Voice Assistant

A modular voice assistant built with C#, Avalonia UI, and Azure AI services(Replaceable).

Features

• Voice recognition with Azure Speech Services
• Natural language processing with Azure Language Understanding
• Text-to-speech capabilities
• Cross-platform UI with Avalonia
• Runs on macOS and Windows with platform-specific services
• Built-in commands for screenshots, folder opening and time queries
• Clean architecture design

Azure Language Service Setup

Step 1: Create Azure Language Service Resource

1. Go to the Azure Portal
2. Create a new "Language Service" resource
3. Select the appropriate subscription, resource group, and region
4. Complete the resource creation process

Step 2: Create a Conversational Language Understanding Project

1. Go to Language Studio
2. Sign in with your Azure account
3. Create a new "Conversational Language Understanding" project
4. Name your project "VoiceAssistant" (or update the config accordingly)

Step 3: Define Intents and Entities

Create the following intents:

1. TakeScreenshot

   • Add example utterances like:
     • "Take a screenshot"
     • "Capture my screen"
     • "Take screenshot"
     • "Screenshot please"

2. GetTime

   • Add example utterances like:
     • "What time is it"
     • "Tell me the current time"
     • "What's the time"
     • "Current time please"

3. OpenFolder

   • Add example utterances like:
     • "Open folder Documents"
     • "Browse to Downloads"
     • "Show me the Pictures folder"
   • Add an entity "FolderName" to extract the folder name from utterances

Step 4: Train and Deploy Your Model

1. Train your model
2. Deploy it with the name "production" (or update the config accordingly)

Step 5: Update Configuration

Create the .env file with your Azure service keys:

1. Open .env.example
2. Create .env based on it (or simply rename)
3. Replace the placeholder values with your actual Azure keys and endpoints.

Local Development

For local development, you can set useMockServices = true in Program.cs to use mock implementations instead of making actual Azure API calls.

Architecture

The application follows a clean architecture approach:

• VoiceAssistant.Domain: Core entities and interfaces
• VoiceAssistant.Application: Application logic and use cases
• VoiceAssistant.Infrastructure: External services implementation
• VoiceAssistant.UI: User interface with Avalonia

Extensibility

This project is designed with extensibility in mind through the use of abstractions and dependency injection:

For example, to implement a different speech recognition service:

1. Create a new class implementing ISpeechRecognitionService
2. Register your implementation in the dependency injection container
3. No other code changes required!

Running the Application

1. Set up your Azure services as described above
2. Create the .env with your API keys based on .env.example
3. Build and run the application:

dotnet run --project VoiceAssistant.UI

Running Tests

Run unit tests with:

dotnet test