PDF to Audio

This Python-based project provides a reproducible workflow to convert PDF documents into audio files. It targets developers, researchers, and accessibility engineers who need scalable, scriptable conversion for long reports, manuals, or academic papers. The pipeline handles plain text PDFs and, optionally, OCR-processed content for scanned documents, returning a speech-ready audio asset.

Conceptually, the tool extracts text from each PDF page, segments content into natural speech units, and feeds the result to a TTS engine chosen by the user. It supports multiple languages, adjustable voice characteristics, and speech rate, with options to produce MP3 or WAV outputs. When a document contains non-text elements, OCR can be enabled to recover hidden content.

Core differentiators include a modular, open-source pipeline, local processing options for privacy, and batch-automation capabilities suitable for large corpora. Use cases span accessibility production, podcast-style narration of manuals, language-learning materials, and archival transcription workflows. The design emphasizes predictable results, configurable chunking, and straightforward integration into CI/CD or server environments.

Usage scenarios and outputs: users can script end-to-end conversions, specify which pages to include, control output bitrate and sample rate, and retrieve metadata such as duration and word count. The project uses a pluggable architecture so alternative OCR or TTS backends can be swapped without rewriting core logic.

This description avoids UI specifics and focuses on the domain logic, inputs, outputs, and integration considerations for developers. The tool can be executed in local environments, Docker containers, or serverless pipelines, enabling reproducible experiments and audit trails.