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

AI Anytime
10 Sept 202348:26

TLDRThis video tutorial guides viewers on creating a Streamlit application for summarizing YouTube videos using open-source tools. It leverages the Haystack framework combined with the Llama 2 and Whisper models for text summarization and speech-to-text conversion, respectively. The app allows users to input a YouTube URL and receive a concise summary of the video's content without relying on paid APIs, demonstrating a cost-effective solution for video summarization that's entirely based on open-source technology.

Takeaways

  • 🌟 Building a YouTube video summarization app with open-source tools like Haystack, Llama 2, Whisper, and Streamlit.
  • 🔍 The app allows users to input a YouTube URL and receive a summarized version of the video's content.
  • 📚 Utilizing the Haystack framework for integrating a large language model (LLM) with Whisper, a state-of-the-art speech-to-text model.
  • 💬 The video demonstrates creating an entirely open-source application without relying on paid APIs or closed-source models.
  • 🛠️ Introduction to Haystack's documentation and resources, emphasizing the use of their Whisper transcriber and summarization nodes.
  • 🔧 Explanation of using a 32k context size Llama 2 model for handling larger videos and providing a comprehensive summary.
  • 🎥 Discussion on using the Pi Tube library for downloading and manipulating YouTube video streams.
  • 🤖 Demonstration of the summarization process involving transcription by the Whisper model followed by summarization with Llama 2.
  • 📝 The app provides a user interface showing the video and the summarized text, with options to expand for more details.
  • 🔄 Mention of the app's functionality to summarize while watching a video, providing an interactive experience.
  • 🔗 The video includes a step-by-step guide for setting up the environment, installing necessary libraries, and writing the application code.

Q & A

  • What is the purpose of the application developed in the video?

    -The 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 application in the video?

    -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 for its state-of-the-art speech-to-text capabilities provided by OpenAI, converting the audio from YouTube videos into text for summarization.

  • 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 YouTube videos without relying on an API, ensuring no additional costs.

  • What is the role of the Llama 2 model in the application?

    -The Llama 2 model is used in combination with the Haystack framework to process the transcribed text and generate a summary of the video content.

  • Why is the application built to be entirely open-source?

    -The application is built to be entirely open-source to avoid reliance on closed-source models or APIs, which may incur costs, and to promote accessibility and customization by the community.

  • What is the expected time for the application to provide a summary of a YouTube video?

    -The application is expected to provide a summary in around two to three minutes, depending on the length of the video and the processing time of the Whisper model.

  • How does the application handle video downloads from YouTube?

    -The application uses the PyTube library to download YouTube videos, specifically to extract audio or video streams for transcription.

  • What is the importance of using a 32k context size model like Llama 2 in the application?

    -A 32k context size model is important for handling larger videos with many tokens, ensuring that the model can process and summarize longer videos effectively.

  • Can the application be used for real-time summarization during video playback?

    -The application allows for summarization while watching a YouTube video, but due to the processing time required for transcription and summarization, it may not be suitable for real-time use.

  • What are the potential use cases for the application beyond summarizing YouTube videos?

    -Beyond summarizing YouTube videos, the application's approach can be extended to process and summarize other forms of audiovisual content, such as meeting recordings or podcasts.

Outlines

00:00

🚀 Introduction to YouTube Video Summarization App

The video introduces a project to create a Streamlit application for summarizing YouTube videos using open-source tools. The app leverages the Haystack framework combined with a large language model and the Whisper AI model for speech-to-text conversion. The focus is on creating a free application that can provide summaries without relying on paid APIs or closed-source models. Viewers are introduced to the app interface and its functionality, including the ability to input a YouTube URL and receive a summary of the video's content.

05:01

🛠️ Setting Up the Development Environment

The script outlines the setup for developing the YouTube video summarization app. It details the necessary libraries and tools, such as Haystack, Streamlit, Torch, and Pi-Tube, and explains the process of creating a virtual environment. The video also covers the installation of the Whisper model from GitHub and the configuration of FFmpeg for video processing. Additionally, it discusses the use of the Llama 2 model for summarization tasks and the creation of custom scripts for invoking the model within Haystack.

10:03

🔍 Exploring the Haystack Framework and Model Integration

This section delves into the Haystack framework, emphasizing its production-ready status and comparison with other options like Langchain. It discusses the use of Haystack's nodes for various tasks such as summarization and transcription. The video script describes creating a custom invocation layer for integrating the Llama 2 model with Haystack, which involves setting parameters like maximum context size and token limit. It also mentions the use of a Gist from GitHub as a workaround for integrating the model.

15:04

📝 Writing Code for Video Download and Model Initialization

The script provides a detailed walkthrough of writing Python code for the application. It starts with functions for downloading YouTube videos using the Pi-Tube library and setting up the page configuration for the Streamlit app. The video explains how to initialize the Llama 2 model using a custom CPP invocation layer, including setting the model path, invocation layer, and other parameters like GPU usage and maximum length for summarization.

20:05

🔧 Initializing Prompt Nodes and Transcription Pipeline

The paragraph describes the process of setting up the prompt node in Haystack using a predefined summarization prompt. It explains the configuration of the node with the model and prompt template, as well as GPU settings. The script then moves on to creating a transcription function that utilizes the Whisper model for transcribing audio from the downloaded YouTube video. This function sets up a pipeline with nodes for transcription and summarization, detailing the process of adding each node and running the pipeline.

