The Podcast Summarizer is an AI-driven application that streamlines podcast consumption by providing automated summaries of podcast episodes, identifying prominent guests, and highlighting key points. This project is submitted as partial fulfillment of the requirements for the Building AI Products with OpenAI course on Uplimit.
Automated Summarization: The app utilizes the OpenAI gpt-3.5-turbo model to generate concise and coherent summaries of podcast episodes. This enables users to quickly grasp the main ideas and highlights without listening to the entire episode.
Guest Identification: Leveraging advanced natural language processing, the application identifies and lists the guests featured in each podcast episode. This feature helps users recognize episodes featuring their favorite speakers.
Key Highlights: The summarization process also identifies and extracts key highlights and important insights from the podcast. This saves users time and allows them to focus on the most valuable content.
Accurate Transcription: The project integrates OpenAI Whisper for accurate podcast episode transcription. This ensures that the generated summaries are based on precise and reliable input.
Modal Labs: Cloud infrastructure for hosting and deployment.
OpenAI Whisper: Automatic transcription service for podcast episodes.
OpenAI gpt-3.5-turbo: Language model for generating podcast summaries.
Python: Programming language used for app development.
This project is licensed under the MIT License.