change grinffin-lim algorithm to wavenet-based vocoder
lsq357 opened this issue · 7 comments
Does anyone plan to use wavenet-based vocoder, insteading of griffin-lim algoritm, which greatly increase the audio quality in the single-speaker experiment of Deep Voice 2 and Deep Voice 3. And in Deep Voice 3,both deep voice 3 + wavenet and tacotron + wavenet achieves the highest MOS of 3.78,Furthmore it is claimed that deep voice 3 + wavenet is faster to trian and faster to convege than tacotron+wavenet
I've been trying to use wavenet insteading of griffin-lim recently .
@feiyuhong Finish it? Can you show me some experienment result and loss curve?
@feiyuhong can you share link to some code? I'm interested in porting it to pytorch!
In my result, I can't get the clear voice after a week training when batch_size=32
Also, it is too slow that only train 1.6k+ step each day in my single GTX1080ＴＩ ＧＰＵ, I need more GPUs.
@keithito Do you plan to use Wavenet Vocoder with Tacotron? There is a new repo to do this task https://github.com/r9y9/wavenet_vocoder. Hope to see the good quality sound just like the white paper did.
I wonder why Griffin-Lim samples from here https://nv-adlr.github.io/WaveGlow have as good quality as other methods? Is it large number of iterations or it's just good output of tacotron?