Comments (4)
Hi,
Training using a pre-trained model can lead to faster convergence
By default, the speaker embedding layer is ignored
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the pre-trained model does have speaker embedding as you can load the model and see that layer.
But it does seem to be quite picth/rythm related. you can try to extract pitch and rythm from a different wav to see/test
from mellotron.
Nevermind, I think there was a bug in loading the speaker dictionary in the inference on my end. Although for some speakers, the voice does not quite match the data. Maybe because of fewer corresponding speaker samples during training.
from mellotron.
@paarthneekhara - I am also facing this similar issue. I am using libritts pretrained model and trying to generate voice for a custom text using a reference style wav file. Though I specify a different speake_rid (in the example_filelists.txt along with style wav, its transcript), the voice generated is always same of a female voice. Do you know how to generate voice of a different speaker that is present in the pre-trained model?
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Related Issues (20)
- NoneType' object is not iterable
- Mismatch model volume
- Training on a different language HOT 1
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- Training time
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- colab demo for inferenece
- How to generate .musicxml files like the examples in `/data`? HOT 1
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- Something wrong with text padding HOT 5
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