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(1 edit)

After playing around with it for a bit, I do think the best solution would be to improve the Translation models.

I use an Open Source voice dictation app that uses the FasterWhisper and the Ctranslate2 library, which is wicked fast (less than 500ms to transcribe and translate more than 30secs of voice recording) on a RTX4070. I’m saying this because I saw that you were unsure of how heavy models would play. I’m confident there must be a way to try out CUDA compatible implementations to see how a “heavier” model would run.

No GPU support for translation yet, but as of 0.4.4, RapidOCR can now be run with the GPU. Hopefully that increases the speed somewhat on your PC!

(12 edits)

Fortunately, or unfortunately, the slowdown isn’t OCR (which is already impressively fast); it’s the DeepL translation that’s killing responsiveness. I know you’re focused on OCR perf, but from my end, that’s not the bottleneck.

Examples (0.4.4)


⬆️ The translation Online with DeepL
 
 
 
 


⬆️ The translation Offline with Internal Engine
 
 

This is one of the many reasons why an internal model is the preferred solution in my use case.

Hi mate!

I've implemented a solution to this by letting users use Custom APIs as suggested by another user. It's available in the 0.4.9_beta version.

Please let me know if you do try it and if you have any suggestions for it! I can confirm it works when I self-hosted LibreTranslate, which uses Argos Translate (..which in turn uses Ctranslate2).

As always, many thanks for all your suggestions, testing and feedback! 🎖️