Note-gen is a Python-based repository designed to automatically generate notes from various input sources, primarily focusing on YouTube videos and PDFs. It leverages a combination of Large Language Models (LLMs) – specifically OpenAI's GPT models – speech-to-text technology (Whisper), and document parsing libraries to create concise and informative summaries. The core goal is to streamline the note-taking process, saving users time and effort while ensuring they capture key information.
The repository's functionality is built around several key components. First, it utilizes OpenAI's Whisper for transcribing audio from YouTube videos. This transcription forms the basis for the note generation process. For PDFs, it employs libraries like PyPDF2 and potentially others (depending on the PDF's structure) to extract text content. Once the text is available, it's then fed into a GPT model (configurable via environment variables, defaulting to gpt-3.5-turbo) with a carefully crafted prompt. This prompt instructs the LLM to summarize the content, identify key topics, and present the information in a structured format. The user can customize the prompt to influence the style and detail of the generated notes.
A significant feature of note-gen is its modular design. The code is organized into distinct modules responsible for specific tasks: transcription, PDF parsing, prompting the LLM, and output formatting. This modularity makes the code more maintainable, testable, and extensible. It also allows for easier integration with other tools or services. The repository includes example scripts demonstrating how to use these modules for both YouTube video summarization and PDF note generation. Configuration is largely handled through environment variables, allowing users to specify their OpenAI API key, preferred GPT model, and other parameters without modifying the core code.
The project supports different output formats. Currently, it primarily generates notes in Markdown format, which is widely used for documentation and note-taking. This makes the generated notes easily readable and editable in various text editors and note-taking applications. The Markdown output includes headings, bullet points, and other formatting elements to enhance readability and organization. Future development could potentially include support for other output formats like plain text, HTML, or even more structured formats like JSON.
Beyond the core functionality, the repository includes helpful utilities like a `requirements.txt` file for easy dependency installation and a `README.md` file providing clear instructions on how to set up and use the tool. The author actively maintains the repository, addressing issues and incorporating improvements based on user feedback. While still under development, note-gen represents a practical and efficient solution for automating the tedious task of note-taking from diverse sources, leveraging the power of modern AI technologies. It's a valuable tool for students, researchers, and anyone who needs to quickly extract and summarize information from lengthy audio or text content.
