Energy and transcript heuristics for voice turn endpoint detection.
Runs on your machine from its package; the steps are below.
At most: only reads or looks up information.
Odel charges nothing per call. The publisher may charge for its own service.
Runs locally over stdio
This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).
Distributed as an MCP bundle (`https://github.com/Alpha-Park/genpark-voice-turn-taking-endpoint-detector-skill/releases/download/v1.0.1/genpark-voice-vad.mcpb`) — download it and import the bundle in your MCP client.
Energy and transcript heuristics for voice turn endpoint detection.
This is a heuristic endpoint detector over precomputed dB energies and a transcript. It does not decode audio, transcribe speech, or run a trained VAD model. Confidence values are heuristic scores, not calibrated probabilities.
Python 3.9 or newer. The library and stdio MCP server have no runtime dependencies.
python -m pip install https://github.com/Alpha-Park/genpark-voice-turn-taking-endpoint-detector-skill/releases/download/v1.0.1/genpark_voice_vad-1.0.1-py3-none-any.whl
PyPI publication is pending account setup. The intended PyPI project is genpark-voice-vad;
do not assume pip install genpark-voice-vad is available until the project is published.
from genpark_voice_vad import VoiceTurnTakingEndpointDetector
client = VoiceTurnTakingEndpointDetector()
print(client.run_benchmark_turn_detection())
After installing the wheel, configure your MCP client with the installed command:
{
"mcpServers": {
"genpark-voice-vad": {
"command": "genpark-voice-vad",
"args": []
}
}
}
If the command is not on PATH, use its absolute path or python -m genpark_voice_vad
with the same interpreter where you installed the wheel.
The GitHub release also contains a .mcpb bundle for clients supporting desktop extensions.
That bundle requires a Python 3.9+ interpreter on PATH; it bundles the server source.
Available tools: analyze_turn_status, calibrate_acoustic_thresholds, predict_semantic_closure, run_benchmark_turn_detection.
tools/list returns required arguments and JSON schemas.
Each MCP process holds its own state. Benchmark tools use isolated instances.
python -m unittest discover -s tests
python -m pip install mcp
python tests/check_mcp.py
python -m pip install build twine
python -m build
python -m twine check dist/*
python mcp_server.py --test runs the deterministic example; it is not a protocol conformance test.
The MCP client check exercises initialize, tools/list, tools/call and ping over stdio.
GitHub source and release artifacts are the primary distribution until PyPI is configured. Registry submissions are tracked separately; a manifest is not proof of registry acceptance. See PUBLISHING.md for the repeatable PyPI workflow.
MIT license. Maintained by GenPark.