Rhino

Made in Vancouver, Canada by Picovoice

Rhino is Picovoice's Speech-to-Intent engine. It directly infers intent from spoken commands within a given context of interest, in real-time. For example, given a spoken command:

Can I have a small double-shot espresso?

Rhino infers that the user and emits the following inference result:

{
  "isUnderstood": "true",
  "intent": "orderBeverage",
  "slots": {
    "beverage": "espresso",
    "size": "small",
    "numberOfShots": "2"
  }
}

Rhino is:

• using deep neural networks trained in real-world environments.
• compact and computationally-efficient. It is perfect for IoT.
• cross-platform: Raspberry Pi, BeagleBone, Android, iOS, Linux (x86_64), Mac (x86_64), Windows (x86_64), and web browsers are supported. Additionally, enterprise customers have access to the ARM Cortex-M SDK.
• self-service. Developers can train custom models using Picovoice Console.

Table of Contents

• Rhino
  • Table of Contents
  • License & Terms
  • Use Cases
  • Try It Out
  • Language Support
  • Performance
  • Terminology
  • Demos
    • Python
    • .NET
    • Java
    • Go
    • Unity
    • Flutter
    • React Native
    • Android
    • iOS
    • Web
      • Vanilla JavaScript and HTML
      • Angular
      • React
      • Vue
    • NodeJS
    • Rust
    • C
  • 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
  • Releases
  • FAQ

License & Terms

The Rhino SDK is free and licensed under Apache 2.0, including the pre-trained models available within the repository. Picovoice Console offers two types of subscriptions: Personal and Enterprise. Personal accounts can train custom wake word models, subject to limitations and strictly for non-commercial purposes. Personal accounts empower researchers, hobbyists, and tinkerers to experiment. Enterprise accounts can unlock all capabilities of Picovoice Console, are permitted for use in commercial settings, and have a path to graduate to commercial distribution^*.

Use Cases

Rhino is the right choice if the domain of voice interactions is specific (limited).

• If you want to create voice experiences similar to Alexa or Google, see the Picovoice platform.
• If you need to recognize a few static (always listening) voice commands, see Porcupine.

Try It Out

• Interactive Web Demo

• Rhino and Porcupine on an ARM Cortex-M7

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 the accuracy of Rhino and major cloud-based alternatives is provided here. Below is the summary of the benchmark:

Terminology

Rhino infers the user's intent from spoken commands within a domain of interest. We refer to such a specialized domain as a Context. A context can be thought of a set of voice commands, each mapped to an intent:

turnLightOff:
  - Turn off the lights in the office
  - Turn off all lights
setLightColor:
  - Set the kitchen lights to blue

In examples above, each voice command is called an Expression. Expressions are what we expect the user to utter to interact with our voice application.

Consider the expression:

Turn off the lights in the office

What we require from Rhino is:

1. To infer the intent (turnLightOff)
2. Record the specific details from the utterance, in this case the location (office)

We can capture these details using slots by updating the expression:

turnLightOff:
  - Turn off the lights in the $location:lightLocation.

$location:lightLocation means that we expect a variable of type location to occur and we want to capture its value in a variable named lightLocation. We call such variable a Slot. Slots give us the ability to capture details of the spoken commands. Each slot type is be defined as a set of phrases. For example:

lightLocation:
  - "attic"
  - "balcony"
  - "basement"
  - "bathroom"
  - "bedroom"
  - "entrance"
  - "kitchen"
  - "living room"
  - ...

You can create custom contexts using the Picovoice Console.

To learn the complete expression syntax of Rhino, see the Speech-to-Intent Syntax Cheat Sheet.

Demos

If using SSH, clone the repository with:

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

If using HTTPS, clone the repository with:

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

Python Demos

Install the demo package:

sudo pip3 install pvrhinodemo

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

rhino_demo_mic --context_path ${CONTEXT_PATH}

Replace ${CONTEXT_PATH} with either a context file created using Picovoice Console or one within the repository.

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

.NET Demos

Rhino .NET demo is a command-line application that lets you choose between running Rhino on an audio file or on real-time microphone input.

