Installation

pip install voxcpm

Requirements: Python ≥ 3.10 (<3.13), PyTorch ≥ 2.5.0, CUDA ≥ 12.0. See Quick Start Docs for details.

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🚢 Production Deployment (Nano-vLLM)

For high-throughput serving, use Nano-vLLM-VoxCPM — a dedicated inference engine built on Nano-vLLM with concurrent request support and an async API.

pip install nano-vllm-voxcpm

from nanovllm_voxcpm import VoxCPM
import numpy as np, soundfile as sf

server = VoxCPM.from_pretrained(model="/path/to/VoxCPM", devices=[0])
chunks = list(server.generate(target_text="Hello from VoxCPM!"))
sf.write("out.wav", np.concatenate(chunks), 48000)
server.stop()

RTF as low as ~0.13 on NVIDIA RTX 4090 (vs ~0.3 with the standard PyTorch implementation), with support for batched concurrent requests and a FastAPI HTTP server. See the Nano-vLLM-VoxCPM repo for deployment details.

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Install from source (latest main — vllm-omni is rapidly evolving)

uv pip install vllm==0.19.0 --torch-backend=auto git clone https://github.com/vllm-project/vllm-omni.git && cd vllm-omni uv pip install -e .

See the [vLLM-Omni installation guide](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/) for other platforms (ROCm, XPU, MUSA, NPU) and Docker images.

```bash

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## 📄 License

VoxCPM model weights and code are open-sourced under the [Apache-2.0](LICENSE) license.