YouTube Video Summarization App built using open source LLM and Framework like Llama 2, Haystack, Whisper, and Streamlit. This app smoothly runs on CPU as Llama 2 model is in GGUF format loaded through Llama.cpp.
YouTube Video Summarization App, is a powerful and customizable tool at your disposal, capable of automatically summarizing YouTube videos.
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🔍 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.
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🤖 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.
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🗣️ 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.
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🚀 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.
- Haystack: https://haystack.deepset.ai/
- Llama 2 32K Model: https://huggingface.co/togethercomputer/LLaMA-2-7B-32K
- Llama 2 32K GGUF Model: 32K-Instruct-GGUF🦌
Distributed under the MIT License. See LICENSE for more information.
