2023/09/07 by Brendan Walsh, Walsh, Brendan, Mark Hamilton +19 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Digital Libraries (cs.DL) #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #Parallel #Sound (cs.SD) #Video Analysis and Summarization #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.03926
openalex publication_date 2023/09/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
An audiobook can dramatically improve a work of literature's accessibility and improve reader engagement. However, audiobooks can take hundreds of hours of human effort to create, edit, and publish. In this work, we present a system that can automatically generate high-quality audiobooks from online e-books. In particular, we leverage recent advances in neural text-to-speech to create and release thousands of human-quality, open-license audiobooks from the Project Gutenberg e-book collection. Our method can identify the proper subset of e-book content to read for a wide collection of diversely structured books and can operate on hundreds of books in parallel. Our system allows users to customize an audiobook's speaking speed and style, emotional intonation, and can even match a desired voice using a small amount of sample audio. This work contributed over five thousand open-license audiobooks and an interactive demo that allows users to quickly create their own customized audiobooks. To listen to the audiobook collection visit \urlhttps://aka.ms/audiobook.