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

dotnet run -c MicDemo.Release -- --context_path ${CONTEXT_FILE_PATH}

Replace ${CONTEXT_FILE_PATH} with either a context file created using Picovoice Console or one within the repository.

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

Java Demos

The Rhino Java demo is a command-line application that lets you choose between running Rhino on a audio file or on real-time microphone input.

To try the real-time demo, 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 rhino-mic-demo.jar -c ${CONTEXT_FILE_PATH}

Replace ${CONTEXT_FILE_PATH} with either a context file created using Picovoice Console or one within the repository.

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/rhino_mic_demo.go -context_path ${CONTEXT_FILE_PATH}

Replace ${CONTEXT_FILE_PATH} with either a context file created using Picovoice Console or one within the repository.

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

Unity Demos

To run the Rhino Unity demo, import the Rhino Unity package into your project, open the RhinoDemo 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 Rhino 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.

Before launching the app, use the copy_assets.sh script to copy the rhino demo context file into the demo project. (NOTE: on Windows, Git Bash or another bash shell is required, or you will have to manually copy the context into the project.).

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

flutter run

The demo uses a smart lighting context, which can understand commands such as:

Turn off the lights.

or

Set the lights in the living room to purple.

React Native Demos

To run the React Native Rhino demo app you will first need to setup 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

Both demos use a smart lighting context, which can understand commands such as:

Turn off the lights.

or

Set the lights in the living room to purple.

Android Demos

Using Android Studio, open demo/android/Activity as an Android project and then run the application. After pressing the start button you can issue commands such as:

Turn off the lights.

or:

Set the lights in the living room to purple.

For more information about Android demo and the complete list of available expressions, go to demo/android.

iOS Demos

Before building the demo app, run the following from this directory to install the Rhino-iOS Cocoapod:

pod install

Then, using Xcode, open the generated RhinoDemo.xcworkspace and run the application. After pressing the start button you can issue commands such as:

Turn off the lights.

or:

Set the lights in the living room to purple.

For more information about Android demo and the complete list of available expressions go to demo/ios.

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/rhino-node-demo

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

rhn-mic-demo --context_path ${CONTEXT_FILE_PATH}

Replace ${CONTEXT_FILE_PATH} with either a context file created using Picovoice Console or one within the repository.

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

Rust Demos

This demo opens an audio stream from a microphone and performs inference on spoken commands. From demo/rust/micdemo run the following:

cargo run --release -- --context_path ${CONTEXT_FILE_PATH}

Replace ${CONTEXT_FILE_PATH} with either a context file created using Picovoice Console or one within the repository.

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 rhino_demo_mic

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

List input audio devices with:

./demo/c/build/rhino_demo_mic --show_audio_devices

Run the demo using:

./demo/c/build/rhino_demo_mic ${RHINO_LIBRARY_PATH} lib/common/rhino_params.pv \
resources/contexts/${PLATFORM}/smart_lighting_${PLATFORM}.rhn ${AUDIO_DEVICE_INDEX}

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 ${AUDIO_DEVICE_INDEX} with the index of your audio device.

Windows

List input audio devices with:

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

Run the demo using:

.\\demo\\c\\build\\rhino_demo_mic.exe lib/windows/amd64/libpv_rhino.dll lib/common/rhino_params.pv resources/contexts/windows/smart_lighting_windows.rhn ${AUDIO_DEVICE_INDEX}

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

The demo opens an audio stream and infers your intent from spoken commands in the context of a smart lighting system. For example, you can say:

"Turn on the lights in the bedroom."

File Demo

At the root of the repository, build with:

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

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

Run the demo using:

./demo/c/build/rhino_demo_file ${LIBRARY_PATH} lib/common/rhino_params.pv \
resources/contexts/${PLATFORM}/coffee_maker_${PLATFORM}.rhn resources/audio_samples/test_within_context.wav 

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).

Windows

Run the demo using:

.\\demo\\c\\build\\rhino_demo_file.exe lib/windows/amd64/libpv_rhino.dll lib/common/rhino_params.pv resources/contexts/windows/coffee_maker_windows.rhn resources/audio_samples/test_within_context.wav

The demo opens up the WAV file and infers the intent in the context of a coffee maker system.

