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

AI Anytime
10 Sept 202348:26

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

  • ๐Ÿ˜€ The video demonstrates how to create a Streamlit application that summarizes YouTube videos using open-source tools.
  • ๐Ÿ”ง It utilizes the Haystack framework combined with a large language model and Whisper, an AI speech-to-text model by OpenAI.
  • ๐ŸŒ The application is designed to be entirely open-source, avoiding reliance on closed-source models or paid APIs.
  • ๐Ÿ’ป 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.
  • ๐ŸŽฅ The process involves downloading YouTube videos using the Pytube library, transcribing the audio with the Whisper model, and summarizing the text with Llama 2.
  • ๐Ÿ“ The summarization is achieved through a prompt engineering technique, leveraging a pre-defined prompt from Haystack for summarization tasks.
  • ๐Ÿ”— The video provides a link to the GitHub repository containing the code for the application, ensuring transparency and accessibility.
  • ๐Ÿ“ˆ The speaker emphasizes the scalability of the application, mentioning the use of V8 as a vector database for handling large volumes of data.
  • ๐Ÿ› ๏ธ Customization options are discussed, such as adjusting the maximum context size and token limit depending on the Llama model used.
  • โฑ๏ธ 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

00:00

๐ŸŒ 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.

05:01

๐Ÿ› ๏ธ 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.

10:03

๐Ÿ’ป 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.

15:04

๐Ÿ”— 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.

20:05

๐Ÿ“ 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.

25:07

๐ŸŽฅ 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.

30:08

๐Ÿ”ง 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

๐Ÿ’กHaystack

Haystack is an open-source framework developed by Deepset that is designed to build production-ready applications using large language models (LLMs). In the context of the video, Haystack is used as a foundation to create the YouTube video summarization app. It provides a way to integrate various components such as document stores and LLMs through its nodes and pipelines architecture, which is essential for the app's functionality.

๐Ÿ’กStreamlit

Streamlit is an open-source Python library used to create and share data apps quickly. In the video, Streamlit is utilized to develop the front-end interface of the YouTube video summarization app, allowing users to input a YouTube URL and receive a summary of the video content. Streamlit's simplicity and efficiency make it an ideal choice for deploying the app for public use.

๐Ÿ’กLlama 2

Llama 2 refers to a large language model that is used in the video for summarization tasks. The model is part of the open-source stack that the video aims to use, avoiding reliance on closed-source models or APIs. In the script, Llama 2 is mentioned as the LLM that processes the transcribed text from the YouTube video to generate a concise summary.

๐Ÿ’กWhisper

Whisper is an AI model for speech recognition, developed by OpenAI, which converts speech to text. In the video, Whisper is used locally to transcribe the audio from the YouTube videos into text, which is then fed into the Llama 2 model for summarization. The use of Whisper showcases the video's commitment to leveraging open-source tools for the app's development.

๐Ÿ’กTranscription

Transcription in the video refers to the process of converting the spoken content from a YouTube video into written text. This is a crucial step before the summarization can take place, as the Llama 2 model requires text input to generate a summary. The transcription is handled by the Whisper model, as mentioned in the script.

๐Ÿ’กSummarization

Summarization is the process of condensing a longer piece of text into a shorter, more concise version while retaining the essential information. In the video, summarization is the main goal of the app, where the Llama 2 model takes the transcribed text from a YouTube video and produces a summary. This is showcased in the script when the app is demonstrated to summarize a video about using LLMs to retrieve information from PDFs.

๐Ÿ’กOpen-source stack

The term 'open-source stack' refers to a collection of software tools and libraries that are available with open-source licenses, allowing for modification and redistribution. The video emphasizes the use of an open-source stack for the app development, including tools like Haystack, Streamlit, Llama 2, and Whisper, which are all used without relying on proprietary or closed-source solutions.

๐Ÿ’กAPI

An API, or Application Programming Interface, is a set of rules and protocols for building and interacting with software applications. The video mentions using APIs in the context of comparing the use of local models like Whisper to API-based services. The decision to use local implementations over APIs is to avoid costs and potential latency issues, as illustrated by the choice to run Whisper locally for transcription.

๐Ÿ’กVector database

A vector database is a type of database designed to store and retrieve data based on numerical characteristics, often used in machine learning for tasks like information retrieval. In the video, the mention of a vector database refers to V8, which is discussed as a tool for building scalable LLM applications. It's an example of the kind of infrastructure that can be used in conjunction with the app being developed.

๐Ÿ’กCustom invocation layer

A custom invocation layer in the context of the video refers to a specific implementation that allows the Haystack framework to interface with a model that is not natively supported, such as Llama 2. The script describes creating a custom class to integrate Llama 2 with Haystack, which is a workaround to leverage the capabilities of Llama 2 within the Haystack ecosystem.

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.