No, that would still be zero shot. Providing inference-time context (in this case, audio) is no different than giving a prompt to an LLM. Think of it as analogous to an AGENTS.md included in a prompt. You're not retraining the model, you're simply putting the rest of the prompt into context.

If you actually stopped and fine-tuned the model weights on that single clip, that would be one-shot learning.

It makes perfect sense; you are simply confusing training samples with inference context. "Zero-shot" refers to zero gradient updates (retraining) required to handle a new class. It does not mean "zero input information."

> how would the model know what voice it should sound like

It uses the reference audio just like a text based model uses a prompt.

> unless it's a celebrity voice or similar included in the training data where it's enough to specify a name

If the voice is in the training data, that is literally the opposite of zero-shot. The entire point of zero-shot is that the model has never encountered the speaker before.

In the olden days of 2023, you didn’t just find instruct-tuned models sitting on every shelf.

You could use a base model that has only undergone pretraining and can only generate text continuations based on the input it receives. If you provided the model with several examples of a question followed by an answer, and then provided a new question followed by a blank for the next answer, the model understood from the context that it needed to answer the question. This is the most primitive use of ICL, and a very basic way to achieve limited instruction following behavior.

With this few-shot example, I would call that few-shot ICL. Not zero shot, even though the model weights are locked.

But, I am learning that it is technically called zero shot, and I will accept this, even if I think it is a confusingly named concept.

It is exactly what happens. You are confusing the task (classification vs. generation) with the learning paradigm (zero-shot).

In the voice cloning context, the class is the speaker's voice (not observed during training), samples of which are generated by the machine learning model.

The definition applies 1:1. During inference, it is predicting the conditional probability distribution of audio samples that belong to that unseen class. It is "predict[ing] the class that they belong to," which very same class was "not observed during training."

You're getting hung up on the semantics.

I don't know about Novichok, but nuclear bombs have stopped world wars, at least so far.

.kkrieger looks like an amazing game for the mid-90s. It's incomprehensible that it's only 96kb.