Those people usually are not duplicates. They are various versions of subtitles for a similar Film. If you check the sizes or articles, you are going to notice they're going to all be a little various.
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This can be the first time I've tried using translating anything at all. I'm seeking to discover japanese and it had been an exciting method of getting some observe And that i uncovered a good deal. I applied AI to tear a transcript and then went line by line comparing the hiragana to what was remaining reported to ensure the same old MTL garble was not in it.
panop857 explained: I've a frequent challenge of the initial thirty seconds not obtaining translations or having terrible translations, then the chunk once the thirty next mark to be rushed and horribly mistimed. Click on to develop...
Due to ding73ding. Even though her/his? subtitle file was a Chinese translation, I found an English machine translation on SubtitleCat but I wouldn't have looked experienced ding73ding not posted it. I cleaned the machine translation up a little and attempted to better interpret what was staying explained, but or else did not intentionally twist the storyline.
Transcribed applying WhisperWithVAD_Pro. Whisper skipped lots of traces on this one particular to start with attempt. I managed to acquire all (I do think) on the lines detected by normalizing and slowing the audio down right before transcription.
If your deeplv4 application is simply too too much to handle There is certainly a single button Google translation Developed into subtitle edit it You can utilize the sub cat Web site....
As normal, you can find lines that I have never translated, lines which i'm uncertain of (Particularly one which mentions Ny), and several awkward phrasing, but I have completed my best for making the experience a fantastic one particular.
Once again, this is just a group of every thing Absolutely everyone has posted in this article - almost nothing new. But I think It really is helpful to have everything in one sorted collection, duplicates taken out, and named continuously.
Start out-256 Eng Sub. NTR inside a shared home: Trapped by a snowstorm, a helpless weathercaster is forced to share a home by using a sly producer who takes advantage of her.
Deepseek however has the situation of currently being read more actually sluggish in the course of peak times, they supply a discount all through non-peak moments. The very first hour of that remains slow but following that It truly is fine.
Once more, I do not fully grasp Japanese so my re-interpretations might not be thoroughly correct but I try to match what is going on in the scene. In any case, appreciate and allow me to know what you're thinking that.
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