Cheema Text-to-Voice MCP Server

Free, open-source text-to-speech for AI assistants. Generate natural speech directly from Claude Desktop, Claude Code, n8n, or any MCP-compatible platform. No API keys. No cloud. Runs entirely on your machine.

What Can It Do?

Just ask your AI assistant to speak — it handles the rest:

"Say hello in French using the juliette voice"

"Convert this paragraph to speech and save it as intro.wav"

"Clone my voice from this recording and use it to read my essay"

5 built-in voices across 4 languages, plus instant voice cloning from a short audio sample.

Quick Start

1. Install Prerequisites

You need Python 3.10+ and espeak-ng:

# Ubuntu / Debian
sudo apt install espeak-ng

# macOS
brew install espeak-ng

# Windows
choco install espeak-ng

2. Clone & Install

git clone https://github.com/MuhammadTayyabIlyas/CHeema-Text-to-Voice-MCP-Server.git
cd CHeema-Text-to-Voice-MCP-Server

python -m venv venv
source venv/bin/activate        # Linux/macOS
# venv\Scripts\activate          # Windows

pip install -e .
pip install "mcp[cli]"

3. Connect to Your AI Assistant

Pick your platform and follow the steps below.

Setup by Platform

Claude Desktop

Add this to your config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "cheema-tts": {
      "command": "/full/path/to/CHeema-Text-to-Voice-MCP-Server/venv/bin/python",
      "args": ["/full/path/to/CHeema-Text-to-Voice-MCP-Server/mcp_server.py"],
      "env": {}
    }
  }
}

Restart Claude Desktop. You'll see the TTS tools appear in the tools menu.

Claude Code

claude mcp add cheema-tts -- /full/path/to/venv/bin/python /full/path/to/mcp_server.py

Then just ask Claude to generate speech in any conversation.

n8n

Start the server in SSE mode:

cd CHeema-Text-to-Voice-MCP-Server
source venv/bin/activate
python mcp_server.py --transport sse --host 127.0.0.1 --port 8000

In your n8n workflow:

Add an AI Agent node with an MCP Client Tool
Set connection type to SSE
Enter the URL: http://127.0.0.1:8000/sse
The agent can now call any TTS tool

Any Other MCP Client

Start the server with your preferred transport:

# SSE (for web platforms and remote clients)
python mcp_server.py --transport sse --host 0.0.0.0 --port 8000

# Streamable HTTP
python mcp_server.py --transport streamable-http --host 0.0.0.0 --port 8000

Connect your MCP client to:

SSE: http://<your-host>:8000/sse
HTTP: http://<your-host>:8000/mcp

Available Voices

| Voice | Language | Description | |-------|----------|-------------| | jo | English | Default — clear, natural female voice | | dave | English | Male voice | | greta | German | German female voice | | juliette | French | French female voice | | mateo | Spanish | Spanish male voice |

Use tts_list_speakers to see all voices including any custom ones you've added.

Voice Cloning

Clone any voice from a short audio sample:

Record or find a WAV file — 3 to 15 seconds of clean speech
Know the transcript — the exact words spoken in the recording
Ask your AI assistant:

"Add a new speaker called 'alex' from /path/to/recording.wav — the transcript is 'This is what I said in the recording'"

Or call the tool directly:

tts_add_speaker(name="alex", wav_path="/path/to/recording.wav", ref_text="This is what I said in the recording")

Custom voices are saved permanently and available across restarts.

Tips for best results:

Mono audio, 16-44 kHz sample rate
3-15 seconds of continuous, natural speech
Minimal background noise

Available Tools

| Tool | What It Does | |------|-------------| | tts_help | Shows a complete usage guide with examples — start here | | tts_synthesize | Converts text to speech, saves a WAV file | | tts_list_speakers | Lists all available voices | | tts_list_models | Shows the active model and alternatives | | tts_add_speaker | Clones a new voice from an audio sample |

tts_synthesize Parameters

| Parameter | Required | Default | Description | |-----------|----------|---------|-------------| | text | Yes | — | The text to convert to speech | | speaker | No | "jo" | Which voice to use | | output_filename | No | auto-generated | Custom filename for the WAV output |

MCP Prompts (Templates)

| Prompt | Description | |--------|-------------| | quick_speech | Fast speech generation — just provide text and optional speaker | | voice_clone_guide | Step-by-step walkthrough for adding a new voice |

These appear automatically in Claude Desktop's prompt picker.

