@picovoice/leopard-web
v2.0.1
Published
Leopard Speech-to-Text engine for web browsers (via WebAssembly)
Downloads
293
Readme
Leopard Binding for Web
Leopard Speech-to-Text Engine
Made in Vancouver, Canada by Picovoice
Leopard is an on-device speech-to-text engine. Leopard is:
- Private; All voice processing runs locally.
- Accurate
- Compact and Computationally-Efficient
- Cross-Platform:
- Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64)
- Android and iOS
- Chrome, Safari, Firefox, and Edge
- Raspberry Pi (4, 3) and NVIDIA Jetson Nano
Compatibility
- Chrome / Edge
- Firefox
- Safari
Restrictions
IndexedDB is required to use Leopard
in a worker thread. Browsers without IndexedDB support
(i.e. Firefox Incognito Mode) should use Leopard
in the main thread.
Installation
Using yarn
:
yarn add @picovoice/leopard-web
or using npm
:
npm install --save @picovoice/leopard-web
AccessKey
Leopard requires a valid Picovoice AccessKey
at initialization. AccessKey
acts as your credentials when using Leopard 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
Create a model in Picovoice Console or use one of the default language models found in lib/common.
For the web packages, there are two methods to initialize Leopard.
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 Leopard. Copy the model file into the public directory:
cp ${LEOPARD_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 Leopard. Use the built-in script pvbase64
to
base64 your model file:
npx pvbase64 -i ${LEOPARD_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
Leopard Model
Leopard 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 Leopard. If both are set, Leopard will use the base64
model.
const leopardModel = {
publicPath: ${MODEL_RELATIVE_PATH},
// or
base64: ${MODEL_BASE64_STRING},
// Optionals
customWritePath: "leopard_model",
forceWrite: false,
version: 1,
}
Init options
Set enableAutomaticPunctuation
to true, if you wish to enable punctuation in transcript.
// Optional
const options = {
enableAutomaticPunctuation: true
}
Initialize Leopard
Create an instance of Leopard
in the main thread:
const handle = await Leopard.create(
${ACCESS_KEY},
leopardModel,
options // optional options
);
Or create an instance of Leopard
in a worker thread:
const handle = await LeopardWorker.create(
${ACCESS_KEY},
leopardModel,
options // optional options
);
Process Audio Frames
The process result is an object with:
transcript
: A string containing the transcribed data.words
: A list of objects containing aword
,startSec
,endSec
, andconfidence
. Each object indicates the start, end time and confidence (between 0 and 1) of the word.
function getAudioData(): Int16Array {
... // function to get audio data
return new Int16Array();
}
const result = await handle.process(getAudioData());
console.log(result.transcript);
console.log(result.words);
For processing using worker, you may consider transferring the buffer instead for performance:
let pcm = new Int16Array();
const result = await handle.process(pcm, {
transfer: true,
transferCallback: (data) => { pcm = data }
});
console.log(result.transcript);
console.log(result.words);
Clean Up
Clean up used resources by Leopard
or LeopardWorker
:
await handle.release();
Terminate
Terminate LeopardWorker
instance:
await handle.terminate();
Demo
For example usage refer to our Web demo application.