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

SDKs

Python

Install the Python SDK:

pip3 install pvrhino

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

import pvrhino

handle = pvrhino.create(context_path='/absolute/path/to/context')

Where context_path is the absolute path to the Speech-to-Intent context created either using Picovoice Console or one of the default contexts available on Rhino's GitHub repository.

When initialized, the required sample rate can be obtained using rhino.sample_rate. The expected frame length (number of audio samples in an input array) is provided by rhino.frame_length. The object can be used to infer intent from spoken commands as below:

import pvrhino

handle = pvrhino.create(context_path='/absolute/path/to/context')

def get_next_audio_frame():
    pass

while True:
    is_finalized = handle.process(get_next_audio_frame())

    if is_finalized:
        inference = handle.get_inference()
        if not inference.is_understood:
            # add code to handle unsupported commands
            pass
        else:
            intent = inference.intent
            slots = inference.slots
            # add code to take action based on inferred intent and slot values

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 Rhino

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

using Pv

Rhino handle = Rhino.Create(contextPath:"/absolute/path/to/context");

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

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

while(true)
{
    bool isFinalized = handle.Process(GetNextAudioFrame());
    if(isFinalized)
    {
        Inference inference = handle.GetInference();
        if(inference.IsUnderstood)
        {
            string intent = inference.Intent;
            Dictionary<string, string> slots = inference.Slots;
            // .. code to take action based on inferred intent and slot values
        }
        else
        {
            // .. code to handle unsupported commands
        }
    }
}

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

using(Rhino handle = Rhino.Create(contextPath:"/absolute/path/to/context"))
{
    // .. Rhino usage here
}

Java

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

The SDK exposes a Builder that allows you to create an instance of the engine:

import ai.picovoice.rhino.*;

try{
    Rhino handle = new Rhino.Builder()
                    .setContextPath("/absolute/path/to/context")
                    .build();
} catch (RhinoException e) { }

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

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

while(true) {
    boolean isFinalized = handle.process(getNextAudioFrame());
    if(isFinalized){
        RhinoInference inference = handle.getInference();
        if(inference.getIsUnderstood()){
            String intent = inference.getIntent();
            Map<string, string> slots = inference.getSlots();
            // .. code to take action based on inferred intent and slot values
        } else {
            // .. code to handle unsupported commands
        }
    }
}

Once you are done with Rhino, ensure you release its resources explicitly:

handle.delete();

Go

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

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

To create an instance of the engine with default parameters, pass a path to a Rhino context file (.rhn) to the NewRhino function and then make a call to .Init().

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

rhino = NewRhino("/path/to/context/file.rhn")
err := rhino.Init()
if err != nil {
    // handle error
}

Once initialized, you can start passing in frames of audio for processing. The engine accepts 16-bit linearly-encoded PCM and operates on single-channel audio. The sample rate that is required by the engine is given by SampleRate and number of samples per frame is FrameLength.

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

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

for {
    isFinalized, err := rhino.Process(getNextFrameAudio())
    if isFinalized {
        inference, err := rhino.GetInference()
        if inference.IsUnderstood {
            intent := inference.Intent
            slots := inference.Slots
            // add code to take action based on inferred intent and slot values
        } else {
            // add code to handle unsupported commands
        }
    }
}

When done resources have to be released explicitly.

rhino.Delete()

Unity

Import the Rhino Unity Package into your Unity project.

The SDK provides two APIs:

High-Level API

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

Using the constructor RhinoManager.Create will create an instance of the RhinoManager using the provided context file.

using Pv.Unity;

try
{
    RhinoManager _rhinoManager = RhinoManager.Create(
                                    "/path/to/context/file.rhn",
                                    (inference) => {});
}
catch (Exception ex)
{
    // handle rhino init error
}

Once you have instantiated a RhinoManager, you can start audio capture and intent inference by calling:

_rhinoManager.Process();

Audio capture stops and Rhino resets once an inference result is returned via the inference callback. When you wish to result, call .Process() again.

Once the app is done with using an instance of RhinoManager, you can explicitly release the audio resources and the resources allocated to Rhino:

_rhinoManager.Delete();

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

Low-Level API

Rhino provides low-level access to the inference engine for those who want to incorporate speech-to-intent into a already existing audio processing pipeline.

