• High-quality speech recognition using WhisperKit
• Multiple model sizes (216MB to 955MB) for different accuracy/speed trade-offs
• Support for multiple languages with auto-detection
• Real-time waveform visualization during recording

• Local LLM processing for grammar correction and text improvement
• Multiple AI models including Gemma, Llama, Qwen, and Mistral
• Custom prompts for different use cases:
  • Grammar fixing and email formatting
  • Language translation
  • Custom text processing workflows

• 100% local processing - your voice never leaves your device
• No cloud services, no data collection
• Open source - audit the code yourself
• Secure sandboxed environment

• Global hotkey support (Option+Space by default)
• Auto-copy to clipboard
• Auto-paste functionality
• Auto-enter for instant message sending
• Menu bar integration
• Auto-stop recording after 10 minutes

• Beautiful dark-themed interface
• Real-time recording visualization
• Comprehensive onboarding guide
• Easy model management and downloads
• Customizable shortcuts and prompts

• macOS 14.0 or later
• 20GB free disk space (for AI models)
• Microphone access permission
• Accessibility permissions (for global hotkeys)
• Apple Events permissions (for clipboard operations)

1. Visit whisperclip.com
2. Download the latest release
3. Drag WhisperClip.app to your Applications folder
4. Follow the setup guide for permissions

# Clone the repository
git clone https://github.com/cydanix/whisperclip.git
cd whisperclip

# Build the app
./build.sh

# For development
./local_build.sh Debug
./local_run.sh Debug

1. Launch WhisperClip from Applications or menu bar
2. Grant permissions when prompted (microphone, accessibility)
3. Download AI models through the setup guide
4. Press Option+Space (or click Record) to start recording
5. Press again to stop - text will be automatically copied to clipboard

• Change hotkey: Settings → Hotkey preferences
• Add custom prompts: Settings → Prompts → Add new prompt
• Switch AI models: Setup Guide → Download different models
• Configure auto-actions: Settings → Enable auto-paste/auto-enter

• OpenAI Whisper Small (216MB) - Fast, good quality
• OpenAI Whisper Large v3 Turbo (632MB) - Best balance
• Distil Whisper Large v3 Turbo (600MB) - Optimized speed
• OpenAI Whisper Large v2 Turbo (955MB) - Maximum accuracy

• Gemma 2 (2B/9B) - Google's efficient models
• Llama 3/3.2 (3B/8B) - Meta's powerful models
• Qwen 2.5/3 (1.5B-8B) - Alibaba's multilingual models
• Mistral 7B - High-quality French company model
• Phi 3.5 Mini - Microsoft's compact model
• DeepSeek R1 - Advanced reasoning model

All models run locally using MLX for Apple Silicon optimization.

WhisperClip is designed with privacy as the cornerstone:

• Local Processing Only: All voice recognition and AI processing happens on your device
• No Network Requests: Except for downloading models from Hugging Face
• No Analytics: No usage tracking, no telemetry, no data collection
• Open Source: Full transparency - inspect the code yourself
• Sandboxed: Runs in Apple's secure app sandbox
• Encrypted Storage: AI models stored securely on device

Sources/
├── WhisperClip.swift      # Main app entry point
├── ContentView.swift      # Main UI interface
├── AudioRecorder.swift    # Voice recording logic
├── VoiceToText*.swift     # Transcription engine
├── LLM*.swift            # AI text enhancement
├── ModelStorage.swift     # Model management
├── SettingsStore.swift    # User preferences
└── HotkeyManager.swift    # Global shortcuts

• WhisperKit: Apple's optimized Whisper implementation
• MLX: Apple Silicon ML framework
• MLX-Swift-Examples: LLM implementations
• Hub: Hugging Face model downloads

# Debug build
./local_build.sh Debug

# Release build with code signing
./build.sh

# Notarization (requires Apple Developer account)
./notarize.sh

We welcome contributions! Please see our contributing guidelines:

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

• New AI model integrations
• UI/UX improvements
• Performance optimizations
• Language support
• Accessibility features
• Documentation improvements

WhisperClip is licensed under the MIT License - see the LICENSE file for details.

This means you can:

• ✅ Use commercially
• ✅ Modify and distribute
• ✅ Use privately
• ✅ Fork and create derivatives

Attribution required: Please include the original license notice.

WhisperClip is developed by Cydanix LLC.

• Website: whisperclip.com
• Support: support@cydanix.com
• Version: 1.0.43

• Apple - WhisperKit and MLX frameworks
• OpenAI - Original Whisper models
• Hugging Face - Model hosting and Hub library
• ML Community - Open source AI models (Gemma, Llama, Qwen, etc.)