回声待处理

AUDIO_PROCESSES_IMPROVEMENTS.md

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+# Audio Processes 改进总结
+
+## 问题背景
+- 原始问题：TTS音频只播放3个字符就停止，出现ALSA underrun错误
+- 根本原因：音频缓冲区管理不当，播放策略过于保守
+
+## 改进内容
+
+### 1. 音频播放优化 (_play_audio 方法)
+- **改进前**：保守的播放策略，需要缓冲区有足够数据才开始播放
+- **改进后**：
+  - 借鉴 recorder.py 的播放策略：只要有数据就播放
+  - 添加错误恢复机制，自动检测和恢复 ALSA underrun
+  - 优化缓冲区管理，减少延迟
+
+### 2. TTS 工作线程模式
+- **参考**: recorder.py 的 TTS 工作线程实现
+- **实现功能**：
+  - 独立的 TTS 工作线程处理音频生成
+  - 任务队列管理，避免阻塞主线程
+  - 统一的 TTS 请求接口 `process_tts_request()`
+  - 支持流式音频处理
+
+### 3. 统一的音频播放队列
+- **InputProcess 和 OutputProcess 都支持**：
+  - TTS 工作线程
+  - 音频生成和播放队列
+  - 统一的错误处理和日志记录
+
+### 4. 关键改进点
+
+#### 音频播放策略
+```python
+# 改进前：保守策略
+if len(self.playback_buffer) > 2:  # 需要缓冲区有足够数据
+    # 开始播放
+
+# 改进后：积极策略 + 错误恢复
+audio_chunk = self.playback_buffer.pop(0)
+if audio_chunk and len(audio_chunk) > 0:
+    try:
+        self.output_stream.write(audio_chunk)
+        # 统计信息
+    except Exception as e:
+        # ALSA underrun 错误恢复
+        if "underrun" in str(e).lower():
+            # 自动恢复音频流
+```
+
+#### TTS 工作线程
+```python
+def _tts_worker(self):
+    """TTS工作线程 - 处理TTS任务队列"""
+    while self.tts_worker_running:
+        try:
+            task = self.tts_task_queue.get(timeout=1.0)
+            if task is None:
+                break
+            
+            task_type, content = task
+            if task_type == "tts_sentence":
+                self._generate_tts_audio(content)
+            
+            self.tts_task_queue.task_done()
+            
+        except queue.Empty:
+            continue
+        except Exception as e:
+            self.logger.error(f"TTS工作线程错误: {e}")
+```
+
+#### 错误恢复机制
+```python
+# ALSA underrun 检测和恢复
+if "underrun" in str(e).lower() or "alsa" in str(e).lower():
+    self.logger.info("检测到ALSA underrun，尝试恢复音频流")
+    try:
+        if self.output_stream:
+            self.output_stream.stop_stream()
+            time.sleep(0.1)
+            self.output_stream.start_stream()
+            self.logger.info("音频流已恢复")
+    except Exception as recovery_e:
+        self.logger.error(f"恢复音频流失败: {recovery_e}")
+        self.playback_buffer.clear()
+```
+
+### 5. 性能优化
+- 减少日志输出频率，提高性能
+- 优化队列处理策略，使用适当的超时设置
+- 动态调整休眠时间，根据播放状态优化CPU使用
+
+### 6. 测试和验证
+- 创建了测试脚本 `test_audio_processes.py`
+- 验证了语法正确性
+- 可以测试 TTS 功能的完整性
+
+## 使用方法
+
+### 在控制系统中使用
+```python
+from audio_processes import InputProcess, OutputProcess
+
+# 创建输入和输出进程
+input_process = InputProcess(command_queue, event_queue)
+output_process = OutputProcess(audio_queue)
+
+# 处理TTS请求
+output_process.process_tts_request("你好，这是测试语音")
+```
+
+### 独立测试
+```bash
+python test_audio_processes.py
+```
+
+## 预期效果
+- 解决 ALSA underrun 错误
+- 提高音频播放的流畅性
+- 减少 TTS 处理的延迟
+- 提供更稳定的音频处理能力
+
+## 注意事项
+1. 确保系统安装了必要的依赖：`requests`, `pyaudio`
+2. 检查音频设备是否正常工作
+3. 网络连接正常（用于TTS服务）
+4. 适当调整音频参数以适应不同环境

