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Generate audiobooks from e-books

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README.md

Audiblez: Generate audiobooks from e-books

Audiblez generates .m4b audiobooks from regular .epub e-books, using Kokoro's high-quality speech synthesis.

Kokoro v0.19 is a recently published text-to-speech model with just 82M params and very natural sounding output. It's released under Apache licence and it was trained on < 100 hours of audio. It currently supports American, British English, French, Korean, Japanese and Mandarin, and a bunch of very good voices.

On my M2 MacBook Pro, it takes about 2 hours to convert to mp3 the Selfish Gene by Richard Dawkins, which is about 100,000 words (or 600,000 characters), at a rate of about 80 characters per second.

How to install and run

If you have Python 3 on your computer, you can install it with pip. Be aware that it won't work with Python 3.13. Then you also need to download a couple of additional files in the same folder, which are about ~360MB:

pip install audiblez
wget https://github.com/thewh1teagle/kokoro-onnx/releases/download/model-files/kokoro-v0_19.onnx
wget https://github.com/thewh1teagle/kokoro-onnx/releases/download/model-files/voices.json

Then, to convert an epub file into an audiobook, just run:

audiblez book.epub -l en-gb -v af_sky

It will first create a bunch of book_chapter_1.wav, book_chapter_2.wav, etc. files in the same directory, and at the end it will produce a book.m4b file with the whole book you can listen with VLC or any audiobook player. It will only produce the .m4b file if you have ffmpeg installed on your machine.

Supported Languages

Use -l option to specify the language, available language codes are: 🇺🇸 en-us, 🇬🇧 en-gb, 🇫🇷 fr-fr, 🇯🇵 ja, 🇰🇷 kr and 🇨🇳 cmn.

Speed

By default the audio is generated using a normal speed, but you can make it up to twice slower or faster by specifying a speed argument between 0.5 to 2.0:

audiblez book.epub -l en-gb -v af_sky -s 1.5

Supported Voices

Use -v option to specify the voice: available voices are af, af_bella, af_nicole, af_sarah, af_sky, am_adam, am_michael, bf_emma, bf_isabella, bm_george, bm_lewis. You can try them here: https://huggingface.co/spaces/hexgrad/Kokoro-TTS

Author

by Claudio Santini in 2025, distributed under MIT licence.