.

.gitignore

@@ -14,4 +14,6 @@ venv/
 # Cython
 *.pyd
 
-
+logs/**
+User/**
+data/**

Dockerfile

@@ -0,0 +1,51 @@
+# Use NVIDIA CUDA base image with Python
+FROM nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
+
+# Install Python 3.12
+RUN apt-get update && apt-get install -y \
+    software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa \
+    && apt-get update && apt-get install -y \
+    python3.12 \
+    python3.12-venv \
+    python3.12-dev \
+    python3-pip \
+    ffmpeg \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Make python3.12 the default
+RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.12 1
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.12 1
+
+# Upgrade pip
+RUN python -m pip install --upgrade pip
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements first for better caching
+COPY fast-whisper-mcp-server/requirements.txt .
+
+# Install Python dependencies with CUDA support
+RUN pip install --no-cache-dir \
+    faster-whisper \
+    torch==2.5.1 --index-url https://download.pytorch.org/whl/cu121 \
+    torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121 \
+    mcp[cli]
+
+# Copy application code
+COPY fast-whisper-mcp-server/ .
+
+# Create directories for models and outputs
+RUN mkdir -p /models /outputs
+
+# Set environment variables for GPU
+ENV WHISPER_MODEL_DIR=/models
+ENV TRANSCRIPTION_OUTPUT_DIR=/outputs
+ENV TRANSCRIPTION_MODEL=large-v3
+ENV TRANSCRIPTION_DEVICE=cuda
+ENV TRANSCRIPTION_COMPUTE_TYPE=float16
+
+# Run the server
+CMD ["python", "whisper_server.py"]

README-CN.md

@@ -1,184 +0,0 @@
-# Whisper 语音识别 MCP 服务器
-
-基于 Faster Whisper 的语音识别 MCP 服务器，提供高性能的音频转录功能。
-
-## 功能特点
-
-- 集成 Faster Whisper 进行高效语音识别
-- 支持批处理加速，提高转录速度
-- 自动使用 CUDA 加速（如果可用）
-- 支持多种模型大小（tiny 到 large-v3）
-- 输出格式支持 VTT 字幕和 JSON
-- 支持批量转录文件夹中的音频文件
-- 模型实例缓存，避免重复加载
-
-## 安装
-
-### 依赖项
-
-- Python 3.10+
-- faster-whisper>=0.9.0
-- torch==2.6.0+cu126
-- torchaudio==2.6.0+cu126
-- mcp[cli]>=1.2.0
-
-### 安装步骤
-
-1. 克隆或下载此仓库
-2. 创建并激活虚拟环境（推荐）
-3. 安装依赖项：
-
-```bash
-pip install -r requirements.txt
-```
-
-## 使用方法
-
-### 启动服务器
-
-在 Windows 上，直接运行 `start_server.bat`。
-
-在其他平台上，运行：
-
-```bash
-python whisper_server.py
-```
-
-### 配置 Claude Desktop
-
-1. 打开 Claude Desktop 配置文件：
-   - Windows: `%APPDATA%\Claude\claude_desktop_config.json`
-   - macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
-
-2. 添加 Whisper 服务器配置：
-
-```json
-{
-  "mcpServers": {
-    "whisper": {
-      "command": "python",
-      "args": ["D:/path/to/whisper_server.py"],
-      "env": {}
-    }
-  }
-}
-```
-
-3. 重启 Claude Desktop
-
-### 可用工具
-
-服务器提供以下工具：
-
-1. **get_model_info** - 获取可用的 Whisper 模型信息
-2. **transcribe** - 转录单个音频文件
-3. **batch_transcribe** - 批量转录文件夹中的音频文件
-
-## 性能优化建议
-
-- 使用 CUDA 加速可显著提高转录速度
-- 对于大量短音频，批处理模式效率更高
-- 根据 GPU 显存大小自动调整批处理大小
-- 对于长音频，使用 VAD 过滤可提高准确性
-- 指定正确的语言可提高转录质量
-
-## 本地测试方案
-
-1. 使用 MCP Inspector 进行快速测试：
-
-```bash
-mcp dev whisper_server.py
-```
-
-2. 使用 Claude Desktop 进行集成测试
-
-3. 使用命令行直接调用（需要安装 mcp[cli]）：
-
-```bash
-mcp run whisper_server.py
-```
-
-## 错误处理
-
-服务器实现了以下错误处理机制：
-
-- 音频文件不存在检查
-- 模型加载失败处理
-- 转录过程异常捕获
-- GPU 内存管理
-- 批处理参数自适应调整
-
-## 许可证
-
-MIT
-
-## 致谢
-
-本项目在开发过程中得到了以下优秀AI工具和模型的帮助：
-
-- [GitHub Copilot](https://github.com/features/copilot) - AI结对编程助手
-- [Trae](https://trae.ai/) - 智能AI编码助手
-- [Cline](https://cline.ai/) - AI驱动的终端
-- [DeepSeek](https://www.deepseek.com/) - 先进的AI模型
-- [Claude-3.7-Sonnet](https://www.anthropic.com/claude) - Anthropic强大的AI助手
-- [Gemini-2.0-Flash](https://ai.google/gemini/) - Google的多模态AI模型
-- [VS Code](https://code.visualstudio.com/) - 强大的代码编辑器
-- [Whisper](https://github.com/openai/whisper) - OpenAI的语音识别模型
-- [Faster Whisper](https://github.com/guillaumekln/faster-whisper) - 优化的Whisper实现
-
-特别感谢这些出色的工具和背后的团队。
-
----
-
-# Whisper 语音识别 MCP 服务器（cline claude sonnet 3.7 完成所有任务后的说明）
-
-我已经成功创建了基于Python的语音识别MCP服务器，该服务器集成了Faster Whisper并支持批处理加速。服务器具有以下特点：
-
-## 核心功能
-
-- **集成Faster Whisper**：使用最新版本(>=0.9.0)进行高效语音识别
-- **CUDA加速**：自动检测并使用CUDA加速，默认使用torch==2.6.0+cu126
-- **批处理优化**：使用BatchedInferencePipeline实现批处理，根据GPU显存自动调整批大小
-- **模型缓存**：实现模型实例缓存，避免重复加载模型
-- **多格式输出**：支持VTT字幕和JSON格式的转录结果
-- **批量处理**：支持批量转录整个文件夹的音频文件
-
-## 主要工具
-
-服务器提供了三个主要工具：
-
-1. **get_model_info**：获取可用的Whisper模型信息和系统配置
-2. **transcribe**：转录单个音频文件，支持多种参数配置
-3. **batch_transcribe**：批量转录文件夹中的音频文件
-
-## 错误处理机制
-
-- 音频文件存在性验证
-- 模型加载异常捕获和日志记录
-- 转录过程异常处理
-- GPU内存管理和清理
-- 批处理参数自适应调整
-
-## 性能优化
-
-- 根据GPU显存大小动态调整批处理大小(4-32)
-- 使用VAD(语音活动检测)过滤提高准确性
-- 模型实例缓存避免重复加载
-- 自动选择最佳设备和计算类型
-
-## 本地测试方案
-
-提供了多种测试方法：
-
-- 使用MCP Inspector进行快速测试：`mcp dev whisper_server.py`
-- 使用Claude Desktop进行集成测试
-- 使用命令行直接调用：`mcp run whisper_server.py`
-
-所有文件已准备就绪，包括：
-
-- whisper_server.py：主服务器代码
-- requirements.txt：依赖项列表
-- start_server.bat：Windows启动脚本
-- README.md：详细文档
-
-您可以通过运行start_server.bat或直接执行`python whisper_server.py`来启动服务器。

audio_processor.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
-音频处理模块
-负责音频文件的验证和预处理
+Audio Processing Module
+Responsible for audio file validation and preprocessing
 """
 
 import os
@@ -9,59 +9,59 @@ import logging
 from typing import Union, Any
 from faster_whisper import decode_audio
 
