The Beginner's Guide to Diff-SVC
  • Project Overview
  • Setting Up
    • Requirements
    • Setting up the Environment
  • Start
    • Dataset Preparation
    • Preprocessing
    • Training
    • Inference
  • See also
  • Appendix
  • Resource List
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  • Set up Python Environment
  • Using Conda
  • Install Diff-SVC Dependencies
  • Install Tensorboard (Optional)
  • Install Jupyter Notebook and Create Kernel (Optional)
  • Download the Required Checkpoints

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  1. Setting Up

Setting up the Environment

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Last updated 2 years ago

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Setting up the environment is probably the "hardest" part for many people.

Set up Python Environment

This project is tested under Python 3.8. Other versions may cause issues.

To avoid conflict with other projects, it's recommended to use a virtual environment. We will use in this guide, you can also use or other options if you are familiar with them.

Using Conda

  1. Download and install Anaconda:

If you are on a Windows machine, go to Start -> Anaconda3 -> Anaconda Prompt to use the Anaconda Prompt that you just installed to run the following steps. (NOT Anaconda Powershell Prompt since commands will be slightly different on Powershell)

  1. Run this command to create a virtual environment with Python 3.8 named diff-svc (you can also use other names):

conda create -n diff-svc python=3.8
  1. Next, run this command to activate the virtual environment you just created:

conda activate diff-svc

If the next line starts with (diff-svc), the virtual environment is activated successfully.

Make sure you are in this environment when running the commands later in this guide.

Install Diff-SVC Dependencies

  1. Go to Diff-SVC's , click on code -> Download ZIP

Alternativly, use git clone if you know what it is

Do NOT download from Releases, it's not the latest version.

  1. Extract the zip file, you should now have a diff-svc-main folder.

  2. Run this command to install PyTorch:

    conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

If your supported CUDA version is lower than 11.6 even after upgrading the NVIDIA Driver, try using conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 to install the 11.3 version.

  1. Run this command to install other required packages:

    pip install -r requirements_short.txt

Install Tensorboard (Optional)

Tensorboard helps you visualize training. This step is not required for the program to function but it's recommended to do.

Run this command in the diff-svc environment to install Tensorboard.

pip install tensorboard

Install Jupyter Notebook and Create Kernel (Optional)

  1. Run this command to install Jupyter Notebook

conda install notebook

To make the diff-svc environment visible in Jupyter, you need to create a kernel.

  1. Run this command to install ipykernel:

conda install ipykernel
  1. Create a kernel named diff-svc (you can also use other names):

ipython kernel install --user --name=diff-svc

Download the Required Checkpoints

You also need to download some model checkpoints and place them correctly.

  1. Get checkpoints.zip from the Diff-SVC Discord server or some other mirror links online.

  2. Extract the folders inside and put them under a folder called checkpoints under your diff-svc folder.

    1. Extract the zip file and put the nsf_hifigan folder under checkpoints.

    2. (The 24kHz vocoder is not needed in this case, but you can also keep it there)

to the diff-svc-main folder in the command line and make sure you are in the diff-svc environment.

This command may not apply to everyone. You can run nvidia-smi to check the highest supported CUDA version of your GPU, then use the command from to download the corresponding version.

If you are on a Windows machine, you may encounter an error that says Microsoft Visual C++ 14.0 or greater is required when building webrtcvad. You can follow the instruction on the error message and install or just install .

One of the options for inference is using . This step is not required if you want to use other tools for inference or if you want to use other programs (like ) that also support Jupyter Notebook. You may decide if you want to do it this way and come back later.

(If you want to train a 44.1kHz model), you should also download the 44.1kHz vocoder from . (You only need nsf_hifigan_xxxxxx.zip, ignore the "finetune" and "onnx" ones)

Anaconda
Miniconda
https://www.anaconda.com/products/distribution
GitHub repository
Navigate
https://pytorch.org/get-started/locally/
the latest build tools
Visual Studio
Jupyter Notebook
VSCode
here
Folder Structure