Which methods used in voice synthesis projects today deliver the most stable and high-quality results? For example, are hybrid models (e.g., GAN-based + traditional methods) outperforming purely neural approaches? Considering efficiency and latency, which approach should be preferred?
Which is the most efficient method for voice synthesis?
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Isn't using just a neural approach enough? Lately, diffusion model-based approaches (like VITS-2) have made significant strides in audio quality while also minimizing latency. The issue with hybrid models seems to be that latency increases when working in two steps, don't you think?