Comments (1)
Hi @Pathos0925, thanks for the feedback!
Initially we used HuBERT-Base as the feature extractor. It also works well, maybe slightly worse than WavLM but not by much. I'm not sure if that's due to size or to the training procedure though. We haven't experimented with WavLM-Base or Base+, but I'm confident that they'll work as well. If you do experiment with a smaller model we'd be interested, so please share your results!
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Related Issues (20)
- out_wav is a wav file? HOT 1
- Question about WavLM layer choice HOT 2
- Torch Hub CPU inference support HOT 1
- prematch_dataset run very slow HOT 4
- prematch argument HOT 2
- Choice for k HOT 2
- Conversion output has very strong similarity to source audio. HOT 3
- Considering context around source features HOT 2
- extend to other SSL model features
- Will this work with 44100hz audio? HOT 4
- Error in quickstart HOT 2
- Size mismatch error HOT 2
- loss issues encountered in fine-tuning the model HOT 6
- Will this work for singing voice conversion (svc)? HOT 2
- Maybe mention memory consumption in readme.md? HOT 3
- Using another batch size in training HOT 2
- Some questions about implementation HOT 2
- Matching pool empty HOT 2
- Hints on improvements for training and matching HOT 3
- How to plug-in new finetuned HiFiGAN? HOT 1
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