I know how it workes, the question is if you get better results using whisper or deepl.change the option of translation_mode : No translation
I know how it workes, the question is if you get better results using whisper or deepl.change the option of translation_mode : No translation
This will not work as Whisper generates different subtiltes every time you run it, so there is no one-to-one comparison unless you get the japanese version with no translation and the translated one from the same run.You can test it yourself by running whisper twice on the same file, once with translation and once without.
I know how it workes, the question is if you get better results using whisper or deepl.
I did one file today with only transcribe and it took nearly 1h 30min instad of approx. 30min, then ran it through Deepl and I wasn't impressed with the result. Did not seem better than just straight letting whisper do the whole job.The good things with Whisper end-to-end translation are:
(a) It uses context for translation. It tries to build a context for example guessing gender (he, she), and punctuations for translation task.;(b) It makes the entire Whisper output faster. Translate tsak is faster than transcribe task. It is funny but their main sw engineer was saying that the way the algorithm is written, the end-to-end trasnlation task is performed faster than just transcribe task The good things with DeepL is that It is just a better translator. Fullstop. One bad thing with DeepL is that it often mixes up he/she, it/they, sir/ma'am.
For me I decided to just stick with DeepL. I did some comparisons during the early days of Whisper (v1). I haven't done any comparison with v2 but I understand that the translation capability did not change from v1 to v2. To me, DeepL translations came out better. But then again, I don't speek Japanese so my read might be quite wrong.
In terms of being able to compare the outputs as @SamKook suggested, one can make Whisper to be more deterministic by setting both temperature and beam to zero. That makes the output close to determinstic. But the pitfal is that it produces more halucination and repeating lines in the output.
DeepL is theoretically better, but there's probably some value in doing direct-to-English with the same deep learning model rather than taking the transcribed output and feeding into a second deep learning model that isn't specifically designed to work interact with the first. There's just an additional loss of information during that intermediate step.I know how it workes, the question is if you get better results using whisper or deepl.
I'm using standard settings and the large model from the collab posted here very early on. (VAD threshold 0.4 and chunk_thershold 0.3)DeepL is theoretically better, but there's probably some value in doing direct-to-English with the same deep learning model rather than taking the transcribed output and feeding into a second deep learning model that isn't specifically designed to work interact with the first. There's just an additional loss of information during that intermediate step.
It also depends on whether you are using Medium or Large Whisper, and how tuned your parameters are. Some things like an increased Beam Size are going to produce better translations of proper nouns, and Large is just generally better if you can pull it off.
This will not work as Whisper generates different subtiltes every time you run it, so there is no one-to-one comparison unless you get the japanese version with no translation and the translated one from the same run.
Thats my gripe with using deepL. It will convert the grammar of the original language to english grammar, but it will ignore words/phrases that it doesnt understand. Google API will translate as much as it can, sacrificing grammar for more accurate vocabulary. That said, for AI translation, it is a lot easier to follow sub made from deepl than googleI u
For whatever it's worth, I use DeepL as a translation tool myself, mostly to quickly parse complex sentences and brainstorm translation choices and sentence structures. I wouldn't recommend anybody use it for translation tasks that actually matter, though, unless they have a solid grasp of the target language. DeepL is very good at hiding what it in fact cannot understand, as it prioritizes natural-sounding language above accuracy. And it will straight up ignore details or change the basic meaning to do so.
Through torrent. Youd have to get the multi-language bundle to be able to detect japaneseMay i know where did you get it? Is it virus free?
You do realize that 5 of your 6 total posts are you requesting subs in a thread that has "★NOT A SUB REQUEST THREAD★" in its title.Anyone got a good SRT of SSNI-800?
Who is the source of your adobe? Monkrus? I am searching for a virus free adobe software but all of the source that i know has a negative reviews on reddit.Through torrent. Youd have to get the multi-language bundle to be able to detect japanese