local-transcription/INSTALL.md

# Installation Guide

## Prerequisites

- **Python 3.9 or higher**
- **uv** (Python package installer)
- **FFmpeg** (required by faster-whisper)
- **CUDA-capable GPU** (optional, for GPU acceleration)

### Installing uv

If you don't have `uv` installed:

```bash
# On macOS and Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or with pip
pip install uv
```

### Installing FFmpeg

#### On Ubuntu/Debian:
```bash
sudo apt update
sudo apt install ffmpeg
```

#### On macOS (with Homebrew):
```bash
brew install ffmpeg
```

#### On Windows:
Download from [ffmpeg.org](https://ffmpeg.org/download.html) and add to PATH.

## Installation Steps

### 1. Navigate to Project Directory

```bash
cd /home/jknapp/code/local-transcription
```

### 2. Install Dependencies with uv

```bash
# uv will automatically create a virtual environment and install dependencies
uv sync
```

This single command will:
- Create a virtual environment (`.venv/`)
- Install all dependencies from `pyproject.toml`
- Lock dependencies for reproducibility

**Note for CUDA users:** If you have an NVIDIA GPU, install PyTorch with CUDA support:

```bash
# For CUDA 11.8
uv pip install torch --index-url https://download.pytorch.org/whl/cu118

# For CUDA 12.1
uv pip install torch --index-url https://download.pytorch.org/whl/cu121
```

### 3. Run the Application

```bash
# Option 1: Using uv run (automatically uses the venv)
uv run python main.py

# Option 2: Activate venv manually
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
python main.py
```

On first run, the application will:
- Download the Whisper model (this may take a few minutes)
- Create a configuration file at `~/.local-transcription/config.yaml`

## Quick Start Commands

```bash
# Install everything
uv sync

# Run the application
uv run python main.py

# Install with server dependencies (for Phase 2+)
uv sync --extra server

# Update dependencies
uv sync --upgrade
```

## Configuration

Settings can be changed through the GUI (Settings button) or by editing:
```
~/.local-transcription/config.yaml
```

## Troubleshooting

### Audio Device Issues

If no audio devices are detected:
```bash
uv run python -c "import sounddevice as sd; print(sd.query_devices())"
```

### GPU Not Detected

Check if CUDA is available:
```bash
uv run python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
```

### Model Download Fails

Models are downloaded to `~/.cache/huggingface/`. If download fails:
- Check internet connection
- Ensure sufficient disk space (~1-3 GB depending on model size)

### uv Command Not Found

Make sure uv is in your PATH:
```bash
# Add to ~/.bashrc or ~/.zshrc
export PATH="$HOME/.cargo/bin:$PATH"
```

## Performance Tips

For best real-time performance:

1. **Use GPU if available** - 5-10x faster than CPU
2. **Start with smaller models**:
   - `tiny`: Fastest, ~39M parameters, 1-2s latency
   - `base`: Good balance, ~74M parameters, 2-3s latency
   - `small`: Better accuracy, ~244M parameters, 3-5s latency
3. **Enable VAD** (Voice Activity Detection) to skip silent audio
4. **Adjust chunk duration**: Smaller = lower latency, larger = better accuracy

## System Requirements

### Minimum:
- CPU: Dual-core 2GHz+
- RAM: 4GB
- Model: tiny or base

### Recommended:
- CPU: Quad-core 3GHz+ or GPU (NVIDIA GTX 1060+)
- RAM: 8GB
- Model: base or small

### For Best Performance:
- GPU: NVIDIA RTX 2060 or better
- RAM: 16GB
- Model: small or medium

## Development

### Install development dependencies:
```bash
uv sync --extra dev
```

### Run tests:
```bash
uv run pytest
```

### Format code:
```bash
uv run black .
uv run ruff check .
```

## Why uv?

`uv` is significantly faster than pip:
- **10-100x faster** dependency resolution
- **Automatic virtual environment** management
- **Reproducible builds** with lockfile
- **Drop-in replacement** for pip commands

Learn more at [astral.sh/uv](https://astral.sh/uv)