Hi and welcome!
I would think that GPT 3.5 Turbo base model would already have the knowledge to do that spelling translation already so maybe fine-tuning it would not really have much of an effect.
Fine-tuning is very useful when you have output expectations that are not consistent, you can fine tune your cases to be more consistent by providing fine-tune examples.
I am no expert myself though my guess it the base knowledge is good enough for your use case.