mlsanityData to 14 Oct 2026

Large-Scale Automatic Audiobook Creation

arXiv 2309.03926 ↗First posted 7 Sep 2023cs.SD cs.AI cs.DCcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 7 Sep 2023.

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6 of 8 references.

References · 6

  1. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  2. 2017Tacotron: Towards End-to-End Speech Synthesiscited by 490
  3. 2022AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarioscited by 41
  4. 2020Conversational End-to-End TTS for Voice Agentcited by 37
  5. 2022Self-supervised Context-aware Style Representation for Expressive Speech Synthesiscited by 10
  6. 2018Flexible and Scalable Deep Learning with MMLSparkcited by 2

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