@picovoice/octopus-web-en-factory

octopus-web

The Picovoice Octopus library for web browsers, powered by WebAssembly.

Octopus is Picovoice's Speech-to-Index engine. It directly indexes speech without relying on a text representation. This acoustic-only approach boosts accuracy by removing out-of-vocabulary limitation. All processing is done via WebAssembly and Workers in a separate thread.

Compatibility

• Chrome / Edge
• Firefox
• Safari

This library requires several modern browser features: WebAssembly, Web Workers, and promises. Internet Explorer will not work.

AccessKey

Octopus requires a valid Picovoice AccessKey at initialization. AccessKey acts as your credentials when using Octopus 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.

Packages

The Octopus SDK for Web is split into separate worker and factory packages; import each as required.

Workers

For typical cases, use the worker package. The worker package creates complete OctopusWorker instances that can be immediately used.

• @picovoice/octopus-web-en-worker

Factories

Factory packages allow you to create instances of Octopus directly. Useful for building your own custom Worker/Worklet, or some other bespoke purpose.

• @picovoice/octopus-web-en-factory

Installation & Usage

Worker

To obtain an OctopusWorker, we can use the static create factory method from the OctopusWorkerFactory. Here is a complete example that:

• Obtains an OctopusWorker from the OctopusWorkerFactory
• Passes a standard audio stream to be indexed and stores the result in octopusMetadata object. The audio should be in the voice recognition standard format (16-bit 16kHz linear PCM, single-channel)
• Searches a phrase and receives the occurrences time if there are any matches with their probabilities

E.g.:

yarn add @picovoice/octopus-web-en-worker

import { OctopusWebEnWorker } from "@picovoice/octopus-web-en-worker";

// The metadata object to save the result of indexing for later searches
let octopusMetadata = undefined;

function octopusIndexCallback(metadata) {
  octopusMetadata = metadata;
}

function octopusSearchCallback(matches) {
  console.log(`Search results (${matches.length}):`);
  console.log(
    `Start: ${match.startSec}s -> End: ${match.endSec}s (Probability: ${match.probability})`
  );
}

async function startOctopus() {
  // Create an Octopus Worker
  // Note: you receive a Worker object, _not_ an individual Octopus instance
  const accessKey = ... // .. AccessKey string provided by Picovoice Console (https://console.picovoice.ai/)
  const OctopusWorker = await OctopusWorkerFactory.create(
    accessKey,
    octopusIndexCallback,
    octopusSearchCallback
  );
}

startOctopus();

// Send Octopus the audio signal
const audioSignal = new Int16Array(/* Provide data with correct format*/);
OctopusWorker.postMessage({
  command: "index",
  input: audioSignal,
});

...

const searchText = ...;
OctopusWorker.postMessage({
  command: "search",
  metadata: octopusMetadata,
  searchPhrase: searchText,
});

...

// Finished with Octopus? Release the worker.
if (done) {
  OctopusWorker.sendMessage({ command: "release" });
}

Important Note: Because the workers are all-in-one packages that run an entire machine learning inference model in WebAssembly, they are approximately 9MB in size. While this is tiny for a speech recognition model, it's large for web delivery. Because of this, you likely will want to use dynamic import() instead of static import {} to reduce your app's starting bundle size. See e.g. https://webpack.js.org/guides/code-splitting/ for more information.

Factory

If you wish to build your own worker, or perhaps not use workers at all, use the factory packages. This will let you instantiate Octopus engine instances directly.

E.g.:

import { Octopus } from "@picovoice/octopus-web-en-factory";

async function startOctopus() {
  const accessKey = ""; // .. AccessKey string provided by Picovoice Console (https://console.picovoice.ai/)
  const handle = await Octopus.create(accessKey);
}

startOctopus();

...

// Send Octopus the audio signal
const audioSignal = new Int16Array(/* Provide data with correct format*/);
let octopusMetadata = await handle.index(audioSignal);

...

const searchText = "";
let octopusMatches = await handle.search(octopusMetadata, searchText);
console.log(`Search results (${octopusMatches.length}):`);
console.log(
  `Start: ${octopusMatches.startSec}s -> End: ${octopusMatches.endSec}s (Probability: ${octopusMatches.probability})`
);

Important Note: Because the workers are all-in-one packages that run an entire machine learning inference model in WebAssembly, they are approximately 9MB in size. While this is tiny for a speech recognition model, it's large for web delivery. Because of this, you likely will want to use dynamic import() instead of static import {} to reduce your app's starting bundle size. See e.g. https://webpack.js.org/guides/code-splitting/ for more information.

Build from source (IIFE + ESM outputs)

This library uses Rollup and TypeScript along with Babel and other popular rollup plugins. There are two outputs: an IIFE version intended for script tags / CDN usage, and a JavaScript module version intended for use with modern JavaScript/TypeScript development (e.g. Angular, Create React App, Webpack).

yarn
yarn build

The output will appear in the ./dist/ folder.

For example usage refer to the web demo