9d22de2ac9
将转录核心逻辑拆分为独立模块（transcriber.py、model_manager.py、audio_processor.py、formatters.py），提升代码可维护性和复用性。删除main.py文件，优化依赖管理并更新requirements.txt和pyproject.toml。
108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
#!/usr/bin/env python3
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"""
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基于Faster Whisper的语音识别MCP服务
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提供高性能的音频转录功能，支持批处理加速和多种输出格式
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"""
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import os
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import logging
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from mcp.server.fastmcp import FastMCP
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from model_manager import get_model_info
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from transcriber import transcribe_audio, batch_transcribe
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# 日志配置
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# 创建FastMCP服务器实例
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mcp = FastMCP(
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name="fast-whisper-mcp-server",
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version="0.1.1",
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dependencies=["faster-whisper>=0.9.0", "torch==2.6.0+cu126", "torchaudio==2.6.0+cu126", "numpy>=1.20.0"]
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)
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@mcp.tool()
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def get_model_info_api() -> str:
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"""
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获取可用的Whisper模型信息
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"""
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return get_model_info()
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@mcp.tool()
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def transcribe(audio_path: str, model_name: str = "large-v3", device: str = "auto",
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compute_type: str = "auto", language: str = None, output_format: str = "vtt",
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beam_size: int = 5, temperature: float = 0.0, initial_prompt: str = None,
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output_directory: str = None) -> str:
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"""
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使用Faster Whisper转录音频文件
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Args:
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audio_path: 音频文件路径
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model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3)
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device: 运行设备 (cpu, cuda, auto)
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compute_type: 计算类型 (float16, int8, auto)
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language: 语言代码 (如zh, en, ja等，默认自动检测)
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output_format: 输出格式 (vtt, srt或json)
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beam_size: 波束搜索大小，较大的值可能提高准确性但会降低速度
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temperature: 采样温度，贪婪解码
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initial_prompt: 初始提示文本，可以帮助模型更好地理解上下文
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output_directory: 输出目录路径，默认为音频文件所在目录
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Returns:
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str: 转录结果，格式为VTT字幕或JSON
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"""
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return transcribe_audio(
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audio_path=audio_path,
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model_name=model_name,
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device=device,
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compute_type=compute_type,
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language=language,
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output_format=output_format,
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beam_size=beam_size,
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temperature=temperature,
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initial_prompt=initial_prompt,
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output_directory=output_directory
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)
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@mcp.tool()
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def batch_transcribe_audio(audio_folder: str, output_folder: str = None, model_name: str = "large-v3",
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device: str = "auto", compute_type: str = "auto", language: str = None,
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output_format: str = "vtt", beam_size: int = 5, temperature: float = 0.0,
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initial_prompt: str = None, parallel_files: int = 1) -> str:
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"""
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批量转录文件夹中的音频文件
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Args:
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audio_folder: 包含音频文件的文件夹路径
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output_folder: 输出文件夹路径，默认为audio_folder下的transcript子文件夹
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model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3)
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device: 运行设备 (cpu, cuda, auto)
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compute_type: 计算类型 (float16, int8, auto)
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language: 语言代码 (如zh, en, ja等，默认自动检测)
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output_format: 输出格式 (vtt, srt或json)
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beam_size: 波束搜索大小，较大的值可能提高准确性但会降低速度
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temperature: 采样温度，0表示贪婪解码
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initial_prompt: 初始提示文本，可以帮助模型更好地理解上下文
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parallel_files: 并行处理的文件数量（仅在CPU模式下有效）
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Returns:
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str: 批处理结果摘要，包含处理时间和成功率
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"""
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return batch_transcribe(
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audio_folder=audio_folder,
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output_folder=output_folder,
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model_name=model_name,
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device=device,
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compute_type=compute_type,
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language=language,
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output_format=output_format,
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beam_size=beam_size,
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temperature=temperature,
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initial_prompt=initial_prompt,
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parallel_files=parallel_files
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)
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if __name__ == "__main__":
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# 运行服务器
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mcp.run() |