STT2TTS

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.

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

Source-only releases ship on main for clean installs. The dev branch carries the test suite (tests/test_config_loader.py, tests/test_mcp_integration.py, tests/test_piper_no_json.py) for contributors.

git clone https://github.com/pygodzilla/stt2tts-mcp
cd stt2tts-mcp
git checkout dev                 # for tests + dev iteration
pip install -e ".[all]"
python -m stt2tts_mcp.server

License

MIT