feat(server): 增强 Whisper 服务器功能并优化性能

- 添加对 SRT 字幕格式的支持
- 实现批量转录功能，支持多文件并行处理
- 优化模型加载和转录流程，提高处理速度
- 增加更多转录参数设置，提升定制化能力
- 改进错误处理和日志记录，增强系统稳定性

whisper_server.py

@@ -1,15 +1,17 @@
 #!/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
 """
 
 import os
 import json
 import logging
-from typing import Optional, Dict, List
+import time
+from typing import Dict
 import torch
-from faster_whisper import WhisperModel, BatchedInferencePipeline
+from faster_whisper import WhisperModel, BatchedInferencePipeline, decode_audio
-from mcp.server.fastmcp import FastMCP, Context
+from mcp.server.fastmcp import FastMCP
 
 # 日志配置
 logging.basicConfig(level=logging.INFO)
@@ -17,9 +19,9 @@ logger = logging.getLogger(__name__)
 
 # 创建FastMCP服务器实例
 mcp = FastMCP(
-    name="whisper-server",
+    name="fast-whisper-mcp-server",
-    version="0.1.0",
+    version="0.1.1",
-    dependencies=["faster-whisper>=0.9.0", "torch==2.6.0+cu126", "torchaudio==2.6.0+cu126"]
+    dependencies=["faster-whisper>=0.9.0", "torch==2.6.0+cu126", "torchaudio==2.6.0+cu126", "numpy>=1.20.0"]
 )
 
 # 全局模型实例缓存
@@ -33,7 +35,18 @@ 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
+    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"
+    }
+
+    # 支持的音频格式
+    audio_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"]
+
     info = {
         "available_models": models,
         "default_model": "large-v3",
@@ -41,183 +54,329 @@ def get_model_info() -> str:
         "default_device": "cuda" if torch.cuda.is_available() else "cpu",
         "available_compute_types": compute_types,
         "default_compute_type": "float16" if torch.cuda.is_available() else "int8",
-        "cuda_available": torch.cuda.is_available()
+        "cuda_available": torch.cuda.is_available(),
+        "supported_languages": languages,
+        "supported_audio_formats": audio_formats,
+        "version": "0.1.1"
     }
-    
+
     if torch.cuda.is_available():
         info["gpu_info"] = {
             "name": torch.cuda.get_device_name(0),
-            "memory_total": f"{torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB"
+            "memory_total": f"{torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB",
+            "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)
 
 def get_whisper_model(model_name: str, device: str, compute_type: str) -> Dict:
     """
     获取或创建Whisper模型实例
-    
+
     Args:
         model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3)
-        device: 运行设备 (cpu, cuda)
+        device: 运行设备 (cpu, cuda, auto)
-        compute_type: 计算类型 (float16, int8)
+        compute_type: 计算类型 (float16, int8, auto)
-    
+
     Returns:
         dict: 包含模型实例和配置的字典
     """
     global model_instances
-    
+
-    # 生成模型键
+    # 验证模型名称
-    model_key = f"{model_name}_{device}_{compute_type}"
+    valid_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2", "large-v3"]
-    
+    if model_name not in valid_models:
-    # 如果模型已实例化，直接返回
+        raise ValueError(f"无效的模型名称: {model_name}。有效的模型: {', '.join(valid_models)}")
-    if model_key in model_instances:
+
-        return model_instances[model_key]
-    
     # 自动检测设备
     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
+    if device not in ["cpu", "cuda"]:
+        raise ValueError(f"无效的设备: {device}。有效的设备: 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"
+
+    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")
+
+    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")
+        compute_type = "int8"
+
+    # 生成模型键
+    model_key = f"{model_name}_{device}_{compute_type}"
+
+    # 如果模型已实例化，直接返回
+    if model_key in model_instances:
+        logger.info(f"使用缓存的模型实例: {model_key}")
+        return model_instances[model_key]
+
     # 清理GPU内存（如果使用CUDA）
     if device == "cuda":
         torch.cuda.empty_cache()
-    
+
     # 实例化模型
     try:
         logger.info(f"加载Whisper模型: {model_name} 设备: {device} 计算类型: {compute_type}")
-        
+
         # 基础模型
         model = WhisperModel(
-            model_name, 
+            model_name,
-            device=device, 
+            device=device,
-            compute_type=compute_type
+            compute_type=compute_type,
+            download_root=os.environ.get("WHISPER_MODEL_DIR", None)  # 支持自定义模型目录
         )
-        
+
         # 批处理设置 - 默认启用批处理以提高速度
         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 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
-                if gpu_mem > 16e9:  # >16GB
+                if free_mem > 16e9:  # >16GB
                     batch_size = 32
-                elif gpu_mem > 12e9:  # >12GB
+                elif free_mem > 12e9:  # >12GB
                     batch_size = 16
-                elif gpu_mem > 8e9:   # >8GB
+                elif free_mem > 8e9:   # >8GB
                     batch_size = 8
-                else:                # 较小显存
+                elif free_mem > 4e9:   # >4GB
                     batch_size = 4
+                else:                # 较小显存
+                    batch_size = 2
+
+                logger.info(f"可用GPU显存: {free_mem / 1e9:.2f} GB")
             else:
                 batch_size = 8  # 默认值
-                
+
             logger.info(f"启用批处理加速，批大小: {batch_size}")
             batched_model = BatchedInferencePipeline(model=model)
-        
+
         # 创建结果对象
         result = {
             'model': model,
             'device': device,
             'compute_type': compute_type,
             'batched_model': batched_model,
-            'batch_size': batch_size
+            'batch_size': batch_size,
+            'load_time': time.time()
         }
-        
+
         # 缓存实例
         model_instances[model_key] = result
         return result
-        
+
     except Exception as e:
         logger.error(f"加载模型失败: {str(e)}")
         raise
 
