YouLama

A powerful web application for transcribing and summarizing YouTube videos and local media files using faster-whisper and Ollama.

Features

• 🎥 YouTube video transcription with subtitle extraction
• 🎙️ Local audio/video file transcription
• 🤖 Automatic language detection
• 📝 Multiple Whisper model options
• 📚 AI-powered text summarization using Ollama
• 🎨 Modern web interface with Gradio
• 🐳 Docker support with CUDA
• ⚙️ Configurable settings via config.ini

Requirements

• Docker and Docker Compose
• NVIDIA GPU with CUDA support
• NVIDIA Container Toolkit
• Ollama installed locally (optional, for summarization)

Installation

1. Clone the repository:

git clone <repository-url>
cd youlama

2. Install NVIDIA Container Toolkit (if not already installed):

# Add NVIDIA package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# Install nvidia-docker2 package
sudo apt-get update
sudo apt-get install -y nvidia-docker2

# Restart the Docker daemon
sudo systemctl restart docker

3. Install Ollama locally (optional, for summarization):

curl https://ollama.ai/install.sh | sh

4. Copy the example configuration file:

cp .env.example .env

5. Edit the configuration files:

• .env: Set your environment variables
• config.ini: Configure Whisper, Ollama, and application settings

Running the Application

1. Start Ollama locally (if you want to use summarization):

ollama serve

2. Build and start the YouLama container:

docker-compose up --build

3. Open your web browser and navigate to:

http://localhost:7860

Configuration

Environment Variables (.env)

# Server configuration
SERVER_NAME=0.0.0.0
SERVER_PORT=7860
SHARE=true

Application Settings (config.ini)

[whisper]
default_model = base
device = cuda
compute_type = float16
beam_size = 5
vad_filter = true

[app]
max_duration = 3600
server_name = 0.0.0.0
server_port = 7860
share = true

[models]
available_models = tiny,base,small,medium,large-v1,large-v2,large-v3

[languages]
available_languages = en,es,fr,de,it,pt,nl,ja,ko,zh

[ollama]
enabled = false
url = http://host.docker.internal:11434
default_model = mistral
summarize_prompt = Please provide a comprehensive yet concise summary of the following text. Focus on the main points, key arguments, and important details while maintaining accuracy and completeness. Here's the text to summarize: 

Features in Detail

YouTube Video Processing

• Supports youtube.com, youtu.be, and invidious URLs
• Automatically extracts subtitles if available
• Falls back to transcription if no subtitles found
• Optional AI-powered summarization with Ollama

Local File Transcription

• Supports various audio and video formats
• Automatic language detection
• Multiple Whisper model options
• Optional AI-powered summarization with Ollama

AI Summarization

• Uses locally running Ollama for text summarization
• Configurable model selection
• Customizable prompt
• Available for both local files and YouTube videos

Tips

• For better accuracy, use larger models (medium, large)
• Processing time increases with model size
• GPU is recommended for faster processing
• Maximum audio duration is configurable (default: 60 minutes)
• YouTube videos will first try to use available subtitles
• If no subtitles are available, the video will be transcribed
• Ollama summarization is optional and requires Ollama to be running locally
• The application runs in a Docker container with CUDA support
• Models are downloaded and cached in the models directory
• The container connects to the local Ollama instance using host.docker.internal

License

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