# Installing Whisper for Local Transcription

This guide explains how to install OpenAI's Whisper for local speech-to-text transcription.

## Prerequisites

- Python 3.8 or newer
- pip (Python package manager)
- ffmpeg

## Installation Steps

### 1. Install Python Dependencies

```bash
# Install Whisper
pip install openai-whisper

# Or install with specific version
pip install openai-whisper==20230918
```

### 2. Install ffmpeg

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

#### macOS (using Homebrew):
```bash
brew install ffmpeg
```

#### Windows:
1. Download ffmpeg from https://ffmpeg.org/download.html
2. Extract the archive
3. Add the bin folder to your PATH

### 3. Verify Installation

```bash
# Test Whisper installation
whisper --help

# Test with an audio file
whisper audio.mp3 --model base
```

## Available Models

Whisper offers several models with different speed/accuracy tradeoffs:

| Model  | Parameters | Relative Speed | Required VRAM |
|--------|------------|----------------|---------------|
| tiny   | 39 M       | ~32x           | ~1 GB         |
| base   | 74 M       | ~16x           | ~1 GB         |
| small  | 244 M      | ~6x            | ~2 GB         |
| medium | 769 M      | ~2x            | ~5 GB         |
| large  | 1550 M     | 1x             | ~10 GB        |

## Configuration

Set your preferred model in the `.env` file:

```env
WHISPER_MODEL=base  # Options: tiny, base, small, medium, large
```

## GPU Acceleration Setup

### NVIDIA GPUs (CUDA)
```bash
# Install PyTorch with CUDA support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# Verify CUDA is available
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
```

### AMD GPUs (ROCm) - Linux Only
```bash
# Install ROCm (Ubuntu/Debian)
wget -q -O - https://repo.radeon.com/rocm/rocm.gpg.key | sudo apt-key add -
echo 'deb [arch=amd64] https://repo.radeon.com/rocm/apt/debian/ ubuntu main' | sudo tee /etc/apt/sources.list.d/rocm.list
sudo apt update
sudo apt install rocm-dkms

# Install PyTorch with ROCm support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2

# Add user to render/video groups
sudo usermod -a -G render,video $USER
# Logout and login for group changes to take effect
```

### Intel GPUs (OpenVINO)
```bash
# Install OpenVINO toolkit
pip install openvino-dev[pytorch]

# For Arc GPUs, you may need the compute runtime
# Ubuntu/Debian:
wget -qO - https://repositories.intel.com/graphics/intel-graphics.key | sudo apt-key add -
sudo apt-add-repository 'deb [arch=amd64] https://repositories.intel.com/graphics/ubuntu focal main'
sudo apt update
sudo apt install intel-opencl-icd intel-level-zero-gpu level-zero

# Note: You may need a Whisper fork with OpenVINO support
pip install git+https://github.com/openvinotoolkit/whisper.git
```

## Troubleshooting

### Issue: "whisper: command not found"
- Make sure Python's Scripts directory is in your PATH
- On Windows: `C:\Users\[Username]\AppData\Local\Programs\Python\Python3X\Scripts`
- On macOS/Linux: `~/.local/bin`

### Issue: GPU not detected
- Use the "Detect GPU" button in the app's settings page
- For NVIDIA: Ensure `nvidia-smi` command works
- For AMD: Ensure `rocm-smi` command works
- For Intel: Check `lspci | grep -i intel` shows your GPU

### Issue: CUDA out of memory
- Try using a smaller Whisper model (tiny or base)
- Reduce batch size or use CPU for very long audio

### Issue: Audio Format Not Supported
Whisper works best with WAV, MP3, and M4A files. The app automatically converts audio to a compatible format.

## Alternative: Docker Installation

You can also run Whisper in a Docker container:

```bash
docker pull openai/whisper
docker run -it -v $(pwd):/app openai/whisper audio.mp3 --model base
```