I would caution that using the term "example" suggests further learning happens at inference-time, which isn't the case.

For LLMs, the entire prompt is the input and conveys both the style and the content vectors. In zero-shot voice cloning, we provide the exact same inputs vectors but just decoupled. Providing reference audio is no different than including "Answer in the style of Sir Isaac Newton" in an LLM's prompt. The model doesn't 'learn' the voice; it simply applies the style vector to the content during the forward pass.

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 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.

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 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.

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

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