Build Your Own YouTube Video Summarization App with Haystack, Llama 2, Whisper, and Streamlit
TLDRThis video tutorial guides viewers on creating an open-source Streamlit application for summarizing YouTube videos. Utilizing the Haystack framework combined with the Llama 2 and Whisper models, the app transcribes video content and generates concise summaries without the need for proprietary APIs or models. Viewers learn to build the app from scratch, including setting up a virtual environment, installing necessary libraries, and scripting the application logic. The process involves downloading YouTube videos, transcribing audio to text with Whisper, and summarizing the text using Llama 2 through Haystack's prompt engineering. The final app provides a user-friendly interface for inputting YouTube URLs and receiving video summaries, making it an accessible tool for those looking to quickly grasp the key points of lengthy videos.
Takeaways
- 🌟 Building a YouTube video summarization app using open-source tools.
- 🛠️ Utilizing the Haystack framework for combining large language models with other AI capabilities.
- 🗣️ Incorporating Whisper, an AI speech-to-text model by OpenAI, for transcribing video audio.
- 📚 The app is designed to work entirely on open-source software, avoiding paid APIs or models.
- 🔗 Demonstrating the app with a user input of a YouTube URL to generate a video summary.
- 💾 Using a local model, Llama 2, with a 32k context size for handling large videos.
- 🔍 Employing a vector database like V8 for scalable LLM applications.
- 📝 The process involves downloading YouTube videos, transcribing the audio, and summarizing the text.
- 🔧 Customizing the app with Streamlit for an interactive user interface.
- 🔄 Discussing the use of a pipeline in Haystack to add nodes for transcription and summarization.
- 🔗 The final app allows users to submit a YouTube video and receive a summarized version of its content.
Q & A
What is the main purpose of the application developed in the video?
-The main purpose of the application is to summarize YouTube videos using a user-input URL, providing a summary of the video's content without the need to watch the entire video.
Which framework is used to develop the video summarization application?
-The Haystack framework is used to develop the video summarization application.
What is the role of the Whisper model in the application?
-The Whisper model is used for converting the speech in the YouTube video to text through its state-of-the-art speech-to-text capabilities.
Is the application reliant on any closed-source models or APIs?
-No, the application is built entirely on open-source technology and does not rely on any closed-source models or APIs.
What is the significance of using a 32k context size model like Llama 2?
-The 32k context size model, such as Llama 2, is significant because it can handle larger videos with more tokens, providing a bigger context which is necessary for summarizing longer videos effectively.
How does the application handle the process of summarizing a YouTube video?
-The application first connects to YouTube to download the video using the Pi Tube library. It then uses the Whisper model to transcribe the video's audio to text. Finally, it feeds the transcription to the Llama 2 model through a prompt engineered prompt to generate a summary.
What is the expected time for the application to provide a summary of a YouTube video?
-The application is expected to take around two to three minutes to provide a summary, depending on the size and length of the video.
How does the application leverage the Llama CPP invocation layer?
-The application uses a custom script for Llama CPP invocation to load any Llama model within the Haystack framework, allowing for the summarization of the video's transcription.
What is the role of the Vector Database (V8) in the context of this application?
-V8, a vector database, is mentioned as a tool that can be used to build scalable LLM applications, although in this specific application script, it is not directly utilized.
What are some additional features or capabilities that the application could potentially offer?
-The application could potentially offer features such as summarizing while watching a video, providing a detailed view of the video alongside the summary, and possibly extending to chat with videos or images in future updates.
How can one access the code for the YouTube video summarization application?
-The code for the application is available on the presenter's GitHub repository, with the link provided in the video description.
Outlines
🚀 Introduction to YouTube Video Summarization App
The video introduces a project to develop a Streamlit application using the Haystack framework and open-source models like Llama 2 and Whisper. The app will allow users to input a YouTube URL and receive a summary of the video's content. The focus is on creating an entirely open-source solution without relying on paid APIs or closed-source models. The app is demonstrated with an example, showcasing its user interface and functionality.