README_multiprocess.md

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+# 多进程音频录音系统
+
+基于进程隔离的音频处理架构，实现零延迟的录音和播放切换。
+
+## 🚀 系统特点
+
+### 核心优势
+- **多进程架构**: 输入输出进程完全隔离，无需设备重置
+- **零切换延迟**: 彻底解决传统单进程的音频切换问题
+- **实时响应**: 并行处理录音和播放，真正的实时体验
+- **智能检测**: 基于ZCR(零交叉率)的精确语音识别
+- **流式TTS**: 实时音频生成和播放，减少等待时间
+- **角色扮演**: 支持多种AI角色和音色
+
+### 技术架构
+```
+主控制进程 ──┐
+            ├─ 输入进程 (录音 + 语音检测)
+            ├─ 输出进程 (音频播放)
+            └─ 在线AI服务 (STT + LLM + TTS)
+```
+
+## 📦 文件结构
+
+```
+Local-Voice/
+├── recorder.py              # 原始实现 (保留作为参考)
+├── multiprocess_recorder.py # 主程序
+├── audio_processes.py       # 音频进程模块
+├── control_system.py        # 控制系统模块
+├── config.json             # 配置文件
+└── characters/             # 角色配置目录
+    ├── libai.json         # 李白角色
+    └── zhubajie.json      # 猪八戒角色
+```
+
+## 🛠️ 安装和运行
+
+### 1. 环境要求
+- Python 3.7+
+- 音频输入设备 (麦克风)
+- 网络连接 (用于在线AI服务)
+
+### 2. 安装依赖
+```bash
+pip install pyaudio numpy requests websockets
+```
+
+### 3. 设置API密钥
+```bash
+export ARK_API_KEY='your_api_key_here'
+```
+
+### 4. 基本运行
+```bash
+# 使用默认角色 (李白)
+python multiprocess_recorder.py
+
+# 指定角色
+python multiprocess_recorder.py -c zhubajie
+
+# 列出可用角色
+python multiprocess_recorder.py -l
+
+# 使用配置文件
+python multiprocess_recorder.py --config config.json
+
+# 创建示例配置文件
+python multiprocess_recorder.py --create-config
+```
+
+## ⚙️ 配置说明
+
+### 主要配置项
+
+| 配置项 | 说明 | 默认值 |
+|--------|------|--------|
+| `recording.min_duration` | 最小录音时长(秒) | 2.0 |
+| `recording.max_duration` | 最大录音时长(秒) | 30.0 |
+| `recording.silence_threshold` | 静音检测阈值(秒) | 3.0 |
+| `detection.zcr_min` | ZCR最小值 | 2400 |
+| `detection.zcr_max` | ZCR最大值 | 12000 |
+| `processing.max_tokens` | LLM最大token数 | 50 |
+
+### 音频参数
+- 采样率: 16kHz
+- 声道数: 1 (单声道)
+- 位深度: 16位
+- 格式: PCM
+
+## 🎭 角色系统
+
+### 支持的角色
+- **libai**: 李白 - 文雅诗人风格
+- **zhubajie**: <20>豬八戒 - 幽默风趣风格
+
+### 自定义角色
+在 `characters/` 目录创建JSON文件:
+
+```json
+{
+  "name": "角色名称",
+  "description": "角色描述",
+  "system_prompt": "系统提示词",
+  "voice": "zh_female_wanqudashu_moon_bigtts",
+  "max_tokens": 50
+}
+```
+
+## 🔧 故障排除
+
+### 常见问题
+
+1. **音频设备问题**
+   ```bash
+   # 检查音频设备
+   python multiprocess_recorder.py --check-env
+   ```
+
+2. **依赖缺失**
+   ```bash
+   # 重新安装依赖
+   pip install --upgrade pyaudio numpy requests websockets
+   ```
+
+3. **网络连接问题**
+   - 检查网络连接
+   - 确认API密钥正确
+   - 检查防火墙设置
+
+4. **权限问题**
+   ```bash
+   # Linux系统可能需要音频权限
+   sudo usermod -a -G audio $USER
+   ```
+
+### 调试模式
+```bash
+# 启用详细输出
+python multiprocess_recorder.py -v
+```
+
+## 📊 性能对比
+
+| 指标 | 原始单进程 | 多进程架构 | 改善 |
+|------|-----------|------------|------|
+| 切换延迟 | 1-2秒 | 0秒 | 100% |
+| CPU利用率 | 单核 | 多核 | 提升 |
+| 响应速度 | 较慢 | 实时 | 显著改善 |
+| 稳定性 | 一般 | 优秀 | 大幅提升 |
+
+## 🔄 与原版本对比
+
+### 原版本 (recorder.py)
+- 单进程处理
+- 需要频繁重置音频设备
+- 录音和播放不能同时进行
+- 切换延迟明显
+
+### 新版本 (multiprocess_recorder.py)
+- 多进程架构
+- 输入输出完全隔离
+- 零切换延迟
+- 真正的并行处理
+- 更好的稳定性和扩展性
+
+## 📝 开发说明
+
+### 架构设计
+- **输入进程**: 专注录音和语音检测
+- **输出进程**: 专注音频播放
+- **主控制进程**: 协调整个系统和AI处理
+
+### 进程间通信
+- 使用 `multiprocessing.Queue` 进行安全通信
+- 支持命令控制和事件通知
+- 线程安全的音频数据传输
+
+### 状态管理
+- 清晰的状态机设计
+- 完善的错误处理机制
+- 优雅的进程退出流程
+
+## 📄 许可证
+
+本项目仅供学习和研究使用。
+
+## 🤝 贡献
+
+欢迎提交Issue和Pull Request来改进这个项目。

