There's ample literature showing that neutral vocoders generalize to new speakers not seen in training. IME, once you train on around ten speakers the models ge

There's ample literature showing that neutral vocoders generalize to new speakers not seen in training. IME, once you train on around ten speakers the models generalize quite well to new speakers, and in practice training sets for production models are often using hundreds of speakers for training. There are also non autoregressive GAN vocoders which work great, with a tiny fraction of the compute of the original WaveNet. Soundstream is a good example of this. My own work on bird song separation uses non AR models for generating separation masks. It works great and generalizes to new habitats and species without difficulty. https://ai.googleblog.com/2022/01/separating-birdsong-in-wild-for.html?m=1 https://ai.googleblog.com/2022/01/separating-birdsong-in-wil... Calling architecture variants a hack is risking no true Scotsman arguments... The only true neutral networks are the ones that don't work, perhaps?