Wake Word Detection with React.js

Learn how to add wake words, like Alexa or Hey Siri, to any React app. This tutorial takes 15 minutes or less from the start to a working demo. We learn how to train custom wake word models, like Hey Jarvis, that fit your product, not Big Tech's brand. In this article, we use Picovoice Porcupine Wake Word Engine React SDK.

Setup the Project

1. Create a new React app:

npx create-react-app porcupine-react

2. Install the dependencies:

npm install @picovoice/porcupine-react @picovoice/web-voice-processor

3. Download the Porcupine model (i.e. Deep Neural Network). From the project folder, run the following to turn the binary model into a base64 string. Remember that you need to replace ${DOWNLOADED_MODEL_PATH} with the path to the model you downloaded (e.g. ~/Downloads/porcupine_params.pv on my Ubuntu machine).

npx pvbase64 -i ${DOWNLOADED_MODEL_PATH} -o src/porcupine_params.js

4. Run the local server to load the page:

yarn start

Train Wake Word Models

1. Sign up for Picovoice Console.
2. Go to the Porcupine Page.
3. Select English as the language for your model.
4. Type in Hey Jarvis as the phrase you want to build the model for.

5. Optionally, you can try it within the browser
6. Once you are happy, click on the train button.
7. Select Web (WASM) as the platform.

8. Click on Download. You should have a .zip file in your download folder now.
9. Unzip it. Inside the folder, you see a file with the suffix .ppn. That's our model. Transform it into a base64 string. Remember that you need to replace ${DOWNLOADED_PPN_PATH} with the path to downloaded file (e.g. ~/Downloads/Hey-Jarvis_en_wasm_v2_1_0/Hey-Jarvis_en_wasm_v2_1_0.ppn on my Ubuntu machine)

npx pvbase64 \
-i ${DOWNLOADED_PPN_PATH} \
-o src/hey_jarvis.js \
-n heyJarvisKeywordModel

Wire it up

1. Go to Picovoice Console's dashboard. Copy your AccessKey.

2. Create a file within src called VoiceWidget.js and paste the below into it. The code uses Porcupine's hook to create and start the wake word detection. Remember to replace ${ACCESS_KEY} with your AccessKey obtained from Picovoice Console.

import {useEffect, useState} from "react";

import {usePorcupine} from "@picovoice/porcupine-react";

import heyJarvisKeywordModel from "./hey_jarvis"
import modelParams from "./porcupine_params";

export default function VoiceWidget() {
  const [keywordDetections, setKeywordDetections] = useState([]);

  const {
    keywordDetection,
    isLoaded,
    isListening,
    error,
    init,
    start,
    stop,
    release
  } = usePorcupine();

  const initEngine = async () => {
    await init(
      ${ACCESS_KEY},
      {
        "base64": heyJarvisKeywordModel,
        "label": "Hey Jarvis"
      },
      {base64: modelParams}
    );
    start()
  }

  useEffect(() => {
    if (keywordDetection !== null) {
      setKeywordDetections((oldVal) =>
        [...oldVal, keywordDetection.label])
    }
  }, [keywordDetection])

  return (
    <div className="voice-widget">
      <h3>
        <label>
          <button
            className="init-button"
            onClick={() => initEngine()}
          >
            Start
          </button>
        </label>
      </h3>
      {keywordDetections.length > 0 && (
        <ul>
          {keywordDetections.map((label, index) => (
            <li key={index}>{label}</li>
          ))}
        </ul>
      )}
    </div>
  );
}

3. Modify the App.js to display the VoiceWidget and click start:

import './App.css';
import VoiceWidget from "./VoiceWidget";

function App() {
  return (
    <div className="App">
      <h1>
        Porcupine React Demo
      </h1>
      <VoiceWidget />
    </div>
  );
}

export default App;

Additional Languages

Porcupine supports many more languages aside from English. To use models in other languages, refer to the quick start.

Source Code

The source code for a fully-working demo with Porcupine is available on its GitHub repository.