Porcupine

Made in Vancouver, Canada by Picovoice

Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications. It is

• using deep neural networks trained in real-world environments.
• compact and computationally-efficient. It is perfect for IoT.
• cross-platform:
  • Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT
  • Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone
  • Android and iOS
  • Chrome, Safari, Firefox, and Edge
  • Linux (x86_64), macOS (x86_64, arm64), and Windows (x86_64)
• scalable. It can detect multiple always-listening voice commands with no added runtime footprint.
• self-service. Developers can train custom wake word models using Picovoice Console.

Table of Contents

• Porcupine
  • Table of Contents
  • Use Cases
  • Try It Out
  • Language Support
  • Performance
  • Demos
    • Python
    • .NET
    • Java
    • Go
    • Unity
    • Flutter
    • React Native
    • Android
    • iOS
    • Web
      • Vanilla JavaScript and HTML
      • Angular
      • React
      • Vue
    • NodeJS
    • Rust
    • C
    • Microcontroller
  • SDKs
    • Python
    • .NET
    • Java
    • Go
    • Unity
    • Flutter
    • React Native
    • Android
    • iOS
    • Web
      • Vanilla JavaScript and HTML (CDN Script Tag)
      • Vanilla JavaScript and HTML (ES Modules)
      • Angular
      • React
      • Vue
    • NodeJS
    • Rust
    • C
    • Microcontroller
  • Releases
  • FAQ

Use Cases

Porcupine is the right product if you need to detect one or a few static (always-listening) voice commands.

• If you want to create voice experiences similar to Alexa or Google, see the Picovoice platform.
• If you need to understand complex and naturally-spoken voice commands within a specific domain, see the Rhino Speech-to-Intent engine.

Try It Out

• Interactive Web Demo

• Android Demo Application

• Porcupine on a Raspberry Pi Zero

Language Support

• English, German, French, and Spanish.
• Support for additional languages is available for commercial customers on a case-by-case basis.

Performance

A comparison between accuracy and runtime metrics of Porcupine and two other widely-used libraries, PocketSphinx and Snowboy, is provided here. Compared to the best-performing engine of these two, Porcupine is 11.0 times more accurate and 6.5 times faster (on Raspberry Pi 3).

Demos

If using SSH, clone the repository with:

git clone --recurse-submodules [email protected]:Picovoice/porcupine.git

If using HTTPS, clone the repository with:

git clone --recurse-submodules https://github.com/Picovoice/porcupine.git

Python Demos

Install the demo package:

sudo pip3 install pvporcupinedemo

With a working microphone connected to your device run the following in the terminal:

porcupine_demo_mic --access_key ${ACCESS_KEY} --keywords porcupine

The engine starts processing the audio input from the microphone in realtime and outputs to the terminal when it detects utterances of Porcupine.

For more information about Python demos go to demo/python.

.NET Demos

From demo/dotnet/PorcupineDemo run the following in the terminal to build the demo:

dotnet build -c MicDemo.Release

Make sure there is a working microphone connected to your device. From demo/dotnet/PorcupineDemo run the following in the terminal:

dotnet run -c MicDemo.Release -- \
--access_key ${ACCESS_KEY} \
--keywords porcupine

The engine starts processing the audio input from the microphone in realtime and outputs to the terminal when it detects utterances of Porcupine.

For more information about .NET demos go to demo/dotnet.

Java Demos

Make sure there is a working microphone connected to your device. Then invoke the following commands from the terminal:

cd demo/java
./gradlew build
cd build/libs
java -jar porcupine-mic-demo.jar -a ${ACCESS_KEY} -k porcupine

The engine starts processing the audio input from the microphone in realtime and outputs to the terminal when it detects utterances of Porcupine.

For more information about Java demos go to demo/java.

Go Demos

The demo requires cgo, which on Windows may mean that you need to install a gcc compiler like Mingw to build it properly.

From demo/go run the following command from the terminal to build and run the mic demo:

go run micdemo/porcupine_mic_demo.go \
-access_key "${ACCESS_KEY}" \
-keywords porcupine

The engine starts processing the audio input from the microphone in realtime and outputs to the terminal when it detects utterances of the word Porcupine.

For more information about Go demos go to demo/go.

Unity Demos

To run the Porcupine Unity demo, import the Porcupine Unity package into your project, open the PorcupineDemo scene and hit play. To run on other platforms or in the player, go to File > Build Settings, choose your platform and hit the Build and Run button.

To browse the demo source go to demo/unity.

Flutter Demos

To run the Porcupine demo on Android or iOS with Flutter, you must have the Flutter SDK installed on your system. Once installed, you can run flutter doctor to determine any other missing requirements for your relevant platform. Once your environment has been set up, launch a simulator or connect an Android/iOS device.

Run the following command from demo/flutter to build and deploy the demo to your device:

flutter run

React Native Demos

To run the React Native Porcupine demo app you will first need to set up your React Native environment. For this, please refer to React Native's documentation. Once your environment has been set up, navigate to demo/react-native to run the following commands:

For Android:

yarn android-install    # sets up environment
yarn android-run        # builds and deploys to Android

For iOS:

yarn ios-install        # sets up environment
yarn ios-run            # builds and deploys to iOS

Android Demos

Using Android Studio, open demo/android/Activity as an Android project, copy your AccessKey into MainActivity.java and then run the application.

To learn about how to use Porcupine in long-running services go to demo/android/Service.

To learn about how to use Porcupine with Android Speech to Text recognition go to demo/android/STT.

iOS Demos

The BackgroundService demo runs audio recording in the background, enabling detection of wake word while the application is not in focus and remains running in the background. The ForegroundApp demo runs wake word detection only when the application is in focus.

