Awesome

YouTube Summarizer by TCSenpai

YouTube Summarizer is a Streamlit-based web application that allows users to generate summaries of YouTube videos using AI-powered language models and optionally Whisper for transcription.

• YouTube Summarizer by TCSenpai
  • Features
  • Installation
  • Usage
  • Global Installation
  • Run with the included binary
  • Dependencies
  • Project Structure
  • Contributing
  • License
  • Credits

Features

• Supports multiple YouTube frontends (e.g. YouTube, Invidious, etc.)
• Fetch and cache YouTube video transcripts
• Summarize video content using Ollama AI models
• Display video information (title and channel)
• Customizable Ollama URL and model selection
• Fallback to Whisper for transcription if no transcript is found
• Customizable Whisper URL and model selection
• Optional force Whisper transcription

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/youtube-summarizer.git
   cd youtube-summarizer
   

2. Install the required dependencies:

   2a. Using pip:

   pip install -r requirements.txt
   

   2b. Using conda:

   conda env create -f environment.yml
   

   Note: You might need to install conda first.

3. Set up environment variables: Create a .env file in the root directory and add the following:

   YOUTUBE_API_KEY=your_youtube_api_key
   OLLAMA_MODEL=default_model_name
   WHISPER_URL=http://localhost:8000/
   WHISPER_MODEL=Systran/faster-whisper-large-v3
   PASTEBIN_API_KEY=your_pastebin_api_key
   

   • Note: you can copy the env.example file to .env and modify the values.
   • Important: the WHISPER_URL should point to the whisper server you want to use. You can leave it as it is if you are not planning on using Whisper.
   • Important: the PASTEBIN_API_KEY is optional, but if you want to use it, you need to get one from Pastebin.

Usage

1. Run the Streamlit app:

   streamlit run src/main.py
   

2. Open your web browser and navigate to the provided local URL (usually http://localhost:8501).

3. Enter a YouTube video URL in the input field.

4. (Optional) Customize the Ollama URL and select a different AI model.

5. (Optional) Customize the Whisper URL and select a different Whisper model.

6. Click the "Summarize" button to generate a summary of the video.

Global Installation

You can install the application globally on your system by running the following command:

sudo ./install.sh

This will create a new command youlama that you can use to run the application.

Run with the included binary

You can also run the application with the included binary:

./youlama

Dependencies

• Streamlit
• Pytube
• Ollama
• YouTube Data API
• Python-dotenv
• pytubefix
• Gradio

Project Structure

• src/main.py: Main Streamlit application
• src/ollama_client.py: Ollama API client for model interaction
• src/video_info.py: YouTube API integration for video information
• src/whisper_module.py: Whisper API client for transcription
• src/yt_audiophile.py: Audio downloader for YouTube videos
• transcript_cache/: Directory for caching video transcripts
• downloads/: Directory for downloaded audio files, might be empty

Contributing

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

License

WTFPL License

Credits

Icon: "https://www.flaticon.com/free-icons/subtitles" by Freepik - Flaticon