-# 日志配置
+# Log configuration
 logger = logging.getLogger(__name__)
 
 def validate_audio_file(audio_path: str) -> str:
     """
-    验证音频文件是否有效
+    Validate if an audio file is valid
 
     Args:
-        audio_path: 音频文件路径
+        audio_path: Path to the audio file
 
     Returns:
-        str: 验证结果，"ok"表示验证通过，否则返回错误信息
+        str: Validation result, "ok" indicates validation passed, otherwise returns error message
     """
-    # 验证参数
+    # Validate parameters
     if not os.path.exists(audio_path):
-        return f"错误: 音频文件不存在: {audio_path}"
+        return f"Error: Audio file does not exist: {audio_path}"
 
-    # 验证文件格式
+    # Validate file format
     supported_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"]
     file_ext = os.path.splitext(audio_path)[1].lower()
     if file_ext not in supported_formats:
-        return f"错误: 不支持的音频格式: {file_ext}。支持的格式: {', '.join(supported_formats)}"
+        return f"Error: Unsupported audio format: {file_ext}. Supported formats: {', '.join(supported_formats)}"
 
-    # 验证文件大小
+    # Validate file size
     try:
         file_size = os.path.getsize(audio_path)
         if file_size == 0:
-            return f"错误: 音频文件为空: {audio_path}"
+            return f"Error: Audio file is empty: {audio_path}"
 
-        # 大文件警告（超过1GB）
+        # Warning for large files (over 1GB)
         if file_size > 1024 * 1024 * 1024:
-            logger.warning(f"警告: 文件大小超过1GB，可能需要较长处理时间: {audio_path}")
+            logger.warning(f"Warning: File size exceeds 1GB, may require longer processing time: {audio_path}")
     except Exception as e:
-        logger.error(f"检查文件大小失败: {str(e)}")
-        return f"错误: 检查文件大小失败: {str(e)}"
+        logger.error(f"Failed to check file size: {str(e)}")
+        return f"Error: Failed to check file size: {str(e)}"
 
     return "ok"
 
 def process_audio(audio_path: str) -> Union[str, Any]:
     """
-    处理音频文件，进行解码和预处理
+    Process audio file, perform decoding and preprocessing
 
     Args:
-        audio_path: 音频文件路径
+        audio_path: Path to the audio file
 
     Returns:
-        Union[str, Any]: 处理后的音频数据或原始文件路径
+        Union[str, Any]: Processed audio data or original file path
     """
-    # 尝试使用decode_audio预处理音频，以处理更多格式
+    # Try to preprocess audio using decode_audio to handle more formats
     try:
         audio_data = decode_audio(audio_path)
-        logger.info(f"成功预处理音频: {os.path.basename(audio_path)}")
+        logger.info(f"Successfully preprocessed audio: {os.path.basename(audio_path)}")
         return audio_data
     except Exception as audio_error:
-        logger.warning(f"音频预处理失败，将直接使用文件路径: {str(audio_error)}")
+        logger.warning(f"Audio preprocessing failed, will use file path directly: {str(audio_error)}")
         return audio_path

data

@@ -0,0 +1 @@
+/media/raid/agents/tools/mcp-transcriptor

formatters.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
-格式化输出模块
-负责将转录结果格式化为不同的输出格式（VTT、SRT、JSON）
+Formatting Output Module
+Responsible for formatting transcription results into different output formats (VTT, SRT, JSON, TXT)
 """
 
 import json
@@ -9,13 +9,13 @@ from typing import List, Dict, Any
 
 def format_vtt(segments: List) -> str:
     """
-    将转录结果格式化为VTT
+    Format transcription results to VTT
 
     Args:
-        segments: 转录段落列表
+        segments: List of transcription segments
 
     Returns:
-        str: VTT格式的字幕内容
+        str: Subtitle content in VTT format
     """
     vtt_content = "WEBVTT\n\n"
 
@@ -31,13 +31,13 @@ def format_vtt(segments: List) -> str:
 
 def format_srt(segments: List) -> str:
     """
-    将转录结果格式化为SRT
+    Format transcription results to SRT
 
     Args:
-        segments: 转录段落列表
+        segments: List of transcription segments
 
     Returns:
-        str: SRT格式的字幕内容
+        str: Subtitle content in SRT format
     """
     srt_content = ""
     index = 1
@@ -53,16 +53,51 @@ def format_srt(segments: List) -> str:
 
     return srt_content
 
-def format_json(segments: List, info: Any) -> str:
+def format_txt(segments: List) -> str:
     """
-    将转录结果格式化为JSON
+    Format transcription results to plain text
 
     Args:
-        segments: 转录段落列表
-        info: 转录信息对象
+        segments: List of transcription segments
 
     Returns:
-        str: JSON格式的转录结果
+        str: Plain text transcription content
+    """
+    text_content = ""
+    
+    for segment in segments:
+        text = segment.text.strip()
+        if text:
+            # Add the text content
+            text_content += text
+            
+            # Add appropriate spacing between segments
+            # If the text doesn't end with punctuation, add a space
+            if not text.endswith(('.', '!', '?', ':', ';')):
+                text_content += " "
+            else:
+                # If it ends with punctuation, add a space for natural flow
+                text_content += " "
+    
+    # Clean up any trailing whitespace and ensure single line breaks
+    text_content = text_content.strip()
+    
+    # Replace multiple spaces with single spaces
+    while "  " in text_content:
+        text_content = text_content.replace("  ", " ")
+    
+    return text_content
+
+def format_json(segments: List, info: Any) -> str:
+    """
+    Format transcription results to JSON
+
+    Args:
+        segments: List of transcription segments
+        info: Transcription information object
+
+    Returns:
+        str: Transcription results in JSON format
     """
     result = {
         "segments": [{
@@ -86,13 +121,13 @@ def format_json(segments: List, info: Any) -> str:
 
 def format_timestamp(seconds: float) -> str:
     """
-    格式化时间戳为VTT格式
+    Format timestamp for VTT format
 
     Args:
-        seconds: 秒数
+        seconds: Number of seconds
 
     Returns:
-        str: 格式化的时间戳 (HH:MM:SS.mmm)
+        str: Formatted timestamp (HH:MM:SS.mmm)
     """
     hours = int(seconds // 3600)
     minutes = int((seconds % 3600) // 60)
@@ -101,13 +136,13 @@ def format_timestamp(seconds: float) -> str:
 
 def format_timestamp_srt(seconds: float) -> str:
     """
-    格式化时间戳为SRT格式
+    Format timestamp for SRT format
 
     Args:
-        seconds: 秒数
+        seconds: Number of seconds
 
     Returns:
-        str: 格式化的时间戳 (HH:MM:SS,mmm)
+        str: Formatted timestamp (HH:MM:SS,mmm)
     """
     hours = int(seconds // 3600)
     minutes = int((seconds % 3600) // 60)
@@ -117,13 +152,13 @@ def format_timestamp_srt(seconds: float) -> str:
 
 def format_time(seconds: float) -> str:
     """
-    格式化时间为可读格式
+    Format time into readable format
 
