How can I fine-tune voice cloning models for better emotion rendering?

Example Concept: Fine-tuning voice cloning models involves using a dataset rich in emotional diversity and adjusting model parameters to better capture and render emotional tones. This can include training the model with audio samples that exhibit a wide range of emotions, such as happiness, sadness, and anger, and using these to refine the model's ability to synthesize voices that convey these emotions naturally. Additionally, some platforms offer sliders or controls to manually adjust emotion levels in the generated audio.