To create an instance of Rhino, use the .Create static constructor and a context file.

using Pv.Unity;

try
{
    Rhino _rhino = Rhino.Create("path/to/context/file.rhn");
}
catch (Exception ex)
{
    // handle rhino init error
}

To feed Rhino your audio, you must send it frames of audio to its Process function until it has made an inference.

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

try
{
    bool isFinalized = _rhino.Process(GetNextAudioFrame());
    if(isFinalized)
    {
        Inference inference = _rhino.GetInference();
        if(inference.IsUnderstood)
        {
            string intent = inference.Intent;
            Dictionary<string, string> slots = inference.Slots;
            // .. code to take action based on inferred intent and slot values
        }
        else
        {
            // .. code to handle unsupported commands
        }
    }
}
catch (Exception ex)
{
    Debug.LogError(ex.ToString());
}

For process to work correctly, the audio data must be in the audio format required by Picovoice.

Rhino implements the IDisposable interface, so you can use Rhino in a using block. If you don't use a using block, resources will be released by the garbage collector automatically or you can explicitly release the resources like so:

_rhino.Dispose();

Flutter

Add the Rhino Flutter plugin to your pub.yaml.

dependencies:
  rhino: ^<version>

The SDK provides two APIs:

High-Level API

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

The constructor RhinoManager.create will create an instance of the RhinoManager using a context file that you pass to it.

import 'package:rhino/rhino_manager.dart';
import 'package:rhino/rhino_error.dart';

void createRhinoManager() async {
    try{
        _rhinoManager = await RhinoManager.create(
            "/path/to/context/file.rhn",
            _inferenceCallback);
    } on PvError catch (err) {
        // handle rhino init error
    }
}

The inferenceCallback parameter is a function that you want to execute when Rhino makes an inference. The function should accept a map that represents the inference result.

void _infererence(Map<String, dynamic> inference){
    if(inference['isUnderstood']){
        String intent = inference['intent']
        Map<String, String> = inference['slots']
        // add code to take action based on inferred intent and slot values
    }
    else{
        // add code to handle unsupported commands
    }
}

Once you have instantiated a RhinoManager, you can start audio capture and intent inference using the .process() function. Audio capture stops and rhino resets once an inference result is returned via the inference callback.

try{
    await _rhinoManager.process();
} on PvAudioException catch (ex) { }

Once your app is done with using RhinoManager, be sure you explicitly release the resources allocated for it:

_rhinoManager.delete();

Our flutter_voice_processor Flutter plugin captures the frames of audio and automatically passes it to the speech-to-intent engine.

Low-Level API

Rhino provides low-level access to the inference engine for those who want to incorporate speech-to-intent into a already existing audio processing pipeline.

Rhino is created by passing a context file to its static constructor create:

import 'package:rhino/rhino_manager.dart';
import 'package:rhino/rhino_error.dart';

void createRhino() async {
    try{
        _rhino = await Rhino.create('/path/to/context/file.rhn');
    } on PvError catch (err) {
        // handle rhino init error
    }
}

To deliver audio to the engine, you must send audio frames to its process function. Each call to process will return a Map object that will contain the following items:

• isFinalized - whether Rhino has made an inference
• isUnderstood - if isFinalized, whether Rhino understood what it heard based on the context
• intent - if isUnderstood, name of intent that were inferred
• slots - if isUnderstood, dictionary of slot keys and values that were inferred

List<int> buffer = getAudioFrame();

try {
    Map<String, dynamic> inference = _rhino.process(buffer);
    if(inference['isFinalized']){
        if(inference['isUnderstood']){
            String intent = inference['intent']
            Map<String, String> = inference['slots']
            // add code to take action based on inferred intent and slot values
        }
    }
} on PvError catch (error) {
    // handle error
}

// once you are done
this._rhino.delete();

React Native

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

High-Level API

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

The constructor RhinoManager.create will create an instance of a RhinoManager using a context file that you pass to it.

async createRhinoManager(){
    try{
        this._rhinoManager = await RhinoManager.create(
            '/path/to/context/file.rhn',
            inferenceCallback);
    } catch (err) {
        // handle error
    }
}

Once you have instantiated a RhinoManager, you can start/stop audio capture and intent inference by calling .process(). Upon receiving an inference callback, audio capture will stop automatically and Rhino will reset. To restart it you must call .process() again.

let didStart = await this._rhinoManager.process();

When you are done using Rhino, release you must explicitly resources:

this._rhinoManager.delete();

@picovoice/react-native-voice-processor handles audio capture and RhinoManager passes frames to the inference engine for you.

