This small tutorial takes 15 minutes from the start to a working demo. We use Picovoice Porcupine Wake Word Engine to enable a Vaadin-based Java web application.
(If you don't have that much time: clone the repo, create a accesskey and run it.)
Wake Word Detection is also known as Keyword Spotting, Hotword Detection, Always-Listening Voice Commands, Trigger Word Detection, and Voice Activation.
Setup the Project
The first step is to create a new Vaadin Java application project:
1. Download a new project from start.vaadin.com with an empty view.
2. Create new Java API for always-on keyword detection and import Picovoice libraries:
package com.example.application;
import com.vaadin.flow.component.UI;
import com.vaadin.flow.component.dependency.JsModule;
import com.vaadin.flow.component.dependency.NpmPackage;
@NpmPackage(value = "@picovoice/porcupine-web", version = "2.1.16")
@NpmPackage(value = "@picovoice/web-voice-processor", version = "4.0.5")
@JsModule("./porcupine-integration.js")
public class Porcupine {
boolean started = false;
public Porcupine(String picovoiceAccesskey) {
UI.getCurrent().getPage().executeJs("window.vaadinPorcupine.key=$0;"
, picovoiceAccesskey);
}
public void start() {
this.started = true;
UI.getCurrent().getPage().executeJs("window.vaadinPorcupine.start()");
}
public void stop() {
this.started = false;
UI.getCurrent().getPage().executeJs("window.vaadinPorcupine.stop()");
}
public boolean isStarted() {
return this.started;
}
}
This is the server API available for the rest of the Vaadin Java application.
3. Create porcupine-integration.js in project’s frontend folder.
import { WebVoiceProcessor } from "@picovoice/web-voice-processor";
import { PorcupineWorker, BuiltInKeyword } from "@picovoice/porcupine-web";
import modelParams from "./porcupine_params.js";
// a global 'vaadinPorcupine' integration instance is enough
window.vaadinPorcupine = window.vaadinPorcupine || {
key: null,
async start() {
console.log('Starting wake word detection');
window.vaadinPorcupine._worker = window.vaadinPorcupine._worker ||
await PorcupineWorker.create(
window.vaadinPorcupine.key,
[BuiltInKeyword.Computer],
window.vaadinPorcupine.keywordDetectionCallback,
{base64: modelParams }
);
await WebVoiceProcessor.subscribe(window.vaadinPorcupine._worker);
},
async stop() {
console.log('Stopping wake word detection');
await WebVoiceProcessor.unsubscribe(window.vaadinPorcupine._worker);
},
keywordDetectionCallback(detection) {
console.log(`Detected keyword: ${detection.label}`);
const e = new CustomEvent("voice-wakeword",
{ "detail": detection.label });
document.body.dispatchEvent(e);
}
}
This is the client side part of the API integrating the in-browser wake word detection library.
4. Download the Porcupine model (i.e. Deep Neural Network). From the project frontend folder, run the following to turn the binary .pv model into a base64 string model.
echo "const model_params='$( cat porcupine_params.pv | base64 )';\nexport default model_params;\n\n" > porcupine_params.js
5. Get your Picovoice AccessKey
Go to Picovoice Console's dashboard. Copy your AccessKey.
6. Add a test button to the EmptyView.java to turn the wake word detection on and off:
final Porcupine porcupine = new Porcupine(System.getenv("PICOVOICE_ACCESSKEY"));
add(new Button("Start/Stop wake word detection", e -> {
if (!porcupine.isStarted()) {porcupine.start(); }
else { porcupine.stop();}
}));
Run the application using your own accessKey:
PICOVOICE_ACCESSKEY=your_accesskey_here ./mvnw
Source Code and more examples?
The source code for a fully-working Vaadin demo with Porcupine is available in my GitHub repository. I’ve added a custom event and handler example there and later I’ll show how to train and add your own custom wake word using Picovoice Porcupine… What should I do next?
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