using deep neural networks trained in real-world environments.
compact and computationally-efficient. It is perfect for IoT.
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.

Requirements

.NET Core 3.1

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 3.1+:

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

Installation

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

dotnet add package Porcupine

AccessKey

Porcupine requires a valid Picovoice AccessKey at initialization. AccessKeys act as your credentials when using Porcupine SDKs. You can create your AccessKey for free. Make sure to keep your AccessKey secret.

To obtain your AccessKey:

Login or Signup for a free account on the Picovoice Console.
Once logged in, go to the AccessKey tab to create one or use an existing AccessKey.

Usage

Create an instance of the engine:

using Pv;

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

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

handle is an instance of Porcupine that detects utterances of "Picovoice". 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.

Porcupine can detect multiple keywords concurrently:

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

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

To detect custom keywords, use the FromKeywordPaths constructor instead:

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);

In addition to custom keywords, you can override the default Porcupine english model file and/or keyword sensitivities.

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

The model file contains the parameters for the wake word engine. To change the language that Porcupine understands, you'll pass in a different model file.

const string accessKey = "${ACCESS_KEY}";
var keywords = new List<BuiltInKeyword> { 
        BuiltInKeyword.GRAPEFRUIT,
        BuiltInKeyword.PORCUPINE 
    };
string modelPath = "/path/to/model.pv"
var sensitivities = new List<float>{ 0.6f, 0.35f };

Porcupine handle = Porcupine.FromBuiltInKeywords(
    accessKey,
    keywords,
    modelPath: modelPath,
    sensitivities: sensitivities);

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)
{
    var keywordIndex = handle.Process(GetNextAudioFrame());
    if(keywordIndex >= 0)
    {
	    // .. detection event logic/callback
    }
}

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
}

Non-English Wake Words

In order to detect non-English wake words you need to use the corresponding model file. The model files for all supported languages are available here.

Demos

The Porcupine dotnet demo project is a .NET Core command line application that allows for processing real-time audio (i.e. microphone) and files using Porcupine.