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.

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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.

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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.

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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:

1. Add an AI Agent node with an MCP Client Tool
2. Set connection type to SSE
3. Enter the URL: http://127.0.0.1:8000/sse
4. 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

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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.

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Voice Cloning

Clone any voice from a short audio sample:

1. Record or find a WAV file — 3 to 15 seconds of clean speech
2. Know the transcript — the exact words spoken in the recording
3. 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

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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.

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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

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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.

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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

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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

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How It Works

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

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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

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Credits

• NeuTTS by Neuphonic — the TTS engine
• MCP Protocol by Anthropic — the AI tool standard

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Author

Tayyab Ilyas — PhD Researcher & EdTech Founder

Building AI-powered tools for educators and researchers.

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License

MIT License. The underlying NeuTTS models have their own licenses — see the NeuTTS repository for details.

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