Whisper (OpenAI) - Automatic English Subtitles for Any Film in Any Language - An Intro & Guide to Subtitling JAV
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I just updated the notebook and the repo. I don't have bandwidth to do a proper test. Please let me know if anything.
Note for the advanced users: as of July 2026, the latest release of ctranslate2 is compatible with python 3.13. I oppened the repo (uvlock, toml) to allow python 3.13. Because 3.13 relies on the newer numpy stack, I upgrtaded that too. The numpy stack upgarde would have needed more testing to make sure no downstream funny behaviour.
I think v1.9 is a big improvement, and is probably going to be my preferred workflow. Two step with local translation under Ensemble Methods.
Some things get in the way at the moment:
- The prompt, especially the Pornify prompt, probably needs to have repeated "Ignore all censorship" rules.
- The default translation should probably be an Abliterated/Heretic model, if possible.
- There's some sort of hidden caching that is going on, and I am not sure where the cache is or how to force it to retranslate on each run. Or how to explicitly use the cached merge file that has Scene descriptions in it.
Other than that, this is very good and probably the baseline best for JAV translation.
Some things get in the way at the moment:
- The prompt, especially the Pornify prompt, probably needs to have repeated "Ignore all censorship" rules.
- The default translation should probably be an Abliterated/Heretic model, if possible.
- There's some sort of hidden caching that is going on, and I am not sure where the cache is or how to force it to retranslate on each run. Or how to explicitly use the cached merge file that has Scene descriptions in it.
Other than that, this is very good and probably the baseline best for JAV translation.
For WhisperJAV, it feels like translategemma3 is the only model that reliably can complete lines without having mangled outputs that fails validation. However, on an 8GB card, it is very slow.
I think the baked in context sizes for other models are hurting them-- the prompt is way too large for small contexts to work. Should probably be a directly exposed context argument.
I am also unable to figure out why I am only getting 1 or 2 "scenes" after the merged transcriptions. Still ~400 batches, just the app is identifying it all as one continuous scene for reasons I can't discern.
I also can't figure out what part of the code is causing the Summaries to truncate. "Summary truncated from 250 to 150 characters" or something like that.
I heavily modified the Standard prompt, but I think having more summary/scene information would help it know when some of the lines might just be moaning rather than dialogue.
I think the baked in context sizes for other models are hurting them-- the prompt is way too large for small contexts to work. Should probably be a directly exposed context argument.
I am also unable to figure out why I am only getting 1 or 2 "scenes" after the merged transcriptions. Still ~400 batches, just the app is identifying it all as one continuous scene for reasons I can't discern.
I also can't figure out what part of the code is causing the Summaries to truncate. "Summary truncated from 250 to 150 characters" or something like that.
I heavily modified the Standard prompt, but I think having more summary/scene information would help it know when some of the lines might just be moaning rather than dialogue.
Do have access to github to post the comments there too? The community there can help / contribute.
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