PDF to Audiobook Converter

Transform any PDF into an immersive audiobook with AI-powered voice synthesis

Source Code View Demo
PDF to Audiobook Converter Interface

About This Project

The PDF to Audiobook Converter is a sophisticated Python application that transforms static PDF documents into engaging audiobooks. Using advanced text-to-speech technologies, it creates natural-sounding narrations with character voice differentiation and intelligent dialogue processing.

This project solves the accessibility challenge of consuming written content through audio. Whether for visually impaired users, multitaskers, or anyone who prefers audio learning, this tool makes literature and documents accessible in a new format while maintaining the richness of the original text through voice modulation and character recognition.

Key Features

  • Multiple TTS engine support (pyttsx3, Google Cloud, ElevenLabs)
  • Intelligent dialogue detection and speaker identification
  • Character voice mapping with distinct voices
  • PDF text extraction with PyMuPDF
  • Audio file management and chapter organization
  • Customizable voice parameters and speed control

Technical Challenges

The primary challenge was developing accurate dialogue detection algorithms to distinguish between narrative text and character speech. This required natural language processing techniques to identify speech patterns, quotation marks, and dialogue tags. Additionally, managing multiple TTS engines with different APIs and rate limits while maintaining consistent audio quality across the entire audiobook proved complex.

Conversion Process

1

PDF Analysis

Extract and clean text from PDF, preserving structure and formatting cues

2

Dialogue Detection

Identify speakers and dialogue sections using NLP techniques

3

Voice Mapping

Assign unique voices to characters and narrator based on analysis

4

Audio Generation

Convert text to speech using selected TTS engine with voice modulation

Code Highlights

PDF Text Extraction

class PDFTextExtractor:
    def extract_text(self, pdf_path):
        doc = fitz.open(pdf_path)
        text = ""
        for page in doc:
            text += page.get_text()
        return self.clean_text(text)

Dialogue Processing

class DialogueProcessor:
    def detect_dialogue(self, text):
        dialogue_pattern = r'"([^"]*)"'
        matches = re.finditer(dialogue_pattern, text)
        return [(m.start(), m.end(), m.group(1)) 
                for m in matches]