YouTube Summarizer: AI-Powered Summaries for YouTube Videos and Playlists
This repository profile is provided by osrepos.com, an open source repository discovery platform.
Summary
YouTube Summarizer is a Flask web application designed to generate concise, AI-powered summaries of YouTube videos and entire playlists. It leverages advanced AI models like Google Gemini and OpenAI GPT, extracts transcripts, and can even convert summaries into audio using Google's Text-to-Speech API, offering a comprehensive tool for efficient content digestion.
Repository Information
Topics
Click on any tag to explore related repositories
Use at your own risk
OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of code from these repositories is the user's own responsibility. Always review the repository, source code, dependencies, licenses, and security implications before running or installing anything. OSRepos is not responsible for issues, damages, or losses resulting from third-party repositories.
Introduction
The youtube-summarizer project is a powerful Flask web application that provides AI-powered summarization for YouTube videos and playlists. It streamlines the process of extracting key information from long-form video content by generating concise summaries using state-of-the-art AI models. Beyond just text, the application also offers the unique ability to convert these summaries into audio files, enhancing accessibility and convenience.
Installation
The recommended way to set up and run YouTube Summarizer is using Docker. This ensures a consistent environment and simplifies dependency management.
Prerequisites:
- Google API Key: Required for YouTube Data API v3, Google Generative AI (Gemini), and Google Cloud Text-to-Speech API.
- OpenAI API Key: Optional, for utilizing OpenAI GPT models.
Steps for Docker Installation:
- Clone the Repository:
git clone https://github.com/jaye773/youtube-summarizer.git cd youtube-summarizer - Set Up Environment Variables: Create a
.envfile in the project root and add your API keys:GOOGLE_API_KEY=your_google_api_key_here OPENAI_API_KEY=your_openai_api_key_here # Optional, for GPT models - Initialize Data Directory: Run the initialization script to create the necessary directory structure:
./init_data.sh - Run with Docker Compose:
docker-compose up -dThe application will be accessible at http://localhost:5001.
Examples
Using the YouTube Summarizer is straightforward through its clean web interface.
- Open the Web Interface: Navigate to http://localhost:5001 in your browser.
- Login (if enabled): If authentication is configured, enter the passcode to gain access.
- Enter YouTube URLs: Paste one or more YouTube video or playlist URLs into the input field. Multiple URLs can be entered on separate lines.
- Generate Summaries: Click the "Summarize" button to begin processing the videos.
- View Results: Summaries will appear below each video. You can also find cached summaries in the sidebar. Click the speaker icon to generate and play audio versions of the summaries.
Why Use It
YouTube Summarizer offers a comprehensive solution for efficient video content consumption. Its key advantages include:
- Multi-Model AI Support: Choose between Google Gemini and OpenAI GPT models for summarization, allowing flexibility and access to diverse AI capabilities.
- Playlist Support: Effortlessly process and summarize entire YouTube playlists, saving significant time.
- Audio Generation: Convert text summaries into MP3 audio files, perfect for on-the-go learning or accessibility.
- Smart Caching: Minimize API calls and speed up retrieval with intelligent caching of summaries and audio files.
- Optional Authentication and Proxy Support: Enhance security with passcode-based login and bypass IP restrictions using Webshare proxy integration.
- Clean and Responsive Interface: A user-friendly web UI ensures a smooth and intuitive experience.
Links
- GitHub Repository: https://github.com/jaye773/youtube-summarizer
Related repositories
Similar repositories that may be relevant next.

PromptBench: A Unified Framework for LLM Evaluation and Robustness
July 1, 2026
PromptBench is a comprehensive Python library designed for the evaluation and understanding of Large Language Models (LLMs). It provides a unified framework for assessing model performance, exploring various prompt engineering techniques, and evaluating robustness against adversarial attacks. This tool empowers researchers to conduct in-depth analyses of LLMs across diverse datasets and models.

LangTest: A Comprehensive Library for Safe & Effective Language Models
June 30, 2026
LangTest is an open-source Python library dedicated to ensuring the safety and effectiveness of language models. It offers a comprehensive framework for testing model quality, covering robustness, bias, fairness, and accuracy across various NLP tasks and LLM providers. With LangTest, developers can generate and execute over 60 distinct test types with just one line of code, promoting responsible AI development.

EvalPlus: Rigorous Evaluation for LLM-Synthesized Code
June 30, 2026
EvalPlus is a robust framework designed for the rigorous evaluation of code generated by Large Language Models (LLMs). It extends standard benchmarks like HumanEval and MBPP with significantly more tests, offering precise assessment of code correctness and efficiency. This tool is crucial for developers and researchers aiming to thoroughly validate LLM-synthesized code.

AgentEvals: Robust Evaluation Tools for LLM Agent Trajectories
June 30, 2026
AgentEvals is a powerful open-source package from LangChain designed to simplify the evaluation of agentic applications. It provides a collection of ready-made evaluators and utilities, with a particular focus on analyzing agent trajectories, the intermediate steps an agent takes to solve problems. This helps developers understand and improve the reliability and performance of their LLM agents.
Source repository
Open the original repository on GitHub.