Models

The default model (neutts-nano) works great on CPU. Larger models produce higher quality but need more resources.

| Model | Language | Size | Notes | |-------|----------|------|-------| | neuphonic/neutts-nano | English | ~229M | Default — fast, good quality | | neuphonic/neutts-air | English | ~552M | Higher quality, slower | | neuphonic/neutts-nano-german | German | ~229M | German language | | neuphonic/neutts-nano-french | French | ~229M | French language | | neuphonic/neutts-nano-spanish | Spanish | ~229M | Spanish language | | neuphonic/neutts-*-q4-gguf | varies | smaller | Quantized — faster, less memory | | neuphonic/neutts-*-q8-gguf | varies | medium | Quantized — balanced |

Switch models using environment variables:

NEUTTS_BACKBONE="neuphonic/neutts-air" python mcp_server.py

Configuration

All settings are optional — defaults work out of the box.

| Variable | Default | Description | |----------|---------|-------------| | NEUTTS_BACKBONE | neuphonic/neutts-nano | HuggingFace model repo | | NEUTTS_BACKBONE_DEVICE | cpu | cpu or cuda for GPU | | NEUTTS_CODEC | neuphonic/neucodec | Audio codec model | | NEUTTS_CODEC_DEVICE | cpu | cpu or cuda for GPU | | NEUTTS_OUTPUT_DIR | ./output | Where WAV files are saved | | NEUTTS_SAMPLES_DIR | ./samples | Built-in speaker samples | | NEUTTS_SPEAKERS_DIR | ./speakers | Custom voice data | | NEUTTS_TRANSPORT | stdio | stdio, sse, or streamable-http | | NEUTTS_HOST | 127.0.0.1 | Bind address (SSE/HTTP only) | | NEUTTS_PORT | 8000 | Bind port (SSE/HTTP only) |

GPU Acceleration

For faster synthesis on NVIDIA GPUs:

NEUTTS_BACKBONE_DEVICE=cuda NEUTTS_CODEC_DEVICE=cuda python mcp_server.py

Requires PyTorch with CUDA support.

Running as a Service

For production use, create a systemd service so it starts automatically:

# /etc/systemd/system/cheema-tts.service
[Unit]
Description=Cheema Text-to-Voice MCP Server
After=network.target

[Service]
Type=simple
WorkingDirectory=/path/to/CHeema-Text-to-Voice-MCP-Server
ExecStart=/path/to/venv/bin/python mcp_server.py --transport sse --host 127.0.0.1 --port 8000
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target

sudo systemctl enable --now cheema-tts

To expose it over HTTPS, put an Nginx or Caddy reverse proxy in front with these key settings for SSE:

Disable proxy buffering (proxy_buffering off)
Set a long read timeout (proxy_read_timeout 86400)
Add X-Accel-Buffering: no header

Troubleshooting

Server won't start?

Check espeak-ng: espeak-ng --version
Check dependencies: pip list | grep -E "mcp|neutts|torch|soundfile"

No audio output?

Check the output/ directory for WAV files
Verify speaker name with tts_list_speakers

Slow first run?

Normal — the first run downloads model weights from HuggingFace (~200-500MB). Cached after that.

Want GPU acceleration?

Set NEUTTS_BACKBONE_DEVICE=cuda and NEUTTS_CODEC_DEVICE=cuda

How It Works

The server loads the NeuTTS backbone model and audio codec on startup
Speaker voice prints (.pt files) are loaded into memory
When you request speech, text is phonemized and combined with the speaker's voice reference
The model generates speech tokens, decoded into a 24kHz waveform
Output is saved as a standard WAV file

Project Structure

CHeema-Text-to-Voice-MCP-Server/
├── mcp_server.py       # MCP server entry point
├── neutts/             # NeuTTS engine
├── samples/            # Built-in speaker voices (.wav, .pt, .txt)
├── speakers/           # Custom cloned voices (auto-created)
├── output/             # Generated audio files (auto-created)
└── examples/           # Usage examples