Comments (1)
Hi, sorry for the late reply, I was busy finishing my work in the last few weeks :)
Fine-tuned global embedding is used for the condition, while directly using the features generated by wav2vec2 for training should harm the style similarity. As for the VQ codebook, we did not try the Gaussian-based VQVAE (i.e., include a regularization loss before quantification), and it may help to stabilize model training. For shuffle operation, it is similar to the style-aware dropout operation, and I think these two operations should have similar performance.
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Related Issues (18)
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