Comments (1)
Hi @Kristopher-Chen, thanks for the feedback!
There are some definite similarities between PPGs and the Soft Speech Units we proposed. The main difference is that soft units don't require text transcriptions to train. This can be useful for training VC systems in languages without large corpora of annotated speech. Additionally, things like laughter, breathing, etc. may be captured better by soft units than PPGs. Unfortunately, I haven't compared the approaches directly yet. I think it would be a useful benchmark but haven't had the chance to look into it.
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Related Issues (14)
- Interesting work! HOT 2
- speech resynthesis? HOT 1
- Can you share the training scripts? HOT 1
- Bug:TypeError: hubert_soft() got an unexpected keyword argument 'trust_repo'
- skipped phonemes in generated audio
- How to get discrete units from soft content encoder as its predicting discrete tokens in theory but in implementation its otherwise?
- Will the training code be available?
- inference and training scripts
- K-means training HOT 1
- Discrete content encoder example HOT 2
- About fine-tuning.
- can the pre-trained hubert-soft or discrete model be used for encoding mandarin Chinese language data ? HOT 3
- is real-time voice conversion possible?
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