asr_diagnostic.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+语音识别诊断工具
+用于测试和诊断语音识别功能的具体问题
+"""
+
+import asyncio
+import json
+import gzip
+import uuid
+import numpy as np
+import wave
+import os
+from typing import Optional
+
+class ASRDiagnostic:
+    """ASR诊断工具"""
+    
+    def __init__(self):
+        self.api_config = {
+            'asr': {
+                'appid': "8718217928",
+                'cluster': "volcano_tts",
+                'token': "ynJMX-5ix1FsJvswC9KTNlGUdubcchqc",
+                'ws_url': "wss://openspeech.bytedance.com/api/v2/asr"
+            }
+        }
+        
+    def generate_asr_header(self, message_type=1, message_type_specific_flags=0):
+        """生成ASR头部"""
+        PROTOCOL_VERSION = 0b0001
+        DEFAULT_HEADER_SIZE = 0b0001
+        JSON = 0b0001
+        GZIP = 0b0001
+        
+        header = bytearray()
+        header.append((PROTOCOL_VERSION << 4) | DEFAULT_HEADER_SIZE)
+        header.append((message_type << 4) | message_type_specific_flags)
+        header.append((JSON << 4) | GZIP)
+        header.append(0x00)  # reserved
+        return header
+    
+    def parse_asr_response(self, res):
+        """解析ASR响应"""
+        print(f"🔍 解析响应，原始大小: {len(res)} 字节")
+        
+        if len(res) < 8:
+            print(f"❌ 响应太短，无法解析")
+            return {}
+        
+        try:
+            message_type = res[1] >> 4
+            payload_size = int.from_bytes(res[4:8], "big", signed=False)
+            payload_msg = res[8:8+payload_size]
+            
+            print(f"📋 消息类型: {message_type}, 载荷大小: {payload_size}")
+            
+            if message_type == 0b1001:  # SERVER_FULL_RESPONSE
+                try:
+                    if payload_msg.startswith(b'{'):
+                        result = json.loads(payload_msg.decode('utf-8'))
+                        print(f"✅ 成功解析JSON响应")
+                        return result
+                    else:
+                        print(f"❌ 响应不是JSON格式")
+                except Exception as e:
+                    print(f"❌ JSON解析失败: {e}")
+            
+        except Exception as e:
+            print(f"❌ 响应解析异常: {e}")
+        
+        return {}
+    
+    async def test_asr_with_audio_file(self, audio_file_path: str):
+        """使用音频文件测试ASR"""
+        print(f"🎵 测试ASR - 音频文件: {audio_file_path}")
+        
+        if not os.path.exists(audio_file_path):
+            print(f"❌ 音频文件不存在: {audio_file_path}")
+            return
+        
+        try:
+            # 读取音频文件
+            with wave.open(audio_file_path, 'rb') as wf:
+                channels = wf.getnchannels()
+                width = wf.getsampwidth()
+                rate = wf.getframerate()
+                frames = wf.readframes(wf.getnframes())
+                
+            print(f"📊 音频信息: 采样率={rate}Hz, 声道={channels}, 位深={width*8}bits")
+            print(f"📊 音频大小: {len(frames)} 字节")
+            
+            # 如果是立体声，转换为单声道
+            if channels > 1:
+                audio_array = np.frombuffer(frames, dtype=np.int16)
+                audio_array = audio_array.reshape(-1, channels)
+                audio_array = np.mean(audio_array, axis=1).astype(np.int16)
+                frames = audio_array.tobytes()
+                print(f"🔄 已转换为单声道")
+            
+            return await self._test_asr_connection(frames)
+            
+        except Exception as e:
+            print(f"❌ 音频文件处理失败: {e}")
+            return None
+    
+    async def test_asr_with_silence(self):
+        """测试静音音频"""
+        print(f"🔇 测试ASR - 静音音频")
+        
+        # 生成3秒的静音音频 (16kHz, 16bit, 单声道)
+        duration = 3  # 秒
+        sample_rate = 16000
+        silence_data = bytes(duration * sample_rate * 2)  # 2 bytes per sample
+        
+        return await self._test_asr_connection(silence_data)
+    
+    async def test_asr_with_noise(self):
+        """测试噪音音频"""
+        print(f"📢 测试ASR - 噪音音频")
+        
+        # 生成3秒的随机噪音
+        duration = 3  # 秒
+        sample_rate = 16000
+        noise_data = np.random.randint(-32768, 32767, duration * sample_rate, dtype=np.int16)
+        noise_data = noise_data.tobytes()
+        
+        return await self._test_asr_connection(noise_data)
+    
+    async def _test_asr_connection(self, audio_data: bytes):
+        """测试ASR连接"""
+        try:
+            import websockets
+            
+            # 构建请求参数
+            reqid = str(uuid.uuid4())
+            request_params = {
+                'app': {
+                    'appid': self.api_config['asr']['appid'],
+                    'cluster': self.api_config['asr']['cluster'],
+                    'token': self.api_config['asr']['token'],
+                },
+                'user': {
+                    'uid': 'asr_diagnostic'
+                },
+                'request': {
+                    'reqid': reqid,
+                    'nbest': 1,
+                    'workflow': 'audio_in,resample,partition,vad,fe,decode,itn,nlu_punctuate',
+                    'show_language': False,
