See lazysys for the revamped version.

LazyShorts

A command-line tool to convert long-form videos into multiple short-form videos, with burned-in text and subtitles. It also cuts out unwanted silence.

Preview

Original video

https://user-images.githubusercontent.com/29675001/210073372-f2a3fa63-29c2-4bc0-ab03-31188a772021.mp4

Result (with manual subtitle correction)

https://user-images.githubusercontent.com/29675001/210073848-09bcd7b7-6a86-4114-ab0b-72ddf674e298.mp4

Result (without manual subtitle correction)

As you can see, in Hungarian the medium model works quite well: considering the bad quality of my input. The large model could be even better: if you have the hardware. :)

https://user-images.githubusercontent.com/29675001/210073868-f650cef7-093c-4260-9407-c6bc7de1b59e.mp4

Notes

Arguments

See lazyshorts -h

Subtitles

I use Whisper to transcribe audible voices to text.

Obviously with non-english languages the accuracy can be lower: you can help that by...

• ...using a different (for now, only Whisper) model (be wary, the medium model is hard to run even with 8GBs of RAM.)
• ...editing subtitles manually from segment to segment. ({lazyshorts-py} e1 2 45 78...)

Not tested

• I don't know if running Whisper on GPU works, you could try CUDA. See --whisper_device and PyTorch/Whisper documentation. Also, get the CUDA enabled PyTorch as I define the CPU one in the requirements.txt.

Known issues

• 'subprocess.run' somehow blocks UI process.
• We could use rich to have nice progress bars, as currently you have to manually poll the status of the renders.
• Cropping is just arbitrary: I wanted to use MediaPipe. It's not easy to even get it to run, but my resources were not enough. Maybe a less demanding model or cloud is needed?
• Don't combine segments that are less than end_time, you'll get an exception.