BackgroundService Demo

To run the demo, go to demo/ios/BackgroundService and run:

pod install

Replace let accessKey = "${YOUR_ACCESS_KEY_HERE}" in the file ViewController.swift with your AccessKey.

Then, using Xcode, open the generated PorcupineBackgroundServiceDemo.xcworkspace and run the application.

ForegroundApp Demo

To run the demo, go to demo/ios/ForegroundApp and run:

pod install

Replace let accessKey = "${YOUR_ACCESS_KEY_HERE}" in the file ViewController.swift with your AccessKey.

Then, using Xcode, open the generated PorcupineForegroundAppDemo.xcworkspace and run the application.

Web Demos

Vanilla JavaScript and HTML

From demo/web run the following in the terminal:

yarn
yarn start

(or)

npm install
npm run start

Open http://localhost:5000 in your browser to try the demo.

Angular Demos

From demo/angular run the following in the terminal:

yarn
yarn start

(or)

npm install
npm run start

Open http://localhost:4200 in your browser to try the demo.

React Demos

From demo/react run the following in the terminal:

yarn
yarn start

(or)

npm install
npm run start

Open http://localhost:3000 in your browser to try the demo.

Vue Demos

From demo/vue run the following in the terminal:

yarn
yarn serve

(or)

npm install
npm run serve

Open http://localhost:8080 in your browser to try the demo.

NodeJS Demos

Install the demo package:

yarn global add @picovoice/porcupine-node-demo

With a working microphone connected to your device run the following in the terminal:

ppn-mic-demo --access_key ${ACCESS_KEY} --keywords porcupine

The engine starts processing the audio input from the microphone in realtime and outputs to the terminal when it detects utterances of Porcupine.

For more information about NodeJS demos go to demo/nodejs.

Rust Demos

This demo opens an audio stream from a microphone and detects utterances of a given wake word. From demo/rust/micdemo the following opens the default microphone and detects occurrences of "Picovoice":

cargo run --release -- --access_key ${ACCESS_KEY} --keywords picovoice

For more information about Rust demos go to demo/rust.

C Demos

The C demo requires CMake version 3.4 or higher.

The Microphone demo requires miniaudio for accessing microphone audio data.

Windows Requires MinGW to build the demo.

Microphone Demo

At the root of the repository, build with:

cmake -S demo/c/. -B demo/c/build && cmake --build demo/c/build --target porcupine_demo_mic

Linux (x86_64), macOS (x86_64), Raspberry Pi, BeagleBone, and Jetson

List input audio devices with:

./demo/c/build/porcupine_demo_mic --show_audio_devices

Run the demo using:

./demo/c/build/porcupine_demo_mic -l ${LIBRARY_PATH} -m lib/common/porcupine_params.pv \
-k resources/keyword_files/${PLATFORM}/porcupine_${PLATFORM}.ppn -t 0.5 \
-d ${AUDIO_DEVICE_INDEX} -a ${ACCESS_KEY}

Replace ${LIBRARY_PATH} with path to appropriate library available under lib, ${PLATFORM} with the name of the platform you are running on (linux, raspberry-pi, mac, beaglebone, or jetson), ${AUDIO_DEVICE_INDEX} with the index of your audio device and ${ACCESS_KEY} with your AccessKey.

Windows

List input audio devices with:

.\\demo\\c\\build\\porcupine_demo_mic.exe --show_audio_devices

Run the demo using:

.\\demo\\c\\build\\porcupine_demo_mic.exe -l lib/windows/amd64/libpv_porcupine.dll -m lib/common/porcupine_params.pv -k resources/keyword_files/windows/porcupine_windows.ppn -t 0.5 -d ${AUDIO_DEVICE_INDEX} -a ${ACCESS_KEY}

Replace ${AUDIO_DEVICE_INDEX} with the index of your audio device and ${ACCESS_KEY} with your AccessKey.

The demo opens an audio stream and detects utterances of Porcupine.

File Demo

At the root of the repository, build with:

cmake -S demo/c/. -B demo/c/build && cmake --build demo/c/build --target porcupine_demo_file

Linux (x86_64), macOS (x86_64), Raspberry Pi, BeagleBone, and Jetson

Run the demo using:

./demo/c/build/porcupine_demo_file -l ${LIBRARY_PATH} -m lib/common/porcupine_params.pv \
-k resources/keyword_files/${PLATFORM}/porcupine_${PLATFORM}.ppn -t 0.5 \
-w resources/audio_samples/multiple_keywords.wav -a ${ACCESS_KEY}

Replace ${LIBRARY_PATH} with path to appropriate library available under lib, ${PLATFORM} with the name of the platform you are running on (linux, raspberry-pi, mac, beaglebone, or jetson) and ${ACCESS_KEY} with your AccessKey.

Windows

Run the demo using:

.\\demo\\c\\build\\porcupine_demo_file.exe -l lib/windows/amd64/libpv_porcupine.dll -m lib/common/porcupine_params.pv -k resources/keyword_files/windows/porcupine_windows.ppn -t 0.5 -w resources/audio_samples/multiple_keywords.wav -a ${ACCESS_KEY}

Replace ${ACCESS_KEY} with your AccessKey.

The demo opens up the file and detects utterances of Porcupine.

For more information about C demos go to demo/c.

Microcontroller Demos

There are several projects for various development boards inside the mcu demo folder.

