STT2TTS
FreeNot checkedLocal-first speech-to-text and text-to-speech MCP server. Hot-swappable engines via config.yaml — no code changes, no API keys required.
About
Local-first speech-to-text and text-to-speech MCP server. Hot-swappable engines via config.yaml — no code changes, no API keys required.
README
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
Install STT2TTS in Claude Desktop, Claude Code & Cursor
unyly install stt2tts-mcpInstalls into Claude Desktop, Claude Code, Cursor & VS Code — handles npx, uvx and build-from-source repos for you.
First time? Get the CLI: curl -fsSL https://unyly.org/install | sh
Or configure manually
Run in your terminal:
claude mcp add stt2tts-mcp -- uvx --from git+https://github.com/pygodzilla/stt2tts-mcp stt2tts-mcpFAQ
Is STT2TTS MCP free?
Yes, STT2TTS MCP is free — one-click install via Unyly at no cost.
Does STT2TTS need an API key?
No, STT2TTS runs without API keys or environment variables.
Is STT2TTS hosted or self-hosted?
Self-hosted: the server runs locally on your machine via the install command above.
How do I install STT2TTS in Claude Desktop, Claude Code or Cursor?
Open STT2TTS on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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