Porcupine Binding for Web

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 is required to use Porcupine in a worker thread. Browsers without IndexedDB support (i.e. Firefox Incognito Mode) should use Porcupine in the main thread.

Installation

Package

Using Yarn:

yarn add @picovoice/porcupine-web

or using npm:

npm install --save @picovoice/porcupine-web

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

Create a keywordDetectionCallback function to get the results from the engine:

function keywordDetectionCallback(keyword) {
  console.log(`Porcupine detected keyword: ${keyword.label}`);
}

create an options object and add a processErrorCallback function if you would like to catch errors:

function processErrorCallback(error: string) {
...
}

options.processErrorCallback = processErrorCallback;

Initialize an instance of Porcupine in the main thread:

const handle = await Porcupine.create(
  ${ACCESS_KEY},
  PorcupineWeb.BuiltInKeyword.Porcupine,
  keywordDetectionCallback,
  porcupineModel,
  options // optional options
);

or initialize an instance of Porcupine in a worker thread:

const handle = await PorcupineWorker.create(
  ${ACCESS_KEY},
  PorcupineWeb.BuiltInKeyword.Porcupine,
  keywordDetectionCallback,
  porcupineModel,
  options // optional options
);

Process Audio Frames

The result is received from keywordDetectionCallback as defined above.

function getAudioData(): Int16Array {
... // function to get audio data
  return new Int16Array();
}

for (; ;) {
  await handle.process(getAudioData());
  // break on some condition
}

Clean Up

Clean up used resources by Porcupine or PorcupineWorker:

await handle.release();

Terminate

Terminate PorcupineWorker instance:

await handle.terminate();

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 an instance of Porcupine:

const handle = await Porcupine.create(
  ${ACCESS_KEY},
  [keywordModel],
  keywordDetectionCallback,
  porcupineModel,
  options
);

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.

Demo

For example usage refer to our Web demo application.