@picovoice/eagle-web
v1.0.0
Published
Eagle Speaker Recognition engine for web browsers (via WebAssembly)
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Eagle Binding for Web
Eagle Speaker Recognition Engine
Made in Vancouver, Canada by Picovoice
Eagle is an on-device speaker recognition engine. Eagle is:
- Private; All voice processing runs locally.
- 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 Eagle
in a worker thread. Browsers without IndexedDB support
(i.e. Firefox Incognito Mode) should use Eagle
in the main thread.
Installation
Using yarn
:
yarn add @picovoice/eagle-web
or using npm
:
npm install --save @picovoice/eagle-web
AccessKey
Eagle requires a valid Picovoice AccessKey
at initialization. AccessKey
acts as your credentials when using Eagle
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
Eagle has two distinct steps: Enrollment and Recognition. In the enrollment step, Eagle analyzes a series of utterances from a particular speaker to learn their unique voiceprint. This step produces a profile object, which can be stored and utilized during inference. During the Recognition step, Eagle compares the incoming frames of audio to the voiceprints of all enrolled speakers in real-time to determine the similarity between them.
Speaker Enrollment
Create an instance of the EagleProfiler
:
const eagleModel = {
publicPath: ${MODEL_RELATIVE_PATH},
// or
base64: ${MODEL_BASE64_STRING},
}
// Main thread
const eagleProfiler = await EagleProfiler.create(
${ACCESS_KEY},
eagleModel);
// or on worker thread
const eagleProfiler = await EagleProfilerWorker.create(
${ACCESS_KEY},
eagleModel);
EagleProfiler
is responsible for processing and enrolling PCM audio data, with the valid audio sample rate determined
by eagleProfiler.sampleRate
. The audio data must be 16-bit linearly-encoded and single-channel.
When passing samples to eagleProfiler.enroll
, the number of samples must be at
least eagleProfiler.minEnrollSamples
to ensure sufficient data for enrollment. The percentage value
returned from this process indicates the progress of enrollment, while the feedback value can be utilized to determine the status of the enrollment process.
function getAudioData(numSamples): Int16Array {
// get audio frame of size `numSamples`
}
let percentage = 0;
while (percentage < 100) {
const audioData = getAudioData(eagleProfiler.minEnrollSamples);
const result: EagleProfilerEnrollResult = await eagleProfiler.enroll(audioData);
if (result.feedback === EagleProfilerEnrollFeedback.AUDIO_OK) {
// audio is good!
} else {
// feedback code will tell you why audio was not used in enrollment
}
percentage = result.percentage;
}
After the percentage reaches 100%, the enrollment process is considered complete. While it is possible to continue providing additional audio data to the profiler to improve the accuracy of the voiceprint, it is not necessary to do so. Moreover, if the audio data submitted is unsuitable for enrollment, the feedback value will indicate the reason, and the enrollment progress will remain unchanged.
const speakerProfile: EagleProfile = eagleProfiler.export();
The eagleProfiler.export()
function produces a binary array, which can be saved to a file.
To reset the profiler and enroll a new speaker, the eagleProfiler.reset()
method can be used. This method clears all
previously stored data, making it possible to start a new enrollment session with a different speaker.
Finally, when done be sure to explicitly release the resources:
eagleProfiler.release();
// if on worker thread
eagleProfiler.terminate();
Speaker Recognition
Create an instance of the engine with one or more speaker profiles created by the EagleProfiler
:
// Main thread
const eagle = await Eagle.create(
${ACCESS_KEY},
eagleModel,
speakerProfile);
// or, on a worker thread
const eagle = await EagleWorker.create(
${ACCESS_KEY},
eagleModel,
speakerProfile);
When initialized, eagle.sampleRate
specifies the valid sample rate for Eagle. The expected length of a frame, or the
number of audio samples in an input array, is defined by eagle.frameLength
.
Like the profiler, Eagle is designed to work with single-channel audio that is encoded using 16-bit linear PCM.
function getAudioData(numSamples): Int16Array {
// get audio frame of size `numSamples`
}
while (true) {
const audioData = getAudioData(eagle.frameLength);
const scores: number[] = await eagle.process(audioData);
}
The return value scores
represents the degree of similarity between the input audio frame and the enrolled speakers.
Each value is a floating-point number ranging from 0 to 1, with higher values indicating a greater degree of similarity.
Finally, when done be sure to explicitly release the resources:
eagle.release();
// if on worker thread
eagle.terminate();
Eagle Model
The default model is located in lib/common. Use it with the EagleModel
type:
const eagleModel = {
publicPath: ${MODEL_RELATIVE_PATH},
// or
base64: ${MODEL_BASE64_STRING},
// Optionals
customWritePath: "eagle_model",
forceWrite: false,
version: 1,
}
Eagle 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 Eagle. If both are set, Eagle will use the base64
model.
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 passes it to Eagle. Copy the model file into the 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 passes it to Eagle. Use the built-in script pvbase64
to
base64 your model file:
npx pvbase64 -i ${EAGLE_MODEL_PATH} -o ${BASE64_MODEL_PATH}.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
Demos
For example usage refer to our Web demo application.