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The steps of transcription and translation currently appear to be relatively tightly coupled. We can see that the subtitles generated by the transcription are processed in the translation step.
This seems to violate the SRP.
At the same time, even specified skip trans=True , the translation thread will still be started. Users pay for the additional performance overhead even though they are not using it.
I wish we could decouple the two steps of transcription and translation: - The translation step no longer processes transcribed files. - The translation thread is no longer started when skip_trans=False is specified.
I am not familiar with nlp related knowledge. But if you agree, maybe I can try to complete this improvement.
The text was updated successfully, but these errors were encountered:
I do think the structure of openlrc.py is not good - the tight coupling between transcription and translation makes the code less maintainable and less efficient. I actually started addressing some architectural issues in commit c7db967, but there's still room for improvement.
Feel free to open a PR with your proposed changes!
The steps of transcription and translation currently appear to be relatively tightly coupled. We can see that the subtitles generated by the transcription are processed in the translation step.
And finally generated in translation worker.
This seems to violate the SRP.
At the same time, even specified
skip trans=True
, the translation thread will still be started. Users pay for the additional performance overhead even though they are not using it.I wish we could decouple the two steps of transcription and translation:
- The translation step no longer processes transcribed files.
- The translation thread is no longer started when
skip_trans=False
is specified.I am not familiar with nlp related knowledge. But if you agree, maybe I can try to complete this improvement.
The text was updated successfully, but these errors were encountered: