我们在此版本增加了 HiFiSinger 架构支持 (见 configs/svc_hifisinger.py), 它有以下优势:
In this version, we added HiFiSinger architecture support (see configs/svc_hifisinger.py) with the following advantages:
- 推理速度远远快于 DiffSVC.
- The inference speed is much faster than DiffSVC.
- 在脏样本情况下表现更好 (但是极限性能不如 DiffSVC).
- It performs better under noisy sample conditions (although the ultimate performance is not as good as DiffSVC).
同时, 我们在此架构中加入了响度嵌入, 提高了模型的表现力.
At the same time, we added loudness (power) embedding to this architecture, enhancing the model's expressiveness.
2023-03-29 更新:
2023-03-29 Update:
我们新增了音色混合功能, 现有模型均可使用, 只需在推理命令中加入:
We added the timbre (or speaker) mixing function. You only need to update your inference command to:
--speaker "speaker_a:0.5,speaker_b:0.5"我们发布了一个 HiFiSinger + Content Vec 的预训练模型
We released a HiFiSinger + Content Vec pre-trained model.
我们强烈建议您参考随附的配置进行微调.
We strongly recommend that you refer to the config I've attached for finetuning.
Model Info
- Dataset Size: ~50 hours (M4Singer, OpenCpop, and In-House Data), 2.25x data aug
- Feature Extractor: ContentVec
- MD5: 45a84d1b626cbdb23f72042c7eac680f
- Steps: 540k on a 2x3090 server
本模型根据 CC-BY-NC-SA 4.0 license 发布, 下载前请仔细阅读.
This model is released under CC-BY-NC-SA 4.0 license, please read it before you download.