Cheetah Speech-to-Text engine for web browsers (via WebAssembly)
npm install @picovoice/cheetah-webMade 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)
- Chrome / Edge
- Firefox
- Safari
The Cheetah 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.
IndexedDB is required to use Cheetah in a worker thread. Browsers without IndexedDB supportCheetah
(i.e. Firefox Incognito Mode) should use in the main thread.
Multi-threading is only enabled for Cheetah when using on a web worker.
Using Yarn:
`console`
yarn add @picovoice/cheetah-web
or using npm:
`console`
npm install --save @picovoice/cheetah-web
Cheetah requires a valid Picovoice AccessKey at initialization. AccessKey acts as your credentials when using Cheetah SDKs.AccessKey
You can get your for free. Make sure to keep your AccessKey secret.AccessKey
Signup or Login to Picovoice Console to get your .
Create a model in Picovoice Console or use the default model.
For the web packages, 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:
`console`
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:
`console`
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:
`console`
npx pvbase64 -h
#### Cheetah Model
Cheetah saves and caches your model file in IndexedDB to be used by WebAssembly. Use a different customWritePath variableforceWrite
to hold multiple models and set the value to true to force re-save a model file.
Either base64 or publicPath must be set to instantiate Cheetah. If both are set, Cheetah will use the base64 model.
`typescript
const cheetahModel = {
publicPath: ${MODEL_RELATIVE_PATH},
// or
base64: ${MODEL_BASE64_STRING},
// Optionals
customWritePath: "cheetah_model",
forceWrite: false,
version: 1,
}
`
#### Init options
Set endpointDurationSec value to 0 if you do not wish to detect endpoint (moment of silence). Set enableAutomaticPunctuation toprocessErrorCallback
true to enable punctuation in transcript. Set to handle errors if an error occurs while transcribing.
`typescript`
// Optional, these are default
const options = {
endpointDurationSec: 1.0,
enableAutomaticPunctuation: false,
processErrorCallback: (error) => {}
}
#### Initialize Cheetah
Create a transcriptCallback function to get the streaming results
from the engine:
`typescript
let transcript = "";
function transcriptCallback(cheetahTranscript: CheetahTranscript) {
transcript += cheetahTranscript.transcript;
if (cheetahTranscript.isEndpoint) {
transcript += ". ";
}
if (cheetahTranscript.isFlushed) {
transcript += "\n"
}
}
`
Create an instance of Cheetah on the main thread:
`typescript`
const handle = await Cheetah.create(
${ACCESS_KEY},
transcriptCallback,
cheetahModel,
options // optional options
);
Or create an instance of Cheetah in a worker thread:
`typescript`
const handle = await CheetahWorker.create(
${ACCESS_KEY},
transcriptCallback,
cheetahModel,
options // optional options
);
#### Process Audio Frames
The process function will send the input frames to the engine.transcriptCallback
The transcript is received from as mentioned above.
`typescript
function getAudioData(): Int16Array {
... // function to get audio data
return new Int16Array();
}
for (;;) {
handle.process(getAudioData());
// break on some condition
}
handle.flush(); // runs transcriptCallback on remaining data.
`
#### Clean Up
Clean up used resources by Cheetah or CheetahWorker:
`typescript`
await handle.release();
#### Terminate (Worker only)
Terminate CheetahWorker instance:
`typescript``
await handle.terminate();
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.
For example usage refer to our Web demo application.