Video Transcriber App

A powerful desktop application that converts video files into accurate text transcripts using OpenAI's Whisper AI model. Features a modern GUI built with PyQt6, batch processing capabilities, and advanced text post-processing with filler word removal.

✨ Features

• 🎥 Multi-Format Support: Process MP4, AVI, MKV, MOV, WEBM, and MP3 files
• 🚀 GPU Acceleration: Automatic CUDA detection for 10-20x faster processing
• 📦 Batch Processing: Queue multiple files for automated transcription
• 🧹 Advanced Text Processing:
  • Automatic filler word removal ("um", "uh", "like", "you know")
  • Smart punctuation and capitalization
  • Paragraph formatting for readability
• 🎯 Flexible Model Selection: Choose from tiny, base, small, medium, or large Whisper models
• 💾 Custom Model Loading: Load pre-downloaded models to work offline
• 📊 Real-time Progress: Track processing with time estimates and progress bars
• ⏸️ Pause/Resume: Control processing without losing progress
• 🎨 Modern UI: Clean, intuitive interface with drag-and-drop support

📥 Installation

Option 1: Download Pre-built Executable (Windows)

1. Download the latest VideoTranscriber.exe from the Releases page
2. Download Whisper model files (see Model Setup below)
3. Run VideoTranscriber.exe

Option 2: Run from Source

Prerequisites

• Python 3.11 or higher
• NVIDIA GPU (optional, for faster processing)
• CUDA 11.8 or 12.1 (if using GPU)

Step 1: Clone the Repository

git clone https://github.com/yourusername/video-transcriber.git
cd video-transcriber

Step 2: Create Virtual Environment

python -m venv venv

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Install PyTorch with CUDA (for GPU support)

# For CUDA 11.8
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

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

# For CPU only
pip install torch torchvision torchaudio

Step 5: Run the Application

python run.py

🎯 Model Setup

Understanding Whisper Models

The app uses OpenAI's Whisper models for transcription. Each model size offers different trade-offs:

Model	Parameters	Speed	Quality	Download Size
tiny	39M	Very Fast	Basic	~39 MB
base	74M	Fast	Good	~74 MB
small	244M	Moderate	Better	~244 MB
medium	769M	Slow	Very Good	~769 MB
large	1550M	Very Slow	Best	~1.5 GB

Automatic Model Download

On first use, the app will automatically download the selected model from OpenAI (requires internet connection).

Manual Model Setup (Recommended for Offline Use)

1. Download Model Files

   • Download .pt files from OpenAI Whisper
   • Or from Hugging Face

2. Place Models in a Folder

   C:\WhisperModels\
   ├── tiny.pt
   ├── base.pt
   ├── small.pt
   ├── medium.pt
   └── large-v3.pt
   

3. Load in Application

   • Click "Load Model Folder" button
   • Navigate to your models folder
   • Select the folder containing .pt files
   • The app will remember this location

🚀 Usage Guide

Basic Workflow

1. Start the Application

   • Run VideoTranscriber.exe or python run.py

2. Configure Settings

   • Select output directory for transcripts
   • Choose Whisper model size (larger = better quality, slower)
   • (Optional) Load custom model folder

3. Add Videos

   • Click "Add Files" to select videos
   • Or "Add Directory" to process entire folders
   • Or drag and drop files directly

4. Process Videos

   • Click "Start Processing"
   • Monitor progress in real-time
   • Pause/resume as needed

5. Get Results

   • Transcripts saved as .txt files
   • Same filename as video with .txt extension
   • Located in your selected output directory

Advanced Features

GPU Acceleration

The app automatically detects and uses NVIDIA GPUs. Check status in console output:

• Model loaded successfully on cuda = GPU active ✅
• Model loaded successfully on cpu = CPU only ⚠️

Batch Processing Tips

• Queue processes videos in order (FIFO)
• Each video's transcript is saved immediately upon completion
• Failed videos don't stop the queue
• Time estimates improve as more videos are processed

Text Processing Options

The app automatically:

• Removes filler words while preserving meaning
• Adds proper punctuation and capitalization
• Creates readable paragraphs
• Fixes common transcription errors

🔧 Building from Source

Creating Executable

1. Install PyInstaller

   pip install pyinstaller

2. Run Build Script

   # Windows
   build_exe.bat
   
   # Or manually
   pyinstaller VideoTranscriber.spec --clean

3. Find Executable

   • Located in dist/VideoTranscriber.exe
   • Single file, ready for distribution

Customizing Build

Edit VideoTranscriber.spec to:

• Add custom icon
• Include additional files
• Modify build options

🐛 Troubleshooting

Common Issues

"No model found" error

• Ensure .pt files are in the selected folder
• File names should contain model size (e.g., large.pt, large-v3.pt)

Slow processing on CPU

• Install CUDA-enabled PyTorch (see installation)
• Use smaller model (base or small)
• Check GPU is detected in console output

"CUDA out of memory" error

• Use smaller model
• Close other GPU applications
• Process shorter videos

Transcription has repeated text

• App includes automatic repetition removal
• Update to latest version
• Report persistent issues

Performance Tips

1. For Speed: Use GPU + smaller models (base/small)
2. For Quality: Use large model with GPU
3. For Long Videos: Videos auto-split into segments
4. For Batch Processing: Queue overnight with large model

📁 Project Structure

video-transcriber/
├── src/
│   ├── ui/                    # GUI components
│   ├── transcription/          # Whisper integration
│   ├── audio_processing/       # Video/audio conversion
│   ├── post_processing/        # Text enhancement
│   ├── input_handling/         # File management
│   └── config/                 # Settings management
├── run.py                      # Application entry point
├── requirements.txt            # Python dependencies
├── VideoTranscriber.spec       # PyInstaller configuration
└── build_exe.bat              # Build script

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• OpenAI Whisper for the amazing transcription model
• PyQt6 for the GUI framework
• MoviePy for video processing
• PyTorch for ML framework

💬 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions

🚀 Roadmap

• Support for more video formats
• Real-time transcription preview
• Speaker diarization
• Multiple language support
• Cloud processing option
• Export to SRT/VTT subtitles
• Integration with video editing software