Closed cdemir1919 closed 5 years ago
Yes, it can be done. We often do it in the scenario of voice conversion.
For example, the recipe of multi-speaker WaveNet vocoder is available in egs/arctic/si-close
of egs/arctic/si-open
, you can train WaveNet vocoder using 6 speakers' data via the recipe.
And then you can re-train the WaveNet using the multi-speaker WaveNet as the initial model.
This can be done with --resume
option in train.py
.
https://github.com/kan-bayashi/PytorchWaveNetVocoder/blob/86688e34510a9e2382b148a6c0f865c0c85ce215/src/bin/train.py#L496-L505
Maybe it is not necessary to load the states of optimizer, only that of model is OK.
Thank you for your explanation.
Thank you for your work. is it possible to train wavenet vocoder with multi-speaker data and adapt it to a target speaker with limited data? if yes, how can i do that?