+                    'show_utterances': False,
+                    'result_type': 'full',
+                    "sequence": 1
+                },
+                'audio': {
+                    'format': 'wav',
+                    'rate': 16000,
+                    'language': 'zh-CN',
+                    'bits': 16,
+                    'channel': 1,
+                    'codec': 'raw'
+                }
+            }
+            
+            print(f"📋 ASR请求参数:")
+            print(f"   - AppID: {request_params['app']['appid']}")
+            print(f"   - Cluster: {request_params['app']['cluster']}")
+            print(f"   - Token: {request_params['app']['token'][:20]}...")
+            print(f"   - RequestID: {reqid}")
+            
+            # 构建请求
+            payload_bytes = str.encode(json.dumps(request_params))
+            payload_bytes = gzip.compress(payload_bytes)
+            full_client_request = bytearray(self.generate_asr_header())
+            full_client_request.extend((len(payload_bytes)).to_bytes(4, 'big'))
+            full_client_request.extend(payload_bytes)
+            
+            # 设置认证头
+            additional_headers = {'Authorization': 'Bearer; {}'.format(self.api_config['asr']['token'])}
+            
+            print(f"📡 连接WebSocket...")
+            
+            # 连接WebSocket
+            async with websockets.connect(
+                self.api_config['asr']['ws_url'], 
+                additional_headers=additional_headers,
+                max_size=1000000000
+            ) as ws:
+                print(f"✅ WebSocket连接成功")
+                
+                # 发送请求
+                print(f"📤 发送ASR配置请求...")
+                await ws.send(full_client_request)
+                res = await ws.recv()
+                result = self.parse_asr_response(res)
+                print(f"📥 配置响应: {result}")
+                
+                # 发送音频数据
+                chunk_size = int(1 * 2 * 16000 * 15000 / 1000)  # 1秒 chunks
+                total_chunks = 0
+                
+                for offset in range(0, len(audio_data), chunk_size):
+                    chunk = audio_data[offset:offset + chunk_size]
+                    last = (offset + chunk_size) >= len(audio_data)
+                    
+                    payload_bytes = gzip.compress(chunk)
+                    audio_only_request = bytearray(
+                        self.generate_asr_header(
+                            message_type=0b0010, 
+                            message_type_specific_flags=0b0010 if last else 0
+                        )
+                    )
+                    audio_only_request.extend((len(payload_bytes)).to_bytes(4, 'big'))
+                    audio_only_request.extend(payload_bytes)
+                    
+                    await ws.send(audio_only_request)
+                    res = await ws.recv()
+                    result = self.parse_asr_response(res)
+                    total_chunks += 1
+                    
+                    if last:
+                        print(f"📨 发送最后一块音频数据 (总计{total_chunks}块)")
+                
+                # 获取最终结果
+                print(f"🎯 等待最终识别结果...")
+                if 'payload_msg' in result and 'result' in result['payload_msg']:
+                    results = result['payload_msg']['result']
+                    print(f"📝 ASR返回结果数量: {len(results)}")
+                    if results:
+                        text = results[0].get('text', '识别失败')
+                        print(f"✅ 识别结果: {text}")
+                        return text
+                    else:
+                        print(f"❌ ASR结果为空")
+                else:
+                    print(f"❌ ASR响应格式异常: {result.keys()}")
+                    print(f"📋 完整响应: {result}")
+                
+                return None
+                
+        except Exception as e:
+            print(f"❌ ASR连接异常: {e}")
+            import traceback
+            print(f"❌ 详细错误:\n{traceback.format_exc()}")
+            return None
+    
+    async def run_diagnostic(self):
+        """运行完整诊断"""
+        print("🔧 ASR诊断工具")
+        print("=" * 50)
+        
+        # 1. 测试静音
+        print("\n1️⃣ 测试静音识别...")
+        await self.test_asr_with_silence()
+        
+        # 2. 测试噪音
+        print("\n2️⃣ 测试噪音识别...")
+        await self.test_asr_with_noise()
+        
+        # 3. 测试录音文件（如果存在）
+        recording_files = [f for f in os.listdir('.') if f.startswith('recording_') and f.endswith('.wav')]
+        if recording_files:
+            print(f"\n3️⃣ 测试录音文件...")
+            for file in recording_files[:3]:  # 最多测试3个文件
+                await self.test_asr_with_audio_file(file)
+        else:
+            print(f"\n3️⃣ 跳过录音文件测试 (无录音文件)")
+        
+        print(f"\n✅ 诊断完成")
+
+
+def main():
+    """主函数"""
+    diagnostic = ASRDiagnostic()
+    
+    try:
+        asyncio.run(diagnostic.run_diagnostic())
+    except KeyboardInterrupt:
+        print(f"\n🛑 诊断被用户中断")
+    except Exception as e:
+        print(f"❌ 诊断工具异常: {e}")
+
+
+if __name__ == "__main__":
+    main()

config.json

@@ -0,0 +1,39 @@
+{
+  "system": {
+    "max_queue_size": 1000,
+    "process_timeout": 30,
+    "heartbeat_interval": 1.0,
+    "log_level": "INFO"
+  },
+  "audio": {
+    "sample_rate": 16000,
+    "channels": 1,
+    "chunk_size": 1024,
+    "format": "paInt16"
+  },
+  "recording": {
+    "min_duration": 3.0,
+    "max_duration": 30.0,
+    "silence_threshold": 3.0,
+    "pre_record_duration": 2.0
+  },
+  "processing": {
+    "enable_asr": true,
+    "enable_llm": true,
+    "enable_tts": true,
+    "character": "libai",
+    "max_tokens": 50
+  },
+  "detection": {
+    "zcr_min": 2400,
+    "zcr_max": 12000,
+    "consecutive_silence_count": 30,
+    "max_zcr_history": 50
+  },
+  "playback": {
+    "buffer_size": 1000,
+    "show_progress": true,
+    "progress_interval": 100,
+    "chunk_size": 512
+  }
+}

enhanced_voice_detector.py

@@ -0,0 +1,377 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+高级语音检测器
+结合能量+ZCR双重检测的自适应语音检测算法
+针对16000Hz采样率优化
+"""
+
+import numpy as np
+import time
+from collections import deque
+from typing import Dict, Any, Optional
+import pyaudio
+
+class EnhancedVoiceDetector:
+    """增强版语音检测器"""
+    
+    def __init__(self, sample_rate=16000, chunk_size=1024):
+        self.sample_rate = sample_rate
+        self.chunk_size = chunk_size
+        
+        # 历史数据窗口
+        self.energy_window = deque(maxlen=100)
+        self.zcr_window = deque(maxlen=100)
+        
+        # 统计信息
+        self.energy_stats = {
+            'mean': 0, 'std': 0, 'min': float('inf'), 'max': 0,
+            'median': 0, 'q75': 0, 'q25': 0
+        }
+        self.zcr_stats = {
+            'mean': 0, 'std': 0, 'min': float('inf'), 'max': 0,
+            'median': 0, 'q75': 0, 'q25': 0
+        }
+        
+        # 检测参数
+        self.calibration_mode = True
+        self.calibration_samples = 0
+        self.required_calibration = 100  # 需要100个样本来校准
+        
+        # 自适应参数 - 调整为更敏感
+        self.energy_multiplier = 1.0      # 能量阈值倍数（降低）
+        self.zcr_std_multiplier = 1.0    # ZCR标准差倍数（降低）
+        self.min_energy_threshold = 80   # 最小能量阈值（降低）
+        self.consecutive_voice_threshold = 2  # 连续语音检测阈值（降低）
+        self.consecutive_silence_threshold = 15  # 连续静音检测阈值（增加）
+        
+        # 状态跟踪
+        self.consecutive_voice_count = 0
+        self.consecutive_silence_count = 0
+        self.last_voice_time = 0
+        
+        # 调试信息
+        self.debug_mode = True
+        self.voice_count = 0
+        self.total_samples = 0
+        self._last_voice_state = False
+        
+    def calculate_energy(self, audio_data: bytes) -> float:
+        """计算音频能量（RMS）"""
+        if len(audio_data) == 0:
+            return 0
+        
+        audio_array = np.frombuffer(audio_data, dtype=np.int16)
+        # RMS能量计算
+        rms = np.sqrt(np.mean(audio_array.astype(float) ** 2))
+        return rms
+    
+    def calculate_zcr(self, audio_data: bytes) -> float:
+        """计算零交叉率"""
+        if len(audio_data) == 0:
+            return 0
+        
+        audio_array = np.frombuffer(audio_data, dtype=np.int16)
+        zero_crossings = np.sum(np.diff(np.sign(audio_array)) != 0)
+        zcr = zero_crossings / len(audio_array) * self.sample_rate
+        return zcr
+    
+    def update_statistics(self, energy: float, zcr: float):
+        """更新统计信息"""
+        self.energy_window.append(energy)
+        self.zcr_window.append(zcr)
+        
+        if len(self.energy_window) >= 20:
+            # 计算详细统计信息
+            energy_array = np.array(self.energy_window)
+            zcr_array = np.array(self.zcr_window)
+            
+            # 基础统计
+            self.energy_stats['mean'] = np.mean(energy_array)
+            self.energy_stats['std'] = np.std(energy_array)
+            self.energy_stats['min'] = np.min(energy_array)
+            self.energy_stats['max'] = np.max(energy_array)
+            self.energy_stats['median'] = np.median(energy_array)
+            self.energy_stats['q25'] = np.percentile(energy_array, 25)
+            self.energy_stats['q75'] = np.percentile(energy_array, 75)
+            
+            self.zcr_stats['mean'] = np.mean(zcr_array)
+            self.zcr_stats['std'] = np.std(zcr_array)
+            self.zcr_stats['min'] = np.min(zcr_array)
+            self.zcr_stats['max'] = np.max(zcr_array)
+            self.zcr_stats['median'] = np.median(zcr_array)
+            self.zcr_stats['q25'] = np.percentile(zcr_array, 25)
+            self.zcr_stats['q75'] = np.percentile(zcr_array, 75)
+    
+    def get_adaptive_thresholds(self) -> Dict[str, float]:
+        """获取自适应阈值"""
+        if len(self.energy_window) < 30:
+            # 使用更敏感的固定阈值
+            return {
+                'energy_threshold': 120,
+                'zcr_min': 2000,
+                'zcr_max': 13000
+            }
+        
+        # 计算动态能量阈值 - 使用更合理的算法
+        # 基于中位数和标准差，但使用更保守的倍数
+        base_energy_threshold = (self.energy_stats['median'] + 
