Install and convert with RVC GUI

This guide covers Tiger14n's archived desktop app for converting audio files. Prepare a voice checkpoint and a short, dry WAV recording before you start.

1. Open the Windows app

Open the project's Windows package. Download the packaged asset from the release, and extract the contents to a writable folder. Keep the runtime and model assets in the extracted layout.
Open the included launcher. A source checkout can be started with RVC-GUI.bat on Windows or python rvcgui.py once the runtime is set up. The archived source expects a compatible Python and PyTorch environment plus hubert_base.pt in its project root. Use a separate environment if you are testing the source so other RVC installations retain their dependencies.
If the packaged app closes immediately, run its launcher from an open terminal and read the first error. An archive that contains source files only will not have the packaged runtime. If the error names HuBERT in a source install, place the project's HuBERT weights in the project root.

Generic HuBERT checkpoints are not recommended as the default encoder for voice conversion. Their speech prediction training does not explicitly remove speaker identity, so the features can carry source timbre into the output. For RVC, use the encoder expected by the model, such as ContentVec when the voice was trained with it.

2. Add a voice

Click Import model from .zip and select a ZIP containing an inference .pth and its matching .index if supplied. For manual placement, put the files in a per-voice subfolder under the app's models directory. For example:

models/
  MyVoice/
    MyVoice.pth
    MyVoice.index

Select the imported voice in the GUI. If it is absent, reopen the model list or restart the app after checking the folder. Use an inference model compatible with the archived build. A model created with an encoder or architecture the build cannot load should be tested in a compatible app.

3. Convert a baseline clip

In Input audio file, click Browse and select a short, dry vocal recording with a clearly voiced vowel and a consonant. Use a full WAV file as your baseline; separate a vocal stem first if the source is a full song.
Select the voice from the model controls. If your model has several supported speaker slots, choose the documented slot; otherwise use the default.
Leave Pitch at zero and set Feature retrieval rate to zero for the baseline. Leave F0 method at the default for this test.
Click Convert once and wait for Output path to show the generated file. Click Open to play it and compare it with the original at a similar volume. Copy the generated file to a distinct name if you want to keep it for comparison. Compare the same sentence before making another adjustment.
If you want stronger target character and have the matching index, try a small retrieval blend. If words become less clear, return to the baseline.

Common problems

What happens	What to check
Import reports missing weights	Open the archive and confirm it contains an inference `.pth` rather than just audio, documentation, or a training checkpoint.
No model appears	Check the extracted folder layout and ensure you opened the app from the same installation.
Wrong voice or metallic output	Check the selected checkpoint, compatible encoder, optional index and the source recording. Use an unprocessed clip with retrieval off.
File conversion appears to stop	Test a short local WAV and inspect the console. Check the first error before retrying a long render.
Performance is poor	Check which device the installed PyTorch build uses. For older platforms or newer model features, see the RVC tools comparison.