🎙️ PDF to Podcast Conversion with LLMs 📄➡️🎧

This repository hosts a pipeline for transforming PDF files into audio podcasts! Leveraging advanced Language Models (LLMs) and Text-to-Speech (TTS) technology, this tool converts textual PDF information into engaging, high-quality audio content. It’s ideal for creators, researchers, or anyone looking to make static documents more accessible through audio.

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📑 Table of Contents

1. 📋 Project Overview
2. ✨ Features
3. 🔧 Installation
4. 🔄 Workflow
5. 📝 Detailed Steps
   • 📕 Step 1: PDF Text Extraction
   • ✍️ Step 2: Podcast Script Generation
   • 🛠️ Step 3: Transcript Refinement
   • 🎙️ Step 4: Audio Generation
6. 📋 Requirements
7. 🚀 Usage
8. 📂 File Structure
9. 🙏 Acknowledgments

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📋 Project Overview

This project uses modern LLMs and TTS technology to automate the conversion of PDFs into audio podcasts. Built on Llama 3.1 8b and high-quality TTS models (Parler and Bark), this tool delivers a seamless process from raw PDF to professional-sounding podcast with minimal manual input. The modular Jupyter Notebook structure makes each step transparent and easy to customize.

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✨ Features

• 📄 Automated PDF Text Extraction: Quickly extracts text from PDFs, accommodating various formats.
• 🧠 LLM-Driven Content Generation: Creates scripts that are rephrased and summarized for podcast-style delivery.
• 📝 Script Refinement: Enhances readability and flow, tailoring content for audio listeners.
• 🎙️ Natural Sounding TTS Output: Uses Parler and Bark TTS models to produce clear, natural-sounding audio.

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🔧 Installation

To set up the environment, ensure Python is installed, then execute:

pip install -r requirements.txt

This installs dependencies, including foundational packages like torch and transformers needed for TTS and NLP tasks.

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🔄 Workflow

The project workflow includes four primary stages, each with a dedicated notebook:

1. Extract Text 📄 from the PDF.
2. Generate Podcast Script 📝 using Llama 3.1 8b.
3. Refine Script ✍️ for clarity and flow.
4. Synthesize Audio 🎧 for podcast-ready MP3 output.

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📝 Detailed Steps

📕 Step 1: PDF Text Extraction

• Notebook: Step1_PDF_preprocessing.ipynb
• Purpose: Extracts text from PDF documents for easy processing.
• Libraries: Utilizes PyPDF2 for PDF parsing.
• Process:
  • Converts each PDF page into cleaned text.
  • Saves extracted text as a .txt file, ready for LLM processing.

✍️ Step 2: Podcast Script Generation

• Notebook: Step2_Transcript_Writer.ipynb
• Purpose: Uses Llama 3.1 8b to summarize and rephrase content for audio.
• Highlights:
  • The LLM converts extracted text into a listener-friendly script.
  • Produces a draft script saved as a text file for refinement.

🛠️ Step 3: Transcript Refinement

• Notebook: Step3_Rewriter.ipynb
• Purpose: Polishes the initial script, optimizing it for audio narration.
• Process:
  • Improves readability and conversational flow.
  • Reduces redundancy and clarifies complex ideas.

🎙️ Step 4: Audio Generation

• Notebook: Step4_TTS_Workflow.ipynb
• Purpose: Converts the refined script into high-quality audio.
• Models Used: Combines Parler and Bark TTS models.
• Output: Creates an .mp3 file that’s ready for playback or distribution.

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📋 Requirements

The requirements.txt file provides a comprehensive list of dependencies, including:

• NLP and LLM: transformers, torch, datasets
• PDF Parsing: PyPDF2
• Audio Processing: pydub, audioread, librosa
• TTS Models: parler_tts, bark

Key Dependencies

• LLM Processing: transformers, torch
• Text Extraction: PyPDF2
• Audio Tools: pydub

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🚀 Usage

1. Prepare PDF Files 📄: Place PDFs in a folder accessible to the notebooks.
2. Run Notebooks Sequentially:
   • Step 1: Run Step1_PDF_preprocessing.ipynb to extract text.
   • Step 2: Run Step2_Transcript_Writer.ipynb to create a script.
   • Step 3: Run Step3_Rewriter.ipynb to refine the script.
   • Step 4: Run Step4_TTS_Workflow.ipynb to generate audio.
3. Review Output 📄: Check the output at each step to ensure quality.

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📂 File Structure

The project includes the following files:

• Step1_PDF_preprocessing.ipynb: PDF text extraction.
• Step2_Transcript_Writer.ipynb: Script generation.
• Step3_Rewriter.ipynb: Script refinement.
• Step4_TTS_Workflow.ipynb: Audio synthesis.
• requirements.txt: Lists all dependencies.

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🙏 Acknowledgments

This project is powered by the amazing open-source tools provided by:

• Meta AI for the Llama 3.1 8b language model.
• Parler and Bark TTS for high-quality audio generation.
• PyPDF2 and Pydub for essential PDF and audio handling.

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🔗 General Links & Resources

• Our Website: www.apcmasterypath.co.uk
• APC Mastery Path Blogposts: APC Blogposts
• LinkedIn Pages: Personal | APC Mastery Path