     Args:
-        seconds: 秒数
+        seconds: Number of seconds
 
     Returns:
-        str: 格式化的时间 (HH:MM:SS)
+        str: Formatted time (HH:MM:SS)
     """
     hours = int(seconds // 3600)
     minutes = int((seconds % 3600) // 60)

mcp.logs

@@ -0,0 +1,6 @@
+{"jsonrpc":"2.0","id":1,"result":{"protocolVersion":"2025-03-26","capabilities":{"experimental":{},"prompts":{"listChanged":false},"resources":{"subscribe":false,"listChanged":false},"tools":{"listChanged":false}},"serverInfo":{"name":"fast-whisper-mcp-server","version":"1.9.4"}}}
+INFO:mcp.server.lowlevel.server:Processing request of type ListToolsRequest
+{"jsonrpc":"2.0","id":2,"result":{"tools":[{"name":"get_model_info_api","description":"\n    Get available Whisper model information\n    ","inputSchema":{"properties":{},"title":"get_model_info_apiArguments","type":"object"}},{"name":"transcribe","description":"\n    Transcribe audio files using Faster Whisper\n\n    Args:\n        audio_path: Path to the audio file\n        model_name: Model name (tiny, base, small, medium, large-v1, large-v2, large-v3)\n        device: Execution device (cpu, cuda, auto)\n        compute_type: Computation type (float16, int8, auto)\n        language: Language code (such as zh, en, ja, etc., auto-detect by default)\n        output_format: Output format (vtt, srt, json or txt)\n        beam_size: Beam search size, larger values may improve accuracy but reduce speed\n        temperature: Sampling temperature, greedy decoding\n        initial_prompt: Initial prompt text, can help the model better understand context\n        output_directory: Output directory path, defaults to the audio file's directory\n\n    Returns:\n        str: Transcription result, in VTT subtitle or JSON format\n    ","inputSchema":{"properties":{"audio_path":{"title":"Audio Path","type":"string"},"model_name":{"default":"large-v3","title":"Model Name","type":"string"},"device":{"default":"auto","title":"Device","type":"string"},"compute_type":{"default":"auto","title":"Compute Type","type":"string"},"language":{"default":null,"title":"Language","type":"string"},"output_format":{"default":"vtt","title":"Output Format","type":"string"},"beam_size":{"default":5,"title":"Beam Size","type":"integer"},"temperature":{"default":0.0,"title":"Temperature","type":"number"},"initial_prompt":{"default":null,"title":"Initial Prompt","type":"string"},"output_directory":{"default":null,"title":"Output Directory","type":"string"}},"required":["audio_path"],"title":"transcribeArguments","type":"object"}},{"name":"batch_transcribe_audio","description":"\n    Batch transcribe audio files in a folder\n\n    Args:\n        audio_folder: Path to the folder containing audio files\n        output_folder: Output folder path, defaults to a 'transcript' subfolder in audio_folder\n        model_name: Model name (tiny, base, small, medium, large-v1, large-v2, large-v3)\n        device: Execution device (cpu, cuda, auto)\n        compute_type: Computation type (float16, int8, auto)\n        language: Language code (such as zh, en, ja, etc., auto-detect by default)\n        output_format: Output format (vtt, srt, json or txt)\n        beam_size: Beam search size, larger values may improve accuracy but reduce speed\n        temperature: Sampling temperature, 0 means greedy decoding\n        initial_prompt: Initial prompt text, can help the model better understand context\n        parallel_files: Number of files to process in parallel (only effective in CPU mode)\n\n    Returns:\n        str: Batch processing summary, including processing time and success rate\n    ","inputSchema":{"properties":{"audio_folder":{"title":"Audio Folder","type":"string"},"output_folder":{"default":null,"title":"Output Folder","type":"string"},"model_name":{"default":"large-v3","title":"Model Name","type":"string"},"device":{"default":"auto","title":"Device","type":"string"},"compute_type":{"default":"auto","title":"Compute Type","type":"string"},"language":{"default":null,"title":"Language","type":"string"},"output_format":{"default":"vtt","title":"Output Format","type":"string"},"beam_size":{"default":5,"title":"Beam Size","type":"integer"},"temperature":{"default":0.0,"title":"Temperature","type":"number"},"initial_prompt":{"default":null,"title":"Initial Prompt","type":"string"},"parallel_files":{"default":1,"title":"Parallel Files","type":"integer"}},"required":["audio_folder"],"title":"batch_transcribe_audioArguments","type":"object"}}]}}
+INFO:mcp.server.lowlevel.server:Processing request of type CallToolRequest
+INFO:model_manager:GPU test passed: NVIDIA GeForce RTX 3060 (12.5GB)
+INFO:model_manager:Loading Whisper model: large-v3 device: cuda compute type: float16

model_manager.py

@@ -1,96 +1,120 @@
 #!/usr/bin/env python3
 """
-模型管理模块
-负责Whisper模型的加载、缓存和管理
+Model Management Module
+Responsible for loading, caching, and managing Whisper models
 """
 
-import os
+import os; print(os.environ.get("WHISPER_MODEL_DIR"))
 import time
 import logging
 from typing import Dict, Any
 import torch
 from faster_whisper import WhisperModel, BatchedInferencePipeline
 
-# 日志配置
+# Log configuration
 logger = logging.getLogger(__name__)
 
-# 全局模型实例缓存
+# Global model instance cache
 model_instances = {}
 
+def test_gpu_driver():
+    """Simple GPU driver test"""
+    try:
+        if not torch.cuda.is_available():
+            logger.error("CUDA not available in PyTorch")
+            raise RuntimeError("CUDA not available")
+        
+        gpu_count = torch.cuda.device_count()
+        if gpu_count == 0:
+            logger.error("No CUDA devices found")
+            raise RuntimeError("No CUDA devices")
+        
+        # Quick GPU test
+        test_tensor = torch.randn(10, 10).cuda()
+        _ = test_tensor @ test_tensor.T
+        
+        device_name = torch.cuda.get_device_name(0)
+        memory_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
+        logger.info(f"GPU test passed: {device_name} ({memory_gb:.1f}GB)")
+        
+    except Exception as e:
+        logger.error(f"GPU test failed: {e}")
+        raise RuntimeError(f"GPU initialization failed: {e}")
+
 def get_whisper_model(model_name: str, device: str, compute_type: str) -> Dict[str, Any]:
     """
-    获取或创建Whisper模型实例
+    Get or create Whisper model instance
 
     Args:
-        model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3)
-        device: 运行设备 (cpu, cuda, auto)
-        compute_type: 计算类型 (float16, int8, auto)
+        model_name: Model name (tiny, base, small, medium, large-v1, large-v2, large-v3)
+        device: Running device (cpu, cuda, auto)
+        compute_type: Computation type (float16, int8, auto)
 
     Returns:
-        dict: 包含模型实例和配置的字典
+        dict: Dictionary containing model instance and configuration
     """
     global model_instances
 
-    # 验证模型名称
+    # Validate model name
     valid_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2", "large-v3"]
     if model_name not in valid_models:
-        raise ValueError(f"无效的模型名称: {model_name}。有效的模型: {', '.join(valid_models)}")
+        raise ValueError(f"Invalid model name: {model_name}. Valid models: {', '.join(valid_models)}")
 
-    # 自动检测设备
+    # Auto-detect device
     if device == "auto":
         device = "cuda" if torch.cuda.is_available() else "cpu"
         compute_type = "float16" if device == "cuda" else "int8"
 