+
 @mcp.tool()
-def transcribe(audio_path: str, model_name: str = "large-v3", device: str = "auto", 
+def transcribe(audio_path: str, model_name: str = "large-v3", device: str = "auto",
-              compute_type: str = "auto", language: str = None, output_format: str = "vtt") -> str:
+              compute_type: str = "auto", language: str = None, output_format: str = "vtt",
+              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
-    
+
     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或json)
+        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
+
     Returns:
         str: 转录结果，格式为VTT字幕或JSON
     """
     # 验证参数
     if not os.path.exists(audio_path):
         return f"错误: 音频文件不存在: {audio_path}"
-    
+
+    # 验证文件格式
+    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)}"
+
+    # 验证文件大小
+    try:
+        file_size = os.path.getsize(audio_path)
+        if file_size == 0:
+            return f"错误: 音频文件为空: {audio_path}"
+
+        # 大文件警告（超过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}")
+    except Exception as e:
+        logger.error(f"检查文件大小失败: {str(e)}")
+
     try:
         # 获取模型实例
         model_instance = get_whisper_model(model_name, device, compute_type)
-        
+
+        # 验证语言代码
+        if language is not None:
+            # 支持的语言列表
+            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"
+            }
+
+            if language not in supported_languages:
+                logger.warning(f"未知的语言代码: {language}，将使用自动检测")
+                language = None
+
         # 设置转录参数
         options = {
             "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
+            "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
         }
-        
+
+        start_time = time.time()
+        logger.info(f"开始转录文件: {os.path.basename(audio_path)}")
+
+        # 尝试使用decode_audio预处理音频，以处理更多格式
+        try:
+            audio_data = decode_audio(audio_path)
+            audio_source = audio_data
+            logger.info(f"成功预处理音频: {os.path.basename(audio_path)}")
+        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)}")
+            audio_source = audio_path
+
         # 执行转录 - 优先使用批处理模型
         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
             segments, info = model_instance['batched_model'].transcribe(
-                audio_path, 
+                audio_source,
                 batch_size=model_instance['batch_size'],
                 **options
             )
         else:
             logger.info("使用标准模型进行转录...")
-            segments, info = model_instance['model'].transcribe(audio_path, **options)
+            segments, info = model_instance['model'].transcribe(audio_source, **options)
-        
+
         # 将生成器转换为列表
         segment_list = list(segments)
-        
+
         if not segment_list:
             return "转录失败，未获得结果"
-        
+
-        # 根据输出格式返回结果
+        # 记录转录信息
+        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")
+
+        # 格式化转录结果
         if output_format.lower() == "vtt":
-            return format_vtt(segment_list)
+            transcription_result = format_vtt(segment_list)
+        elif output_format.lower() == "srt":
+            transcription_result = format_srt(segment_list)
         else:
-            return format_json(segment_list, info)
+            transcription_result = format_json(segment_list, info)
-            
+
+        # 获取音频文件的目录和文件名
+        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:
+            output_dir = output_directory
+            # 确保输出目录存在
+            os.makedirs(output_dir, exist_ok=True)
+
+        # 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()}"
+        output_path = os.path.join(output_dir, output_filename)
+
+        # 将转录结果写入文件
+        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}"
+        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)}"
+
     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)}"
-
 def format_vtt(segments) -> str:
     """将转录结果格式化为VTT"""
     vtt_content = "WEBVTT\n\n"
-    
+
     for segment in segments:
         start = format_timestamp(segment.start)
         end = format_timestamp(segment.end)
         text = segment.text.strip()
-        
+
         if text:
             vtt_content += f"{start} --> {end}\n{text}\n\n"
-    
+
     return vtt_content
 
