@picovoice/porcupine-angular NPM

Porcupine Binding for Angular

Porcupine wake word engine

Made in Vancouver, Canada by Picovoice

Porcupine is a highly accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications using cutting edge voice AI.

Porcupine is:

private and offline
accurate
resource efficient (runs even on microcontrollers)
data efficient (wake words can be easily generated by simply typing them, without needing thousands of hours of bespoke audio training data and manual effort)
scalable to many simultaneous wake-words / always-on voice commands
cross-platform

Compatibility

Chrome / Edge
Firefox
Safari

Restrictions

IndexedDB and WebWorkers are required to use Porcupine Angular. Browsers without support (i.e. Firefox Incognito Mode) should use the PorcupineWeb binding main thread method.

Installation

Package

Using Yarn:

yarn add @picovoice/porcupine-angular @picovoice/web-voice-processor

or using npm:

npm install --save @picovoice/porcupine-angular @picovoice/web-voice-processor

AccessKey

Porcupine requires a valid Picovoice AccessKey at initialization. AccessKey acts as your credentials when using Porcupine SDKs. You can get your AccessKey for free. Make sure to keep your AccessKey secret. Signup or Login to Picovoice Console to get your AccessKey.

Usage

There are two methods to pass model files and initialize Porcupine:

Public Directory

NOTE: Due to modern browser limitations of using a file URL, this method does not work if used without hosting a server.

This method fetches the model file from the public directory and feeds it to Porcupine. Copy the model file into the public directory:

cp ${PORCUPINE_MODEL_FILE} ${PATH_TO_PUBLIC_DIRECTORY}

The same procedure can be used for the custom keyword files (.ppn) files.

Base64

NOTE: This method works without hosting a server, but increases the size of the model file roughly by 33%.

This method uses a base64 string of the model file and feeds it to Porcupine. Use the built-in script pvbase64 to base64 your model file:

npx pvbase64 -i ${MODEL_FILE} -o ${OUTPUT_DIRECTORY}/${MODEL_NAME}.js

The output will be a js file which you can import into any file of your project. For detailed information about pvbase64, run:

npx pvbase64 -h

The same procedure can be used for the custom keyword files (.ppn) files.

Porcupine Model

Porcupine saves and caches your parameter model file (.pv) in IndexedDB to be used by Web Assembly. Use a different customWritePath variable to hold multiple model values and set the forceWrite value to true to force re-save the model file. If the model file changes, version should be incremented to force the cached models to be updated. Either base64 or publicPath must be set to instantiate Porcupine. If both are set, Porcupine will use the base64 model.

// Model (.pv)
const porcupineModel = {
  publicPath: ${MODEL_RELATIVE_PATH},
  // or
  base64: ${MODEL_BASE64_STRING},

  // Optional
  customWritePath: 'custom_model',
  forceWrite: true,
  version: 1,
}

Initialize Porcupine

First subscribe to the events from PorcupineService. There are four subscription events:

keywordDetection$: Returns the detected keyword.
isLoaded$: Returns true if Porcupine has loaded successfully.
isListening$: Returns true if WebVoiceProcessor has started successfully.
error$: Returns any errors occurred.

import { Subscription } from "rxjs"
import { PorcupineService } from "@picovoice/porcupine-angular"

...

  constructor(private porcupineService: PorcupineService) {
    this.keywordSubscription = porcupineService.keywordDetection$.subscribe(
      porcupineDetection => {
        console.log(`Porcupine Detected "${porcupineDetection.label}"`)
      });
    this.isLoadedSubscription = porcupineService.isLoaded$.subscribe(
      isLoaded => {
        console.log(isLoaded);
      });
    this.isListeningSubscription = porcupineService.isListening$.subscribe(
      isListening => {
        console.log(isListening);
      });
    this.errorSubscription = porcupineService.error$.subscribe(
      error => {
        console.error(error);
      });
  }

After setting up the subscriber events, initialize Porcupine:

import {BuiltInKeyword} from '@picovoice/porcupine-web';

async ngOnInit() {
  await this.porcupineService.init(
    ${ACCESS_KEY},
    [BuiltInKeyword.Porcupine],
    porcupineModel,
  );
}

Process Audio Frames

Run the following to start wake word detection:

await this.porcupineService.start();

The results are available on porcupineService.keywordDetection$ as mentioned above.

To stop wake word detection run:

await this.porcupineService.stop();

Clean Up

Clean up used resources with:

ngOnDestroy(): void {
  this.keywordSubscription.unsubscribe();
  this.isLoadedSubscription.unsubscribe();
  this.isListeningSubscription.unsubscribe();
  this.errorSubscription.unsubscribe();
  this.porcupineService.release();
}

Custom Keywords

Create custom keywords using the Picovoice Console. Train and download a Porcupine keyword model (.ppn) for the target platform Web (WASM). This model file can be used directly with publicPath, but, if base64 is preferable, convert the .ppn file to a base64 JavaScript variable using the built-in pvbase64 script:

npx pvbase64 -i ${KEYWORD_FILE}.ppn -o ${KEYWORD_BASE64}.js -n ${KEYWORD_BASE64_VAR_NAME}

Similar to the model file (.pv), keyword files (.ppn) are saved in IndexedDB to be used by Web Assembly. Either base64 or publicPath must be set for each keyword to instantiate Porcupine. If both are set, Porcupine will use the base64 model. An arbitrary label is required to identify the keyword once the detection occurs.

// custom keyword (.ppn)
const keywordModel = {
  publicPath: ${KEYWORD_RELATIVE_PATH},
  // or
  base64: ${KEYWORD_BASE64_STRING},
  label: ${KEYWORD_LABEL},
  // Optional
  customWritePath: 'custom_keyword',
  forceWrite: true,
  version: 1,
}

Then, initialize porcupineService:

await this.porcupineService.init(
  ${ACCESS_KEY},
  keywordModel,
  porcupineModel,
);

Non-English Languages

In order to detect non-English wake words you need to use the corresponding model file (.pv). The model files for all supported languages are available here.