Skip to main content
Glama
yangsenessa

Voice Recognition MCP Service

by yangsenessa

语音识别MCP服务

该服务通过 stdio 和 MCP 模式提供语音识别和文本提取功能。

特征

  • 文件中的语音识别

  • 通过 base64 编码数据进行语音识别

  • 文本提取

  • 支持 stdio 和 MCP 模式

  • 结构化语音识别结果

Related MCP server: GhostMinutes MCP

项目结构

  • voice_service.py - 核心服务实现

  • stdio_server.py - stdio 模式入口点

  • mcp_server.py - MCP 模式入口点

  • build.py可执行文件的构建脚本

  • build_exec.sh - 构建执行脚本

  • test_*.sh不同功能的测试脚本

安装

  1. 克隆存储库:

git clone https://github.com/AIO-2030/mcp_voice_identify.git
cd mcp_voice_identify
  1. 安装依赖项:

pip install -r requirements.txt
  1. .env中设置环境变量:

API_URL=your_api_url
API_KEY=your_api_key

用法

stdio模式

  1. 运行服务:

python stdio_server.py
  1. 通过 stdin 发送 JSON-RPC 请求:

{
    "jsonrpc": "2.0",
    "method": "help",
    "params": {},
    "id": 1
}
  1. 或者使用可执行文件:

./dist/voice_stdio

MCP 模式

  1. 运行服务:

python mcp_server.py
  1. 或者使用可执行文件:

./dist/voice_mcp

语音识别结果

该服务提供结构化的语音识别结果。以下是响应格式的示例:

原始 API 响应

{
    "jsonrpc": "2.0",
    "result": {
        "message": "input processed successfully",
        "results": "test test test",
        "label_result": "<|en|><|EMO_UNKNOWN|><|Speech|><|woitn|>test test test"
    },
    "id": 1
}

重组响应

{
    "jsonrpc": "2.0",
    "result": {
        "message": "input processed successfully",
        "results": "test test test",
        "label_result": {
            "lan": "en",
            "emo": "unknown",
            "type": "speech",
            "speaker": "woitn",
            "text": "test test test"
        }
    },
    "id": 1
}

标签结果字段

label_result字段包含以下结构化信息:

场地

描述

示例值

局域网

语言代码

“en”

情绪摇滚

情绪状态

“未知”

类型

音频类型

“演讲”

扬声器

说话人识别器

“沃因”

文本

识别的文本内容

“测试测试测试”

特殊标签

该服务识别并处理原始响应中的以下特殊标签:

  • <|en|> - 语言代码

  • <|EMO_UNKNOWN|> - 情绪状态

  • <|Speech|> - 音频类型

  • <|woitn|> - 说话人标识符

构建可执行文件

  1. 使构建脚本可执行:

chmod +x build_exec.sh
  1. 构建 stdio 模式可执行文件:

./build_exec.sh
  1. 构建 MCP 模式可执行文件:

./build_exec.sh mcp

可执行文件将在以下位置创建:

  • stdio 模式: dist/voice_stdio

  • MCP 模式: dist/voice_mcp

测试

运行测试脚本:

chmod +x test_*.sh
./test_help.sh
./test_voice_file.sh
./test_voice_base64.sh

执照

该项目根据 MIT 许可证获得许可 - 有关详细信息,请参阅 LICENSE 文件。

A
license - permissive license
Not graded
quality - not tested
D
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

  • F
    license
    Not graded
    quality
    D
    maintenance
    A powerful speech-to-text MCP server that supports multiple audio formats and recognition engines including remote APIs (Bailian, OpenAI Whisper, iFLYTEK), Google Speech Recognition, and CMU Sphinx.
    1
  • F
    license
    Not graded
    quality
    D
    maintenance
    A high-performance speech recognition MCP server based on Faster Whisper, providing efficient audio transcription capabilities with support for multiple model sizes, batch processing, and various output formats.
    17
  • F
    license
    Not graded
    quality
    C
    maintenance
    A Model Context Protocol (MCP) server that gives AI agents the ability to process audio files — transcribe speech to text, detect spoken languages, and extract audio metadata.
    1

View all related MCP servers

Related MCP Connectors

  • OCR, transcription, file extraction, and image generation for AI agents via MCP.

  • MCP server exposing the AceDataCloud Fish Audio API (text-to-speech with voice conditioning)

  • A paid remote MCP for AI SDK data query MCP, built to return verdicts, receipts, usage logs, and aud

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/yangsenessa/mcp_voice_identify'

If you have feedback or need assistance with the MCP directory API, please join our Discord server