voxcpm-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@voxcpm-mcpsynthesize 'Welcome to the demonstration of autonomous forensic incident response.'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
voxcpm-mcp
VoxCPM2 diffusion TTS as an MCP server for Claude Code.
Synthesize speech, clone voices, and narrate anything — from inside Claude Code with a/voxcpmslash command.
VoxCPM2 is a 2B-parameter diffusion text-to-speech model by OpenBMB that produces 48 kHz speech with expressive prosody and accurate voice cloning. This package wraps it in an MCP server so Claude Code (or any MCP client) can call it as a tool.
Architecture
Claude Code (MCP client)
│ stdio MCP
▼
voxcpm-mcp server (any Python ≥ 3.10)
│ subprocess stdin/stdout JSON
▼
worker.py (runs inside CUDA venv — torch + voxcpm installed)
│
VoxCPM2 model (openbmb/VoxCPM2, loaded once, stays in VRAM)The server and CUDA worker are deliberately separated so the MCP server itself has zero heavy dependencies — only mcp. The CUDA venv (with torch, voxcpm, soundfile, etc.) is pointed to via VOXCPM_PYTHON.
Related MCP server: Voice MCP
Requirements
Component | Requirement |
MCP server | Python 3.10+, |
Worker (CUDA venv) | Python 3.12, torch 2.x + CUDA 12.x, |
GPU | NVIDIA GPU with ≥ 6 GB VRAM (tested on RTX 4060 Laptop) |
Model |
|
Installation
1. Install the MCP server package
pip install -e .
# or, without cloning:
pip install git+https://github.com/OLGTX303/voxcpm-mcp.git2. Point to your CUDA venv
Set VOXCPM_PYTHON to the Python executable inside a venv that has voxcpm and torch+CUDA installed:
# Windows
set VOXCPM_PYTHON=C:\path\to\cuda-venv\Scripts\python.exe
# Linux / macOS
export VOXCPM_PYTHON=/path/to/cuda-venv/bin/pythonIf you used the fraudsentinel demo tools setup, the venv is already at:
F:\5Gcase\hackton\fraudsentinel\demotools\fraudsentinel-demo\.venv312\Scripts\python.exe3. Download VoxCPM2 model (if not already cached)
from huggingface_hub import snapshot_download
snapshot_download("openbmb/VoxCPM2")4. Register with Claude Code
claude mcp add voxcpm-tts \
-e VOXCPM_PYTHON="C:\path\to\cuda-venv\Scripts\python.exe" \
-e VOXCPM_OUTPUT_DIR="C:\path\to\output" \
-- voxcpm-mcp5. Install the /voxcpm skill
Copy the skill file to your Claude Code commands directory:
# Windows
copy .claude\commands\voxcpm.md %APPDATA%\Claude\commands\voxcpm.md
# Linux / macOS
cp .claude/commands/voxcpm.md ~/.claude/commands/voxcpm.mdOr place it in your project's .claude/commands/ folder to make it project-local.
MCP tools
Tool | Description |
| Text → WAV using default VoxCPM2 voice |
| Text → WAV cloning a reference speaker voice |
| Load model into VRAM (warm-up, ~10 s) |
| Check worker subprocess health |
synthesize parameters
Parameter | Type | Default | Description |
| string | required | Text to synthesize |
| string |
| Output filename inside |
| integer | 30 | Diffusion steps (10=fast draft, 50=best quality) |
synthesize_with_clone parameters
Parameter | Type | Default | Description |
| string | required | Text to synthesize |
| string | required | Absolute path to reference WAV (48 kHz mono) |
| string | required | Transcript of the reference WAV |
| string |
| Output filename |
| integer | 30 | Diffusion steps |
/voxcpm skill
Once installed, use the slash command in Claude Code:
/voxcpm Welcome to the demonstration of autonomous forensic incident response./voxcpm Clone my voice from ref.wav — it says "Hello world". Now say: Good morning./voxcpm warm upEnvironment variables
Variable | Default | Description |
|
| Python executable with VoxCPM2 + CUDA |
|
| Directory where WAV files are saved |
License
MIT — see LICENSE.
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