But, clearly, that’s already part of the Bark model, so in the abstract one should be able to leverage the existing model with appropriate code to do that, rather than developing a new model. This seems too simple but…isn’t that just “generate_text_semantic” from the existing source (with None as the history_prompt. since you don’t want it in the context of some pre-existing speaker)?

EDIT: Looking at the SERP voice clone, that’s what they are doing. The one thing that I’m intuitively skeptical about (and this is way out of the kind of programming I do normally, so I could be way off) is that the temp they use is kept at the level normally used for synthesis (0.7). I’d think you’d want the temp low, since you’d want generating a baseline for a new speaker to be more deterministic than generating content from an existing speaker.