SDKs

Python

Install the Python SDK:

pip3 install pvporcupine

The SDK exposes a factory method to create instances of the engine:

import pvporcupine

access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

handle = pvporcupine.create(access_key=access_key, keywords=['picovoice', 'bumblebee'])

keywords argument is a shorthand for accessing default keyword files shipped with the library. The default keyword files available can be retrieved via

import pvporcupine

print(pvporcupine.KEYWORDS)

If you wish to use a non-default keyword file you need to identify its path:

import pvporcupine

access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

handle = pvporcupine.create(access_key=access_key, keyword_paths=['path/to/non/default/keyword/file'])

When initialized, valid sample rate can be obtained using handle.sample_rate. The required frame length (number of audio samples in an input array) is handle.frame_length. The object can be used to monitor incoming audio as follows:

import pvporcupine

access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

handle = pvporcupine.create(access_key=access_key, keywords=['porcupine'])

def get_next_audio_frame():
    pass

while True:
    keyword_index = handle.process(get_next_audio_frame())
    if keyword_index >= 0:
        # Insert detection event callback here
        pass

Finally, when done be sure to explicitly release the resources using handle.delete().

.NET

Install the .NET SDK using NuGet or the dotnet CLI:

dotnet add package Porcupine

The SDK exposes a factory method to create instances of the engine:

using Pv

const string accessKey = "${ACCESS_KEY}";
var keyword = new List<BuiltInKeyword> { BuiltInKeyword.PICOVOICE };

Porcupine handle = Porcupine.FromBuiltInKeywords(accessKey, keyword);

Using the FromBuiltInKeywords constructor allows you to initialize the Porcupine engine to detect any of the free, built-in keywords that come with the library. These built-ins are represented by the BuiltInKeyword enum.

If you wish to use a custom keyword file (i.e. a keyword file generated by Picovoice Console, with a .ppn extension), you need to specify its path:

const string accessKey = "${ACCESS_KEY}";
var keywordPaths = new List<string> { 
    "/absolute/path/to/keyword/one", 
    "/absolute/path/to/keyword/two", 
    ... }

Porcupine handle = Porcupine.FromKeywordPaths(accessKey, keywordPaths);

When initialized, the required sample rate can be obtained using handle.SampleRate. Expected frame length (number of audio samples in an input array) is handle.FrameLength. The object can be used to monitor incoming audio as below:

short[] getNextAudioFrame()
{
    // .. get a frame of audio
    return audioFrame;
}

while(true)
{
    var keywordIndex = handle.Process(getNextAudioFrame())
    if(keywordIndex >= 0)
    {
        // .. Insert detection event callback here
    }
}

Porcupine will have its resources freed by the garbage collector, but to have resources freed immediately after use, wrap it in a using statement:

using(Porcupine handle = Porcupine.FromBuiltInKeywords(
    accessKey, 
    new List<BuiltInKeyword> { BuiltInKeyword.PICOVOICE }))
{
    // .. Porcupine usage here
}

Java

The Porcupine Java binding is available from the Maven Central Repository at ai.picovoice:porcupine-java:${version}.

import ai.picovoice.porcupine.*;

final String accessKey = "${ACCESS_KEY}"; // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)
try{
    Porcupine handle = new Porcupine.Builder()
                        .setAccessKey(accessKey)
                        .setBuiltInKeyword(BuiltInKeyword.PORCUPINE)
                        .build();
} catch (PorcupineException e) { }

The setBuiltInKeyword() builder argument is a shorthand for accessing built-in keyword model files shipped with the package.

The list of built-in keywords can be found in the BuiltInKeyword enum, and can be retrieved by:

import ai.picovoice.porcupine.*;

for(BuiltInKeyword keyword : BuiltInKeyword.values()) {
    System.out.println(keyword.name());
}

If you wish to use a custom keyword file (i.e. a keyword file generated by Picovoice Console, with a .ppn extension) you need to the file path as demonstrated below:

import ai.picovoice.porcupine.*;

final String accessKey = "${ACCESS_KEY}"; // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)
try{
    Porcupine handle = new Porcupine.Builder()
                        .setAccessKey(accessKey)
                        .setKeywordPath("path/to/custom/keyword/file")
                        .build();
} catch (PorcupineException e) { }

When initialized, valid sample rate can be obtained using handle.getSampleRate(). Expected frame length (number of audio samples in an input array) is handle.getFrameLength(). The object can be used to monitor incoming audio as below:

short[] getNextAudioFrame(){
    // .. get audioFrame
    return audioFrame;
}

while(true){
    int keywordIndex = handle.Process(getNextAudioFrame());
    if(keywordIndex >= 0){
        // .. detection event logic/callback
    }
}

Once you're done with Porcupine, ensure you release its resources explicitly:

handle.delete();

Go

To install the Porcupine Go module to your project, use the command:

go get github.com/Picovoice/porcupine/binding/go

To create an instance of the engine you first create a Porcupine struct with the configuration parameters for the wake word engine and then make a call to .Init().

import . "github.com/Picovoice/porcupine/binding/go"

porcupine := Porcupine{
  AccessKey: "${ACCESS_KEY}", // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/) 
  BuiltInKeywords: []BuiltInKeyword{PICOVOICE}}
err := porcupine.Init()
if err != nil {
    // handle init fail
}

In the above example, we've initialized the engine to detect the built-in wake word "Picovoice". Built-in keywords are constants in the package with the BuiltInKeyword type.

To detect non-default keywords, use KeywordPaths parameter instead

porcupine := Porcupine{
  AccessKey: "${ACCESS_KEY}", // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/) 
  KeywordPaths: []string{"/path/to/keyword.ppn"}}
err := porcupine.Init()

When initialized, the valid sample rate is given by SampleRate. Expected frame length (number of audio samples in an input array) is given by FrameLength. The engine accepts 16-bit linearly-encoded PCM and operates on single-channel audio.

To feed audio into Porcupine, use the Process function in your capture loop. You must call Init() before calling Process.

func getNextFrameAudio() []int16{
    // get audio frame
}

for {
    keywordIndex, err := porcupine.Process(getNextFrameAudio())
    if keywordIndex >= 0 {
        // wake word detected!
    }
}

When done resources have to be released explicitly.

porcupine.Delete()

Unity

Import the Porcupine Unity Package into your Unity project.

