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@patrickenfuego patrickenfuego released this 14 Oct 15:29
· 54 commits to main since this release
v0.1.0
d6210a7

What's New

The first stable release. Chapterize-Audiobooks uses speech-to-text Machine Learning to discover chapter timecodes in .mp3 audiobook files and ffmpeg to extract the metadata and split the file. The user may also pass various ID3-compliant metadata tags to the script, which will always take precedence over any existing ID3 tags extracted from the audiobook file.

Improvement

The script is still being improved, and I encourage anyone who uses it to report problems; particularly false positive words or phrases that trigger a chapter marker.

So far, I've found the following words or phrases (enclosed in "") which cause a false positive chapter marker to be created:

  • chapters
  • "chapter and verse"

My goal is to grow this list over time, improving the accuracy of detection.

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