OpenVoice V2

Our Take

A solid dense audio model from MyShell AI. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing.

Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.

The tone color converter operates on mel-spectrogram representations. It captures the timbral qualities of the reference speaker — pitch range, resonance, vocal tract characteristics — and maps them onto the base speaker's output. The base speaker provides the language-specific phonetics and prosody for each of the six supported languages.

Because parameters are undisclosed, you cannot estimate VRAM requirements from parameter count alone. However, the model's practical behavior on consumer hardware is well-documented by the community. The model does not specify a context length, which is typical for TTS models — inference is driven by input text length and audio duration rather than token context windows.

OpenVoice V2's core capability is accurate tone color cloning with fine-grained style control. This translates to several practical applications:

• Content creation: Generate consistent voiceovers for video, podcasts, or audiobooks using a cloned voice, with the ability to adjust emotional tone per segment.
• Language learning: Clone a learner's voice and generate pronunciation examples in multiple languages, maintaining their natural voice identity.
• Voice preservation: Archive a specific voice by creating a digital clone from short recordings, then generate new speech in that voice across languages.
• Accessibility: Generate personalized synthetic voices for individuals who have lost their natural speech, using minimal reference audio.
• Localization: Dub content into multiple languages while preserving the original speaker's voice characteristics.

The model does not support real-time streaming out of the box. It processes text input and generates audio output as a complete inference pass. For production deployments, you would need to handle streaming at the application layer.

OpenVoice V2 runs on consumer hardware, but you need to understand the resource requirements to avoid surprises. The model is not quantized by default, and community quantization support varies.

• Minimum: 8 GB VRAM (RTX 3070, RTX 4060) — runs at reduced batch sizes, acceptable for single-sentence generation.
• Recommended: 12-16 GB VRAM (RTX 4070 Ti, RTX 4080, RTX 3090) — comfortable for paragraph-length generation with reasonable latency.
• Ideal: 24 GB VRAM (RTX 4090, RTX 3090, A5000) — allows batch processing and longer audio generation.
• Apple Silicon: M2 Pro or better with 16 GB unified memory. M4 Max handles the model without issue.

Inference speed depends on output audio length and hardware. On an RTX 4090, expect approximately 2-4 seconds of audio generated per second of wall-clock time for short clips (under 30 seconds). On an RTX 3060 with 12 GB, expect roughly 0.5-1 second of audio per second of processing time. These figures vary significantly based on audio sample rate and output length.

OpenVoice V2 does not have widely standardized quantization formats like GGUF or GPTQ. The model uses PyTorch checkpoints with FP16 weights. You can convert to FP16 or INT8 using standard PyTorch quantization, but community-supported quantized versions are limited. For most users, running the model in FP16 on a GPU with 12 GB VRAM is the most reliable approach.

The quickest way to run OpenVoice V2 locally is to clone the [official GitHub repository](https://github.com/myshell-ai/OpenVoice) and follow the Linux installation instructions. The model requires Python 3.9, PyTorch, and the dependencies listed in requirements.txt. The repository includes Jupyter notebooks (demo_part1.ipynb, demo_part2.ipynb, demo_part3.ipynb) that walk through voice cloning, style control, and cross-lingual generation.

For non-Linux platforms, community installation guides exist but are not officially maintained. Windows users will need WSL2 or a Docker container. macOS users on Apple Silicon can run the model using PyTorch's MPS backend, though performance is lower than CUDA.

If you are constrained on VRAM:

• Reduce the audio sample rate from 24 kHz to 16 kHz
• Limit generated audio to shorter segments (under 15 seconds)
• Use FP16 inference instead of FP32
• Process one utterance at a time rather than batching

OpenVoice V2 vs. ElevenLabs: ElevenLabs offers higher audio quality and more polished output, but it is a cloud-only service with usage-based pricing. OpenVoice V2 runs entirely offline, costs nothing per inference, and gives you full control over the pipeline. If you need production-grade audio quality and have budget for API costs, ElevenLabs is the better choice. If you need local inference, privacy, or unlimited usage, OpenVoice V2 wins.

OpenVoice V2 vs. Coqui TTS: Coqui TTS provides similar voice cloning capabilities with a broader range of pretrained models. Coqui has more active community development and better quantization support. OpenVoice V2's advantage is its native multilingual support with consistent quality across six languages, whereas Coqui's multilingual models vary in quality depending on language. OpenVoice V2 also offers more granular style control through its tone-color separation architecture.

Choose OpenVoice V2 when you need cross-lingual voice cloning with independent style control, and you want to run everything on your own hardware under the MIT License.