flutterRhino - Flutter API

  • Speech-to-Intent Engine
  • Domain Specific NLU
  • Offline NLU
  • Local Voice Recognition
  • Flutter
  • Dart
  • Mobile
  • Android
  • iOS

This document outlines how to integrate the Rhino Speech-to-Intent engine within an application with the Rhino Flutter API.


  • Flutter SDK
  • Android SDK 16+
  • JDK 8+
  • Xcode 9+

Follow Flutter's guide to install the Flutter SDK on your system. Once installed, you can run flutter doctor to determine any other missing requirements.


  • Flutter 1.20.0
  • Android 4.1+ (API 16+)
  • iOS 9.0+


To add the Rhino plugin to your app project, you can reference it in your pub.yaml:

rhino: ^<version>

If you prefer to clone the repo and use it locally, first run copy_resources.sh (NOTE: on Windows, Git Bash or another bash shell is required, or you will have to manually copy the libs into the project.). Then you can reference the local binding location:

path: /path/to/rhino/flutter/binding


To enable recording with the hardware's microphone, you must first ensure that you have enabled the proper permission on both iOS and Android.

On iOS, open your Info.plist and add the following line:

<string>[Permission explanation]</string>

On Android, open your AndroidManifest.xml and add the following line:

<uses-permission android:name="android.permission.RECORD_AUDIO" />


The module provides you with two levels of API to choose from depending on your needs.

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 {
_rhinoManager = await RhinoManager.create(
} on PvError catch (err) {
// handle rhino init error

NOTE: the call is asynchronous and therefore should be called in an async block with a try/catch.

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

You can override also the default Rhino model file and/or the inference sensitivity. There is also an optional errorCallback that is called if there is a problem encountered while processing audio. These optional parameters can be passed in like so:

_rhinoManager = await RhinoManager.create(
modelPath: 'path/to/model/file.pv',
sensitivity: 0.75,
errorCallback: _errorCallback);
void _errorCallback(PvError error){
// handle error

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. When you wish to result, call .process() again.

await _rhinoManager.process();
} on PvAudioException catch (ex) {
// deal with either audio exception

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

await _rhinoManager.delete();

There is no need to deal with audio capture to enable inference with RhinoManager. This is because it uses our flutter_voice_processor Flutter plugin to capture frames of audio and automatically pass it to Rhino.

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

To feed Rhino your audio, you must send it frames of audio 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);
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

For process to work correctly, the audio data must be in the audio format required by Picovoice. The required audio format is found by calling .sampleRate to get the required sample rate and .frameLength to get the required frame size.

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


Custom Contexts

You can create custom Rhino context models using Picovoice Console.

Custom Context Integration

To add a custom context to your Flutter application, first add the rhn file to an assets folder in your project directory. Then add them to you your pubspec.yaml:

- assets/context.rhn

In your Flutter code, using the path_provider plugin, extract the asset files to your device like so:

String contextAsset = "assets/context.rhn"
String extractedContextPath = await _extractAsset(contextAsset);
// create Rhino
// ...
Future<String> _extractAsset(String resourcePath) async {
// extraction destination
String resourceDirectory = (await getApplicationDocumentsDirectory()).path;
String outputPath = '$resourceDirectory/$resourcePath';
File outputFile = new File(outputPath);
ByteData data = await rootBundle.load(resourcePath);
final buffer = data.buffer;
await outputFile.create(recursive: true);
await outputFile.writeAsBytes(
buffer.asUint8List(data.offsetInBytes, data.lengthInBytes));
return outputPath;

Non-English Contexts

In order to run inference on non-English contexts you need to use the corresponding model file. The model files for all supported languages are available here.

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