`model_0.pt` is a pre-trained model using contentvec768l12 encoder. A demo of training from scratch (without using a pre-trained model) is [here](https://www.bilibili.com/audio/au4270993).

Unzip the demo model into ``exp`` directory, unzip the sample audios to the main directory, then run the demo samples: ```bash # opencpop (1st speaker) python main_diff.py -i samples/source.wav -diff exp/diffusion-new-demo/model_200000.pt -o samples/svc-opencpop+12key.wav -id 1 -k 12 -kstep 100 # kiritan (2nd speaker) python main_diff.py -i samples/source.wav -diff exp/diffusion-new-demo/model_200000.pt -o samples/svc-kiritan+12key.wav -id 2 -k 12 -kstep 100 # mix the timbre of opencpop and kiritan in a 0.5 to 0.5 ratio python main_diff.py -i samples/source.wav -diff exp/diffusion-demo/model_200000.pt -o samples/svc-opencpop_kiritan_mix+12key.wav -mix "{1:0.5,2:0.5}" -k 12 -kstep 100 ``` The training data of this 2-speaker model is from [opencpop](https://wenet.org.cn/opencpop/) and [kiritan](https://zunko.jp/kiridev/login.php) Thanks to [CN_ChiTu](https://github.com/CNChTu) for helping to train this model.

Unzip the two demo models into ``exp`` directory, then run the demo samples: ```bash # opencpop (1st speaker) python main_diff.py -i samples/source.wav -ddsp exp/ddsp-demo/model_300000.pt -diff exp/diffusion-demo/model_400000.pt -o samples/svc-opencpop+12key.wav -id 1 -k 12 -kstep 300 # kiritan (2nd speaker) python main_diff.py -i samples/source.wav -ddsp exp/ddsp-demo/model_300000.pt -diff exp/diffusion-demo/model_400000.pt -o samples/svc-kiritan+12key.wav -id 2 -k 12 -kstep 300 # mix the timbre of opencpop and kiritan in a 0.5 to 0.5 ratio python main_diff.py -i samples/source.wav -ddsp exp/ddsp-demo/model_300000.pt -diff exp/diffusion-demo/model_400000.pt -o samples/svc-opencpop_kiritan_mix+12key.wav -mix "{1:0.5,2:0.5}" -k 12 -kstep 300 ``` The training data of this 2-speaker model is from [opencpop](https://wenet.org.cn/opencpop/) and [kiritan](https://zunko.jp/kiridev/login.php) Thanks to [lafi2333](https://github.com/lafi2333) for helping to train the demo models.

Unzip the pretrained model into ``exp`` directory, then run the demo samples: ```bash # opencpop (1st speaker) python main.py -i samples/source.wav -m exp/multi_speaker/model_300000.pt -o samples/svc-opencpop+12key.wav -k 12 -id 1 # kiritan (2nd speaker) python main.py -i samples/source.wav -m exp/multi_speaker/model_300000.pt -o samples/svc-kiritan+12key.wav -k 12 -id 2 # mix the timbre of opencpop and kiritan in a 0.5 to 0.5 ratio python main.py -i samples/source.wav -m exp/multi_speaker/model_300000.pt -o samples/svc-opencpop_kiritan_mix+12key.wav -k 12 -mix "{1:0.5, 2:0.5}" ``` The training data of this 2-speaker model is from [opencpop](https://wenet.org.cn/opencpop/) and [kiritan](https://zunko.jp/kiridev/login.php) Thanks to [CN_ChiTu](https://github.com/CNChTu) for helping to train this model.

Unzip the pretrained model into ``exp`` directory, then run the demo samples: ```bash # opencpop (1st speaker) python main.py -i samples/source.wav -m exp/multi_speaker/model_300000.pt -o samples/svc-opencpop+12key.wav -k 12 -pe crepe -e true -id 1 # kiritan (2nd speaker) python main.py -i samples/source.wav -m exp/multi_speaker/model_300000.pt -o samples/svc-kiritan+12key -k 12 -pe crepe -e true -id 2 # mix the timbre of opencpop and kiritan in a 0.5 to 0.5 ratio python main.py -i samples/source.wav -m exp/multi_speaker/model_300000.pt -o samples/svc-kiritan+12key -k 12 -pe crepe -e true -mix "{1:0.5, 2:0.5}" ``` The training data of this 2-speaker model is from [opencpop](https://wenet.org.cn/opencpop/) and [kiritan](https://zunko.jp/kiridev/login.php)

Unzip the pretrained model into ``exp`` directory, then run the demo samples: ```bash # origin output python main.py -i samples/source.wav -m exp/opencpop/model_300000.pt -o samples/svc-opencpop+10key-origin.wav -k 10 -pe crepe # enhanced output python main.py -i samples/source.wav -m exp/opencpop/model_300000.pt -o samples/svc-opencpop+10key-enhance.wav -k 10 -pe crepe -e true ``` The training data is from [opencpop](https://wenet.org.cn/opencpop/)