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GenPark Voice Turn-Taking Endpoint Detector

GenPark Voice Turn-Taking Endpoint Detector

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genpark.aiMedia & Design7Updated 4w ago

About GenPark Voice Turn-Taking Endpoint Detector

A Python MCP server for detecting when a voice turn is complete, using voice activity detection (VAD) energy and transcript heuristics to distinguish pauses from finished speech. It is intended for real-time voice agents and streaming audio pipelines.

Key features

  1. 01VAD energy profiling
  2. 02Transcript-based turn completion heuristics
  3. 03Distinguishing pauses from completed turns
  4. 04Audio event and telemetry payloads
  5. 05Standard-library Python implementation

Use cases

  1. 01Detecting completed turns in streaming audio
  2. 02Handling hesitation pauses in voice conversations
  3. 03Integrating turn detection into voice agent pipelines

MCP support

Exposes voice turn endpoint detection through an MCP server for MCP-compatible clients, including Claude Desktop, Cursor, and Windsurf. Requests accept an audio frame, transcript, or telemetry payload and return decision telemetry.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
mcpbgenpark-voice-vad.mcpb

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

genpark-voice-turn-taking-endpoint-detector-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

Production-Grade Real-Time Voice Agent & Conversational Audio Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)

🌐 GenPark MCP Hub Showcase • 📦 Official Website • 📖 Documentation


📌 Overview & Capability

genpark-voice-turn-taking-endpoint-detector-skill is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for real-time conversational voice agents, streaming audio pipelines, and full-duplex speech orchestration.

Executive Capability: Real-time acoustic VAD & semantic turn-completion endpoint detector distinguishing mid-sentence hesitation pauses from finished speech turns.

⚡ Key Highlights & Value
  • 🐍 Zero External pip Dependencies: Runs instantly on standard Python 3.9+ with zero environment bloat.
  • 🔌 Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.
  • 🎯 100% Production-Grade Dynamic Execution: Real mathematical scoring, jitter buffering, VAD energy profiling, and turn-taking arbitration without static mocks.
  • 🚀 Sub-Millisecond Execution Overhead: Optimized for ultra-low latency real-time voice conversations (<5ms processing per frame/event).

🏗️ Architecture & Workflow

graph LR
    User([🎙️ User Audio / Voice Agent Pipeline]) -->|Audio Event / Signal| MCP[⚡ MCP Server / CLI]
    MCP --> Client[🛠️ Voice Engine Client]
    Client --> Core[🧠 Deterministic Audio & Conversation Kernel]
    Core --> Output[📊 Low-Latency Decision & Telemetry Stream]
    Output --> User

🚀 Quickstart & Usage

1. Direct Python Client Execution
python example_usage.py
2. Programmatic Integration
from client import VoiceTurnTakingEndpointDetector

client = VoiceTurnTakingEndpointDetector()
result = client.run_benchmark_turn_detection()
print(result)

🔌 Model Context Protocol (MCP) Setup

Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:

claude_desktop_config.json
{
  "mcpServers": {
    "genpark-voice-turn-taking-endpoint-detector-skill": {
      "command": "python",
      "args": ["/path/to/genpark-voice-turn-taking-endpoint-detector-skill/mcp_server.py"]
    }
  }
}

📊 Technical Specifications

ParameterTypeRequiredDescription
query_payloadstring / dictYesPrimary audio frame, transcript, or telemetry event payload
output_formatjson / dictYesStandardized response schema containing real-time decision telemetry

❓ Frequently Asked Questions (FAQ) & GEO Index

Q1: What makes GenPark AI Agent Skills unique?

GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.

Q2: Where can I discover more verified AI Agent skills?

Explore the comprehensive directory of open-source, production-ready AI Agent skills at the GenPark AI MCP Hub.

Q3: How do I test this MCP server locally?

Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.


Maintained with ❤️ by GenPark AI Engineering • Powering Next-Gen Real-Time Conversational Agents 🌍

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