Low-Level API

Rhino provides low-level access to the inference engine for those who want to incorporate speech-to-intent into a already existing audio processing pipeline.

Rhino is created by passing a context file to its static constructor create:

async createRhino(){
    try{
        this._rhino = await Rhino.create('/path/to/context/file.rhn');
    } catch (err) {
        // handle error
    }
}

To deliver audio to the enine, you must pass it audio frames using the process function. The JSON result that is returned from process will have up to four fields:

• isFinalized - whether Rhino has made an inference
• isUnderstood - if isFinalized, whether Rhino understood what it heard based on the context
• intent - if isUnderstood, name of intent that were inferred
• slots - if isUnderstood, dictionary of slot keys and values that were inferred

let buffer = getAudioFrame();
try {
    let result = await this._rhino.process(buffer);
    // use result
    // ..
    }
} catch (e) {
    // handle error
}

// once you are done
this._rhino.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:rhino-android:1.6.0'
}

There are two possibilities for integrating Rhino into an Android application: the High-level API and the Low-level API.

High-Level API

RhinoManager provides a high-level API for integrating Rhino into Android applications. It manages all activities related to creating an input audio stream, feeding it into Rhino, and invoking a user-provided inference callback.

try {
    RhinoManager rhinoManager = new RhinoManager.Builder()
                        .setContextPath("/path/to/context/file.rhn")
                        .setModelPath("/path/to/model/file.pv")
                        .setSensitivity(0.35f)                        
                        .build(appContext, new RhinoManagerCallback() {
                            @Override
                            public void invoke(RhinoInference inference) {
                                if (inference.getIsUnderstood()) {
                                    final String intent = inference.getIntent()));
                                    final Map<String, String> slots = inference.getSlots();
                                    // add code to take action based on inferred intent and slot values
                                }
                                else {
                                    // add code to handle unsupported commands
                                }
                            }
                        });
} catch (RhinoException e) { }

The appContext parameter is the Android application context - this is used to extract Rhino resources from the APK. 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 processed using manager.process(). When done, be sure to release the resources using manager.delete().

Low-Level API

Rhino provides a binding for Android using JNI. It can be initialized using:

import ai.picovoice.rhino.*;

try {    
    Rhino rhino = new Rhino.Builder()
                        .setContextPath("/path/to/context/file.rhn")                        
                        .build(appContext);
} catch (RhinoException e) { }

Once initialized, handle can be used for intent inference:

private short[] getNextAudioFrame();

while (!handle.process(getNextAudioFrame()));

final RhinoInference inference = handle.getInference();
if (inference.getIsUnderstood()) {
    // logic to perform an action given the intent object.
} else {
    // logic for handling out of context or unrecognized command
}

Finally, prior to exiting the application be sure to release resources acquired:

handle.delete()

iOS

The Rhino iOS binding is available via Cocoapods. To import it into your iOS project, add the following line to your Podfile and run pod install:

pod 'Rhino-iOS'

There are two approaches for integrating Rhino into an iOS application: The high-level API and the low-level API.

High-Level API

RhinoManager provides a high-level API for integrating Rhino into iOS applications. It manages all activities related to creating an input audio stream, feeding it to the engine, and invoking a user-provided inference callback.

do {
    RhinoManager manager = try RhinoManager(
        contextPath: "/path/to/context/file.rhn", 
        modelPath: "/path/to/model/file.pv",
        sensitivity: 0.35,
        onInferenceCallback: { inference in
                if inference.isUnderstood {
                    let intent:String = inference.intent
                    let slots:Dictionary<String,String> = inference.slots
                    // use inference results
                }
            })
} catch { }

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 processed using manager.process(). When done, be sure to release the resources using manager.delete().

