Zebra Binding for Web

Zebra Translate

Made in Vancouver, Canada by Picovoice

Zebra is a lightweight, on-device neural machine translation engine. Zebra is:

• Private; All processing runs locally.
• Cross-Platform:
  • Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64)
  • Raspberry Pi (3, 4, 5)
  • Chrome, Safari, Firefox, and Edge

Compatibility

• Chrome / Edge
• Firefox
• Safari

Requirements

The Zebra Web Binding uses SharedArrayBuffer.

Include the following headers in the response to enable the use of SharedArrayBuffers:

Cross-Origin-Opener-Policy: same-origin
Cross-Origin-Embedder-Policy: require-corp

Refer to our Web demo for an example on creating a server with the corresponding response headers.

Browsers that don't support SharedArrayBuffers or applications that don't include the required headers will fall back to using standard ArrayBuffers. This will disable multithreaded processing.

Restrictions

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

Multi-threading is only enabled for Zebra when using on a web worker.

Installation

Using yarn:

yarn add @picovoice/zebra-web

or using npm:

npm install --save @picovoice/zebra-web

AccessKey

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

For the web packages, there are two methods to initialize Zebra.

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 Zebra. Copy the model file into the public directory:

cp ${ZEBRA_MODEL_FILE} ${PATH_TO_PUBLIC_DIRECTORY}

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 Zebra. Use the built-in script pvbase64 to base64 your model file:

npx pvbase64 -i ${ZEBRA_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

Model

Zebra saves and caches your model file in IndexedDB to be used by WebAssembly. Use a different customWritePath variable to hold multiple models and set the forceWrite value to true to force re-save a model file.

Either base64 or publicPath must be set to instantiate Zebra. If both are set, Zebra will use the base64 model.

const zebraModel = {
  publicPath: ${MODEL_RELATIVE_PATH},
  // or
  base64: ${MODEL_BASE64_STRING},

  // Optionals
  customWritePath: "zebra_model",
  forceWrite: false,
  version: 1,
}

Translation Models

Zebra translation models are located here. The selected model decides the source and target translation languages.

The format of the model follows:

zebra_params_${SOURCE}_${TARGET}.pv

Where ${SOURCE} is the language code of the source language and ${TARGET} is the language code of the target language for the translation.

Initialize Zebra

Create an instance of Zebra in the main thread:

const zebra = await Zebra.create(
  "${ACCESS_KEY}",
  zebraModel
);

Or create an instance of Zebra in a worker thread:

const zebra = await ZebraWorker.create(
  "${ACCESS_KEY}",
  zebraModel
);

Translating Text

const translation = await zebra.translate(`${TEXT_TO_TRANSLATE}`);
console.log(translation);

Clean Up

Clean up used resources by Zebra or ZebraWorker:

await zebra.release();

Terminate ZebraWorker instance:

await zebra.terminate();

Demo

For example usage refer to our Web demo application.