GenPark Conversational Barge-In Interruption Arbitrator

io.github.alphaparkinc由 GitHub 账号 alphaparkinc 发布v1.0.1更新于 Oct 28, 2026

Arbitrate conversational interruptions with echo rules and playback truncation estimation

已验证STDIO仅桌面媒体与设计AI 与机器学习

概览

AI 生成的概览

一个本地 Python MCP 技能，用于仲裁实时语音代理中的打断（barge-in），区分用户插话、附和与回声。

可以做什么
  • 依据回声规则与播放截断估算仲裁对话打断
  • 对用户插话、附和声与声学回声进行分类并支持上下文回滚
  • 结合 VAD 能量分析与抖动缓冲对轮流发言进行评分决策
  • 接收音频帧、转写文本或遥测事件负载，按标准 JSON 结构返回决策与遥测
适用场景
适合在构建或调试全双工语音代理与流式音频管线时使用，需要确定性的轮流发言判断，例如区分用户是真的打断还是只是附和。
运行要求
以本地 stdio 进程运行，需要 Python 3.9 及以上（无需外部 pip 依赖）。README 通过让 MCP 客户端指向 mcp_server.py 来配置；未声明任何账号、API 密钥或环境变量。
安装前请注意
该技能在本地运行，未声明认证或凭据，但会处理语音转写与音频遥测，需注意发送哪些对话数据。README 的配置示例使用本地文件路径，需自行调整。

安装

在 SourceWeft 中

  1. 打开 控制台中的 GenPark Conversational Barge-In Interruption Arbitrator，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only，通过 STDIO。 STDIO 服务会启动本地进程，因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

genpark-conversational-barge-in-interruption-arbitrator-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-conversational-barge-in-interruption-arbitrator-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: Full-duplex conversational barge-in arbitrator classifying user interruptions, backchannels, and acoustic echo with context rollback.

⚡ 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

mermaid
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

bash
python example_usage.py

2. Programmatic Integration

python
from client import ConversationalBargeInArbitrator
client = ConversationalBargeInArbitrator()result = client.run_benchmark_barge_in_arbitration()print(result)

🔌 Model Context Protocol (MCP) Setup

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

claude_desktop_config.json

json
{  "mcpServers": {    "genpark-conversational-barge-in-interruption-arbitrator-skill": {      "command": "python",      "args": ["/path/to/genpark-conversational-barge-in-interruption-arbitrator-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 🌍

来源：README.md，提交 2fcee6a

工具

0
SourceWeft 尚未在沙箱中启动过该软件包，因此其工具未知。

版本历史

1
  1. v1.0.1最新Oct 19, 2026

评分

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