# Building Local Transcription

This guide explains how to build standalone executables for Linux and Windows.

## Prerequisites

1. **Python 3.8+** installed on your system
2. **uv** package manager (install from https://docs.astral.sh/uv/)
3. All project dependencies installed (`uv sync`)

## Building for Linux

### Standard Build (CPU-only):

```bash
# Make the build script executable (first time only)
chmod +x build.sh

# Run the build script
./build.sh
```

### CUDA Build (GPU Support):

Build with CUDA support even without NVIDIA hardware:

```bash
# Make the build script executable (first time only)
chmod +x build-cuda.sh

# Run the CUDA build script
./build-cuda.sh
```

This will:
- Install PyTorch with CUDA 12.1 support
- Bundle CUDA runtime libraries (~600MB extra)
- Create an executable that works on both GPU and CPU systems
- Automatically fall back to CPU if no CUDA GPU is available

The executable will be created in `dist/LocalTranscription/LocalTranscription`

### Manual build:
```bash
# Clean previous builds
rm -rf build dist

# Build with PyInstaller
uv run pyinstaller local-transcription.spec
```

### Distribution:
```bash
cd dist
tar -czf LocalTranscription-Linux.tar.gz LocalTranscription/
```

## Building for Windows

### Standard Build (CPU-only):

```cmd
# Run the build script
build.bat
```

### CUDA Build (GPU Support):

Build with CUDA support even without NVIDIA hardware:

```cmd
# Run the CUDA build script
build-cuda.bat
```

This will:
- Install PyTorch with CUDA 12.1 support
- Bundle CUDA runtime libraries (~600MB extra)
- Create an executable that works on both GPU and CPU systems
- Automatically fall back to CPU if no CUDA GPU is available

The executable will be created in `dist\LocalTranscription\LocalTranscription.exe`

### Manual build:
```cmd
# Clean previous builds
rmdir /s /q build
rmdir /s /q dist

# Build with PyInstaller
uv run pyinstaller local-transcription.spec
```

### Distribution:
- Compress the `dist\LocalTranscription` folder to a ZIP file
- Or use an installer creator like NSIS or Inno Setup

## Important Notes

### Cross-Platform Building

**You cannot cross-compile!**
- Linux executables must be built on Linux
- Windows executables must be built on Windows
- Mac executables must be built on macOS

### First Run

On the first run, the application will:
1. Create a config directory at `~/.local-transcription/` (Linux) or `%USERPROFILE%\.local-transcription\` (Windows)
2. Download the Whisper model (if not already present)
3. The model will be cached in `~/.cache/huggingface/` by default

### Executable Size

The built executable will be large (300MB - 2GB+) because it includes:
- Python runtime
- PySide6 (Qt framework)
- PyTorch/faster-whisper
- NumPy, SciPy, and other dependencies

### Console Window

By default, the console window is visible (for debugging). To hide it:

1. Edit `local-transcription.spec`
2. Change `console=True` to `console=False` in the `EXE` section
3. Rebuild

### GPU Support

#### Building with CUDA (Recommended for Distribution)

**Yes, you CAN build with CUDA support on systems without NVIDIA GPUs!**

PyTorch provides CUDA-enabled builds that bundle the CUDA runtime libraries. This means:

1. **You don't need NVIDIA hardware** to create CUDA-enabled builds
2. **The executable will work everywhere** - on systems with or without NVIDIA GPUs
3. **Automatic fallback** - the app detects available hardware and uses GPU if available, CPU otherwise
4. **Larger file size** - adds ~600MB-1GB to the executable size

**How it works:**
```bash
# Linux
./build-cuda.sh

# Windows
build-cuda.bat
```

The build script will:
- Install PyTorch with bundled CUDA 12.1 runtime
- Package all CUDA libraries into the executable
- Create a universal build that runs on any system

**When users run the executable:**
- If they have an NVIDIA GPU with drivers: Uses GPU acceleration
- If they don't have NVIDIA GPU: Automatically uses CPU
- No configuration needed - it just works!

#### Alternative: CPU-Only Builds

If you only want CPU support (smaller file size):
```bash
# Linux
./build.sh

# Windows
build.bat
```

#### AMD GPU Support

- **ROCm**: Requires special PyTorch builds from AMD
- Not recommended for general distribution
- Better to use CUDA build (works on all systems) or CPU build

### Optimizations

To reduce size:

1. **Remove unused model sizes**: The app downloads models on-demand, so you don't need to bundle them
2. **Use UPX compression**: Already enabled in the spec file
3. **Exclude dev dependencies**: Only build dependencies are needed

## Testing the Build

After building, test the executable:

### Linux:
```bash
cd dist/LocalTranscription
./LocalTranscription
```

### Windows:
```cmd
cd dist\LocalTranscription
LocalTranscription.exe
```

## Troubleshooting

### Missing modules error
If you get "No module named X" errors, add the module to the `hiddenimports` list in `local-transcription.spec`

### DLL errors (Windows)
Make sure Visual C++ Redistributable is installed on the target system:
https://aka.ms/vs/17/release/vc_redist.x64.exe

### Audio device errors
The application needs access to audio devices. Ensure:
- Microphone permissions are granted
- Audio drivers are installed
- PulseAudio (Linux) or Windows Audio is running

### Model download fails
Ensure internet connection on first run. Models are downloaded from:
https://huggingface.co/guillaumekln/faster-whisper-base

## Advanced: Adding an Icon

1. Create or obtain an `.ico` file (Windows) or `.png` file (Linux)
2. Edit `local-transcription.spec`
3. Change `icon=None` to `icon='path/to/your/icon.ico'`
4. Rebuild

## Advanced: Creating an Installer

### Windows (using Inno Setup):

1. Install Inno Setup: https://jrsoftware.org/isinfo.php
2. Create an `.iss` script file
3. Build the installer

### Linux (using AppImage):

```bash
# Install appimagetool
wget https://github.com/AppImage/AppImageKit/releases/download/continuous/appimagetool-x86_64.AppImage
chmod +x appimagetool-x86_64.AppImage

# Create AppDir structure
mkdir -p LocalTranscription.AppDir/usr/bin
cp -r dist/LocalTranscription/* LocalTranscription.AppDir/usr/bin/

# Create desktop file and icon
# (Create .desktop file and icon as needed)

# Build AppImage
./appimagetool-x86_64.AppImage LocalTranscription.AppDir
```

## Support

For build issues, check:
1. PyInstaller documentation: https://pyinstaller.org/
2. Project issues: https://github.com/anthropics/claude-code/issues