AI-Powered Podcast Generation System Using Natural Language Processing and Text-to-Speech

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed digital content
creation, enabling automated generation of text, audio, and multimedia. Among these
innovations, podcasting has emerged as a popular medium for information sharing and
entertainment. However, creating high-quality podcast content requires significant time,
effort, and technical expertise. This project presents an AI-Powered Podcast Generation
System that automates the process of generating podcast scripts and converting them into
audio using Natural Language Processing (NLP) and Text-to-Speech (TTS) technologies.
The system is developed using the Django web framework, providing a robust and
scalable backend for handling user requests, content generation, and file management.
Users can input a topic through a web interface, and the system automatically generates a
structured podcast script using an AI-based text generation module. The generated script
is then converted into an audio file using a text-to-speech engine, producing a complete
podcast episode without human intervention.
The application also includes a database component that stores generated episodes,
including the topic, script, and audio file. This enables users to access previously created
podcasts and maintain a history of generated content. Additionally, an RSS feed feature is
implemented, allowing users to subscribe to the generated podcasts through standard
podcast platforms