Comments (1)
안녕하세요!
질문해 주신 내용에 답변 드립니다.
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말씀해 주신 내용이 맞습니다.
더 덧붙이자면, Voice Conversion은 source speech를 target speaker의 목소리로 전환하는 작업입니다.
any to many는 학습 과정에서 보지 못했던 speaker의 음성을 source speech로 넣어주는 것이고,
many to many는 학습에 사용했었던 speaker의 음성을 source speech로 넣어주는 것입니다. -
네 맞습니다.
Multispeaker dataset으로 pretrain된 vocoder를 말씀해주신 assem-vc를 통과한 mel로 finetuning하는 방식입니다.
"ground truth-aligned"이라는 wording은 Tacotron2 논문에서 처음 제시되었으며, 최근에는 HiFi-GAN 논문에서도 같은 방식을 사용하는 등 TTS에서 간혹 사용되던 기법이었습니다.
Thank you for your interest in our work!
from assem-vc.
Related Issues (20)
- Pre-trained model HOT 1
- Reason to use speaker encoder over speaker embeddings? HOT 2
- NaN loss on cotatron_trainer HOT 3
- One-to-Many HOT 1
- Best way to extend the model to a new speaker HOT 6
- Regarding teacher forcing to calculate alignment HOT 2
- Training HIFI-GAN faster HOT 2
- Controllable and Interpretable Singing Voice Decomposition via Assem-VC HOT 9
- Speech+Transcript conditioned phoneme recognition as an alternative to G2P HOT 1
- Trouble importing AttrDict HOT 1
- Changin the sampling rate HOT 3
- 어떻게해야 모델을 한글 음소로 학습시킬 수 있나요? HOT 2
- Extending to n+1 target speakers using pretrained Cotatron
- Build custom non-English dataset with ARPABET HOT 2
- other models
- How to split singing voices
- teacher-forcing HOT 1
- Cross-lingual supported? HOT 1
- Training Cotatron has a problem HOT 1
- Where can I get audio samples? The link in the README is broken. HOT 1
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from assem-vc.