Click on "Deploy 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-mcp synthesize '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.
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.
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: Kokoro MCP Server
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 |
|
pip install -e .
# or, without cloning:
pip install git+https://github.com/OLGTX303/voxcpm-mcp.gitSet 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.exefrom huggingface_hub import snapshot_download
snapshot_download("openbmb/VoxCPM2")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-mcp/voxcpm skillCopy 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.
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 parametersParameter | 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 parametersParameter | 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 skillOnce 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 upVariable | Default | Description |
|
| Python executable with VoxCPM2 + CUDA |
|
| Directory where WAV files are saved |
MIT — see LICENSE.
Check that the VoxCPM2 worker subprocess is alive.
Inputs: no named parameters.
Load VoxCPM2 into VRAM now (takes ~10 s on RTX 4060 Laptop). Call this before synthesize if you want the first synthesis to be fast.
Inputs: no named parameters.
Synthesize speech from text using VoxCPM2 (2B diffusion TTS, 48 kHz). Returns the path to the output WAV file and its duration.
Inputs: text (required), output_filename, steps.
Synthesize speech cloning a voice from a reference WAV. The reference WAV sets the speaker identity, prosody, and style. Both reference and output are 48 kHz mono WAV.
Inputs: text (required), reference_wav_path (required), reference_text (required), output_filename, +1 more.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 4 tools
Each tool targets a distinct operation: health check, model preloading, standard synthesis, and cloned synthesis. There is no overlap in functionality.
All tool names use a consistent snake_case pattern with clear, imperative verbs (ping, preload, synthesize, synthesize_with_clone).
Four tools covers the essential workflow of a TTS server: ensure service is alive, preload model for fast synthesis, generate speech, and clone voice. No extraneous or missing tools.
Core synthesis and cloning are covered, but missing advanced features like listing available voices or managing models. Minor gap for production use.
Lets Claude, Cursor or any MCP client build voice agents and place real phone calls.
AI voice generation: text-to-speech and voice cloning from any MCP client.
Turn Claude into a creative studio: DNA-locked characters, images, video, voiceover — 55 tools.
Transcribe audio and video with Speechmatics speech-to-text from Claude and any MCP client.
