Hi!
The more compatible, slower option removes the WebGPU support, and the AI will work with your CPU instead of the GPU. This option is not a good idea to be used for huge transcriptions; it takes over 20 times slower than the GPU and it could close the browser session. This option is implemented for shortest texts. (and I will review the error code carefully.)
If you can use your GPU, it is the way to go.
I tested Nepali texts, and the model translated almost correctly, but with large transcriptions, it could make hallucinations and some parts became untranslated.
How can you deal with it? If you need quick translations, you can change the untranslated parts manually.
And we need some cases to test how we can harness the model to be more accurate, so if you can send me text fragments of the interview to reproduce the issue, we can optimise the context of the AI, but this will take time.
Note: Don't use this chat for the examples. If they have sensible information (the àgora is a public site), use my email.
Best