Winky

Your cute voice assistant powered by AI

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About Winky

Winky is a cross-platform desktop voice assistant that helps you quickly convert voice to text and run intelligent LLM-powered actions. With a convenient floating microphone overlay, you can interact with Winky from anywhere on your screen, making it perfect for productivity workflows.

Winky supports both cloud-based and local AI processing, giving you the flexibility to choose between speed and privacy. Whether you need quick voice commands, transcription, or AI-powered responses, Winky is ready to help.

Google Chrome Extension:

• Extension repository: https://github.com/Artasov/winky-ext
• Chrome Web Store: https://chromewebstore.google.com/detail/winky/mpinlhhkmpljjlcekiocnglfbfpamkjl

About This Repository

This repository contains the source code for Winky, a cross-platform desktop application built with Tauri + React + Vite. The application provides a modern, efficient voice assistant experience with support for multiple AI providers and local processing options.

Table of Contents

• Key Features
• How to Use
• How to Use Locally
• For Developers

🚀 Key Features

• FREE USAGE - no subscription required, no limits for local processing
• Voice Recognition - advanced speech-to-text conversion with multiple AI models
• LLM Processing - intelligent AI-powered actions and responses
• Floating Microphone - convenient floating microphone overlay for quick access
• Local Speech Recognition - use local AI models for faster processing and privacy
• Quick Actions - customizable hotkeys and actions for productivity
• Privacy & Security - all data processed locally, audio is not stored
• Cross-platform - works on Windows, macOS and Linux
• Simple interface - intuitive and easy to use
• Customizable - configure transcription models, LLM providers, and actions

If you have any issues using the app, please open an issue

🎯 How to Use

1. Setup

1. Open Winky application
2. Complete the initial setup wizard:
   • Sign in with your account (OAuth authentication)
   • Configure your API keys:
     • OpenAI API key (get it from platform.openai.com)
     • Google AI API key (get it from console.cloud.google.com)
3. Choose your speech recognition mode:
   • Cloud - use cloud-based transcription (OpenAI Whisper, Google AI)
   • Local - use local fast-whisper for privacy and speed
4. Configure LLM settings:
   • Choose your preferred LLM provider
   • Select the model suitable for your needs
5. Set up quick actions:
   • Configure custom hotkeys for actions
   • Create and customize your action workflows

2. Usage

1. Use the floating microphone overlay to start voice recognition
2. Speak your command or question
3. Get instant AI-powered responses and actions
4. Use hotkeys for quick access to common actions
5. Access your profile, actions, and settings from the main window

3. Usage Tips

• Position the floating microphone overlay where it's convenient for you
• Customize hotkeys to match your workflow
• Use local speech recognition for better privacy
• Practice with different commands to get the best results

How to Use Locally

The examples below are implemented and tested on Windows 11. Steps may differ on other systems.

The assistant works in two stages:

1. Audio transcription

2. Getting an answer from the LLM

Each stage can be run locally.

Local Speech Recognition

1. In Winky settings select Mode -> Speech Recognition = Local.

2. In Winky settings choose one of Model -> Speech Recognition

3. In Winky settings choose Local transcription device: GPU (Graphics/NVIDIA) or CPU (Processor)

The local speech recognition server will be automatically installed and managed by Winky.

Local LLM Processing

Minimum recommended configuration:

• CPU - 4 cores / 8 threads
• GPU - 6 GB VRAM
• RAM - 16 GB

1. In Winky settings select Mode -> LLM = Local.

2. In Winky settings choose a Model -> LLM from the available models (Ollama models)

3. Install Ollama

   https://ollama.com/

4. Download the model chosen earlier

   ollama pull <model-name>

5. Start Ollama

   ollama serve

The first use after the opening of the program will be slower, since with local use of the AI models will be loaded in GPU or RAM, which takes time. Before important tasks, do a test run so that the subsequent calls are faster.

🔧 For Developers

Contributing

We welcome contributions to the project! If you want to contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

See CONTRIBUTING.md for detailed guidelines.

Local Development

Requirements

• Node.js 20+ (LTS)
• Rust 1.80+ (for building Tauri)
• npm or yarn

Installation

# Clone the repository
git clone https://github.com/placeholder/winky.git
cd winky

# Install dependencies
npm install

# Build the project
npm run build

# Run in development mode
npm run dev

Project Structure

src/
├── renderer/       # React renderer process (UI)
│   ├── app/        # Application logic and hooks
│   ├── components/ # React components
│   ├── context/    # React context providers
│   ├── features/   # Feature modules
│   ├── services/   # API and service layer
│   ├── windows/    # Window components
│   └── ...
├── shared/         # Shared types and utilities
└── ...
src-tauri/
├── src/             # Rust backend (Tauri)
└── ...

Available Commands

• npm run dev - run in development mode
• npm run build - build the project
• npm run build:renderer - build only the renderer (frontend)
• npm run dev:renderer - run renderer dev server only
• npm run lint - check TypeScript types
• npm run typecheck - same as lint
• npm run preview - preview built frontend

Building for Different Platforms

Windows

npm run build

Creates:

• Portable executable in src-tauri/target/release/

macOS

npm run build

Creates:

• DMG archive for Intel and Apple Silicon

Note: For macOS builds, you may need to:

1. Install Xcode Command Line Tools: xcode-select --install

Linux

npm run build

Creates:

• Portable directory in src-tauri/target/release/

Technologies

• Tauri - cross-platform desktop application framework
• React - UI library
• TypeScript - typed JavaScript
• Tailwind CSS - utility-first CSS framework
• Vite - build tool and dev server
• OpenAI API - AI integration
• Google AI API - AI integration

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