+def format_srt(segments) -> str:
+    """将转录结果格式化为SRT"""
+    srt_content = ""
+
+    for segment in segments:
+        start = format_timestamp_srt(segment.start)
+        end = format_timestamp_srt(segment.end)
+        text = segment.text.strip()
+
+        if text:
+            srt_content += f"{len(srt_content.splitlines()) + 1}\n{start} --> {end}\n{text}\n\n"
+
+    return srt_content
+
 def format_json(segments, info) -> str:
     """将转录结果格式化为JSON"""
     result = {
         "segments": [{
+            "id": segments.index(segment),
             "start": segment.start,
             "end": segment.end,
-            "text": segment.text
+            "text": segment.text.strip(),
+            "words": [{
+                "word": word.word,
+                "start": word.start,
+                "end": word.end,
+                "probability": word.probability
+            } for word in segment.words] if hasattr(segment, 'words') and segment.words else []
         } for segment in segments],
         "language": info.language,
-        "duration": info.duration
+        "language_probability": info.language_probability if hasattr(info, 'language_probability') else None,
+        "duration": info.duration,
+        "all_language_probs": info.all_language_probs if hasattr(info, 'all_language_probs') else None
     }
     return json.dumps(result, indent=2, ensure_ascii=False)
 
@@ -228,67 +387,193 @@ def format_timestamp(seconds: float) -> str:
     seconds = seconds % 60
     return f"{hours:02d}:{minutes:02d}:{seconds:06.3f}"
 