🛠️ Setting Up the Development Environment
The script details the initial setup for developing the YouTube video summarization app. It mentions the need for various libraries such as Haystack, Streamlit, and Pi-tube, and the installation of models like Llama 2 and Whisper from GitHub. The video also discusses the importance of having FFmpeg in the system path for video manipulation. The process includes creating a virtual environment, installing necessary libraries, and setting up custom script files for the application.
🔍 Exploring Haystack Framework and Model Integration
This section delves into the Haystack framework, emphasizing its production-ready status and comparison with other frameworks like Langchain. It discusses using Haystack's nodes and pipelines for tasks like summarization and transcription. The video also covers the creation of a custom invocation layer for integrating the Llama 2 model with Haystack, which involves writing a script for the model's configuration and invocation.
📚 Developing the Application's Core Functions
The script outlines the development of core functions for the YouTube video summarization app. It describes creating functions for downloading YouTube videos using Pi-tube, initializing the Llama 2 model with a custom invocation layer, and setting up a prompt node for summarization. Additionally, it details the creation of a pipeline that integrates the Whisper transcriber for converting video audio into text.
🔧 Assembling the Streamlit Application Interface
The video script explains the process of assembling the Streamlit application's user interface. It includes setting the page configuration, creating a title and subtitle with decorative elements, and adding an expander component to explain the app's functionality. The interface is designed to be user-friendly, with a text input for the YouTube URL and a submit button to initiate the summarization process.
🖥️ Implementing the Application Logic in Streamlit
This part of the script focuses on implementing the application logic within the Streamlit framework. It describes the process of defining a main function that integrates all previously developed components. The main function handles the user input, triggers the download and transcription of the video, processes the transcription with the Llama 2 model, and displays the summarized results in a structured format using Streamlit's columns and success message components.
🎬 Demonstrating the Completed YouTube Video Summarization App
The final segment of the script showcases the completed YouTube video summarization app in action. It demonstrates how users can input a YouTube URL, submit it, and receive a summarized output after a processing time of a few minutes. The video emphasizes the app's efficiency and the quality of the summarization, highlighting the potential applications and future developments such as batch processing, containerization, and deployment on platforms like Azure.
Mindmap
Keywords
Haystack
Llama 2
Whisper
Streamlit
YouTube URL
Transcription
Summarization
Open-source
Vector Database
Custom Invocation Layer
Highlights
Developing a Streamlit application to summarize YouTube videos using a user-input URL.
Utilizing the Haystack framework in combination with a large language model for summarization.
Incorporating Whisper, an AI speech-to-text model by OpenAI, for video transcription.
The entire project is open-source, avoiding reliance on closed-source models or paid APIs.
Introduction to Haystack's documentation and resources for Whisper transcriber and summarization.
Building the application without incurring costs, with a wait time of 1-2 minutes for summaries.
Using a 32k context size model, Llama 2, for handling large video sizes.
Haystack's role as an open-source LLM framework for production-ready applications.
Demonstration of the application's user interface for entering a YouTube URL.
Explanation of using a vector database V8 for building scalable LLM applications.
Process of connecting to YouTube, downloading videos, and using Whisper for transcription.
Using the transcription with Llama2 and a prompt engineer to generate a summary.
The app's functionality to summarize while watching a YouTube video.
Providing a detailed view of the video alongside the summarization results.
Discussion on how to chunk text data into smaller parts for model focus and accuracy.
Introduction to the custom script for Llama CPP invocation in Haystack.
Instructions for setting up the environment with necessary libraries and tools.
Explanation of using GGUF models and the transition from GGML in the LLM ecosystem.
Importance of the 32k context size for handling larger videos in production.
Details on implementing the Streamlit application layout and user interface.
Writing functions for downloading YouTube videos, initializing models, and transcribing audio.
Building the pipeline with nodes for transcription and summarization using Haystack.
Finalizing the Streamlit app with a main function that integrates all components.
Demonstration of the complete application in action, summarizing a given YouTube video.