Rhino 2.2.3

Rhino Binding for .NET

Rhino Speech-to-Intent Engine

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 with a lot of sugar, and some milk", Rhino infers that the user would like to order a drink and emits the following inference result:

{
  "type": "espresso",
  "size": "small",
  "numberOfShots": "2"
}

Rhino is:

• using deep neural networks trained in real-world environments.
• compact and computationally-efficient, making it perfect for IoT.
• self-service. Developers and designers can train custom models using Picovoice Console.

Requirements

• .NET 6.0

Compatibility

Platform compatible with .NET Framework 4.6.1+:

• Windows (x86_64)

Platforms compatible with .NET Core 2.0+:

• Linux (x86_64)
• macOS (x86_64)
• Windows (x86_64)

Platforms compatible with .NET Core 3.0+:

• Raspberry Pi:
  • 2
  • 3 (32 and 64 bit)
  • 4 (32 and 64 bit)
• NVIDIA Jetson Nano
• BeagleBone

Platform compatible with .NET 6.0+:

• macOS (arm64)

Installation

You can install the latest version of Rhino by getting the latest Rhino Nuget package in Visual Studio or using the .NET CLI:

dotnet add package Rhino

AccessKey

Rhino requires a valid Picovoice AccessKey at initialization. AccessKey acts as your credentials when using Rhino SDKs. You can get your AccessKey for free. Make sure to keep your AccessKey secret. Signup or Login to Picovoice Console to get your AccessKey.

Usage

Create an instance of the engine:

using Pv;

const string accessKey = "${ACCESS_KEY}";
string contextPath = "/absolute/path/to/context.rhn";
Rhino handle = Rhino.Create(accessKey, contextPath);

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

The sensitivity of the engine can be tuned using the sensitivity parameter. It is a floating-point number within [0, 1]. A higher sensitivity value results in fewer misses at the cost of (potentially) increasing the erroneous inference rate.

The model file contains the parameters for the Rhino engine. To change the language that Rhino understands, pass in the relevant model file.

const string accessKey = "${ACCESS_KEY}";
string contextPath = "/absolute/path/to/context.rhn";

Rhino handle = Rhino.Create(
    accessKey
    contextPath,
    sensitivity: 0.25f);

When initialized, the valid sample rate is given by handle.SampleRate. 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(accessKey, contextPath))
{
    // .. Rhino usage here
}

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.

Demos

The Rhino dotnet demo project is a .NET Core console app that allows for processing real-time audio (i.e. microphone) and files using Rhino.