PDF to Podcast Generator: An Intelligent Content Conversion Tool Based on LLM and TTS

Title: PDF to Podcast Generator: An Intelligent Content Conversion Tool Based on LLM and TTS Abstract: An AI-driven application built on Streamlit that automatically converts PDF documents into multi-role podcast dialogues using large language models and speech synthesis technology, supporting multiple podcast styles and bilingual output. Keywords: PDF conversion, podcast generation, text-to-speech, large language model, Streamlit, Edge TTS, AI application, content conversion Source Information: Original author/maintainer utkarshP-11, source platform GitHub, original title PDF to Podcast Generator, release time June 2026.

In the era of information explosion, knowledge workers face challenges in efficiently digesting large amounts of documents, papers, and reports. Traditional reading methods are inconvenient in scenarios such as commuting, fitness, or housework. The PDF to Podcast Generator is an innovative project born to address this pain point—an AI-driven application based on Streamlit that automatically converts PDFs into multi-role podcast dialogues.

Core Technical Components

• Streamlit: Builds web interface
• LangChain: LLM orchestration
• Groq API: Fast LLM inference (llama-3.3-70b-versatile model)
• Edge TTS: Speech synthesis (multilingual and multi-voice support)
• PyMuPDF4LLM: PDF text extraction
• Pydub: Audio merging
• FFmpeg: Audio processing

System Workflow

1. PDF upload → 2. Text extraction →3. Chunk processing →4. Content summarization →5. Script generation →6. Multi-role speech synthesis →7. Audio merging

Intelligent PDF Processing: PyMuPDF4LLM efficiently extracts text, and chunking avoids model context limitations. AI Script Generation: Supports 7 podcast styles (educational, casual chat, technical deep dive, etc.). Multi-role Audio: Edge TTS generates realistic voices with asynchronous parallel synthesis. Multilingual Support: English and Hindi. Other Features: Optional background music, performance metrics dashboard (extraction time, generation time, etc.).

Application scenarios include:

• Learning assistance: Students convert textbooks/papers into podcasts for fragmented learning
• Content creation: Podcast creators quickly convert written content
• Accessibility: Audio documents for visually impaired individuals
• Multilingual content: Convert English content into local language podcasts
• Corporate training: Convert training manuals into podcasts to increase engagement

Current Limitations: Scanned PDFs require OCR, extremely large PDFs take time, background music needs manual provision, podcast duration is approximate. Future Plans: RAG retrieval pipeline, interactive editing, streaming generation, cloud deployment, user authentication, chapter generation, emotional TTS, YouTube export, cross-chunk memory.

The PDF to Podcast Generator combines document processing, LLM, and TTS technologies to create a practical new way of content consumption. It demonstrates how AI can change the way information is accessed, allowing users to learn in more scenarios in an age of attention scarcity. As technology advances, such applications will become more intelligent and practical, and this project provides a good starting point.