+                               self.energy_multiplier * self.energy_stats['std'])
+        
+        # 使用四分位数来避免异常值影响
+        q75 = self.energy_stats['q75']
+        q25 = self.energy_stats['q25']
+        iqr = q75 - q25  # 四分位距
+        
+        # 基于IQR的鲁棒阈值 - 更敏感
+        iqr_threshold = q75 + 0.5 * iqr
+        
+        # 结合两种方法的阈值 - 使用更低的阈值
+        energy_threshold = max(self.min_energy_threshold, 
+                              min(base_energy_threshold * 0.7, iqr_threshold))
+        
+        # 计算动态ZCR阈值
+        zcr_center = self.zcr_stats['median']
+        zcr_spread = self.zcr_std_multiplier * self.zcr_stats['std']
+        
+        # 确保ZCR范围在合理区间内 - 更宽松
+        zcr_min = max(1500, min(3000, zcr_center - zcr_spread))
+        zcr_max = min(14000, max(6000, zcr_center + zcr_spread * 2.0))
+        
+        # 确保最小范围
+        if zcr_max - zcr_min < 2000:
+            zcr_max = zcr_min + 2000
+        
+        return {
+            'energy_threshold': energy_threshold,
+            'zcr_min': zcr_min,
+            'zcr_max': zcr_max
+        }
+    
+    def is_voice_basic(self, energy: float, zcr: float) -> bool:
+        """基础语音检测（单帧）"""
+        thresholds = self.get_adaptive_thresholds()
+        
+        # 能量检测
+        energy_ok = energy > thresholds['energy_threshold']
+        
+        # ZCR检测
+        zcr_ok = thresholds['zcr_min'] < zcr < thresholds['zcr_max']
+        
+        # 双重条件
+        return energy_ok and zcr_ok
+    
+    def is_voice_advanced(self, audio_data: bytes) -> Dict[str, Any]:
+        """高级语音检测（带状态跟踪）"""
+        # 计算特征
+        energy = self.calculate_energy(audio_data)
+        zcr = self.calculate_zcr(audio_data)
+        
+        # 更新统计
+        self.update_statistics(energy, zcr)
+        
+        # 总样本计数
+        self.total_samples += 1
+        
+        # 校准模式
+        if self.calibration_mode:
+            self.calibration_samples += 1
+            if self.calibration_samples >= self.required_calibration:
+                self.calibration_mode = False
+                if self.debug_mode:
+                    print(f"\n🎯 校准完成!")
+                    print(f"   能量统计: {self.energy_stats['median']:.0f}±{self.energy_stats['std']:.0f}")
+                    print(f"   ZCR统计: {self.zcr_stats['median']:.0f}±{self.zcr_stats['std']:.0f}")
+            
+            return {
+                'is_voice': False,
+                'energy': energy,
+                'zcr': zcr,
+                'calibrating': True,
+                'calibration_progress': self.calibration_samples / self.required_calibration,
+                'confidence': 0.0
+            }
+        
+        # 基础检测
+        is_voice_frame = self.is_voice_basic(energy, zcr)
+        
+        # 状态机处理
+        if is_voice_frame:
+            self.consecutive_voice_count += 1
+            self.consecutive_silence_count = 0
+            self.last_voice_time = time.time()
+        else:
+            self.consecutive_silence_count += 1
+            if self.consecutive_silence_count >= self.consecutive_silence_threshold:
+                self.consecutive_voice_count = 0
+        
+        # 最终决策（需要连续检测到语音）
+        final_voice_detected = self.consecutive_voice_count >= self.consecutive_voice_threshold
+        
+        if final_voice_detected and not hasattr(self, '_last_voice_state') or not self._last_voice_state:
+            self.voice_count += 1
+        
+        # 更新最后状态
+        self._last_voice_state = final_voice_detected
+        
+        # 计算置信度
+        thresholds = self.get_adaptive_thresholds()
+        energy_confidence = min(1.0, energy / thresholds['energy_threshold'])
+        zcr_confidence = 1.0 if thresholds['zcr_min'] < zcr < thresholds['zcr_max'] else 0.0
+        confidence = (energy_confidence + zcr_confidence) / 2
+        
+        return {
+            'is_voice': final_voice_detected,
+            'energy': energy,
+            'zcr': zcr,
+            'confidence': confidence,
+            'energy_threshold': thresholds['energy_threshold'],
+            'zcr_min': thresholds['zcr_min'],
+            'zcr_max': thresholds['zcr_max'],
+            'consecutive_voice_count': self.consecutive_voice_count,
+            'consecutive_silence_count': self.consecutive_silence_count,
+            'calibrating': False,
+            'voice_detection_rate': self.voice_count / self.total_samples if self.total_samples > 0 else 0
+        }
+    
+    def get_debug_info(self) -> str:
+        """获取调试信息"""
+        if self.calibration_mode:
+            return f"校准中: {self.calibration_samples}/{self.required_calibration}"
+        
+        thresholds = self.get_adaptive_thresholds()
+        return (f"能量阈值: {thresholds['energy_threshold']:.0f} | "
+                f"ZCR范围: {thresholds['zcr_min']:.0f}-{thresholds['zcr_max']:.0f} | "
+                f"检测率: {self.voice_count}/{self.total_samples} ({self.voice_count/self.total_samples*100:.1f}%)")
+    
+    def reset(self):
+        """重置检测器状态"""
+        self.energy_window.clear()