The SDK provides two APIs:

High-Level API

PorcupineManager provides a high-level API that takes care of audio recording. This is the quickest way to get started.

The static constructor PorcupineManager.FromBuiltInKeywords will create an instance of the PorcupineManager using one or more of the built-in keywords.

using Pv.Unity;

string accessKey = "${ACCESS_KEY}"; // // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

try {
    List<Porcupine.BuiltInKeyword> keywords = new List<Porcupine.BuiltInKeyword>(){ Porcupine.BuiltInKeyword.PICOVOICE, Porcupine.BuiltInKeyword.PORCUPINE };
    PorcupineManager _porcupineManager = PorcupineManager.FromBuiltInKeywords(
                                            accessKey,
                                            keywords,
                                            OnWakeWordDetected);
}
catch (Exception ex)
{
    // handle porcupine init error
}

To create an instance of PorcupineManager that detects custom keywords, you can use the PorcupineManager.FromKeywordPaths static constructor and provide the paths to the .ppn file(s).

string accessKey = "${ACCESS_KEY}"; // // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

List<string> keywordPaths = new List<string>(){ "/path/to/keyword.ppn" };
PorcupineManager _porcupineManager = PorcupineManager.FromKeywordPaths(
                                        accessKey,
                                        keywordPaths,
                                        OnWakeWordDetected);

Once you have instantiated a PorcupineManager, you can start/stop audio capture and wake word detection by calling:

_porcupineManager.Start();
// .. use porcupine
_porcupineManager.Stop();

Once the app is done with using PorcupineManager, you can explicitly release the resources allocated to Porcupine:

_porcupineManager.Delete();

There is no need to deal with audio capture to enable wake word detection with PorcupineManager. This is because it uses our unity-voice-processor Unity package to capture frames of audio and automatically pass it to the wake word engine.

Low-Level API

Porcupine provides low-level access to the wake word engine for those who want to incorporate wake word detection into an already existing audio processing pipeline. To create an instance of Porcupine, use the .FromBuiltInKeywords static constructor.

using Pv.Unity;

string accessKey = "${ACCESS_KEY}"; // // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

try
{
    List<Porcupine.BuiltInKeyword> keywords = new List<Porcupine.BuiltInKeyword>(){ Porcupine.BuiltInKeyword.PORCUPINE, Porcupine.BuiltInKeyword.PICOVOICE };
    Porcupine _porcupine = Porcupine.FromBuiltInKeywords(accessKey: accessKey, keywords: keywords);
}
catch (Exception ex)
{
    // handle porcupine init error
}

To search for a keyword in audio, you must pass frames of audio to Porcupine using the Process function. The keywordIndex returned will either be -1 if no detection was made or an integer specifying which keyword was detected.

short[] frame = getAudioFrame();

try
{
    int keywordIndex = _porcupine.Process(frame);
    if (keywordIndex >= 0)
    {
        // detection made!
    }
}
catch (Exception ex)
{
    Debug.LogError(ex.ToString());
}

For Process to work correctly, the provided audio must be single-channel and 16-bit linearly-encoded.

Finally, once you no longer need the wake word engine, you can explicitly release the resources allocated to Porcupine:

_porcupine.Dispose();

Flutter

Add the Porcupine Flutter plugin to your pub.yaml.

dependencies:
  flutter_porcupine: ^<version>

The SDK provides two APIs:

High-Level API

PorcupineManager provides a high-level API that takes care of audio recording. This class is the quickest way to get started.

The static constructor PorcupineManager.fromBuiltInKeywords will create an instance of the PorcupineManager using one or more of the built-in keywords.

import 'package:porcupine_flutter/porcupine_manager.dart';
import 'package:porcupine_flutter/porcupine_error.dart';

const accessKey = "{ACCESS_KEY}"  // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

void createPorcupineManager() async {
    try{
        _porcupineManager = await PorcupineManager.fromBuiltInKeywords(
            accessKey,
            [BuiltInKeyword.PICOVOICE, BuiltInKeyword.PORCUPINE],
            _wakeWordCallback);
    } on PorcupineException catch (err) {
        // handle porcupine init error
    }
}

To create an instance of PorcupineManager that detects custom keywords, you can use the PorcupineManager.fromKeywordPaths static constructor and provide the paths to the .ppn file(s).

const accessKey = "{ACCESS_KEY}"  // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

_porcupineManager = await PorcupineManager.fromKeywordPaths(
    accessKey,
    ["/path/to/keyword.ppn"],
    _wakeWordCallback);

Once you have instantiated a PorcupineManager, you can start/stop audio capture and wake word detection by calling:

try{
    await _porcupineManager.start();
} on PorcupineException catch (ex) {
    // deal with either audio exception
}
// .. use porcupine
await _porcupineManager.stop();

Once the app is done with using PorcupineManager, be sure you explicitly release the resources allocated to Porcupine:

await _porcupineManager.delete();

There is no need to deal with audio capture to enable wake word detection with PorcupineManager. This is because it uses flutter_voice_processor plugin to capture frames of audio and automatically pass it to the wake word engine.

Low-Level API

Porcupine provides low-level access to the wake word engine for those who want to incorporate wake word detection into an already existing audio processing pipeline.Porcupine has fromBuiltInKeywords and fromKeywordPaths static constructors.

import 'package:porcupine_flutter/porcupine_manager.dart';
import 'package:porcupine_flutter/porcupine_error.dart';

const accessKey = "{ACCESS_KEY}"  // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

void createPorcupine() async {
    try{
        _porcupine = await Porcupine.fromBuiltInKeywords(
          accessKey,
          [BuiltInKeyword.PICOVOICE]);
    } on PorcupineException catch (err) {
        // handle porcupine init error
    }
}

To search for a keyword in audio, you must pass frames of audio to Porcupine using the process function. The keywordIndex returned will either be -1 if no detection was made or an integer specifying which keyword was detected.

