The *-shot jargon is just in-crowd nonsense that has been meaningless since day one (or zero). Like Big O notation but even more arbitrary (as evidenced by all the answers to your comment).

> Zero-shot learning (ZSL) is a problem setup in deep learning where, at test time, a learner observes samples from classes which were not observed during training, and needs to predict the class that they belong to. The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples.

https://en.wikipedia.org/wiki/Zero-shot_learning

edit: since there seems to be some degree of confusion regarding this definition, I'll break it down more simply:

We are modeling the conditional probability P(Audio|Voice). If the model samples from this distribution for a Voice class not observed during training, it is by definition zero-shot.

"Prediction" here is not a simple classification, but the estimation of this conditional probability distribution for a Voice class not observed during training.

Providing reference audio to a model at inference-time is no different than including an AGENTS.md when interacting with an LLM. You're providing context, not updating the model weights.