Low-Level API

Rhino provides low-level access to the Speech-to-Intent engine for those who want to incorporate intent inference into a already existing audio processing pipeline.

import Rhino

do {
    Rhino handle = try Rhino(contextPath: "/path/to/context/file.rhn")
} catch { }

Once initialized, handle can be used for intent inference:

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

while true {
    do {
        let isFinalized = try handle.process(getNextAudioFrame())
        if isFinalized {
            let inference = try handle.getInference()
            if inference.isUnderstood {
                let intent:String = inference.intent
                let slots:Dictionary<String, String> = inference.slots
                // add code to take action based on inferred intent and slot values
            }
        }
    } catch { }
}

Finally, prior to exiting the application be sure to release resources acquired:

handle.delete()

Web

Rhino 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. Rhino is provided pre-packaged as a Web Worker.

Each spoken language is available as a dedicated npm package (e.g. @picovoice/rhino-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/rhino-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">
      const RHINO_CONTEXT_BASE64 = /* Base64 representation of .rhn file  */;

      async function startRhino() {
        console.log("Rhino is loading. Please wait...");
        window.rhinoWorker = await RhinoWebEnWorker.RhinoWorkerFactory.create(
          {
            context: {
              base64: RHINO_CONTEXT_BASE64,
              sensitivity: 0.5,
            },
            start: false,
          }
        );

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

        window.rhinoWorker.onmessage = (msg) => {
          if (msg.data.command === "rhn-inference") {
            console.log("Inference detected: " + JSON.stringify(msg.data.inference));
            window.rhinoWorker.postMessage({ command: "pause" });
            document.getElementById("push-to-talk").disabled = false;
            console.log("Rhino is paused. Press the 'Push to Talk' button to speak again.")
          }
        };

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

        try {
          let webVp = await WebVoiceProcessor.WebVoiceProcessor.init({
            engines: [window.rhinoWorker],
          });
          console.log(
            "WebVoiceProcessor ready! Press the 'Push to Talk' button to talk."
          );
        } catch (e) {
          console.log("WebVoiceProcessor failed to initialize: " + e);
        }
      }

      document.addEventListener("DOMContentLoaded", function () {
        startRhino();
        document.getElementById("push-to-talk").onclick = function (event) {
          console.log("Rhino is listening for your commands ...");
          this.disabled = true;
          window.rhinoWorker.postMessage({ command: "resume" });
        };
      });
    </script>
  </head>
  <body>
    <button id="push-to-talk">Push to Talk</button>
  </body>
</html>

Vanilla JavaScript and HTML (ES Modules)

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

(or)

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

import { WebVoiceProcessor } from "@picovoice/web-voice-processor"
import { RhinoWorkerFactory } from "@picovoice/rhino-web-en-worker";
 
const RHN_CONTEXT_BASE64 = /* Base64 representation of a .rhn context */
 
async startRhino()
  // Create a Rhino Worker (English language) to listen for
  // commands in the specified context
  const rhinoWorker = await RhinoWorkerFactory.create(
    {context: RHN_CONTEXT_BASE64 }
  );
 
  // The worker will send a message with data.command = "rhn-inference" upon concluding
  // Here we tell it to log it to the console
  rhinoWorker.onmessage = (msg) => {
    switch (msg.data.command) {
      case 'rhn-inference':
        // Log the event
        console.log("Rhino inference: " + msg.data.inference);
        // Pause Rhino processing until the push-to-talk button is pressed again
        rhinoWorker.postMessage({command: "pause"})
        break;
      default:
        break;
    }
  };
 
  // Start up the web voice processor. It will request microphone permission
  // It downsamples the audio to voice recognition standard format (16-bit 16kHz linear PCM, single-channel)
  // The incoming microphone audio frames will then be forwarded to the Rhino Worker
  // n.b. This promise will reject if the user refuses permission! Make sure you handle that possibility.
  const webVp = await WebVoiceProcessor.init({
    engines: [rhinoWorker],
    start: true,
  });
  }
 
  // Rhino is push-to-talk. We need to to tell it that we
  // are starting a voice interaction:
  function pushToTalk() {
    rhinoWorker.postMessage({command: "resume"})
  }
 
}
startRhino()
 
...
 