List<int> buffer = getAudioFrame();

try {
    int keywordIndex = _porcupine.process(buffer);
    if (keywordIndex >= 0) {
        // detection made!
    }
} on PorcupineException catch (error) {
    // handle error
}

For process to work correctly, the provided audio must be single-channel and 16-bit linearly-encoded.

Finally, once you no longer need the wake word engine, be sure to explicitly release the resources allocated to Porcupine:

_porcupine.delete();

React Native

Install @picovoice/react-native-voice-processor and @picovoice/porcupine-react-native. The SDK provides two APIs:

High-Level API

PorcupineManager provides a high-level API that takes care of audio recording. This class is the quickest way to get started.

Using the constructor PorcupineManager.fromBuiltInKeywords will create an instance of the PorcupineManager using one or more of the built-in keywords.

const accessKey = "${ACCESS_KEY}" // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

async createPorcupineManager(){
    try{
        this._porcupineManager = await PorcupineManager.fromBuiltInKeywords(
            accessKey,
            [BuiltInKeywords.Picovoice, BuiltInKeywords.Porcupine],
            detectionCallback,
            processErrorCallback);
    } catch (err) {
        // handle error
    }
}

To create an instance of PorcupineManager that detects custom keywords, you can use the PorcupineManager.fromKeywordPaths static constructor and provide the paths to the .ppn file(s).

const accessKey = "${ACCESS_KEY}" // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

this._porcupineManager = await PorcupineManager.fromKeywordPaths(
  accessKey,
  ["/path/to/keyword.ppn"],
  detectionCallback,
  processErrorCallback
);

Once you have instantiated a Porcupine manager, you can start/stop audio capture and wake word detection by calling:

let didStart = this._porcupineManager.start();
// .. use Porcupine
let didStop = this._porcupineManager.stop();

Once the app is done with using PorcupineManager, be sure you explicitly release the resources allocated to Porcupine:

this._porcupineManager.delete();

There is no need to deal with audio capture to enable wake word detection with PorcupineManager. This is because it uses @picovoice/react-native-voice-processor module to capture frames of audio and automatically pass it to the wake word engine.

Low-Level API

Porcupine provides low-level access to the wake word engine for those who want to incorporate wake word detection into an already existing audio processing pipeline. Porcupine also has fromBuiltInKeywords and fromKeywordPaths static constructors.

const accessKey = "${ACCESS_KEY}" // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

async createPorcupine(){
    try{
        this._porcupine = await Porcupine.fromBuiltInKeywords(accessKey, [BuiltInKeywords.PICOVOICE]);
    } catch (err) {
        // handle error
    }
}

To search for a keyword in audio, you must pass frames of audio to Porcupine using the process function. The keywordIndex returned will either be -1 if no detection was made or an integer specifying which keyword was detected.

let buffer = getAudioFrame();

try {
  let keywordIndex = await this._porcupine.process(buffer);
  if (keywordIndex >= 0) {
    // detection made!
  }
} catch (e) {
  // handle error
}

For process to work correctly, the provided audio must be single-channel and 16-bit linearly-encoded.

Finally, once you no longer need the wake word engine, be sure to explicitly release the resources allocated to Porcupine:

this._porcupine.delete();

Android

To include the package in your Android project, ensure you have included mavenCentral() in your top-level build.gradle file and then add the following to your app's build.gradle:

dependencies {    
    implementation 'ai.picovoice:porcupine-android:${LATEST_VERSION}'
}

There are two possibilities for integrating Porcupine into an Android application.

High-Level API

PorcupineManager provides a high-level API for integrating Porcupine into Android applications. It manages all activities related to creating an input audio stream, feeding it into the Porcupine library, and invoking a user-provided detection callback.

import ai.picovoice.porcupine.*;

final String accessKey = "${ACCESS_KEY}";
final String keywordPath = "/path/to/keyword.ppn"
try {        

    PorcupineManager porcupineManager = new PorcupineManager.Builder()
                        .setAccessKey(accessKey)
                        .setKeywordPath(keywordPath)
                        .setSensitivity(0.5f)
                        .build(context, 
                        new PorcupineManagerCallback() {
                            @Override
                            public void invoke(int keywordIndex) {
                                // detection event logic/callback
                            }
                        });
} catch (PorcupineException e) { }

Sensitivity is the parameter that enables developers to trade miss rate for false alarm. It is a floating point number within [0, 1]. A higher sensitivity reduces miss rate at cost of increased false alarm rate.

When initialized, input audio can be monitored using manager.start(). Stop the manager using by invoking manager.stop(). When done be sure to release the resources using manager.delete().

Low-Level API

Porcupine provides a binding for Android. It can be initialized using.

import ai.picovoice.porcupine.*;

final String accessKey = "${ACCESS_KEY}";
final String keywordPath = "/path/to/keyword.ppn"
try {    
    Porcupine porcupine = new Porcupine.Builder()
                        .setAccessKey(accessKey)  
                        .setKeywordPath(keywordPath)
                        .setSensitivity(0.5f)
                        .build(context);
} catch (PorcupineException e) { }

Once initialized, porcupine can be used to monitor incoming audio.

private short[] getNextAudioFrame();

while (true) {
    final int keywordIndex = porcupine.process(getNextAudioFrame());
    if (keywordIndex != -1) {
        // detection event logic/callback
    }
}

Finally, be sure to explicitly release resources acquired by porcupine as the binding class does not rely on the garbage collector for releasing native resources.

porcupine.delete();

iOS

There are two approaches for integrating Porcupine into an iOS application.

