Enhance MLX and PyTorch Backend Integration

- Added support for MLX backend on Apple Silicon, enabling optimized performance for TTS and STT tasks.
- Implemented platform detection to dynamically select between MLX and PyTorch based on the runtime environment.
- Updated build process to include MLX-specific dependencies and configurations for macOS.
- Refactored backend code to improve model loading and inference logic, accommodating backend-specific requirements.
- Enhanced documentation to clarify backend selection and performance benefits for different platforms.
- Streamlined installation instructions and troubleshooting guidance for MLX-related issues.

docs/developer/setup.mdx

@@ -80,19 +80,16 @@ venv\Scripts\activate  # Windows
 # Install Python dependencies
 pip install -r requirements.txt
 
+# Install MLX dependencies (Apple Silicon only - for faster inference)
+# On Apple Silicon, this enables native Metal acceleration
+if [[ $(uname -m) == "arm64" ]]; then
+  pip install -r requirements-mlx.txt
+fi
+
 # Install Qwen3-TTS
 pip install git+https://github.com/QwenLM/Qwen3-TTS.git
 ```
 
-### 3. Initialize Database
-
-```bash
-cd backend
-python -c "from database import init_db; init_db()"
-```
-
-This creates the SQLite database at `data/voicebox.db`.
-
 ## Running in Development
 
 Development requires **two terminals**: one for the Python backend, one for the Tauri app.