// Finished with Rhino? Release the WebVoiceProcessor and the worker.
if (done) {
  webVp.release()
  rhinoWorker.sendMessage({command: "release"})
}

Angular

yarn add @picovoice/rhino-web-angular @picovoice/rhino-web-en-worker

(or)

npm install @picovoice/rhino-web-angular @picovoice/rhino-web-en-worker

async ngOnInit() {
  const rhinoFactoryEn = (await import('@picovoice/rhino-web-en-worker')).RhinoWorkerFactory
  // Initialize Rhino Service
  try {
    await this.rhinoService.init(rhinoFactoryEn, {context: { base64: RHN_CONTEXT_BASE64 }})
    console.log("Rhino is now loaded. Press the Push-to-Talk button to activate.")
  }
  catch (error) {
    console.error(error)
  }
}
 
ngOnDestroy() {
  this.rhinoDetection.unsubscribe()
  this.rhinoService.release()
}
 
public pushToTalk() {
  this.rhinoService.pushToTalk();
}

React

yarn add @picovoice/rhino-web-react @picovoice/rhino-web-en-worker

(or)

npm install @picovoice/rhino-web-react @picovoice/rhino-web-en-worker

mport React, { useState } from 'react';
import { RhinoWorkerFactory } from '@picovoice/rhino-web-en-worker';
import { useRhino } from '@picovoice/rhino-web-react';
 
const RHINO_CONTEXT_BASE64 = /* Base64 representation an English language .rhn file, omitted for brevity */
 
function VoiceWidget(props) {
  const [latestInference, setLatestInference] = useState(null)
 
  const inferenceEventHandler = (rhinoInference) => {
    console.log(`Rhino inferred: ${rhinoInference}`);
    setLatestInference(rhinoInference)
  };
 
  const {
    isLoaded,
    isListening,
    isError,
    isTalking,
    errorMessage,
    start,
    resume,
    pause,
    pushToTalk,
  } = useRhino(
    // Pass in the factory to build Rhino workers. This needs to match the context language below
    RhinoWorkerFactory,
    // Initialize Rhino (in a paused state).
    // Immediately start processing microphone audio,
    // Although Rhino itself will not start listening until the Push to Talk button is pressed.
    {
      context: { base64: RHINO_CONTEXT_BASE64 },
      start: true,
    }
    inferenceEventHandler
  );
 
return (
  <div className="voice-widget">
    <button onClick={() => pushToTalk()} disabled={isTalking || isError || !isLoaded}>
      Push to Talk
    </button>
    <p>{JSON.stringify(latestInference)}</p>
  </div>
)

Vue

yarn add @picovoice/rhino-web-vue @picovoice/rhino-web-en-worker

(or)

npm install @picovoice/rhino-web-vue @picovoice/rhino-web-en-worker

<template>
  <div class="voice-widget">
    <Rhino
      ref="rhino"
      v-bind:rhinoFactoryArgs="{
        context: {
          base64: '...', <!-- Base64 representation of a trained Rhino context; i.e. a `.rhn` file, omitted for brevity -->
        },
      }"
      v-bind:rhinoFactory="factory"
      v-on:rhn-error="rhnErrorFn"
      v-on:rhn-inference="rhnInferenceFn"
      v-on:rhn-init="rhnInitFn"
      v-on:rhn-ready="rhnReadyFn"
    />
  </div>
</template>
 
<script>
import Rhino from "@picovoice/rhino-web-vue";
import { RhinoWorkerFactory as RhinoWorkerFactoryEn } from "@picovoice/rhino-web-en-worker";
 
export default {
  name: "VoiceWidget",
  components: {
    Rhino,
  },
  data: function () {
    return {
      inference: null,
      isError: false,
      isLoaded: false,
      isListening: false,
      isTalking: false,
      factory: RhinoWorkerFactoryEn,
    };
  },
  methods: {
    pushToTalk: function () {
      if (this.$refs.rhino.pushToTalk()) {
        this.isTalking = true;
      }
    },
    rhnInitFn: function () {
      this.isError = false;
    },
    rhnReadyFn: function () {
      this.isLoaded = true;
      this.isListening = true;
    },
    rhnInferenceFn: function (inference) {
      this.inference = inference;
      console.log("Rhino inference: " + inference)
      this.isTalking = false;
    },
    rhnErrorFn: function (error) {
      this.isError = true;
      this.errorMessage = error.toString();
    },
  },
};