-    # 验证设备和计算类型
+    # Validate device and compute type
     if device not in ["cpu", "cuda"]:
-        raise ValueError(f"无效的设备: {device}。有效的设备: cpu, cuda")
+        raise ValueError(f"Invalid device: {device}. Valid devices: cpu, cuda")
 
     if device == "cuda" and not torch.cuda.is_available():
-        logger.warning("CUDA不可用，自动切换到CPU")
-        device = "cpu"
-        compute_type = "int8"
+        logger.error("CUDA requested but not available")
+        raise RuntimeError("CUDA not available but explicitly requested")
 
     if compute_type not in ["float16", "int8"]:
-        raise ValueError(f"无效的计算类型: {compute_type}。有效的计算类型: float16, int8")
+        raise ValueError(f"Invalid compute type: {compute_type}. Valid compute types: float16, int8")
 
     if device == "cpu" and compute_type == "float16":
-        logger.warning("CPU设备不支持float16计算类型，自动切换到int8")
+        logger.warning("CPU device does not support float16 computation type, automatically switching to int8")
         compute_type = "int8"
 
-    # 生成模型键
+    # Generate model key
     model_key = f"{model_name}_{device}_{compute_type}"
 
-    # 如果模型已实例化，直接返回
+    # If model is already instantiated, return directly
     if model_key in model_instances:
-        logger.info(f"使用缓存的模型实例: {model_key}")
+        logger.info(f"Using cached model instance: {model_key}")
         return model_instances[model_key]
 
-    # 清理GPU内存（如果使用CUDA）
+    # Test GPU driver before loading model and clean
     if device == "cuda":
+        test_gpu_driver()
         torch.cuda.empty_cache()
 
-    # 实例化模型
+    # Instantiate model
     try:
-        logger.info(f"加载Whisper模型: {model_name} 设备: {device} 计算类型: {compute_type}")
+        logger.info(f"Loading Whisper model: {model_name} device: {device} compute type: {compute_type}")
 
-        # 基础模型
+        # Base model
         model = WhisperModel(
             model_name,
             device=device,
             compute_type=compute_type,
-            download_root=os.environ.get("WHISPER_MODEL_DIR", None)  # 支持自定义模型目录
+            download_root=os.environ.get("WHISPER_MODEL_DIR", None)  # Support custom model directory
         )
 
-        # 批处理设置 - 默认启用批处理以提高速度
+        # Batch processing settings - batch processing enabled by default to improve speed
         batched_model = None
         batch_size = 0
 
-        if device == "cuda":  # 只在CUDA设备上使用批处理
-            # 根据显存大小确定合适的批大小
+        if device == "cuda":  # Only use batch processing on CUDA devices
+            # Determine appropriate batch size based on available memory
             if torch.cuda.is_available():
                 gpu_mem = torch.cuda.get_device_properties(0).total_memory
                 free_mem = gpu_mem - torch.cuda.memory_allocated()
-                # 根据GPU显存动态调整批大小
+                # Dynamically adjust batch size based on GPU memory
                 if free_mem > 16e9:  # >16GB
                     batch_size = 32
                 elif free_mem > 12e9:  # >12GB
@@ -99,17 +123,17 @@ def get_whisper_model(model_name: str, device: str, compute_type: str) -> Dict[s
                     batch_size = 8
                 elif free_mem > 4e9:   # >4GB
                     batch_size = 4
-                else:                # 较小显存
+                else:                # Smaller memory
                     batch_size = 2
 
-                logger.info(f"可用GPU显存: {free_mem / 1e9:.2f} GB")
+                logger.info(f"Available GPU memory: {free_mem / 1e9:.2f} GB")
             else:
-                batch_size = 8  # 默认值
+                batch_size = 8  # Default value
 
-            logger.info(f"启用批处理加速，批大小: {batch_size}")
+            logger.info(f"Enabling batch processing acceleration, batch size: {batch_size}")
             batched_model = BatchedInferencePipeline(model=model)
 
-        # 创建结果对象
+        # Create result object
         result = {
             'model': model,
             'device': device,
@@ -119,20 +143,20 @@ def get_whisper_model(model_name: str, device: str, compute_type: str) -> Dict[s
             'load_time': time.time()
         }
 
-        # 缓存实例
+        # Cache instance
         model_instances[model_key] = result
         return result
 
     except Exception as e:
-        logger.error(f"加载模型失败: {str(e)}")
+        logger.error(f"Failed to load model: {str(e)}")
         raise
 
 def get_model_info() -> str:
     """
-    获取可用的Whisper模型信息
+    Get available Whisper model information
 
     Returns:
-        str: 模型信息的JSON字符串
+        str: JSON string of model information
     """
     import json
 
@@ -142,15 +166,15 @@ def get_model_info() -> str:
     devices = ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"]
     compute_types = ["float16", "int8"] if torch.cuda.is_available() else ["int8"]
 
-    # 支持的语言列表
+    # Supported language list
     languages = {
-        "zh": "中文", "en": "英语", "ja": "日语", "ko": "韩语", "de": "德语",
-        "fr": "法语", "es": "西班牙语", "ru": "俄语", "it": "意大利语",
-        "pt": "葡萄牙语", "nl": "荷兰语", "ar": "阿拉伯语", "hi": "印地语",
-        "tr": "土耳其语", "vi": "越南语", "th": "泰语", "id": "印尼语"
+        "zh": "Chinese", "en": "English", "ja": "Japanese", "ko": "Korean", "de": "German",
+        "fr": "French", "es": "Spanish", "ru": "Russian", "it": "Italian",
+        "pt": "Portuguese", "nl": "Dutch", "ar": "Arabic", "hi": "Hindi",
+        "tr": "Turkish", "vi": "Vietnamese", "th": "Thai", "id": "Indonesian"
     }
 
-    # 支持的音频格式
+    # Supported audio formats
     audio_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"]
 
     info = {
@@ -173,4 +197,4 @@ def get_model_info() -> str:
             "memory_available": f"{torch.cuda.get_device_properties(0).total_memory / 1e9 - torch.cuda.memory_allocated() / 1e9:.2f} GB"
         }
 
-    return json.dumps(info, indent=2)
+    return json.dumps(info, indent=2)

requirements.txt

@@ -1,14 +1,15 @@
 # uv pip install -r ./requirements.txt --index-url https://download.pytorch.org/whl/cu126
 faster-whisper
-torch==2.6.0+cu126
-torchaudio==2.6.0+cu126
+torch #==2.6.0+cu126
+torchaudio #==2.6.0+cu126
 
 # uv pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126
 # pip install faster-whisper>=0.9.0
 # pip install mcp[cli]>=1.2.0
+mcp[cli]
 