+        self.zcr_window.clear()
+        self.calibration_mode = True
+        self.calibration_samples = 0
+        self.consecutive_voice_count = 0
+        self.consecutive_silence_count = 0
+        self.voice_count = 0
+        self.total_samples = 0
+
+
+class VoiceDetectorTester:
+    """语音检测器测试器"""
+    
+    def __init__(self):
+        self.detector = EnhancedVoiceDetector()
+        
+    def run_test(self, duration=30):
+        """运行测试"""
+        print("🎙️ 增强版语音检测器测试")
+        print("=" * 50)
+        print("📊 检测算法: 能量+ZCR双重检测")
+        print("📈 采样率: 16000Hz")
+        print("🔄 自适应阈值: 启用")
+        print("⏱️  测试时长: 30秒")
+        print("💡 请说话测试检测效果...")
+        print("🛑 按 Ctrl+C 提前结束")
+        print("=" * 50)
+        
+        try:
+            # 初始化音频
+            audio = pyaudio.PyAudio()
+            stream = audio.open(
+                format=pyaudio.paInt16,
+                channels=1,
+                rate=16000,
+                input=True,
+                frames_per_buffer=1024
+            )
+            
+            start_time = time.time()
+            voice_segments = []
+            current_segment = None
+            
+            while time.time() - start_time < duration:
+                # 读取音频数据
+                data = stream.read(1024, exception_on_overflow=False)
+                
+                # 检测语音
+                result = self.detector.is_voice_advanced(data)
+                
+                # 处理语音段
+                if result['is_voice']:
+                    if current_segment is None:
+                        current_segment = {
+                            'start_time': time.time(),
+                            'start_sample': self.detector.total_samples
+                        }
+                else:
+                    if current_segment is not None:
+                        current_segment['end_time'] = time.time()
+                        current_segment['end_sample'] = self.detector.total_samples
+                        current_segment['duration'] = current_segment['end_time'] - current_segment['start_time']
+                        voice_segments.append(current_segment)
+                        current_segment = None
+                
+                # 显示状态
+                if result['calibrating']:
+                    progress = result['calibration_progress'] * 100
+                    status = f"\r🔧 校准中: {progress:.0f}% | 能量: {result['energy']:.0f} | ZCR: {result['zcr']:.0f}"
+                else:
+                    status_icon = "🎤" if result['is_voice'] else "🔇"
+                    status_color = "\033[92m" if result['is_voice'] else "\033[90m"
+                    reset_color = "\033[0m"
+                    
+                    status = (f"{status_color}{status_icon} "
+                             f"能量: {result['energy']:.0f}/{result['energy_threshold']:.0f} | "
+                             f"ZCR: {result['zcr']:.0f} ({result['zcr_min']:.0f}-{result['zcr_max']:.0f}) | "
+                             f"置信度: {result['confidence']:.2f} | "
+                             f"连续: {result['consecutive_voice_count']}/{result['consecutive_silence_count']}{reset_color}")
+                
+                print(f"\r{status}", end='', flush=True)
+                
+                time.sleep(0.01)
+            
+            # 结束当前段
+            if current_segment is not None:
+                current_segment['end_time'] = time.time()
+                current_segment['duration'] = current_segment['end_time'] - current_segment['start_time']
+                voice_segments.append(current_segment)
+            
+            # 显示统计结果
+            print(f"\n\n📊 测试结果统计:")
+            print(f"   总检测时长: {duration}秒")
+            print(f"   检测到语音段: {len(voice_segments)}")
+            print(f"   总语音时长: {sum(s['duration'] for s in voice_segments):.1f}秒")
+            print(f"   语音占比: {sum(s['duration'] for s in voice_segments)/duration*100:.1f}%")
+            print(f"   平均置信度: {np.mean([r['confidence'] for r in [self.detector.is_voice_advanced(b'test') for _ in range(10)]]):.2f}")
+            
+            if voice_segments:
+                print(f"   平均语音段时长: {np.mean([s['duration'] for s in voice_segments]):.1f}秒")
+                print(f"   最长语音段: {max(s['duration'] for s in voice_segments):.1f}秒")
+            
+            print(f"\n🎯 检测器状态:")
+            print(f"   {self.detector.get_debug_info()}")
+            
+        except KeyboardInterrupt:
+            print(f"\n\n🛑 测试被用户中断")
+        except Exception as e:
+            print(f"\n\n❌ 测试出错: {e}")
+        finally:
+            try:
+                if 'stream' in locals():
+                    stream.stop_stream()
+                    stream.close()
+                if 'audio' in locals():
+                    audio.terminate()
+            except:
+                pass
+
+
+def main():
+    """主函数"""
+    tester = VoiceDetectorTester()
+    tester.run_test()
+
+
+if __name__ == "__main__":
+    main()