NodeJS

Install the NodeJS SDK:

yarn add @picovoice/rhino-node

Create instances of the Rhino class by specifying the path to the context file:

const Rhino = require("@picovoice/rhino-node");

let handle = new Rhino("/path/to/context/file.rhn");

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

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

let isFinalized = false;
while (!isFinalized) {
  isFinalized = handle.process(getNextAudioFrame());
  if (isFinalized) {
    let inference = engineInstance.getInference();
    // Insert inference 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_rhino to your apps Cargo.toml manifest:

[dependencies]
pv_rhino = "*"

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

use rhino::RhinoBuilder;

let rhino: Rhino = RhinoBuilder::new("/path/to/context/file.rhn").init().expect("Unable to create Rhino");

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

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

loop {
    if let Ok(is_finalized) = rhino.process(&next_audio_frame()) {
        if is_finalized {
            if let Ok(inference) = rhino.get_inference() {
                if inference.is_understood {
                    let intent = inference.intent.unwrap();
                    let slots = inference.slots;
                    // add code to take action based on inferred intent and slot values
                } else {
                    // add code to handle unsupported commands
                }
            }
        }
    }
}

C

Rhino is implemented in ANSI C and therefore can be directly linked to C applications. The pv_rhino.h header file contains relevant information. An instance of the Rhino object can be constructed as follows:

const char *model_path = ... // Available at lib/common/rhino_params.pv
const char *context_path = ... // absolute path to context file for the domain of interest
const float sensitivity = 0.5f;

pv_rhino_t *handle = NULL;
const pv_status_t status = pv_rhino_init(model_path, context_path, sensitivity, &handle);
if (status != PV_STATUS_SUCCESS) {
    // add error handling code
}

Now the handle can be used to infer intent from an incoming audio stream. Rhino accepts single channel, 16-bit PCM audio. The sample rate can be retrieved using pv_sample_rate(). Finally, Rhino accepts input audio in consecutive chunks (frames); the length of each frame can be retrieved using pv_rhino_frame_length().

extern const int16_t *get_next_audio_frame(void);

while (true) {
    const int16_t *pcm = get_next_audio_frame();

    bool is_finalized = false;
    pv_status_t status = pv_rhino_process(handle, pcm, &is_finalized);
    if (status != PV_STATUS_SUCCESS) {
        // add error handling code
    }

    if (is_finalized) {
        bool is_understood = false;
        status = pv_rhino_is_understood(rhino, &is_understood);
        if (status != PV_STATUS_SUCCESS) {
            // add error handling code
        }

        if (is_understood) {
            const char *intent = NULL;
            int32_t num_slots = 0;
            const char **slots = NULL;
            const char **values = NULL;
            status = pv_rhino_get_intent(rhino, &intent, &num_slots, &slots, &values);
            if (status != PV_STATUS_SUCCESS) {
                // add error handling code
            }

            // add code to take action based on inferred intent and slot values

            pv_rhino_free_slots_and_values(rhino, slots, values);
        } else {
            // add code to handle unsupported commands
        }

        pv_rhino_reset(rhino);
    }
}

When done, remember to release the resources acquired.

pv_rhino_delete(rhino);

Releases

v1.6.0 December 2nd, 2020

• Added support for React Native.
• Added support for Java.
• Added support for .NET.
• Added support for NodeJS.

v1.5.0 June 4th, 2020

• Accuracy improvements.

v1.4.0 April 13th, 2020

• Accuracy improvements.
• Builtin slots

v1.3.0 February 13th, 2020

• Accuracy improvements.
• Runtime optimizations.
• Added support for Raspberry Pi 4
• Added support for JavaScript.
• Added support for iOS.
• Updated documentation.

v1.2.0 April 26, 2019

• Accuracy improvements.
• Runtime optimizations.

v1.1.0 December 23rd, 2018

• Accuracy improvements.
• Open-sourced Raspberry Pi build.

v1.0.0 November 2nd, 2018

• Initial Release

FAQ

You can find the FAQ here.