Comments (4)
In reference to the GST part of mellotron, there is no 1:1 lock. You can use GST the same way as in other repos.
If you want to do inference with the mellotron model however, we additionally extract two things from a reference audio: the rhythm and the pitch which creates the 1:1 correspondence. It's the rhythm that creates the 1:1 correspondence actually. But your automatically-extracted pitch might not make sense if you do not additionally condition on the rhythm.
If you don't want rhythm (which you can disable by using model.inferece()) and pitch conditioning (which you can disable by sending zeros as the pitch), you get essentially tacotron 2 with GST and speaker ids.
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Thank you @blisc for the quick reply - much appreciated!
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thanks @blisc
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@blisc .. I have a question on similar lines.. I have trained the model using this repo on LJ speech. During inference i use a out of dataset file as style file. The synthesized speaker quality has changed very much. The quality is decent but it doesn't sound like the original speaker of LJ speech. How to fix that? Please can you help. Thanks.
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Related Issues (20)
- NoneType' object is not iterable
- Mismatch model volume
- Training on a different language HOT 1
- Inference without rhythm and pitch
- parse_output error with Blizzard2013 data
- Training on EmovDB HOT 2
- Voice synthesis by model is not the same as the voice with speaker ID HOT 1
- Try to train some new words
- inference speed on CPU
- Training time
- Two key points of training multispeaker mellotron
- how to train?
- colab demo for inferenece
- How to generate .musicxml files like the examples in `/data`? HOT 1
- Synthesize own text without style transfer gives poor audio results HOT 1
- Here's some code to start mellotron inference by calling a .py file from CLI [Docs]
- What is the reason of filtering "_" and "~" symbols?
- Something wrong with text padding HOT 5
- Can I use TensorRT to speed up model inference?
- colab error HOT 2
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