-# PyTorch安装指南:
-#    请根据您的CUDA版本安装适当版本的PyTorch:
+# PyTorch Installation Guide:
+#    Please install the appropriate version of PyTorch based on your CUDA version:
 #
 #    • CUDA 12.6:
 #      pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126
@@ -16,7 +17,6 @@ torchaudio==2.6.0+cu126
 #    • CUDA 12.1:
 #      pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
 #
-#    • CPU版本:
+#    • CPU version:
 #      pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cpu
 #
-#    可用命令`nvcc --version`或`nvidia-smi`查看CUDA版本

run_server.sh

@@ -0,0 +1,37 @@
+#!/bin/bash
+set -e
+
+datetime_prefix() {
+  date "+[%Y-%m-%d %H:%M:%S]"
+}
+
+# Get current user ID to avoid permission issues
+USER_ID=$(id -u)
+GROUP_ID=$(id -g)
+
+# Set environment variables
+export CUDA_VISIBLE_DEVICES=1
+export WHISPER_MODEL_DIR="/home/uad/agents/tools/mcp-transcriptor/data/models"
+export TRANSCRIPTION_OUTPUT_DIR="/media/raid/agents/tools/mcp-transcriptor/outputs"
+export TRANSCRIPTION_BATCH_OUTPUT_DIR="/media/raid/agents/tools/mcp-transcriptor/outputs/batch"
+export TRANSCRIPTION_MODEL="large-v3"
+export TRANSCRIPTION_DEVICE="cuda"
+export TRANSCRIPTION_COMPUTE_TYPE="cuda"
+export TRANSCRIPTION_OUTPUT_FORMAT="txt"
+export TRANSCRIPTION_BEAM_SIZE="2"
+export TRANSCRIPTION_TEMPERATURE="0.0"
+export TRANSCRIPTION_USE_TIMESTAMP="false"
+export TRANSCRIPTION_FILENAME_PREFIX="test_"
+
+# Log start of the script
+echo "$(datetime_prefix) Starting whisper server script..."
+echo "test: $WHISPER_MODEL_DIR"
+
+# Optional: Verify required directories exist
+if [ ! -d "$WHISPER_MODEL_DIR" ]; then
+  echo "$(datetime_prefix) Error: Whisper model directory does not exist: $WHISPER_MODEL_DIR"
+  exit 1
+fi
+
+# Run the Python script with the defined environment variables
+/home/uad/agents/tools/mcp-transcriptor/venv/bin/python /home/uad/agents/tools/mcp-transcriptor/whisper_server.py 2>&1 | tee /home/uad/agents/tools/mcp-transcriptor/mcp.logs

start_server.bat

@@ -1,16 +0,0 @@
-@echo off
-echo 启动Whisper语音识别MCP服务器...
-
-:: 激活虚拟环境（如果存在）
-if exist "..\venv\Scripts\activate.bat" (
-    call ..\venv\Scripts\activate.bat
-)
-
-:: 运行MCP服务器
-python whisper_server.py
-
-:: 如果出错，暂停以查看错误信息
-if %ERRORLEVEL% neq 0 (
-    echo 服务器启动失败，错误代码: %ERRORLEVEL%
-    pause
-)

transcriber.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
-转录核心模块
-包含音频转录的核心逻辑
+Transcription Core Module with Environment Variable Support
+Contains core logic for audio transcription
 """
 
 import os
@@ -11,211 +11,266 @@ from typing import Dict, Any, Tuple, List, Optional, Union
 
 from model_manager import get_whisper_model
 from audio_processor import validate_audio_file, process_audio
-from formatters import format_vtt, format_srt, format_json, format_time
+from formatters import format_vtt, format_srt, format_json, format_txt, format_time
 
-# 日志配置
+# Logging configuration
 logger = logging.getLogger(__name__)
 
+
+# Environment variable defaults
+DEFAULT_OUTPUT_DIR = os.getenv('TRANSCRIPTION_OUTPUT_DIR')
+DEFAULT_BATCH_OUTPUT_DIR = os.getenv('TRANSCRIPTION_BATCH_OUTPUT_DIR')
+DEFAULT_MODEL = os.getenv('TRANSCRIPTION_MODEL', 'large-v3')
+DEFAULT_DEVICE = os.getenv('TRANSCRIPTION_DEVICE', 'auto')
+DEFAULT_COMPUTE_TYPE = os.getenv('TRANSCRIPTION_COMPUTE_TYPE', 'auto')
+DEFAULT_LANGUAGE = os.getenv('TRANSCRIPTION_LANGUAGE', None)
+DEFAULT_OUTPUT_FORMAT = os.getenv('TRANSCRIPTION_OUTPUT_FORMAT', 'txt')
+DEFAULT_BEAM_SIZE = int(os.getenv('TRANSCRIPTION_BEAM_SIZE', '5'))
+DEFAULT_TEMPERATURE = float(os.getenv('TRANSCRIPTION_TEMPERATURE', '0.0'))
+
+# Model storage configuration
+WHISPER_MODEL_DIR = os.getenv('WHISPER_MODEL_DIR', None)
+
+# File naming configuration
+USE_TIMESTAMP = os.getenv('TRANSCRIPTION_USE_TIMESTAMP', 'false').lower() == 'true'
+FILENAME_PREFIX = os.getenv('TRANSCRIPTION_FILENAME_PREFIX', '')
+FILENAME_SUFFIX = os.getenv('TRANSCRIPTION_FILENAME_SUFFIX', '')
+
 def transcribe_audio(
     audio_path: str,
-    model_name: str = "large-v3",
-    device: str = "auto",
-    compute_type: str = "auto",
+    model_name: str = None,
+    device: str = None,
+    compute_type: str = None,
     language: str = None,
-    output_format: str = "vtt",
-    beam_size: int = 5,
-    temperature: float = 0.0,
+    output_format: str = None,
+    beam_size: int = None,
+    temperature: float = None,
     initial_prompt: str = None,
     output_directory: str = None
 ) -> str:
     """
-    使用Faster Whisper转录音频文件
+    Transcribe audio file using Faster Whisper with ENV VAR support
 
     Args:
-        audio_path: 音频文件路径
-        model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3)
-        device: 运行设备 (cpu, cuda, auto)
-        compute_type: 计算类型 (float16, int8, auto)
-        language: 语言代码 (如zh, en, ja等，默认自动检测)
-        output_format: 输出格式 (vtt, srt或json)
-        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
-        temperature: 采样温度，贪婪解码
-        initial_prompt: 初始提示文本，可以帮助模型更好地理解上下文
-        output_directory: 输出目录路径，默认为音频文件所在目录
+        audio_path: Path to audio file
+        model_name: Model name (defaults to TRANSCRIPTION_MODEL env var or "large-v3")
+        device: Execution device (defaults to TRANSCRIPTION_DEVICE env var or "auto")
+        compute_type: Computation type (defaults to TRANSCRIPTION_COMPUTE_TYPE env var or "auto")
+        language: Language code (defaults to TRANSCRIPTION_LANGUAGE env var or auto-detect)
+        output_format: Output format (defaults to TRANSCRIPTION_OUTPUT_FORMAT env var or "vtt")
+        beam_size: Beam search size (defaults to TRANSCRIPTION_BEAM_SIZE env var or 5)
+        temperature: Sampling temperature (defaults to TRANSCRIPTION_TEMPERATURE env var or 0.0)
+        initial_prompt: Initial prompt text
+        output_directory: Output directory (defaults to TRANSCRIPTION_OUTPUT_DIR env var or audio file directory)
 
     Returns:
-        str: 转录结果，格式为VTT字幕或JSON
+        str: Transcription result path or error message
     """
-    # 验证音频文件
+    # Apply environment variable defaults
+    model_name = model_name or DEFAULT_MODEL
+    device = device or DEFAULT_DEVICE
+    compute_type = compute_type or DEFAULT_COMPUTE_TYPE
+    language = language or DEFAULT_LANGUAGE
+    output_format = output_format or DEFAULT_OUTPUT_FORMAT
+    beam_size = beam_size if beam_size is not None else DEFAULT_BEAM_SIZE
+    temperature = temperature if temperature is not None else DEFAULT_TEMPERATURE
+    
+    # Validate audio file
     validation_result = validate_audio_file(audio_path)
     if validation_result != "ok":
         return validation_result
 
     try:
-        # 获取模型实例
+        # Get model instance
         model_instance = get_whisper_model(model_name, device, compute_type)
 
