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Large-Scale Automatic Audiobook Creation

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arxiv 2309.03926 v1 pith:WKR3XW4T submitted 2023-09-07 cs.SD cs.AIcs.DCcs.DLcs.LGeess.AS

classification cs.SDcs.AIcs.DCcs.DLcs.LGeess.AS
keywords audiobookaudiobookscollectioncreateworkallowsbookse-book
verification ladder T0 review T1 audit T2 compute T3 formal

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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 \url{https://aka.ms/audiobook}.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Multi-Agent AI Framework for Immersive Audiobook Production through Spatial Audio and Neural Narration

    cs.SD 2025-05 reject novelty 2.0 of 10

    The paper proposes an unvalidated agent-based architecture for immersive audiobook creation by combining existing neural TTS, diffusion audio, and alignment methods.

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