Build Your Own YouTube Video Summarization App with Haystack, Llama 2, Whisper, and Streamlit
TLDRIn this AI Anytime video, the host guides viewers on creating a Streamlit app that summarizes YouTube videos using open-source tools. The app leverages the Haystack framework with a large language model and Whisper, an AI speech-to-text model by OpenAI. The tutorial emphasizes using an open-source stack, avoiding paid services for a cost-effective solution. The process includes downloading YouTube videos, transcribing audio with Whisper, and summarizing the text with the Llama 2 model through Haystack's prompt engineering. The result is a user-friendly app that provides video summaries within minutes, showcasing the potential of open-source LLM frameworks for practical applications.
Takeaways
- 😀 The video demonstrates how to create a Streamlit application that summarizes YouTube videos using open-source tools.
- 🔧 It utilizes the Haystack framework combined with a large language model and Whisper, an AI speech-to-text model by OpenAI.
- 🌐 The application is designed to be entirely open-source, avoiding reliance on closed-source models or paid APIs.
- 💻 The video includes a step-by-step guide on setting up the application using a local machine, with no need for an internet connection once the models are downloaded.
- 🎥 The process involves downloading YouTube videos using the Pytube library, transcribing the audio with the Whisper model, and summarizing the text with Llama 2.
- 📝 The summarization is achieved through a prompt engineering technique, leveraging a pre-defined prompt from Haystack for summarization tasks.
- 🔗 The video provides a link to the GitHub repository containing the code for the application, ensuring transparency and accessibility.
- 📈 The speaker emphasizes the scalability of the application, mentioning the use of V8 as a vector database for handling large volumes of data.
- 🛠️ Customization options are discussed, such as adjusting the maximum context size and token limit depending on the Llama model used.
- ⏱️ The video acknowledges the trade-off between latency and cost, as the application operates without incurring API costs but may take a few minutes to process summaries.
Q & A
What is the main purpose of the streamlit application developed in the video?
-The main purpose of the streamlit application is to summarize YouTube videos. Users can input a YouTube URL, and the app will provide a summary of the video's content.
Which framework is used to develop the application?
-The application is developed using the Haystack framework, which is an open-source LLM framework for building production-ready applications.
What is the significance of using the Whisper model in the application?
-The Whisper model is used as a state-of-the-art speech-to-text model by OpenAI. It transcribes the audio from the YouTube video, which is then used to generate a summary.
How does the application handle the transcription of YouTube videos?
-The application uses the local implementation of the Whisper model to transcribe the audio from the YouTube video without relying on an API, ensuring no internet connection is needed for this step.
What is the role of the Llama 2 model in the video summarization process?
-The Llama 2 model is used to summarize the transcribed text from the Whisper model. It processes the text through a prompt engineering process to generate a concise summary of the video.
Why is the 32k context size model of Llama 2 chosen for the application?
-The 32k context size model is chosen to handle larger videos with more tokens, ensuring the model can process lengthy videos effectively.
How does the application avoid relying on closed-source models or APIs?
-The application relies on open-source tools and models like Haystack, Whisper, and Llama 2, avoiding any closed-source models or APIs that would require payment.
What is the expected time for the application to generate a summary of a YouTube video?
-The application takes around two to three minutes to generate a summary, depending on the video's length and the model's processing time.
How does the application handle the user interface for inputting a YouTube URL and displaying the summary?
-The application uses Streamlit for the user interface, allowing users to input a YouTube URL and displaying the video summary in a user-friendly format with columns for video view and detailed results.
What are the future enhancements mentioned for the application?
-Future enhancements include adding the ability to chat with PDFs and videos, containerizing the application for deployment on platforms like Azure, and potentially using GPUs to speed up processing times.
Outlines
🌐 Developing an Open-Source YouTube Video Summarizer
The speaker introduces a project to create an open-source YouTube video summarizer using a combination of the Haystack framework, a large language model, and the Whisper AI model for speech-to-text conversion. The goal is to allow users to input a YouTube URL and receive a summary of the video's content without relying on proprietary APIs or models. The project emphasizes the use of open-source tools and aims to demonstrate the potential of combining different AI models for practical applications.
🛠️ Building the Application with Haystack and Local AI Models
The video script details the process of building the YouTube video summarizer application using the Haystack framework and local AI models. The speaker discusses the use of the Whisper model for transcribing video audio and the Llama 2 model for summarization. The application is designed to be self-contained, running locally without the need for internet connectivity, and is built to be entirely open source, allowing for customization and extension by the user.
💻 Coding the Application: Setting Up the Environment
The speaker outlines the initial steps in coding the application, including setting up the development environment with necessary libraries and tools. The focus is on using Python, Streamlit for the application interface, and various Haystack components for handling AI model integration. The speaker also discusses the importance of having the correct versions of dependencies and the use of virtual environments to manage project dependencies.
🔗 Integrating YouTube Video Download and Transcription
The script describes the function to download YouTube videos using the Pytube library and the process of transcribing the video's audio using the Whisper model. The speaker emphasizes the local implementation of Whisper for transcription, which avoids latency issues and does not require an internet connection once the model is downloaded and set up.
📝 Summarization Process Using Llama 2 and Haystack
The speaker explains how the transcription from the Whisper model is fed into the Llama 2 model for summarization using the Haystack framework. The process involves creating a pipeline with nodes for transcription, summarization, and output handling. The speaker also discusses the configuration of the Llama 2 model, including setting the maximum context size and token limit.
🎥 Demonstrating the Application with a YouTube Video Example
The script includes a demonstration of the application in action, where the speaker inputs a YouTube video URL and the application processes the video to produce a summary. The video used for demonstration discusses the use of large language models for retrieving information from PDFs, and the application successfully summarizes the content, showcasing the effectiveness of the open-source tools and models used.
🔧 Finalizing the Application and Future Enhancements
The speaker concludes the script by summarizing the application's functionality and potential for future enhancements. The application is shown to be fully operational, capable of summarizing YouTube videos using open-source tools. The speaker also hints at upcoming videos that will explore further applications of Haystack and other AI models, suggesting the potential for expanding the current application's capabilities.
Mindmap
Keywords
💡Haystack
💡Streamlit
💡Llama 2
💡Whisper
💡Transcription
💡Summarization
💡Open-source stack
💡API
💡Vector database
💡Custom invocation layer
Highlights
Introduction to developing a Streamlit application for YouTube video summarization.
Utilization of the Haystack framework for combining large language models with other AI models.
Inclusion of Whisper, an AI speech-to-text model by OpenAI, for transcription tasks.
Emphasis on an entirely open-source solution for the application.
Description of the user interface for inputting YouTube URLs and receiving video summaries.
Explanation of using the 'pi tube' library for downloading YouTube videos.
Details on using the Whisper model locally for speech-to-text conversion.
Integration of the Llama 2 model through a custom invocation layer in Haystack for summarization.
Demonstration of the application's functionality with a live example.
Discussion on the use of V8, a vector database, for building scalable LLM applications.
Mention of the app's ability to summarize videos while watching on YouTube.
Explanation of the process flow from video download to transcription and summarization.
Introduction to the custom script for invoking Llama CPP within Haystack.
Instructions for setting up the virtual environment and installing necessary libraries.
Walkthrough of the code for creating the Streamlit application.
Final demonstration of the application with a YouTube video summary.
Conclusion and call to action for feedback, likes, and subscriptions.