Customizing T5 tokenizer for finetuning

I am finetuning a T5 model for QA on my dataset but the vocab is so different than the tokenizer’s, which results in an excessive length of token_ids/tokens. can I train a new tokenizer from the existing one and use it for finetuning? if yes, any tips/resources to aid?