High-Level API

PorcupineManager manages audio recording, passing it into Porcupine, and invoking the user-provided detection callback.

let accessKey = "${ACCESS_KEY}" // Obtained from Picovoice Console (https://console.picovoice.ai)
let modelPath: String = ... // Available at lib/common/porcupine_params.pv
let keywordPaths: [String] = ["/path/to/keyword/file/a", "/path/to/keyword/file/b"]
let sensitivities: [Float32] = [0.35, 0.64]
let keywordCallback: ((Int32) -> Void) = { keywordIndex in
    // Insert detection event logic
}

let manager = try PorcupineManager(
    accessKey: accessKey,
    modelPath: modelPath,
    keywordPaths: keywordPaths,
    sensitivities: sensitivities
    onDetection: keywordCallback)

When initialized, input audio can be monitored using manager.start(). When done be sure to stop the manager using manager.stop().

Low-Level API

Porcupine.swift provides low-level access to the wake word engine for those who want to incorporate wake word detection into an already existing audio processing pipeline.

To construct an instance of Porcupine, pass it a keyword.

import Porcupine

let accessKey = "${ACCESS_KEY}" // Obtained from Picovoice Console (https://console.picovoice.ai)

do {
    Porcupine porcupine = try Porcupine(accessKey: accessKey, keyword: Porcupine.BuiltInKeyword.picovoice)
} catch { }

To search for a keyword in audio, you must pass frames of audio to Porcupine using the process function. The keywordIndex returned will either be -1 if no detection was made or an integer specifying which keyword was detected.

func getNextAudioFrame() -> [Int16] {
    // .. get audioFrame
    return audioFrame;
}

while true {
    do {
        let keywordIndex = try porcupine.process(getNextAudioFrame())
        if keywordIndex >= 0 {
            // .. detection made!
        }
    } catch { }
}

Once you're done with Porcupine you can force it to release its native resources rather than waiting for the garbage collector:

porcupine.delete();

Web

Porcupine is available on modern web browsers (i.e. not Internet Explorer) via WebAssembly. Microphone audio is handled via the Web Audio API and is abstracted by the WebVoiceProcessor, which also handles downsampling to the correct format. Porcupine is provided pre-packaged as a Web Worker.

Each spoken language is available as a dedicated npm package (e.g. @picovoice/porcupine-web-en-worker). These packages can be used with the @picovoice/web-voice-processor. They can also be used with the Angular, React, and Vue bindings, which abstract and hide the web worker communication details.

Vanilla JavaScript and HTML (CDN Script Tag)

<!DOCTYPE html>
<html lang="en">
  <head>
    <script src="https://unpkg.com/@picovoice/porcupine-web-en-worker/dist/iife/index.js"></script>
    <script src="https://unpkg.com/@picovoice/web-voice-processor/dist/iife/index.js"></script>
    <script type="application/javascript">
      function keywordDetectionCallback(keyword) {
        console.log(`Porcupine detected ${keyword}`);
      }

      function processErrorCallback(error) {
        console.error(error); 
      }

      async function startPorcupine() {
        console.log("Porcupine is loading. Please wait...");
        const accessKey = // AccessKey string obtained from Picovoice Console (picovoice.ai/console/)
        let ppnEn = await PorcupineWebEnWorker.PorcupineWorkerFactory.create(
          accessKey, 
          [{builtin: "Picovoice", sensitivity: 0.65},],
          keywordDetectionCallback,
          processErrorCallback
          );

        console.log("Porcupine worker ready!");

        console.log("WebVoiceProcessor initializing. Microphone permissions requested ...");

        try {
          let webVp = await window.WebVoiceProcessor.WebVoiceProcessor.init({
            engines: [ppnEn],
          });
          console.log("WebVoiceProcessor ready and listening!");
        } catch (e) {
          console.log("WebVoiceProcessor failed to initialize: " + e);
        }
      }

      document.addEventListener("DOMContentLoaded", function () {
        startPorcupine();
      });
    </script>
  </head>
  <body></body>
</html>

Vanilla JavaScript and HTML (ES Modules)

yarn add @picovoice/porcupine-web-en-worker @picovoice/web-voice-processor

(or)

npm install @picovoice/porcupine-web-en-worker @picovoice/web-voice-processor

import { PorcupineWorkerFactory } from "@picovoice/porcupine-web-en-worker"
import { WebVoiceProcessor } from "@picovoice/web-voice-processor"

function keywordDetectionCallback(keyword) {
  console.log(`Porcupine detected ${keyword}`);
}

function processErrorCallback(error) {
  console.error(error); 
}

async startPorcupine()
  const accessKey = //AccessKey string provided by Picovoice Console (picovoice.ai/console/)
  const porcupineWorker = await PorcupineWorkerFactory.create(
    accessKey,
    [{builtin: "Picovoice", sensitivity: 0.65}],
    keywordDetectionCallback,
    processErrorCallback
  );

  const webVp = await WebVoiceProcessor.init({
    engines: [porcupineWorker],
    start: true,
  });
  }

}
startPorcupine()

Angular

yarn add @picovoice/porcupine-web-angular

(or)

npm install @picovoice/porcupine-web-angular

async ngOnInit() {
    // Load Porcupine worker chunk with specific language model (large ~1-2MB chunk; dynamically imported)
    const porcupineFactoryEn = (await import('@picovoice/porcupine-web-en-worker')).PorcupineWorkerFactory
    const accessKey = // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)
    // Initialize Porcupine Service
    try {
      await this.porcupineService.init(porcupineFactoryEn,
      {accessKey: accessKey, keywords: [{ builtin: "Okay Google", sensitivity: 0.65 }, { builtin: "Picovoice" }]})
      console.log("Porcupine is now loaded and listening")
    }
    catch (error) {
      console.error(error)
    }
  }

  ngOnDestroy() {
    this.porcupineDetection.unsubscribe()
    this.porcupineService.release()
  }

