Text Generation Model

Select the model to generate the dialogue text.

Reasoning effort (for reasoning models, e.g. o1, o3, o4)

Select reasoning effort used.

Audio Generation Model

Select the model to generate the audio.

Speaker 1 Voice

Select the voice for Speaker 1.

Speaker 2 Voice

Select the voice for Speaker 2.

When enabled, the LLM will call the web search tool during its reasoning.

Instruction Template

Select the instruction template to use. You can also edit any of the fields for more tailored results.


PDF to Audio Converter

This Gradio app converts PDFs into audio podcasts, lectures, summaries, and more. It uses OpenAI's GPT models for text generation and text-to-speech conversion.

Features

  • Upload multiple PDF files
  • Choose from different instruction templates (podcast, lecture, summary, etc.)
  • Customize text generation and audio models
  • Select different voices for speakers

How to Use

  1. Upload one or more PDF files
  2. Select the desired instruction template
  3. Customize the instructions if needed
  4. Click "Generate Audio" to create your audio content

Use in Colab

Audio Example

Your browser does not support the audio element.

Note

This app requires an OpenAI API key to function.

Credits

This project was inspired by and based on the code available at https://github.com/knowsuchagency/pdf-to-podcast and https://github.com/knowsuchagency/promptic.

GitHub repo: lamm-mit/PDF2Audio

@article{ghafarollahi2024sciagentsautomatingscientificdiscovery,
    title={SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning}, 
    author={Alireza Ghafarollahi and Markus J. Buehler},
    year={2024},
    eprint={2409.05556},
    archivePrefix={arXiv},
    primaryClass={cs.AI},
    url={https://arxiv.org/abs/2409.05556}, 
}
@article{buehler2024graphreasoning,
    title={Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning},
    author={Markus J. Buehler},
    journal={Machine Learning: Science and Technology},
    year={2024},
    url={http://iopscience.iop.org/article/10.1088/2632-2153/ad7228},
}