How to Run RVC WebUI on RTX 50-Series GPUs (Python 3.10 + PyTorch 2.7)
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How to Run RVC WebUI on RTX 50-Series GPUs (Python 3.10 + PyTorch 2.7)

A step-by-step setup guide for installing Retrieval-based Voice Conversion WebUI (RVC WebUI) on machines with NVIDIA RTX 50-series GPUs.

I originally had this issue with RTX 5080 and fixed it via a useful github post after spending several days trying to fix. It’s worth noting that the first install of following this didn’t work for me. Also worth noting, although sometimes it states an error on the GUI, it is in fact running. Always check the terminal.

I tried multiple times with different pytorch installs and nightly builds but always ran into more and more issues. Below seems to be the most consistant way of getting it to work.

Credit to: https://github.com/quasiblob

Credit to ChatGPT for doing a lot of the leg work on troubleshooting.

⚠️ Important Notes

  • These instructions are only for RTX 50-series GPUs
  • Assumes usage of Python 3.10.x
  • Uses a venv virtual environment
  • Some steps may need manual tweaks due to evolving dependencies
  • Core WebUI features appear to work:
    • ✅ Inference tab
    • ✅ Training tab
    • ✅ Checkpoint tab
    • ✅ ONNX export (without simplify)

📜 Installation Steps

1. Clone the Repository

git clone https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI RVC_webUI
cd RVC_webUI

2. Create and Activate Virtual Environment (Python 3.10)

py -3.10 -m venv venv
call ./venv/Scripts/activate.bat

3. Update Pip

python.exe -m pip install pip==24.0

4. Install Requirements

pip install -r requirements.txt

5. Install PyTorch (SM 12.0 requires >= 2.7)

pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu128 --force-reinstall

6. Install Missing Dependencies

pip install onnxsim
pip install matplotlib==3.10.3  # Optional

7. Verify ONNX Packages

pip show onnx onnxruntime onnxruntime-gpu
# Recommended to keep either CPU or GPU runtime—not both

8. Verify CUDA-Enabled PyTorch

pip show torch torchaudio

9. Check for Dependency Conflicts

pip check

10. Save Installed Packages (Optional)

pip freeze > install_packages.txt

11. Ensure Pip is Still v24.0

pip --version
python.exe -m pip install pip==24.0  # If not 24.0

12. Confirm ffmpeg is in PATH

ffmpeg  # Should return a version
# If not, install ffmpeg and add it to system PATH

13. Download Required Models

python tools/download_models.py

14. Modify go-web.bat

Open go-web.bat in a text editor and change:

From:
runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897

To:
venv\scripts\python.exe infer-web.py --pycmd venv\scripts\python.exe --port 7897

🛠️ Fixing Known Errors (Do all of the following)

❌ Error #1 – pickle.UnpicklingError (Inference)

  • Fix: Modify checkpoint_utils.py

Open:

.\venv\Lib\site-packages\fairseq\checkpoint_utils.py

At line 315, change:

state = torch.load(f, map_location=torch.device("cpu"), weights_only=False)

❌ Error #2 – 'FigureCanvasAgg' has no attribute 'tostring_rgb' (Training)

Open:

.\infer\lib\train\utils.py

At line 238, replace:

# data = ...
data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8).reshape(fig.canvas.get_width_height()[::-1] + (3,))

Repeat this fix in the plot_alignment_to_numpy function below.

Also, downgrade matplotlib:

pip install matplotlib==3.9.0

❌ Error #3 – PermissionError during ONNX Export

Modify infer-web.py

Locate and change export_onnx function:

def export_onnx(ModelPath, ExportedPath):
    from infer.modules.onnx.export import export_onnx as eo
    result = eo(ModelPath, ExportedPath)
    return result

Modify export.py

Open:

.\infer\modules\onnx\export.py

Update:

cpt = torch.load(ModelPath, map_location="cpu", weights_only=False)

And near the end, replace:

model, _ = onnxsim.simplify(ExportedPath)
onnx.save(model, ExportedPath)
return "Finished"

With:

try:
    # model, _ = onnxsim.simplify(ExportedPath)
    # onnx.save(model, ExportedPath)
    print("export_onnx: onnxsim/onnx.save skipped/completed (if uncommented).")
except NameError:
    print("export_onnx: onnxsim.simplify was skipped.")
except Exception as e:
    print(f"export_onnx: An unexpected error occurred: {e}")

print("export_onnx: About to return 'Finished'.")
return "Finished"

❌ Error #4 – Error during training

Open

.\infer\modules\train\

Create

If you click 'train model' it should work however, sometimes when you click 'Train feature index' it might error out. After clicking 'Train model' create a file called train_index.py and paste in the following:
.\iimport os
import numpy as np
import faiss
from pathlib import Path
import argparse

def compute_faiss_index(feat_dir, index_path):
    feats = []
    for f in Path(feat_dir).glob("*.npy"):
        feats.append(np.load(f))
    feats = np.concatenate(feats, axis=0)
    print(f"Total features shape: {feats.shape}")

    dim = feats.shape[1]
    print(f"Using faiss index of dimension {dim}")
    index = faiss.IndexFlatL2(dim)
    index.add(feats)
    faiss.write_index(index, str(index_path))
    print(f"Index saved to {index_path}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("-e", "--exp_name", required=True)
    parser.add_argument("-sr", "--sampling_rate", default="40k")
    parser.add_argument("-v", "--version", default="v2")
    args = parser.parse_args()

    exp_dir = Path(f"logs/{args.exp_name}/3_feature768")
    index_path = exp_dir / "index.pkl"

    print(f"Building index from {exp_dir}")
    compute_faiss_index(exp_dir, index_path)

Then run:
python infer\modules\train\train_index.py -e NAME_OF_EXPERIMENT -sr 40k -v v2

That should then allow the train feature to work

✅ Run the App

go-web.bat

You’re done! 🎉