VoxCPM2 For Low VRAM (6GB/8GB)

Docker offers the quickest path to setting up this model locally.

Make sure to follow the instructions below.

During setup, the script automatically determines and applies the best settings tailored to your machine.

🧩 Hash sum → f3b4032d9db14831dbfcd62cca98c366 — Update date: 2026-06-28 Verify Processor: high single-core performance needed for token latency RAM: minimum 16 GB for stable 8B model loading Disk Space: 100 GB for multi-modal model vision components Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

VoxCPM2 is a next‑generation speech synthesis model designed to generate highly natural‑sounding audio across dozens of languages. It leverages a conditional parameterization approach that reduces memory footprint by up to 60 % while preserving voice fidelity. The architecture integrates a hierarchical encoder and a diffusion‑based decoder, enabling real‑time inference with latency under 150 ms on standard hardware. A built‑in speaker adaptation module allows users to personalize voice models with just a few seconds of audio, eliminating the need for extensive retraining. These capabilities are showcased in a comparative benchmark where VoxCPM2 outperforms prior models on MOS scores, word error rates, and multilingual consistency, as detailed in the table below.

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