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

This 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

00:00

🚀 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.

05:01

🛠️ 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.

10:03

🔍 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.

15:04

📚 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.

20:05

🔧 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.

25:07

🖥️ 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.

30:08

🎬 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.

Keywords

💡Haystack

Haystack is an open-source framework developed by Deepset that is designed to help build production-ready applications using large language models (LLMs). In the context of the video, Haystack is utilized to integrate various components like the 'Llama 2' model and 'Whisper' for creating a YouTube video summarization application. It provides a structured way to handle different tasks such as summarization, transcription, and more, by using nodes and pipelines.

💡Llama 2

Llama 2 is a large language model (LLM) mentioned in the script, which is used in conjunction with Haystack for generating summaries of YouTube videos. The model is noted for its 32k context size, which is important for handling large volumes of text, such as those found in lengthy videos. It plays a central role in the video's application by processing the transcribed text from YouTube videos and producing concise summaries.

💡Whisper

Whisper is an AI model developed by OpenAI for speech-to-text conversion. It is described as a state-of-the-art model in the video and is used in the application to transcribe the audio from YouTube videos. The local implementation of Whisper is preferred in the video to avoid reliance on an API, which aligns with the video's goal of creating an entirely open-source application.

💡Streamlit

Streamlit is an open-source library used for quickly creating custom web apps for machine learning and data science. In the video, Streamlit is utilized to develop the front-end of the YouTube video summarization app, allowing users to input a YouTube URL and receive a summary of the video's content. It serves as the interface for interacting with the back-end processes powered by Haystack and the Llama 2 model.

💡YouTube URL

A YouTube URL is the web address of a specific video on the YouTube platform. In the context of the video, users are expected to input a YouTube URL into the application developed with Streamlit. This URL is then used to identify and download the video content, which will subsequently be transcribed and summarized by the application.

💡Transcription

Transcription in the video refers to the process of converting the spoken language in a YouTube video into written text. This is achieved using the Whisper model, which is an essential step before the summarization can occur. The transcription allows the Llama 2 model to analyze and process the content of the video to create a summary.

💡Summarization

Summarization is the process of condensing a large piece of text into a shorter version while retaining the key points. In the video, this is the main goal of the application: to provide users with a summary of the content of a YouTube video. The summarization is performed by the Llama 2 model after the video's audio has been transcribed by the Whisper model.

💡Open-source

Open-source refers to software whose source code is available to the public, allowing anyone to view, use, modify, and distribute the software. The video emphasizes the creation of an open-source application, meaning that the code is freely available and does not rely on proprietary or closed-source models or APIs. This aligns with the use of Haystack, Llama 2, and Whisper, which are all open-source components.

💡Vector Database

A vector database, such as V8 mentioned in the script, is a type of database designed to store and retrieve data based on vector similarity rather than exact matches. While not the main focus of the video, the concept is introduced as a potential component for building scalable LLM applications, where it can be used to store and manage large amounts of data efficiently.

💡Custom Invocation Layer

A custom invocation layer, as discussed in the video, is a workaround or custom script created to interface with a specific model or tool within the Haystack framework. In this case, it is used to integrate the Llama 2 model with Haystack, allowing the application to leverage the model's capabilities for summarization tasks.

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.