Unleashing the Power of Whisper, GPT-3.5 and ChatGPT for Transcripts and News

Table of Contents

1. Introduction
2. Project Overview
3. Methodology
4. Step 1: Using PiTube to Download YouTube Video
5. Step 2: Extracting Audio from the Video
6. Step 3: Speech-to-Text Conversion using Whisper Model
7. Step 4: Prompt Engineering using DaVinci 3 Model
8. Step 5: Generating the News Article
9. Integrating the Model into the UI
10. Downloading the Transcript and News Article
11. Conclusion

Introduction

In this article, we will explore how to develop an AI-powered application that can generate a news article from a video using OpenAI's GPT-3 models and Whisper model for speech-to-text conversion. We will utilize the PiTube library to extract audio from a YouTube video, process the audio file using the Whisper model to obtain the transcript, and then leverage the DaVinci 3 GPT-3.5 model for prompt engineering to generate a news article. The article can be downloaded and utilized by journalists, media houses, or anyone interested in automatically generating news articles Based on audio or video recordings. This application can significantly streamline the news article creation process and provide room for further research, validation, and customization.

Project Overview

The project focuses on developing an AI-powered application that leverages OpenAI's models for speech-to-text conversion and news article generation. The application will take a short YouTube video as input and generate a news article based on its content. The process involves several steps, including downloading the video using PiTube, extracting the audio, converting the audio to text using the Whisper model, performing prompt engineering using the DaVinci 3 model, and generating the news article. The application will also provide the option to download both the transcript and news article files.

Methodology

We will follow the following steps to develop the AI-powered application:

1. Use PiTube to download the YouTube video.
2. Extract the audio from the downloaded video.
3. Convert the audio to text using the Whisper model.
4. Perform prompt engineering using the DaVinci 3 model.
5. Generate the news article based on the audio transcript.
6. Integrate the model into a user interface using the Streamlit library.
7. Provide the option to download both the transcript and news article files.

Now, let's dive into each step in Detail to understand how to accomplish this project.

Step 1: Using PiTube to Download YouTube Video

In this step, we will utilize the PiTube library to download the YouTube video. We will provide the YouTube video URL as input, and PiTube will handle the downloading process. We will download the video in audio-only format by setting the "only_audio" parameter to True. This will save the audio file locally for further processing.

Step 2: Extracting Audio from the Video

Once we have downloaded the YouTube video, we will extract the audio from the video file. We will use the extracted audio file for speech-to-text conversion using the Whisper model. The audio file will be saved locally, and its file name will be used in the subsequent steps.

Step 3: Speech-to-Text Conversion using Whisper Model

In this step, we will leverage the Whisper model to convert the extracted audio to text. Whisper is a state-of-the-art Automatic Speech Recognition (ASR) model provided by OpenAI. We will pass the audio file to Whisper and obtain the transcript as output.

Step 4: Prompt Engineering using DaVinci 3 Model

After obtaining the audio transcript, we will perform prompt engineering using the DaVinci 3 model. Prompt engineering involves creating a specific prompt to frame the desired response from the model. We will use the DaVinci 3 model, which is an advanced version of OpenAI's GPT-3 model, to generate a news article based on the provided audio transcript.

Step 5: Generating the News Article

In this step, we will feed the audio transcript to the DaVinci 3 model, leveraging its language generation capabilities to Create a news article. The model will use the provided transcript as a prompt and generate a news article tailored to the input content. The generated news article will be formatted and organized for readability.

Integrating the Model into the UI

To make the AI-powered application accessible and user-friendly, we will integrate the model into a user interface using the StreamLit library. StreamLit allows us to create intuitive and interactive UI components that users can interact with. We will provide a text input for users to enter the YouTube video URL, and once the processing is complete, the generated news article will be displayed on the UI.

Downloading the Transcript and News Article

To enable users to download the transcript and news article, we will implement a functionality that allows them to download both files as a zip file. This feature will provide convenience and flexibility for users who might want to save or share the files for future reference.

Conclusion

In this project, we have developed an AI-powered application that can generate a news article from a YouTube video. We have used OpenAI's GPT-3 and Whisper models for speech-to-text conversion and news article generation. The application allows users to input a YouTube video URL, extract the audio, convert it to text, perform prompt engineering, and generate a news article based on the provided content. The generated news article can be downloaded along with the transcript, providing users with versatile and valuable outputs. The application has the potential to streamline the news article creation process and enable journalists and media houses to leverage AI technology for efficient and accurate news reporting.