@picovoice/cheetah-react v2.1.1

Cheetah Binding for React

Cheetah Speech-to-Text Engine

Made in Vancouver, Canada by Picovoice

Cheetah is an on-device streaming speech-to-text engine. Cheetah is:

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

Compatibility

• Chrome / Edge
• Firefox
• Safari

Restrictions

IndexedDB and WebWorkers are required to use Cheetah React. Browsers without support (e.g. Firefox Incognito Mode) should use the CheetahWeb binding main thread method.

AccessKey

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

Installation

Using yarn:

yarn add @picovoice/cheetah-react @picovoice/web-voice-processor

or using npm:

npm install --save @picovoice/cheetah-react @picovoice/web-voice-processor

Usage

Cheetah requires a model file (.pv) at initialization. Use the default language model found in lib/common, or create a custom Cheetah model (.pv) in the Picovoice Console for the target platform Web (WASM).

There are two methods to initialize Cheetah.

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 Cheetah. Copy the model file into the public directory:

cp ${CHEETAH_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 Cheetah. Use the built-in script pvbase64 to base64 your model file:

npx pvbase64 -i ${CHEETAH_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

Cheetah Model

Cheetah 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. 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 Cheetah. If both are set, Cheetah will use the base64 model.

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

  // Optionals
  customWritePath: "custom_model",
  forceWrite: true,
  version: 1,
}

Additional engine options are provided via the options parameter. Set endpointDurationSec value to 0 if you do not wish to detect endpoint (period of silence). Set enableAutomaticPunctuation to true to enable punctuation in the transcript.

// Optional - below are default values
const options = {
  endpointDurationSec: 1.0,
  enableAutomaticPunctuation: false
}

Initialize Cheetah

Use useCheetah and init to initialize Cheetah:

const {
  result,
  isLoaded,
  isListening,
  error,
  init,
  start,
  stop,
  release,
} = useCheetah();

const initCheetah = async () => {
  await init(
    "${ACCESS_KEY}",
    cheetahModel,
    options
  )
}

In case of any errors, use the error state variable to check the error message. Use the isLoaded state variable to check if Cheetah has loaded.

Transcribe Audio

Cheetah React binding uses WebVoiceProcessor to record audio with a microphone. To start recording and transcribing, run the start function:

await start();

If WebVoiceProcessor has started correctly, isListening will be set to true. Use the result state to get transcription results:

useEffect(() => {
  if (result !== null) {
    console.log(result.transcript);
    console.log(result.isComplete);
  }
}, [result])

• result.transcript: transcript returned from Cheetah
• result.isComplete: whether the corresponding transcript marks the end of a transcript (i.e. the end of a sentence)

Stop

Run stop to stop recording:

await stop();

If WebVoiceProcessor has stopped correctly, isListening will be set to false.

Clean Up

While running in a component, you can call release to clean up all resources used by Cheetah and WebVoiceProcessor:

await release();

This will set isLoaded and isListening to false, and error to null.

If any arguments require changes, call release, then init again to initialize Cheetah with the new settings.

You do not need to call release when your component is unmounted - the hook will clean up automatically on unmount.

Language Model

Default models for supported languages can be found in lib/common.

Create custom language models using the Picovoice Console. Here you can train language models with custom vocabulary and boost words in the existing vocabulary.

Demo

For example usage refer to our React demo application.