@picovoice/porcupine-react
v3.0.3
Published
React component for Porcupine Web SDK
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666
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Porcupine Binding for React
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 and WebWorkers are required to use Porcupine React
. Browsers without support (i.e. Firefox Incognito Mode)
should use the PorcupineWeb binding
main thread method.
Installation
Package
Using Yarn
:
yarn add @picovoice/porcupine-react @picovoice/web-voice-processor
or using npm
:
npm install --save @picovoice/porcupine-react @picovoice/web-voice-processor
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 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}
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 ${PORCUPINE_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
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
Use usePorcupine
and init
to initialize Porcupine
:
import { BuiltInKeyword } from '@picovoice/porcupine-web';
import { usePorcupine } from '@picovoice/porcupine-react';
const {
keywordDetection,
isLoaded,
isListening,
error,
init,
start,
stop,
release,
} = usePorcupine();
await init(
${ACCESS_KEY},
[BuiltInKeyword.Porcupine],
porcupineModel
);
In case of any errors, use error
state to check the error message, else
use the isLoaded
variable to check if Porcupine
has loaded.
Process Audio Frames
Porcupine React binding uses WebVoiceProcessor to record audio.
To start detecting wake word, run the start
function:
await start();
If WebVoiceProcessor
has started correctly, isListening
will be set to true.
Use the keywordDetection
state to get wake word detection results:
useEffect(() => {
if (keywordDetection !== null) {
console.log(keywordDetection.label);
}
}, [keywordDetection])
Stop
Run stop
to stop keyword detection:
await stop();
If WebVoiceProcessor
has stopped correctly, isListening
will be set to false.
Release
While running in a component, you can call release
to clean up all resources used by Porcupine and WebVoiceProcessor:
await release();
This will set isLoaded
and isListening
to false.
You do not need to call release
when your component is unmounted - the hook will clean up automatically on unmount.
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 {
keywordDetection,
isLoaded,
isListening,
error,
init,
start,
stop,
release,
} = usePorcupine();
await init(
${ACCESS_KEY},
keywordModel,
porcupineModel
);
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 react application.