Kristopher-Chen

> @Charlottecuc I'm sorry for the late reply because this issue was closed and I didn't get any notification. Not sure if it has been resolved, but what I meant...

> I believe it could simply be because there's not enough training data. Any-to-many conversion requires a lot of input data for the model to generalize well. I have already...

> @skol101 I don't believe so, if it's not for any-to-any, you only need to have a lot of input speakers. You do not need cycle loss in this case,...

Hi, guys! I appreciate your efforts towards any-to-any voice conversion task. In my understanding, when any input speakers are expected, enlarging the number of training speakers may help. In this...

> Whether to remove the mapping network, if I only use it with reference audio. hi, have you tried to remove the mapping network? Is this way effective for improving...

Hi, I met with similar problems, with small discriminator losses. And in my test, obvious harmonics exist. Have you solved it? 

BTW, the discriminators' loss is quite small, which may suggest the discriminators are too strong? 

> Did you use this repository? Or general question about hifigan? Hi, actually I referred to your repository and the official version. I trained several epochs by the official code...

> OK. How many iterations did you run? In my experiment, around 200k iters can generate reasonable voice. I'm not familiar with official implementation but in my case official optimizer...

> @Kristopher-Chen have you resolved the problems? when I refer to the original codes, this problem is solved. For discriminator losses, the 2nd and 3rd MSD losses are easily becoming...