NVIDIA-AI-Blueprints/pdf-to-podcast

Transform PDFs into AI podcasts for engaging on-the-go audio content.

What it solves

This project provides a framework for transforming static PDF documents into engaging, conversational audio podcasts. It is designed to help developers create applications that extract information from documents and synthesize it into natural-sounding audio, with the flexibility to run securely on private networks.

How it works

The application uses a modular pipeline powered by NVIDIA NIM microservices. It extracts text from target and context PDFs using Docling, processes the content with LLMs (such as Llama 3.1) to generate a conversational transcript, and then converts that text into audio using the ElevenLabs text-to-speech service. The entire stack is orchestrated via Docker Compose, allowing for local development or deployment on infrastructure with GPU acceleration.

Who it’s for

This blueprint is intended for developers and enterprise teams looking to build document-to-audio applications. It is particularly useful for those who need to process sensitive data securely, as it allows for self-hosting the inference components rather than relying solely on public cloud APIs.

Highlights

  • Flexible LLM Integration: Supports various NVIDIA NIM models (Llama 3.1 8B, 70B, or Nemotron 49B) for transcript generation.
  • Document-Aware: Can ingest multiple context PDFs to provide the agent with additional reference material for more accurate content.
  • Customizable Output: Supports both single-speaker monologues and multi-person podcast formats.
  • Production-Ready Architecture: Includes built-in support for Jaeger tracing for monitoring and debugging, with modular components that can be scaled or swapped based on hardware availability.

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