A Complete Guide for Creating an AI Assistant for Summarizing YouTube Videos — Part 1

A Complete Guide for Creating an AI Assistant for Summarizing YouTube Videos — Part 1.

Overview

Author(s): Amin Kamali

Originally published on Towards AI.

Transcribe a YouTube Video Using OpenAI’s Whisper Model
Image generated by the author with Playground.ai

This article is the first in a series of three blog posts explaining step-by-step how I built an AI assistant to summarize YouTube videos. We start this series with in-depth instructions for capturing the transcript of a YouTube video using OpenAI’s Whisper, an open-source voice2text model. In the next post, we will cover the fundamentals of text summarization using Langchain and detailed instructions for implementing a summarization pipeline based on Falcon-7b-instruct, an open-source instruction-tuned LLM. In the final post, we will see how to demonstrate a solution prototype using Gradio and Hugging Face Spaces…. Read the full blog for free on Medium.

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Published via Towards AI

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AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.