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Top RVC AI Voice Conversion Tools 2024

RVC AI – Retrieval-based Voice Conversion: If you’re passionate about voice technologies, this blog post is tailored for you. I’ve simplified complex terminologies and made sure this guide is beginner-friendly.

By the end of this post, you’ll understand what RVC AI is, its features, and how you can set it up and use it. So, let’s get started!

In today’s dynamic technological landscape, RVC AI stands out as a remarkable innovation. It leverages the power of neural networks to transform one voice into another. And the best part? It’s built on the VITS model – a cutting-edge text-to-speech system.

What is RVC AI Voice Conversion?

RVC offers realistic voice conversions without draining your resources. Retrieval-based Voice Conversion (RVC) is a technique that harnesses deep neural networks. Its primary function is to modify the voice of a speaker, making it sound like another distinct voice.

Based on the VITS model, RVC ensures high-quality voice conversions with minimal data and computational efforts.

Features of Retrieval Voice Conversion

  • Minimal Tone Leakage: RVC minimizes tone leakage by substituting the source feature with the training-set feature from top1 retrieval.
  • Efficient Training: Train with ease, even on low-end graphics cards.
  • High Performance with Little Data: Achieve notable results with little data. It is recommended to have at least 10 minutes of low-noise speech.
  • Model Fusion: Alter timbres using model fusion. Check the ckpt processing tab and opt for ckpt merge.
  • User-Friendly Interface: RVC offers a user-friendly Webui interface.
  • UVR5 Model: Use the UVR5 model to separate vocals and instruments swiftly.

How to Use RVC AI Model:

Preparing the environment is the first step. If you’re already familiar with setting up PyTorch, you might find some of these steps familiar. If not, don’t worry! I’ve got you covered.

Setting Up the Environment

  1. Install Core Dependencies for PyTorch:
  2. For Windows Users with Nvidia Ampere Architecture:
    • Install PyTorch with the specific CUDA version using:
      pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
  3. Install Poetry:
  4. Install Dependencies:
    • Use the command:
      poetry install
    • For MacOS users facing issues with faiss 1.7.2, try:
      pip install faiss-cpu==1.7.0
    • Then:
      pip install -r requirements.txt

Preparing Pre-Models

RVC AI relies on some pre-trained models. You can acquire these from Huggingface space. The key pre-trained models and files you’ll need include hubert_base.pt, ./pretrained, ./uvr5_weights, and ./pretrained_v2. For Windows users, you might also need the ffmpeg.exe dictionary.

After preparing the environment and models, kickstart the Webui using:

python infer-web.py

Windows users can directly download RVC-beta.7z, extract it, and use go-web.bat to launch Webui.


What is the primary purpose of RVC AI?

RVC AI is designed to convert one voice into another using deep neural networks.

Is it resource-intensive?

No, RVC AI is optimized for minimal data and computational resources.

Can I use RVC AI on a low-end graphics card?

Absolutely! RVC AI is designed for efficient training, even on low-end graphics cards.


RVC AI is a revolutionary tool in the voice technology domain. Its ability to transform voices with minimal resources makes it a game-changer. Whether you’re a budding enthusiast or a seasoned professional, RVC AI has something to offer.

Dive in, experiment, and explore the endless possibilities. And if you found this guide useful, don’t forget to explore more resources on Sahu4you.com.

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