-        # 验证语言代码
+        # Validate language code
         supported_languages = {
-            "zh": "中文", "en": "英语", "ja": "日语", "ko": "韩语", "de": "德语",
-            "fr": "法语", "es": "西班牙语", "ru": "俄语", "it": "意大利语",
-            "pt": "葡萄牙语", "nl": "荷兰语", "ar": "阿拉伯语", "hi": "印地语",
-            "tr": "土耳其语", "vi": "越南语", "th": "泰语", "id": "印尼语"
+            "zh": "Chinese", "en": "English", "ja": "Japanese", "ko": "Korean", "de": "German",
+            "fr": "French", "es": "Spanish", "ru": "Russian", "it": "Italian",
+            "pt": "Portuguese", "nl": "Dutch", "ar": "Arabic", "hi": "Hindi",
+            "tr": "Turkish", "vi": "Vietnamese", "th": "Thai", "id": "Indonesian"
         }
 
         if language is not None and language not in supported_languages:
-            logger.warning(f"未知的语言代码: {language}，将使用自动检测")
+            logger.warning(f"Unknown language code: {language}, will use auto-detection")
             language = None
 
-        # 设置转录参数
+        # Set transcription parameters
         options = {
+            "verbose": True,
             "language": language,
-            "vad_filter": True,  # 使用语音活动检测
-            "vad_parameters": {"min_silence_duration_ms": 500},  # VAD参数优化
+            "vad_filter": True,
+            "vad_parameters": {"min_silence_duration_ms": 500},
             "beam_size": beam_size,
             "temperature": temperature,
             "initial_prompt": initial_prompt,
-            "word_timestamps": True,  # 启用单词级时间戳
-            "suppress_tokens": [-1],  # 抑制特殊标记
-            "condition_on_previous_text": True,  # 基于前文进行条件生成
-            "compression_ratio_threshold": 2.4  # 压缩比阈值，用于过滤重复内容
+            "word_timestamps": True,
+            "suppress_tokens": [-1],
+            "condition_on_previous_text": True,
+            "compression_ratio_threshold": 2.4,
         }
 
         start_time = time.time()
-        logger.info(f"开始转录文件: {os.path.basename(audio_path)}")
+        logger.info(f"Starting transcription of file: {os.path.basename(audio_path)}")
 
-        # 处理音频
+        # Process audio
         audio_source = process_audio(audio_path)
 
-        # 执行转录 - 优先使用批处理模型
+        # Execute transcription
         if model_instance['batched_model'] is not None and model_instance['device'] == 'cuda':
-            logger.info("使用批处理加速进行转录...")
-            # 批处理模型需要单独设置batch_size参数
+            logger.info("Using batch acceleration for transcription...")
             segments, info = model_instance['batched_model'].transcribe(
                 audio_source,
                 batch_size=model_instance['batch_size'],
                 **options
             )
         else:
-            logger.info("使用标准模型进行转录...")
+            logger.info("Using standard model for transcription...")
             segments, info = model_instance['model'].transcribe(audio_source, **options)
 
-        # 将生成器转换为列表
+        # Convert generator to list
         segment_list = list(segments)
 
         if not segment_list:
-            return "转录失败，未获得结果"
+            return "Transcription failed, no results obtained"
 
-        # 记录转录信息
+        # Record transcription information
         elapsed_time = time.time() - start_time
-        logger.info(f"转录完成，用时: {elapsed_time:.2f}秒，检测语言: {info.language}，音频长度: {info.duration:.2f}秒")
+        logger.info(f"Transcription completed, time used: {elapsed_time:.2f} seconds, detected language: {info.language}, audio length: {info.duration:.2f} seconds")
 
-        # 格式化转录结果
-        if output_format.lower() == "vtt":
+        # Format transcription results based on output format
+        output_format_lower = output_format.lower()
+        
+        if output_format_lower == "vtt":
             transcription_result = format_vtt(segment_list)
-        elif output_format.lower() == "srt":
+        elif output_format_lower == "srt":
             transcription_result = format_srt(segment_list)
-        else:
+        elif output_format_lower == "txt":
+            transcription_result = format_txt(segment_list)
+        elif output_format_lower == "json":
             transcription_result = format_json(segment_list, info)
+        else:
+            raise ValueError(f"Unsupported output format: {output_format}. Supported formats: vtt, srt, txt, json")
 
-        # 获取音频文件的目录和文件名
+        # Determine output directory
         audio_dir = os.path.dirname(audio_path)
         audio_filename = os.path.splitext(os.path.basename(audio_path))[0]
 
-        # 设置输出目录
-        if output_directory is None:
-            output_dir = audio_dir
-        else:
+        # Priority: parameter > env var > audio directory
+        if output_directory is not None:
             output_dir = output_directory
-            # 确保输出目录存在
-            os.makedirs(output_dir, exist_ok=True)
+        elif DEFAULT_OUTPUT_DIR is not None:
+            output_dir = DEFAULT_OUTPUT_DIR
+        else:
+            output_dir = audio_dir
 
-        # 生成带有时间戳的文件名
-        timestamp = time.strftime("%Y%m%d%H%M%S")
-        output_filename = f"{audio_filename}_{timestamp}.{output_format.lower()}"
+        # Ensure output directory exists
+        os.makedirs(output_dir, exist_ok=True)
+
+        # Generate filename with customizable format
+        filename_parts = []
+        
+        # Add prefix if specified
+        if FILENAME_PREFIX:
+            filename_parts.append(FILENAME_PREFIX)
+        
+        # Add base filename
+        filename_parts.append(audio_filename)
+        
+        # Add suffix if specified
+        if FILENAME_SUFFIX:
+            filename_parts.append(FILENAME_SUFFIX)
+        
+        # Add timestamp if enabled
+        if USE_TIMESTAMP:
+            timestamp = time.strftime("%Y%m%d%H%M%S")
+            filename_parts.append(timestamp)
+        
+        # Join parts and add extension
+        base_name = "_".join(filename_parts)
+        output_filename = f"{base_name}.{output_format_lower}"
         output_path = os.path.join(output_dir, output_filename)
 
-        # 将转录结果写入文件
+        # Write transcription results to file
         try:
             with open(output_path, "w", encoding="utf-8") as f:
                 f.write(transcription_result)
-            logger.info(f"转录结果已保存到: {output_path}")
-            return f"转录成功，结果已保存到: {output_path}"
+            logger.info(f"Transcription results saved to: {output_path}")
+            return f"Transcription successful, results saved to: {output_path}"
         except Exception as e:
-            logger.error(f"保存转录结果失败: {str(e)}")
-            return f"转录成功，但保存结果失败: {str(e)}"
+            logger.error(f"Failed to save transcription results: {str(e)}")
+            return f"Transcription successful, but failed to save results: {str(e)}"
 
     except Exception as e:
-        logger.error(f"转录失败: {str(e)}")
-        return f"转录过程中发生错误: {str(e)}"
+        logger.error(f"Transcription failed: {str(e)}")
+        return f"Error occurred during transcription: {str(e)}"
 