React

yarn add @picovoice/porcupine-web-react

(or)

npm install @picovoice/porcupine-web-react

import React, { useState } from "react";
import { PorcupineWorkerFactory } from "@picovoice/porcupine-web-en-worker";
import { usePorcupine } from "@picovoice/porcupine-web-react";

const accessKey = // AccessKey obtained from [Picovoice Console](https://picovoice.ai/console/)
const keywords = [{ builtin: "Picovoice", sensitivity: 0.65 }];

function VoiceWidget(props) {
  const keywordEventHandler = (keywordLabel) => {
    console.log(`Porcupine detected ${keywordLabel}`);
  };

  const {
    isLoaded,
    isListening,
    isError,
    errorMessage,
    start,
    pause,
    setDetectionCallback
  } = usePorcupine(
    PorcupineWorkerFactory,
    { accessKey, keywords, start: true },
    keywordEventHandler
  );
}

Vue

yarn add @picovoice/porcupine-web-vue

(or)

npm install @picovoice/porcupine-web-vue

<script lang="ts">
import porcupineMixin from "@picovoice/porcupine-web-vue";
import { PorcupineWorkerFactoryEn } from "@picovoice/porcupine-web-en-worker";

export default {
  name: "App",
  mixins: [porcupineMixin],
  data: function() {
    return {
      detections: [] as string[],
      isError: false,
      isLoaded: false,
      factory: PorcupineWorkerFactoryEn,
      factoryArgs: {
        accessKey: '${ACCESS_KEY}', // AccessKey obtained from Picovoice Console(https://picovoice.ai/console/)
        keywords: [
          { builtin: 'Grasshopper', sensitivity: 0.5 },
          { builtin: 'Grapefruit', sensitivity: 0.6 },
        ],
      }
    };
  },
  async created() {
    await this.$porcupine.init(
      this.factoryArgs,     // Porcupine factory arguments
      this.factory,         // Porcupine Web Worker component
      this.ppnKeywordFn,    // Callback invoked after detection of keyword
      this.ppnReadyFn,      // Callback invoked after loading Porcupine
      this.ppnErrorFn       // Callback invoked in an error occurs while initializing Porcupine
    );
  },
  methods: {
    start: function () {
      if (this.$porcupine.start()) {
        this.isListening = !this.isListening;
      }
    },
    pause: function () {
      if (this.$porcupine.pause()) {
        this.isListening = !this.isListening;
      }
    },
    ppnReadyFn: function() {
      this.isLoaded = true;
    },
    ppnKeywordFn: function(data: string) {
      this.detections = [...this.detections, data.keywordLabel];
    },
    ppnErrorFn: function(error: Error) {
      this.isError = true;
      this.errorMessage = error.toString();
    },
  },
};
</script>

NodeJS

Install NodeJS SDK:

yarn add @picovoice/porcupine-node

Create instances of the Porcupine class by specifying which keywords you want it to listen for:

const Porcupine = require("@picovoice/porcupine-node");

const {
  GRASSHOPPER,
  BUMBLEBEE,
} = require("@picovoice/porcupine-node/builtin_keywords");

const accessKey = "${ACCESS_KEY}" // Obtained from the Picovoice Console (https://console.picovoice.ai/)

let handle = new Porcupine(
    accessKey,
    [GRASSHOPPER, BUMBLEBEE], 
    [0.5, 0.65]);

GRASSHOPPER and BUMBLEBEE are built-in keywords. If you wish to use a custom keyword file, you need to identify its path:

const Porcupine = require("@picovoice/porcupine-node");

const accessKey = "${ACCESS_KEY}" // Obtained from the Picovoice Console (https://console.picovoice.ai/)

let handle = new Porcupine(
    accessKey, 
    ["/path/to/custom/keyword/file"], 
    [0.5]);

When instantiated, handle can process audio via its .process method.

let getNextAudioFrame = function() {
    ...
};

while (true) {
  let keywordIndex = handle.process(getNextAudioFrame());
  if (keywordIndex !== -1) {
    // detection event callback
  }
}

When done be sure to release resources acquired by WebAssembly using release():

handle.release();

Rust

First you will need Rust and Cargo installed on your system.

To add the porcupine library into your app, add pv_porcupine to your apps Cargo.toml manifest:

[dependencies]
pv_porcupine = "*"

To create an instance of the engine you first create a PorcupineBuilder instance with the configuration parameters for the wake word engine and then make a call to .init():

use porcupine::{BuiltinKeywords, PorcupineBuilder};

let access_key = "${ACCESS_KEY}"; // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)

let porcupine: Porcupine = PorcupineBuilder::new_with_keywords(access_key, &[BuiltinKeywords::Porcupine]).init().expect("Unable to create Porcupine");

In the above example, we've initialized the engine to detect the built-in wake word "Porcupine". Built-in keywords are contained in the package with the BuiltinKeywords enum type.

To detect custom keywords, use PorupineBuilder's new_with_keyword_paths method to pass in *.ppn file paths instead:

let porcupine: Porcupine = PorcupineBuilder::new_with_keyword_paths(&["/absolute/path/to/keyword/one.ppn", "/absolute/path/to/keyword/two.ppn"])
    .init().expect("Unable to create Porcupine");

When initialized, the valid sample rate is given by sample_rate(). Expected frame length (number of audio samples in an input array) is given by frame_length(). The engine accepts 16-bit linearly-encoded PCM and operates on single-channel audio.