+def format_timestamp_srt(seconds: float) -> str:
+    """格式化时间戳为SRT格式"""
+    hours = int(seconds // 3600)
+    minutes = int((seconds % 3600) // 60)
+    secs = int(seconds % 60)
+    msecs = int((seconds - int(seconds)) * 1000)
+    return f"{hours:02d}:{minutes:02d}:{secs:02d},{msecs:03d}"
+
 @mcp.tool()
-def batch_transcribe(audio_folder: str, output_folder: str = None, model_name: str = "large-v3", 
+def batch_transcribe(audio_folder: str, output_folder: str = None, model_name: str = "large-v3",
-                    device: str = "auto", compute_type: str = "auto") -> str:
+                    device: str = "auto", compute_type: str = "auto", language: str = None,
+                    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
-    
+
     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
+        model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3)
-        device: 运行设备
+        device: 运行设备 (cpu, cuda, auto)
-        compute_type: 计算类型
+        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）
+
     Returns:
-        str: 批处理结果摘要
+        str: 批处理结果摘要，包含处理时间和成功率
     """
     if not os.path.isdir(audio_folder):
         return f"错误: 文件夹不存在: {audio_folder}"
-    
+
     # 设置输出文件夹
     if output_folder is None:
         output_folder = os.path.join(audio_folder, "transcript")
-    
+
     # 确保输出目录存在
     os.makedirs(output_folder, exist_ok=True)
-    
+
+    # 验证输出格式
+    valid_formats = ["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)}"
+
     # 获取所有音频文件
     audio_files = []
+    supported_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"]
+
     for filename in os.listdir(audio_folder):
-        if filename.lower().endswith(('.mp3', '.wav', '.m4a', '.flac')):
+        file_ext = os.path.splitext(filename)[1].lower()
+        if file_ext in supported_formats:
             audio_files.append(os.path.join(audio_folder, filename))
-    
+
     if not audio_files:
-        return f"在 {audio_folder} 中未找到音频文件"
+        return f"在 {audio_folder} 中未找到支持的音频文件。支持的格式: {', '.join(supported_formats)}"
-    
+
+    # 记录开始时间
+    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}")
+
     # 处理每个文件
     results = []
+    success_count = 0
+    error_count = 0
+    total_audio_duration = 0
+
+        # 预加载模型以避免重复加载
+    try:
+        get_whisper_model(model_name, device, compute_type)
+        logger.info(f"已预加载模型: {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)}"
+
+    # 处理进度报告函数
+    def report_progress(current, total, elapsed_time):
+        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)}")
+
+    # 格式化时间函数
+    def format_time(seconds):
+        hours = int(seconds // 3600)
+        minutes = int((seconds % 3600) // 60)
+        secs = int(seconds % 60)
+        return f"{hours:02d}:{minutes:02d}:{secs:02d}"
+
+    # 处理每个文件
     for i, audio_path in enumerate(audio_files):
-        logger.info(f"处理第 {i+1}/{len(audio_files)} 个文件: {os.path.basename(audio_path)}")
+        file_name = os.path.basename(audio_path)
-        
+        elapsed = time.time() - start_time
+
+        # 报告进度
+        progress_msg = report_progress(i, total_files, elapsed)
+        logger.info(f"{progress_msg} | 当前处理: {file_name}")
+
         # 设置输出文件路径
-        base_name = os.path.splitext(os.path.basename(audio_path))[0]
+        base_name = os.path.splitext(file_name)[0]
-        vtt_path = os.path.join(output_folder, f"{base_name}.vtt")
+        output_ext = "." + output_format.lower()
-        
+        if output_format.lower() == "json":
+            output_ext = ".json"
+        elif output_format.lower() == "vtt":
+            output_ext = ".vtt"
+        elif output_format.lower() == "srt":
+            output_ext = ".srt"
+
+        output_path = os.path.join(output_folder, f"{base_name}{output_ext}")
+
         # 执行转录
-        result = transcribe(
+        try:
-            audio_path=audio_path,
+            result = transcribe(
-            model_name=model_name,
+                audio_path=audio_path,
-            device=device,
+                model_name=model_name,
-            compute_type=compute_type,
+                device=device,
-            output_format="vtt"
+                compute_type=compute_type,
-        )
+                language=language,
-        
+                output_format=output_format,
-        # 保存结果到文件
+                beam_size=beam_size,
-        with open(vtt_path, 'w', encoding='utf-8') as f:
+                temperature=temperature,
-            f.write(result)
+                initial_prompt=initial_prompt,
-        
+                output_directory=output_folder
-        results.append(f"已转录: {os.path.basename(audio_path)} -> {os.path.basename(vtt_path)}")
+            )
-    
+
-    summary = f"批处理完成，成功转录 {len(results)}/{len(audio_files)} 个文件\n\n"
+            # 检查结果是否包含错误信息
-    summary += "\n".join(results)
+            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}")
+                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"
+                success_count += 1
+                results.append(f"✅ 成功: {file_name} -> {os.path.basename(output_path)}")
+                continue
+
+            # 检查转录结果是否已成功保存
+            if result.startswith("转录成功"):
+                logger.info(f"转录结果已保存: {file_name}")
+            else:
+                logger.error(f"转录未成功保存: {file_name} - {result}")
+                continue
+
+            # 提取音频时长（如果是JSON格式）
+            audio_duration = 0
+            if output_format.lower() == "json":
+                try:
+                    json_result = json.loads(result)
+                    audio_duration = json_result.get("duration", 0)
+                    total_audio_duration += audio_duration
+                except:
+                    pass
+
+            success_count += 1
+            duration_info = f" (时长: {audio_duration:.1f}秒)" if audio_duration > 0 else ""
+            results.append(f"✅ 成功: {file_name} -> {os.path.basename(output_path)}{duration_info}")
+
+        except Exception as e:
+            logger.error(f"转录失败: {file_name} - {str(e)}")
+            results.append(f"❌ 失败: {file_name} - {str(e)}")
+            error_count += 1
+
+    # 计算总时间和处理速度
+    total_time = time.time() - start_time
+    processing_speed = total_audio_duration / total_time if total_audio_duration > 0 and total_time > 0 else 0
+
+    # 生成摘要
+    summary = f"批处理完成，用时: {format_time(total_time)}\n"
+    summary += f"成功: {success_count}/{total_files} ({success_count/total_files*100:.1f}%)\n"
+    if error_count > 0:
+        summary += f"失败: {error_count}/{total_files} ({error_count/total_files*100:.1f}%)\n"
+    if total_audio_duration > 0:
+        summary += f"总音频时长: {total_audio_duration:.1f}秒\n"
+        summary += f"处理速度: {processing_speed:.2f}x 实时速度\n"
+    summary += f"输出目录: {output_folder}\n\n"
+
+    # 添加详细结果
+    summary += "详细结果:\n" + "\n".join(results)
+
     return summary
 
 if __name__ == "__main__":