Tokenizer suggestion for fine tuning cache aware streaming model
If you want to finetune using the original tokenizer, yes you'll need to normalize all numbers to spoken words. 

Changing tokenizer means you'll need a large amount of data to retrain the model, that is not suggested unless you have several thousand hours of speech to reach best results  > If you want to finetune using the original tokenizer, yes you'll need to normalize all numbers to spoken words.
> 
> Changing tokenizer means you'll need a large amount of data to retrain the model, that is not suggested unless you have several thousand hours of speech to reach best results

How to use the original tokenizer? 
I also created [a discussion](https://github.com/NVIDIA/NeMo/discussions/9217) for this.
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Hi
I want to fine tune "stt_en_fastconformer_hybrid_large_streaming_multi" on custom data. 
In my dataset I have things like "Vitamin B12", "Code: c12r5", "hb1ac" etc
For these alphanumeric words:
1. Should I convert the above to "vitamin b twelve", "code c one two r five", "h b one a c" ..... for using the default tokenizer
2. Should I create a custom/new tokenizer for this?

If there is any other suggestion, please let me know.
Thank You