Moonshine Voice for the Web (WebAssembly)

@moonshine-ai/moonshine-wasm runs Moonshine Voice — fast, accurate, on-device speech-to-text, text-to-speech, and voice intent recognition — directly in the browser via WebAssembly. No audio ever leaves the device.

The API mirrors the Python, Swift, and Android bindings: a thin embind bridge over the Moonshine C ABI, wrapped in an idiomatic TypeScript layer (Transcriber, Stream, MicrophoneTranscriber, TextToSpeech, GraphemeToPhonemizer, IntentRecognizer, DialogFlow).

Install

bash

npm install @moonshine-ai/moonshine-wasm

Quick start — streaming speech to text

import { MicrophoneTranscriber, ModelArch } from '@moonshine-ai/moonshine-wasm';

const mic = await MicrophoneTranscriber.load({
  language: 'en',
  modelArch: ModelArch.BaseStreaming,
  listeners: [
    {
      onLineTextChanged: (e) => console.log('…', e.line.text),
      onLineCompleted: (e) => console.log('✓', e.line.text),
    },
  ],
});
await mic.start();
// … later …
await mic.stop();
mic.close();

Transcribe a buffer (non-streaming)

import { Transcriber } from '@moonshine-ai/moonshine-wasm';

const transcriber = await Transcriber.load({ language: 'en' });
const transcript = transcriber.transcribe(float32Pcm, { sampleRate: 16000 });
console.log(transcript.lines.map((l) => l.text).join('\n'));
transcriber.close();

Text to speech

import { TextToSpeech } from '@moonshine-ai/moonshine-wasm';

const tts = await TextToSpeech.load({ language: 'en' });
await tts.speak('Hello from Moonshine.');
tts.close();

Intent recognition + dialog flows

import { IntentRecognizer, DialogFlow } from '@moonshine-ai/moonshine-wasm';

const intent = await IntentRecognizer.load();

const runner = new DialogFlow({ intentRecognizer: intent, tts });
runner.registerFlow('set up wifi', function* (d) {
  const ssid = yield d.ask("What's your wifi network?");
  if (yield d.confirm(`Connect to ${ssid}?`)) {
    yield d.say(`Connecting to ${ssid}.`);
  }
});
// Feed completed transcript lines to `runner` (it's a TranscriptEventListener):
mic.addListener(runner);

Models are downloaded at runtime

To keep the library small (well under 100 MB), only the VAD is embedded in the .wasm. Every other model — STT, TTS, G2P, and the intent embedding model — is fetched from the Moonshine CDN (https://download.moonshine.ai) the first time it's needed and cached in the browser via the Cache API. The exact file list and URLs come from the C ABI manifest helpers, so the JS never hardcodes the layout.

Pass onProgress to any load(...) call to drive a download UI.

Cross-origin isolation (required for the default build)

The default build enables SIMD + multithreading for best performance, which needs SharedArrayBuffer. Browsers only expose that to cross-origin-isolated pages, so your server must send:

Cross-Origin-Opener-Policy: same-origin
Cross-Origin-Embedder-Policy: require-corp

The example server (examples/web/serve.mjs) sets these for you. If you can't set these headers, build the SIMD-only fallback (see below) and load it with -DMOONSHINE_WASM_SINGLE_THREAD=ON.

Examples

See examples/web/: stt/, tts/, and dialog-flow/. The demos import the published binding from the jsDelivr CDN by default. To test a locally-built binding instead, append ?local=1 to the URL (this loads /wasm/dist/index.js). After building the binding, run the dev server (which sets the isolation headers) and open a demo:

bash

scripts/build-wasm.sh
node examples/web/serve.mjs
# → http://localhost:8080/stt/?local=1

Building from source

You need emsdk 4.0.8 (the version ONNX Runtime 1.23 pins) activated on your PATH.

bash

# One-time: build + vendor the ORT-wasm static library (SIMD + threads).
scripts/build-ort-wasm.sh            # add `single-thread` for the fallback too

# Build the module + TypeScript layer into wasm/dist.
scripts/build-wasm.sh                # `single-thread` for the SIMD-only build

# Run the tests.
scripts/test-wasm.sh

scripts/build-wasm.sh accepts publish-npm (npm publish) and upload (attach a dist tarball to the GitHub release). It never uploads the library to download.moonshine.ai — that CDN hosts model assets only.

Why we build ONNX Runtime from source

Microsoft doesn't publish a prebuilt ORT-wasm static library, and the onnxruntime-web npm package only ships a fully-linked .wasm module (no .a to link into our C++ core). scripts/build-ort-wasm.sh builds libonnxruntime_webassembly.a from ORT, pinned to the same version as the native builds for ABI compatibility with the vendored headers.

Versioning

The npm package version tracks the core Moonshine version (see package.json and python/pyproject.toml). Keep the ORT pin in scripts/build-ort-wasm.sh in lockstep with core/third-party/onnxruntime/find-ort-library-path.cmake.

License

MIT.