Comments (1)
Thanks for your interest in our work -- there could be many ways to improve things, I'm not sure on what the best way is to make it better. For some of your suggestions, here's how I predict they might change the output:
- If you use HiFiGAN V2, I think it will do worse than HiFiGAN V1. V2 has much fewer parameters and scores much worse in the original HiFiGAN paper, so I do no think using V2 will improve it.
- Like with just about every model, if you train on more data it will probably improve things.
The training time for training on train-100 data from librispeech took around a week or two on 3x Quadro RTX 6000 GPUs. I hope that helps!
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Related Issues (20)
- 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
- Some question about features KNN HOT 2
- Request
- Question about the Used Hardware HOT 1
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