Leveraging generative AI to create podcasts based on input files
In today’s fast-paced world, consuming information efficiently is crucial. While reading documents can be effective, the dynamic auditory experience offered by podcasts provides a unique level of engagement and accessibility. This is where PodfAI comes in, bridging this gap by transforming various document types — from research papers, lecture notes, project descriptions, and personal resumes — into compelling, podcast-style audio content. This application leverages the power of AI to streamline the content creation process on demand, making it easier than ever to share information in an engaging and digestible format.
How it works
The general workflow starts with the user providing one or more files through the UI interface and receiving the Podcast’s transcript and audio that are generated through a pipeline of generative AI models and some support code. Let’s break down each step to understand it better.
Document Input
The user begins by uploading one or more files (currently supporting text-based formats, with future plans for expanding to images, and videos). The UI that we can easily generate with Streamlit makes this process quite intuitive and simple to implement.
Transcript generation
The uploaded content undergoes formatting (if necessary) and is then sent through the Vertex AI API to be processed by a large language model (currently Gemini-1.5-pro-002, but could be swapped by other offerings). This model generates the podcast script, structuring the information in a conversational and engaging manner, mimicking a dialogue between a host and a guest. Users can customize parameters like temperature and top-k to control the creativity and randomness of the generated script, as well as its length.
Text-to-Speech synthesis
The generated script is fed into a text-to-speech (TTS) engine (leveraging Google Cloud’s Text-to-Speech API). Users can select from a variety of voices for both the host and guest, offering personalization and enhancing the listening experience. This stage relies on Google Cloud’s robust infrastructure, offering fast and high-quality audio output.
Audio and transcript output
The final output is a podcast-style audio file, accompanied by a textual transcript for users to follow along.
The application is built using Streamlit for its intuitive interface and ease of deployment, allowing for fast prototyping and straightforward user interaction. The backend leverages Google Cloud’s Vertex AI for its powerful machine learning capabilities and the Text-to-Speech API for high-quality audio synthesis, both with very low cost.
Example
In the image below you can see an example of the APP’s UI when I provided my own resume for it to generate a podcast.
You can listen to part of the podcast audio at the project’s homepage, but just looking at the first part of the script you can see that it looks pretty good. There you will also be able to find a couple of other examples like a podcast about Andrew Huberman’s Optimal Morning Routine or about a couple of my previous projects “AI trailer” and “AI beats”, for them, the podcast was generated only by providing the project’s readme file.
Use Cases and Applications
PodfAI caters to a broad spectrum of users and applications:
- Academics: Researchers can transform their papers into easily consumable podcasts, increasing accessibility and broadening their audience.
- Educators: Lectures and course materials can be converted into engaging audio resources for students, making learning more convenient and interactive.
- Professionals: Resumes and project descriptions can be transformed into audio presentations, ideal for networking and job applications.
- Content Creators: PodfAI can help streamline the podcast creation process, allowing for rapid prototyping and experimentation with different content formats.
Closing Thoughts and Future Directions
PodfAI represents a significant leap forward in accessible content creation. By automating the transformation of various document types into engaging podcasts, the application significantly reduces the time and effort required for content production. The current version is built upon Google’s powerful AI infrastructure, providing high-quality output, though the project roadmap aims to explore the potential of open-source models for greater flexibility and accessibility.
Future development will focus on expanding input support (images, videos, YouTube URLs), implementing voice cloning for enhanced personalization, supporting a wider range of languages, and exploring the integration of more agentic workflows to further improve the podcast transcript quality. The project actively encourages community contributions, aiming to build a versatile and adaptable tool for anyone looking to create compelling audio content from their existing documents. The project’s GitHub repository is readily available for those interested in contributing or exploring the codebase. Try PodfAI today and experience the future of content consumption!
Acknowledgments
- Google Cloud credits are provided for this project. This project was possible thanks to the support of Google’s ML Developer Programs team.
- This project was based on Google’s [NotebookLM](https://notebooklm.google.com), which, aside from the podcast-style content, has many other features, make sure to check it out.
Do you have any ideas or suggestions? make sure to drop a comment to let me know what you think, are you interested in using PodfAI to your content consumption?
If you are interested in generative AI for content creation, make sure to check out a couple of my previous blog posts related to this subject.
How to use generative AI to create podcast-style content from any input was originally published in Google Developer Experts on Medium, where people are continuing the conversation by highlighting and responding to this story.