 
-def report_progress(current: int, total: int, elapsed_time: float) -> str:
-    """
-    生成进度报告
-
-    Args:
-        current: 当前处理的项目数
-        total: 总项目数
-        elapsed_time: 已用时间（秒）
-
-    Returns:
-        str: 格式化的进度报告
-    """
-    progress = current / total * 100
-    eta = (elapsed_time / current) * (total - current) if current > 0 else 0
-    return (f"进度: {current}/{total} ({progress:.1f}%)" +
-            f" | 已用时间: {format_time(elapsed_time)}" +
-            f" | 预计剩余: {format_time(eta)}")
-
 def batch_transcribe(
     audio_folder: str,
     output_folder: str = None,
-    model_name: str = "large-v3",
-    device: str = "auto",
-    compute_type: str = "auto",
+    model_name: str = None,
+    device: str = None,
+    compute_type: str = None,
     language: str = None,
-    output_format: str = "vtt",
-    beam_size: int = 5,
-    temperature: float = 0.0,
+    output_format: str = None,
+    beam_size: int = None,
+    temperature: float = None,
     initial_prompt: str = None,
     parallel_files: int = 1
 ) -> str:
     """
-    批量转录文件夹中的音频文件
+    Batch transcribe audio files with ENV VAR support
 
     Args:
-        audio_folder: 包含音频文件的文件夹路径
-        output_folder: 输出文件夹路径，默认为audio_folder下的transcript子文件夹
-        model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3)
-        device: 运行设备 (cpu, cuda, auto)
-        compute_type: 计算类型 (float16, int8, auto)
-        language: 语言代码 (如zh, en, ja等，默认自动检测)
-        output_format: 输出格式 (vtt, srt或json)
-        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
-        temperature: 采样温度，0表示贪婪解码
-        initial_prompt: 初始提示文本，可以帮助模型更好地理解上下文
-        parallel_files: 并行处理的文件数量（仅在CPU模式下有效）
+        audio_folder: Path to folder containing audio files
+        output_folder: Output folder (defaults to TRANSCRIPTION_BATCH_OUTPUT_DIR env var or "transcript" subfolder)
+        model_name: Model name (defaults to TRANSCRIPTION_MODEL env var or "large-v3")
+        device: Execution device (defaults to TRANSCRIPTION_DEVICE env var or "auto")
+        compute_type: Computation type (defaults to TRANSCRIPTION_COMPUTE_TYPE env var or "auto")
+        language: Language code (defaults to TRANSCRIPTION_LANGUAGE env var or auto-detect)
+        output_format: Output format (defaults to TRANSCRIPTION_OUTPUT_FORMAT env var or "vtt")
+        beam_size: Beam search size (defaults to TRANSCRIPTION_BEAM_SIZE env var or 5)
+        temperature: Sampling temperature (defaults to TRANSCRIPTION_TEMPERATURE env var or 0.0)
+        initial_prompt: Initial prompt text
+        parallel_files: Number of files to process in parallel (not implemented yet)
 
     Returns:
-        str: 批处理结果摘要，包含处理时间和成功率
+        str: Batch processing summary
     """
+    # Apply environment variable defaults
+    model_name = model_name or DEFAULT_MODEL
+    device = device or DEFAULT_DEVICE
+    compute_type = compute_type or DEFAULT_COMPUTE_TYPE
+    language = language or DEFAULT_LANGUAGE
+    output_format = output_format or DEFAULT_OUTPUT_FORMAT
+    beam_size = beam_size if beam_size is not None else DEFAULT_BEAM_SIZE
+    temperature = temperature if temperature is not None else DEFAULT_TEMPERATURE
+    
     if not os.path.isdir(audio_folder):
-        return f"错误: 文件夹不存在: {audio_folder}"
+        return f"Error: Folder does not exist: {audio_folder}"
 
-    # 设置输出文件夹
-    if output_folder is None:
+    # Determine output folder with environment variable support
+    if output_folder is not None:
+        # Use provided parameter
+        pass
+    elif DEFAULT_BATCH_OUTPUT_DIR is not None:
+        # Use environment variable
+        output_folder = DEFAULT_BATCH_OUTPUT_DIR
+    else:
+        # Use default subfolder
         output_folder = os.path.join(audio_folder, "transcript")
 
-    # 确保输出目录存在
+    # Ensure output directory exists
     os.makedirs(output_folder, exist_ok=True)
 
-    # 验证输出格式
-    valid_formats = ["vtt", "srt", "json"]
+    # Validate output format
+    valid_formats = ["txt", "vtt", "srt", "json"]
     if output_format.lower() not in valid_formats:
-        return f"错误: 不支持的输出格式: {output_format}。支持的格式: {', '.join(valid_formats)}"
+        return f"Error: Unsupported output format: {output_format}. Supported formats: {', '.join(valid_formats)}"
 
-    # 获取所有音频文件
+    # Get all audio files
     audio_files = []
     supported_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"]
 
@@ -225,37 +280,35 @@ def batch_transcribe(
             audio_files.append(os.path.join(audio_folder, filename))
 
     if not audio_files:
-        return f"在 {audio_folder} 中未找到支持的音频文件。支持的格式: {', '.join(supported_formats)}"
+        return f"No supported audio files found in {audio_folder}. Supported formats: {', '.join(supported_formats)}"
 
-    # 记录开始时间
+    # Record start time
     start_time = time.time()
     total_files = len(audio_files)
-    logger.info(f"开始批量转录 {total_files} 个文件，输出格式: {output_format}")
+    logger.info(f"Starting batch transcription of {total_files} files, output format: {output_format}")
 
-    # 预加载模型以避免重复加载
+    # Preload model
     try:
         get_whisper_model(model_name, device, compute_type)
-        logger.info(f"已预加载模型: {model_name}")
+        logger.info(f"Model preloaded: {model_name}")
     except Exception as e:
-        logger.error(f"预加载模型失败: {str(e)}")
-        return f"批处理失败: 无法加载模型 {model_name}: {str(e)}"
+        logger.error(f"Failed to preload model: {str(e)}")
+        return f"Batch processing failed: Cannot load model {model_name}: {str(e)}"
 
-    # 处理每个文件
+    # Process files
     results = []
     success_count = 0
     error_count = 0
-    total_audio_duration = 0
 
-    # 处理每个文件
     for i, audio_path in enumerate(audio_files):
         file_name = os.path.basename(audio_path)
         elapsed = time.time() - start_time
 
-        # 报告进度
+        # Report progress
         progress_msg = report_progress(i, total_files, elapsed)
-        logger.info(f"{progress_msg} | 当前处理: {file_name}")
+        logger.info(f"{progress_msg} | Currently processing: {file_name}")
 
-        # 执行转录
+        # Execute transcription
         try:
             result = transcribe_audio(
                 audio_path=audio_path,
@@ -270,57 +323,39 @@ def batch_transcribe(
                 output_directory=output_folder
             )
 
-            # 检查结果是否包含错误信息
-            if result.startswith("错误:") or result.startswith("转录过程中发生错误:"):
-                logger.error(f"转录失败: {file_name} - {result}")
-                results.append(f"❌ 失败: {file_name} - {result}")
+            if result.startswith("Error:") or result.startswith("Error occurred during transcription:"):
+                logger.error(f"Transcription failed: {file_name} - {result}")
+                results.append(f"❌ Failed: {file_name} - {result}")
                 error_count += 1
-                continue
-
-            # 如果转录成功，提取输出路径信息
-            if result.startswith("转录成功"):
-                # 从返回消息中提取输出路径
-                output_path = result.split(": ")[1] if ": " in result else "未知路径"
+            else:
+                output_path = result.split(": ")[1] if ": " in result else "Unknown path"
                 success_count += 1
-                results.append(f"✅ 成功: {file_name} -> {os.path.basename(output_path)}")
+                results.append(f"✅ Success: {file_name} -> {os.path.basename(output_path)}")
 
