Build Your Own YouTube Video Summarization App with Haystack, Llama 2, Whisper, and Streamlit

AI Anytime

In this exciting tutorial, I’ll show you how to create your very own YouTube Video Summarization App using a powerful combination of cutting-edge technologies: Haystack, Llama 2, Whisper, and Streamlit. That runs on your CPU machine.

πŸ” Haystack: Your AI-Powered Search Engine
Haystack is a versatile framework that allows you to harness the power of Generative AI to efficiently search, extract, and summarize information from vast amounts of text data.

πŸ€– Llama 2: The AI Brain
Meet Llama 2, a massive language model that will assist you in understanding and summarizing the content of YouTube videos. You’ll learn how to leverage Llama 2’s language capabilities to extract key insights from video transcripts. That too 32K context length model in the GGUF format.

πŸ—£οΈ Whisper: Transforming Speech to Text
Whisper, a state-of-the-art automatic speech recognition (ASR) model, will be your go-to tool for transcribing spoken content from your YouTube videos. I’ll show you how to integrate Whisper from Haystack inbuilt class seamlessly into your application, enabling it to work with both spoken and textual data.

πŸš€ Streamlit: The User-Friendly Interface
Streamlit is the secret sauce that ties it all together. With its user-friendly interface design, you can effortlessly create a visually appealing front end for your YouTube Video Summarization App. We’ll guide you through building an intuitive interface that allows users to interact with your app easily.

By the end of this tutorial, you’ll have a powerful and customizable tool at your disposal, capable of automatically summarizing YouTube videos.

Join us on this journey of Generative AI-powered video summarization, and let’s build something amazing together! Don’t forget to like, share, and subscribe for more exciting tutorials like this one. πŸ”—πŸ“ΊπŸš€

GitHub Repo:
Llama 2 32K Model:
Llama 2 32K GGUF Model: 32K-Instruct-GGUF

#llama2 #generativeai #haystack