN VAD or SileroCopy Anchor Link

Both are small neural VADs; which one works better depends on your audio. In short:

TEN reports lower per-frame cost and faster speech-end detection than Silero in its own benchmarks. Evaluate both on recordings of your own users (line quality, noise, languages) before switching, and tune VADParams for the one you pick.