-                # 提取音频时长
-                audio_duration = 0
-                if output_format.lower() == "json":
-                    # 尝试从输出文件中解析音频时长
-                    try:
-                        import json
-                        # 从输出文件中读取JSON内容
-                        with open(output_path, "r", encoding="utf-8") as json_file:
-                            json_content = json_file.read()
-                            json_data = json.loads(json_content)
-                            audio_duration = json_data.get("duration", 0)
-                    except Exception as e:
-                        logger.warning(f"无法从JSON文件中提取音频时长: {str(e)}")
-                        audio_duration = 0
-                else:
-                    # 尝试从文件名中提取音频信息
-                    try:
-                        # 这里我们不能直接访问info对象，因为它在transcribe_audio函数的作用域内
-                        # 使用一个保守的估计值或从结果字符串中提取信息
-                        audio_duration = 0  # 默认为0
-                    except Exception as e:
-                        logger.warning(f"无法从文件名中提取音频时长: {str(e)}")
-                        audio_duration = 0
-
-                # 累加音频时长
-                total_audio_duration += audio_duration
         except Exception as e:
-            logger.error(f"转录过程中发生错误: {file_name} - {str(e)}")
-            results.append(f"❌ 失败: {file_name} - {str(e)}")
+            logger.error(f"Error occurred during transcription process: {file_name} - {str(e)}")
+            results.append(f"❌ Failed: {file_name} - {str(e)}")
             error_count += 1
-    # 计算总转录时间
+
+    # Calculate total transcription time
     total_transcription_time = time.time() - start_time
-    # 生成批处理结果摘要
-    summary = f"批处理完成，总转录时间: {format_time(total_transcription_time)}"
-    summary += f" | 成功: {success_count}/{total_files}"
-    summary += f" | 失败: {error_count}/{total_files}"
-    # 输出结果
+    
+    # Generate summary
+    summary = f"Batch processing completed, total transcription time: {format_time(total_transcription_time)}"
+    summary += f" | Success: {success_count}/{total_files}"
+    summary += f" | Failed: {error_count}/{total_files}"
+    
+    # Output results
     for result in results:
         logger.info(result)
+    
     return summary
+
+
+def report_progress(current: int, total: int, elapsed_time: float) -> str:
+    """Generate progress report"""
+    progress = current / total * 100
+    eta = (elapsed_time / current) * (total - current) if current > 0 else 0
+    return (f"Progress: {current}/{total} ({progress:.1f}%)" +
+            f" | Time used: {format_time(elapsed_time)}" +
+            f" | Estimated remaining: {format_time(eta)}")

whisper_server.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
-基于Faster Whisper的语音识别MCP服务
-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
+Faster Whisper-based Speech Recognition MCP Service
+Provides high-performance audio transcription with batch processing acceleration and multiple output formats
 """
 
 import os
@@ -11,11 +11,11 @@ from mcp.server.fastmcp import FastMCP
 from model_manager import get_model_info
 from transcriber import transcribe_audio, batch_transcribe
 
-# 日志配置
+# Log configuration
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# 创建FastMCP服务器实例
+# Create FastMCP server instance
 mcp = FastMCP(
     name="fast-whisper-mcp-server",
     version="0.1.1",
@@ -25,7 +25,7 @@ mcp = FastMCP(
 @mcp.tool()
 def get_model_info_api() -> str:
     """
-    获取可用的Whisper模型信息
+    Get available Whisper model information
     """
     return get_model_info()
 
@@ -35,22 +35,22 @@ def transcribe(audio_path: str, model_name: str = "large-v3", device: str = "aut
               beam_size: int = 5, temperature: float = 0.0, initial_prompt: str = None,
               output_directory: str = None) -> str:
     """
-    使用Faster Whisper转录音频文件
+    Transcribe audio files using Faster Whisper
 
     Args:
-        audio_path: 音频文件路径
-        model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3)
-        device: 运行设备 (cpu, cuda, auto)
-        compute_type: 计算类型 (float16, int8, auto)
-        language: 语言代码 (如zh, en, ja等，默认自动检测)
-        output_format: 输出格式 (vtt, srt或json)
-        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
-        temperature: 采样温度，贪婪解码
-        initial_prompt: 初始提示文本，可以帮助模型更好地理解上下文
-        output_directory: 输出目录路径，默认为音频文件所在目录
+        audio_path: Path to the audio file
+        model_name: Model name (tiny, base, small, medium, large-v1, large-v2, large-v3)
+        device: Execution device (cpu, cuda, auto)
+        compute_type: Computation type (float16, int8, auto)
+        language: Language code (such as zh, en, ja, etc., auto-detect by default)
+        output_format: Output format (vtt, srt, json or txt)
+        beam_size: Beam search size, larger values may improve accuracy but reduce speed
+        temperature: Sampling temperature, greedy decoding
+        initial_prompt: Initial prompt text, can help the model better understand context
+        output_directory: Output directory path, defaults to the audio file's directory
 
     Returns:
-        str: 转录结果，格式为VTT字幕或JSON
+        str: Transcription result, in VTT subtitle or JSON format
     """
     return transcribe_audio(
         audio_path=audio_path,
@@ -71,23 +71,23 @@ def batch_transcribe_audio(audio_folder: str, output_folder: str = None, model_n
                     output_format: str = "vtt", beam_size: int = 5, temperature: float = 0.0,
                     initial_prompt: str = None, parallel_files: int = 1) -> str:
     """
-    批量转录文件夹中的音频文件
+    Batch transcribe audio files in a folder
 
     Args:
-        audio_folder: 包含音频文件的文件夹路径
-        output_folder: 输出文件夹路径，默认为audio_folder下的transcript子文件夹
-        model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3)
-        device: 运行设备 (cpu, cuda, auto)
-        compute_type: 计算类型 (float16, int8, auto)
-        language: 语言代码 (如zh, en, ja等，默认自动检测)
-        output_format: 输出格式 (vtt, srt或json)
-        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
-        temperature: 采样温度，0表示贪婪解码
-        initial_prompt: 初始提示文本，可以帮助模型更好地理解上下文
-        parallel_files: 并行处理的文件数量（仅在CPU模式下有效）
+        audio_folder: Path to the folder containing audio files
+        output_folder: Output folder path, defaults to a 'transcript' subfolder in audio_folder
+        model_name: Model name (tiny, base, small, medium, large-v1, large-v2, large-v3)
+        device: Execution device (cpu, cuda, auto)
+        compute_type: Computation type (float16, int8, auto)
+        language: Language code (such as zh, en, ja, etc., auto-detect by default)
+        output_format: Output format (vtt, srt, json or txt)
+        beam_size: Beam search size, larger values may improve accuracy but reduce speed
+        temperature: Sampling temperature, 0 means greedy decoding
+        initial_prompt: Initial prompt text, can help the model better understand context
+        parallel_files: Number of files to process in parallel (only effective in CPU mode)
 
     Returns:
-        str: 批处理结果摘要，包含处理时间和成功率
+        str: Batch processing summary, including processing time and success rate
     """
     return batch_transcribe(
         audio_folder=audio_folder,
@@ -104,5 +104,5 @@ def batch_transcribe_audio(audio_folder: str, output_folder: str = None, model_n
     )
 
 if __name__ == "__main__":
-    # 运行服务器
+    print("starting mcp server for whisper stt transcriptor")
     mcp.run()