25:07

🖥️ Building the Streamlit App Interface

This section focuses on constructing the user interface for the Streamlit application. It discusses adding a title, subtitle, and expander component to provide information about the app. The script explains how to use Markdown for styling the text and includes emojis for a more engaging interface. It also outlines the process of creating an input field for the YouTube URL and a submit button to trigger the summarization process.

30:08

🔄 Implementing Video Summarization Flow

The script details the implementation of the video summarization flow in the Streamlit app. It explains how to handle the user input, download the video, initialize the model, and process the transcription and summarization using the previously defined functions. The video describes the use of columns in Streamlit to display the video and the summarization results side by side, providing a comprehensive user experience.

35:10

📊 Displaying Summarization Results and Next Steps

The final part of the script discusses displaying the results of the video summarization in the Streamlit app. It explains how to present the summary and the complete transcription to the user, allowing them to view detailed information or just the summary. The video also mentions potential future enhancements, such as increasing the summary length or containerizing the app for deployment on platforms like Azure. The script concludes with a call to action for feedback and suggestions from the viewers.

Mindmap

Keywords

💡YouTube Video Summarization

YouTube Video Summarization refers to the process of condensing a video's content into a shorter, more digestible format. In the context of the video, this process is automated through an application that uses AI to provide users with a summary of a YouTube video when they input a URL. The script mentions developing a streamlit application for this purpose, demonstrating how it can be used to summarize content while watching a video.

💡Streamlit

Streamlit is an open-source Python library used to create custom web apps for machine learning and data science. In the video, Streamlit is utilized to develop an application that interfaces with the user, allowing them to input a YouTube URL and receive a summary of the video's content. It is highlighted as a key component in building the front end of the summarization app.

💡Haystack

Haystack is an open-source framework mentioned in the script for building AI applications. It is utilized for its semantic search capabilities and is combined with other models like Llama 2 and Whisper to create the video summarization app. The script discusses leveraging Haystack's documentation and resources to build the application without relying on closed-source models or APIs.

💡Llama 2

Llama 2 is a large language model used within the context of the video for summarizing the transcribed text from a YouTube video. The script specifies using a 32k context size model, which is important for handling large videos and ensuring that the model can process a significant amount of text at once. It is part of the open-source stack used in the application.

💡Whisper

Whisper is an AI model for speech-to-text conversion, developed by OpenAI. In the script, it is mentioned as being used locally for transcribing the audio from a YouTube video into text, which is then fed into the Llama 2 model for summarization. The script emphasizes using the open-source version of Whisper to avoid reliance on APIs or closed-source solutions.

💡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 script emphasizes building the application using an open-source stack, which includes Haystack, Llama 2, and Whisper models. This approach avoids the need for paid APIs or closed-source models, making the application freely accessible.

💡API

API stands for Application Programming Interface, which is a set of rules and protocols for building software applications. The script mentions that while Whisper is available through both open source and an API, the tutorial opts for the open-source version to avoid costs. APIs are often used to access external services or data but can incur fees based on usage.

💡Vector Database

A Vector Database is a type of database designed to store and search vector spaces, which are used in AI and machine learning for tasks like semantic search. The script mentions V8 as an example of a vector database that can be used to build scalable LLM applications, although it is not directly used in the summarization app's workflow described.

💡Prompt Engineering

Prompt Engineering is the practice of designing input prompts for AI models to guide their output in a desired direction. In the context of the video, a prompt from Haystack is used to instruct the Llama 2 model to summarize the transcribed text. The script explains that this process does not require custom prompts, as Haystack provides suitable ones for summarization tasks.

💡Custom Invocation Layer

A Custom Invocation Layer in the context of the video refers to a workaround for integrating the Llama 2 model with Haystack, as Haystack may not officially support Llama 2 at the time of the video's creation. The script describes creating a custom class to load the Llama 2 model within Haystack's framework, allowing for its use in the summarization process.

Highlights

Developing a Streamlit application to summarize YouTube videos using a user-input URL.

Utilizing the Haystack framework with a large language model for summarization without relying on paid services.

Combining the Whisper AI model for speech-to-text conversion with Haystack for an open-source solution.

The beauty of the project being entirely open-source, avoiding the need for payment for closed-source models or APIs.

Introduction to Haystack's documentation and resources for Whisper transcriber and summarization.

Haystack's role as an open-source LLM framework for building production-ready applications.

Demonstration of the app's user interface for entering a YouTube URL and receiving a video summary.

Explanation of using the local machine to run Whisper model for transcription, independent of internet connection.

Process description of downloading YouTube videos using the Pi Tube library and passing them to the Whisper model.

The use of a prompt engineer prompt to interface the transcription with the Llama2 model for summarization.

The app's capability to summarize while watching a YouTube video, enhancing user interactivity.

The speaker discussing how to use a large language model to retrieve information from a PDF document.

Demonstration of chunking methods to process text data into smaller chunks for model focus and accuracy.

The development process starting from scratch, including setting up a virtual environment and installing libraries.

Instructions for using the terminal, code editor, and specific files for the application setup.

Details on the requirements for the application, including Haystack, Streamlit, torch, and other libraries.

Guidance on using the Llama 2 model with a 32k context size for handling large videos.

Discussion on the use of gguf models in the LLM ecosystem and the transition from ggml to gguf.

Final demonstration of the application's output, summarizing the content of a given YouTube video.

Invitation for feedback and comments, encouraging community engagement with the project.

Future plans for additional videos on Haystack and other applications of the technology.

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