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STT2TTS MCP

by pygodzilla
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Python
Local

STT2TTS MCP Server

Local-first speech-to-text and text-to-speech MCP server. Hot-swappable engines via config.yaml — no code changes, no API keys required.

┌──────────────┐     stdio      ┌──────────────────┐
│ MCP client   │ ◀────────────▶ │ stt2tts-mcp      │
│              │                │  ├─ STT engine   │ ──▶ faster-whisper
│              │                │  └─ TTS engine   │ ──▶ piper / kokoro / coqui
└──────────────┘                └──────────────────┘
                                       │
                                       ▼
                              config.yaml (hot-reload)

Why

Replaces whisper-mcp. Works offline, ships with five STT and six TTS engines, switches per-task via config.

Related MCP server: speaches-mcp

Install

pip install stt2tts-mcp

# Add the engines you actually use:
pip install stt2tts-mcp[stt-faster-whisper]   # local STT
pip install stt2tts-mcp[tts-piper]            # local TTS (~50MB voices)

# Register with your MCP client (consult your client's docs for the exact
# config file location — most use mcp_config.json or a per-client equivalent):
{
  "mcp": {
    "stt2tts": {
      "type": "local",
      "command": ["stt2tts-mcp"],
      "enabled": true
    }
  }
}

Engines

STT

Size

License

Best for

faster-whisper

39M – 2.9 GB

MIT

English, INT8 CPU, fastest

sherpa-onnx

39M – large

Apache 2.0

Multilingual

OpenAI API

cloud

Proprietary

Highest accuracy, needs key

Ollama

varies

MIT

Local LLM integration

LMStudio

varies

MIT

Local model server

TTS

Voice size

License

Best for

Piper

20 – 50 MB

Apache 2.0

Smallest, 10-20× realtime

Kokoro-82M

~330 MB

Apache 2.0

Quality/size ratio

Coqui XTTS

~1.5 GB

MPL 2.0

Voice cloning, needs GPU

OpenAI API

cloud

Proprietary

All voices, needs key

Ollama

varies

MIT

LLM-based voices

LMStudio

varies

MIT

Local model server

Configure

config.yaml:

stt:
  engine: faster_whisper   # sherpa_onnx | openai_api | ollama | lmstudio
  enabled: true
  params:
    model_size: base.en     # tiny.en | base.en | small.en | medium.en
    device: cpu             # cpu | cuda

tts:
  engine: piper             # kokoro | coqui | openai_api | ollama | lmstudio
  enabled: true
  params:
    voice: en_US-lessac-medium
    model_dir: ~/.cache/piper

Reload without restart by calling the reload_config MCP tool.

MCP Tools

Tool

What it does

transcribe(audio_path, language?)

Audio file → text

speak(text, output_path, voice?)

Text → WAV file

list_stt_models

Available STT models

list_tts_voices

Available TTS voices

reload_config

Re-read config.yaml, rebuild engines

health_check

Engine status

All formats ffmpeg supports (wav, mp3, ogg, flac, m4a) are accepted; STT input is auto-converted to 16 kHz mono.

Develop

git clone https://github.com/your-org/stt2tts-mcp
cd stt2tts-mcp
pip install -e ".[all]"
python -m stt2tts_mcp.server

License

Apache 2.0

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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