Voice Recognition MCP Service
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
克隆存储库:
git clone https://github.com/AIO-2030/mcp_voice_identify.git
cd mcp_voice_identify安装依赖项:
pip install -r requirements.txt在
.env中设置环境变量:
API_URL=your_api_url
API_KEY=your_api_key用法
stdio模式
运行服务:
python stdio_server.py通过 stdin 发送 JSON-RPC 请求:
{
"jsonrpc": "2.0",
"method": "help",
"params": {},
"id": 1
}或者使用可执行文件:
./dist/voice_stdioMCP 模式
运行服务:
python mcp_server.py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
使构建脚本可执行:
chmod +x build_exec.sh构建 stdio 模式可执行文件:
./build_exec.sh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_stdioMCP 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。
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