To feed audio into Porcupine, use the process function in your capture loop:

fn next_audio_frame() -> Vec<i16> {
    // get audio frame
}

loop {
    if let Ok(keyword_index) = porcupine.process(&next_audio_frame()) {
        if keyword_index >= 0 {
            // wake word detected!
        }   
    }
}

C

Porcupine is implemented in ANSI C and therefore can be directly linked to C applications. include/pv_porcupine.h header file contains relevant information. An instance of Porcupine object can be constructed as follows.

const char *model_path = ... // Available at lib/common/porcupine_params.pv
const char *access_key = "${ACCESS_KEY}"; // AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)
const char *keyword_path = ...
const float sensitivity = 0.5f;

pv_porcupine_t *handle = NULL;
const pv_status_t status = pv_porcupine_init(
    access_key,
    model_path,
    1,
    &keyword_path,
    &sensitivity,
    &handle);
if (status != PV_STATUS_SUCCESS) {
    // Insert error handling logic
}

Sensitivity is the parameter that enables developers to trade miss rate for false alarm. It is a floating point number within [0, 1]. A higher sensitivity reduces miss rate (false reject rate) at cost of (potentially) increased false alarm rate.

Now the handle can be used to monitor incoming audio stream. Porcupine accepts single channel, 16-bit linearly-encoded PCM audio. The sample rate can be retrieved using pv_sample_rate(). Finally, Porcupine accepts input audio in consecutive chunks (aka frames) the length of each frame can be retrieved using pv_porcupine_frame_length().

extern const int16_t *get_next_audio_frame(void);

while (true) {
    const int16_t *pcm = get_next_audio_frame();
    int32_t keyword_index = -1;
    const pv_status_t status = pv_porcupine_process(handle, pcm, &keyword_index);
    if (status != PV_STATUS_SUCCESS) {
        // error handling logic
    }
    if (keyword_index != -1) {
        // Insert detection event callback
    }
}

Finally, when done be sure to release the acquired resources:

pv_porcupine_delete(handle);

Microcontroller

Porcupine is implemented in ANSI C and therefore can be directly linked to embedded C projects. Its public header file contains relevant information. An instance of the Porcupine object can be constructed as follows.

#define MEMORY_BUFFER_SIZE ...
uint8_t memory_buffer[MEMORY_BUFFER_SIZE] __attribute__((aligned(16)));

static const char* ACCESS_KEY = ... //AccessKey string obtained from Picovoice Console (https://picovoice.ai/console/)

const uint8_t keyword_array[] = {...};

const int32_t keyword_model_sizes = sizeof(keyword_array);
const void *keyword_models = keyword_array;
const float sensitivity = 0.5f;

pv_porcupine_t *handle = NULL;

const pv_status_t status = pv_porcupine_init(
        ACCESS_KEY,
        MEMORY_BUFFER_SIZE,
        memory_buffer,
        1,
        &keyword_model_sizes,
        &keyword_models,
        &sensitivity,
        &handle);

if (status != PV_STATUS_SUCCESS) {
    // error handling logic
}

Sensitivity is the parameter that enables developers to trade miss rate for false alarm. It is a floating-point number within [0, 1]. A higher sensitivity reduces miss rate (false reject rate) at cost of increased false alarm rate.

Now the handle can be used to monitor incoming audio stream. Porcupine accepts single channel, 16-bit PCM audio. The sample rate can be retrieved using pv_sample_rate(). Finally, Picovoice accepts input audio in consecutive chunks (aka frames) the length of each frame can be retrieved using pv_porcupine_frame_length().

extern const int16_t *get_next_audio_frame(void);

while (true) {
    const int16_t *pcm = get_next_audio_frame();
    int32_t keyword_index;
    const pv_status_t status = pv_porcupine_process(handle, pcm, &keyword_index);
    if (status != PV_STATUS_SUCCESS) {
        // error handling logic
    }
    if (keyword_index != -1) {
        // detection event logic/callback
    }
}

Finally, when done be sure to release the acquired resources.

pv_porcupine_delete(handle);

Releases

v2.0.0 - Nov 25th, 2021

• Improved accuracy.
• Added Rust SDK.
• macOS arm64 support.
• Added NodeJS support for Windows, NVIDIA Jetson Nano, and BeagleBone.
• Added .NET support for NVIDIA Jetson Nano and BeagleBone.
• Runtime optimization.

v1.9.0 - December 2nd, 2020

• Added React Native SDK.
• Added Java SDK.
• Added .NET SDK.
• Added NodeJS SDK.
• Improved accuracy.
• Runtime optimization.

v1.8.0 - May 27th, 2020

• Improved accuracy.
• Runtime optimization.

v1.7.0 - Feb 13th, 2020

• Improved accuracy.
• Runtime optimization.
• Added support for Raspberry Pi 4.
• Added service-based Android demo application.
• Added C demo applications.
• Updated documentation.

v1.6.0 - April 25th, 2019

• Improved accuracy.
• Runtime optimization.
• Added support for BeagleBone.
• iOS build can run on simulator now.

v1.5.0 - November 13, 2018

• Improved accuracy.
• Runtime optimization.
• Added support for running within web browsers (WebAssembly).

v1.4.0 - July 20, 2018

• Improved accuracy.
• Runtime optimizations.
• Updated documentation.

v1.3.0 - June 19, 2018

• Improved accuracy.
• Runtime optimizations

v1.2.0 - April 21, 2018

• Runtime optimizations.

v1.1.0 - April 11, 2018

• Added multiple command detection capability.

v1.0.0 - March 13, 2018

• Initial release.

FAQ

You can find the FAQ here.