This project details the development of an automated pipeline designed to transform ebooks into high-quality audiobooks efficiently. It addresses the
time-intensive and cost-prohibitive nature of manual audiobook production through a multi-step content transformation process with integrated quality
checkpoints.
Manual audiobook production is a labor-intensive and expensive process, creating a significant barrier for content creators and publishers looking to
diversify their offerings.
I engineered a robust, multi-step content transformation pipeline that incorporates quality checkpoints throughout the process. This end-to-end AI
automation handles text parsing with advanced prosody, multilingual voice synthesis, and final packaging, significantly streamlining production.
By automating the entire workflow, this pipeline dramatically reduces the time and cost associated with audiobook production, enabling scalable content
creation and broader market reach for authors and publishers.
Workflow: Production Pipeline
Tools: Python, EdgeTTS Models, FFmpeg, CLI-pipeline
- AI/ML Engineering: Designing and implementing AI-driven automation for complex content transformation.
- Python Development: Building robust backend pipelines and integrating various tools and APIs.
- Audio Engineering & Processing: Utilizing EdgeTTS Models and FFmpeg for high-quality voice synthesis and audio manipulation.
- Workflow Automation: Creating efficient, multi-step pipelines with automated quality control.
- Problem Solving: Addressing industry challenges (time/cost) with innovative technological solutions.