Home

Welcome to the so-vits-svc-fork wiki!

Creating a virtual environment

Windows:

py -3.10 -m venv venv
venv\Scripts\activate

Linux/MacOS:

python3.10 -m venv venv
source venv/bin/activate

Anaconda:

conda create -n so-vits-svc-fork python=3.10 pip
conda activate so-vits-svc-fork

Installing without creating a virtual environment may cause a PermissionError if Python is installed in Program Files, etc.

Hyperparameter Tuning

Most parameters have not been changed from those in VITS and seems to be "empirical" (or left alone).

Changing the train parameter basically will not cause the need to reset the model; changing LR, etc. may improve the training speed. The segment_size parameter is the length of the audio and the tensor to be passed to the decoder, and increasing it may increase the training speed of the decoder, but may not make much difference because of increased VRAM usage.

Changing the model parameter may reset some or all of the weights. The current model may be too large for one speaker or may not. Simply reducing the number of channels does not seem to be effective. Changing the decoder to MS-iSTFT-Generator etc. seems to double the inference speed.

The ssl_dim is the number of input channels, and the correct number of output channels for the officially trained ContentVec model is 768, but after applying final_proj it is 256.

Lacking understanding about this model and need to study more basics .... I shouldn't waste my time at the computer working on these too unimportant bugs...

Samples

Note that these trained models differ in some training hyperparameters, so this is not an exact comparison. Training was conducted using the same initial weights. Weights that did not match the size were reset.

NSF-Hifi-GAN Generator, ~1.3?k epochs, 192/768 channels

individualAudio.4.mp4

MS-iSTFT Generator, 29.4k epochs (somehow has noise)

individualAudio.2.mp4

NSF-Hifi-GAN Generator, 17.4k epochs, 32/64 channels

individualAudio.1.mp4

GPU Specs

Name	fp32 (TFLOPS)	fp16 (TFLOPS)	VRAM (GB)
T4	8.141	65.13	16
P100	9.626	19.05	16
P4000	5.304	0.08288	8
P5000	8.873	0.1386	16
P6000	12.63	0.1974	24
V100 PCIe	14.13	28.26	16/32
V100 SXM2	15.67	31.33	16/32
RTX4000	7.119	14.24	8
RTX5000	11.15	22.3	16
A4000	19.17	19.17	16
A5000	27.77	27.77	24
A6000	38.71	38.71	48
A100 PCIe/SXM4	19.49	77.97	40/80

Citing [The Best GPUs for Deep Learning in 2023 — An In\-depth Analysis](https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/#) https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/#, viewed 2023/03/28

View GPU Usage from console

wookayin/gpustat: 📊 A simple command-line utility for querying and monitoring GPU status.

Debugging realtime inference on YouTube, etc. with RTX Voice

• Windows: Speakers (NVidia RTX Voice)
• RTX Voice Input: (Default Device)
• RTX Voice Output: `CABLE Input (VB-Audio Virtual Cable)
• so-vits-svc-fork GUI Input: `CABLE Output (VB-Audio Virtual Cable)
• so-vits-svc-fork GUI Output: (your device)