OpenVoice vs RVC
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source model that clones a speaker's voice from a short sample and speaks new text in it.
Open source · MIT
Open-source tool that converts one voice into another using a short voice recording to train on.
The short answer
Choose OpenVoice if you need…
- Developers who want to build voice cloning or text-to-speech features into their own projects
- Researchers studying zero-shot voice cloning and cross-lingual speech generation
- Creators who want to experiment with synthetic voices in several languages
- Teams that need a permissively licensed (MIT) voice model
Choose RVC if you need…
- Hobbyists who want to make cover songs or voice-changed clips with a trained voice
- Developers comfortable with Python who want to experiment with voice conversion
- People with a clean, low-noise voice dataset of about 10 minutes or more
- Users with an NVIDIA GPU or willing to run on CPU
Side-by-side
| OpenVoice | RVC | |
|---|---|---|
| Type | AI model / agent | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 38.1k | 39k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Clone and build from source | Clone and build from source |
| Main language | Python | Python |
| Last code update | 19 months ago | 3 months ago |
What each one is
OpenVoice
OpenVoice is a voice cloning model released by MIT and MyShell. You give it a short reference recording of a voice and text to say, and it generates speech that tries to match that voice's tone color, with control over style such as emotion, accent, rhythm and pauses. The README says V2 supports English, Spanish, French, Chinese, Japanese and Korean and is licensed under MIT for commercial and research use.
RVC
RVC is a voice conversion framework with a web interface. You train a model on a voice recording (the README recommends at least 10 minutes of clean speech), then use it to make one voice sound like the trained voice. It also includes a real-time voice changer mode and an option to separate vocals from music.
Best for
OpenVoice
- Developers who want to build voice cloning or text-to-speech features into their own projects
- Researchers studying zero-shot voice cloning and cross-lingual speech generation
- Creators who want to experiment with synthetic voices in several languages
- Teams that need a permissively licensed (MIT) voice model
RVC
- Hobbyists who want to make cover songs or voice-changed clips with a trained voice
- Developers comfortable with Python who want to experiment with voice conversion
- People with a clean, low-noise voice dataset of about 10 minutes or more
- Users with an NVIDIA GPU or willing to run on CPU
Strengths
OpenVoice
- MIT licensed for both commercial and research use, according to the README
- Supports multiple languages and accents, including zero-shot cross-lingual cloning
- Offers granular control over style parameters such as emotion and accent
RVC
- Offers a web UI with options for training, inference, and real-time conversion
- Documented to train with a small dataset (about 10 minutes recommended)
- MIT licensed with pretrained models hosted on Hugging Face
Not ideal for
OpenVoice
- Non-technical users looking for a ready-made app: the README points to usage docs for running it, and no end-user app is described
- People who need guaranteed production-grade audio quality or support, since the README does not describe SLAs or support channels
- Anyone wanting to clone voices without the speaker's consent, which raises ethical and legal concerns the README does not address in detail
RVC
- Anyone needing a no-setup tool: installation involves Python 3.12, PyTorch, and downloading several model files
- People who want to clone voices without consent, since the README does not address rights or ethics beyond saying the base model is copyright-clear
- Users needing guaranteed low latency: the README reports about 170ms end-to-end, and 90ms only with ASIO devices that depend on driver support
Trade-offs
OpenVoice
- Latest release field is empty and the last push was April 2025, so maintenance status is unclear
- The README is brief; detailed setup and usage are delegated to separate docs the analysis did not see
- Requires comfort with Python and command-line setup
RVC
- Setup is multi-step and hardware-specific (separate requirements for CPU, NVIDIA RTX 50 series, and older NVIDIA cards)
- The README is partly in Chinese, and the English docs are linked separately, so some details may be harder to follow
- Latency and performance depend heavily on hardware and drivers
Getting started
OpenVoice
Clone the repository
git clone https://github.com/myshell-ai/OpenVoiceRVC
Create and activate a virtual environment (Ubuntu)
python3.12 -m venv .venvInstall CPU/AMD/Intel dependencies
python -m pip install -r requirments_cpu_py312.txtFrequently asked questions
What is the difference between OpenVoice and RVC?
OpenVoice: Open-source model that clones a speaker's voice from a short sample and speaks new text in it. RVC: Open-source tool that converts one voice into another using a short voice recording to train on. OpenVoice suits developers who want to build voice cloning or text-to-speech features into their own projects, while RVC suits hobbyists who want to make cover songs or voice-changed clips with a trained voice.
Are OpenVoice and RVC free?
OpenVoice: Free and open source. RVC: Free and open source.
Which is easier to set up, OpenVoice or RVC?
RVC is easier to get started with (rated intermediate); OpenVoice is rated advanced and usually needs more setup.
Is OpenVoice or RVC more popular?
RVC has more GitHub stars (39k) than OpenVoice (38.1k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to OpenVoice and RVC?
Other options people compare them with include GPT-SoVITS.
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.