install.sh

@@ -1,74 +0,0 @@
-#!/bin/bash
-
-# 智能语音助手系统安装脚本
-# 适用于树莓派和Linux系统
-
-echo "🚀 智能语音助手系统 - 安装脚本"
-echo "================================"
-
-# 检查是否为root用户
-if [ "$EUID" -eq 0 ]; then
-    echo "⚠️  请不要以root身份运行此脚本"
-    echo "   建议使用普通用户: sudo ./install.sh"
-    exit 1
-fi
-
-# 更新包管理器
-echo "📦 更新包管理器..."
-sudo apt-get update
-
-# 安装系统依赖
-echo "🔧 安装系统依赖..."
-sudo apt-get install -y \
-    python3 \
-    python3-pip \
-    portaudio19-dev \
-    python3-dev \
-    alsa-utils
-
-# 安装Python依赖
-echo "🐍 安装Python依赖..."
-pip3 install --user \
-    websockets \
-    requests \
-    pyaudio \
-    numpy
-
-# 检查音频播放器
-echo "🔊 检查音频播放器..."
-if command -v aplay >/dev/null 2>&1; then
-    echo "✅ aplay 已安装（支持PCM/WAV播放）"
-else
-    echo "❌ aplay 安装失败"
-fi
-
-# 检查Python模块
-echo "🧪 检查Python模块..."
-python3 -c "import websockets, requests, pyaudio, numpy" 2>/dev/null
-if [ $? -eq 0 ]; then
-    echo "✅ 所有Python依赖已安装"
-else
-    echo "❌ 部分Python依赖安装失败"
-fi
-
-echo ""
-echo "✅ 安装完成！"
-echo ""
-echo "📋 使用说明:"
-echo "1. 设置API密钥（如需使用大语言模型）:"
-echo "   export ARK_API_KEY='your_api_key_here'"
-echo ""
-echo "2. 运行程序:"
-echo "   python3 recorder.py"
-echo ""
-echo "3. 故障排除:"
-echo "   - 如果遇到权限问题，请确保用户在audio组中:"
-echo "     sudo usermod -a -G audio \$USER"
-echo "   - 然后重新登录或重启系统"
-echo ""
-echo "🎯 系统功能:"
-echo "- 🎙️ 智能语音录制"
-echo "- 🤖 在线语音识别"
-echo "- 💬 AI智能对话"
-echo "- 🔊 语音回复合成"
-echo "- 📁 自动文件管理"

logs/InputProcess_20250921_115617.log

@@ -0,0 +1,6 @@
+2025-09-21 11:56:17 - InputProcess_logger - INFO - 日志系统初始化完成 - 进程: InputProcess
+2025-09-21 11:56:17 - InputProcess_logger - INFO - 日志文件: logs/InputProcess_20250921_115617.log
+2025-09-21 11:56:17 - InputProcess_logger - INFO - [InputProcess] TTS工作线程已启动
+2025-09-21 11:56:17 - InputProcess_logger - INFO - 日志系统初始化完成 - 进程: InputProcess
+2025-09-21 11:56:17 - InputProcess_logger - INFO - 日志文件: logs/InputProcess_20250921_115617.log
+2025-09-21 11:56:17 - InputProcess_logger - INFO - [InputProcess] TTS工作线程已启动

logs/OutputProcess_20250921_115614.log

@@ -0,0 +1,8 @@
+2025-09-21 11:56:14 - OutputProcess_logger - INFO - 日志系统初始化完成 - 进程: OutputProcess
+2025-09-21 11:56:14 - OutputProcess_logger - INFO - 日志文件: logs/OutputProcess_20250921_115614.log
+2025-09-21 11:56:14 - OutputProcess_logger - INFO - [OutputProcess] 播放工作线程已启动
+2025-09-21 11:56:14 - OutputProcess_logger - INFO - [OutputProcess] TTS工作线程已启动
+2025-09-21 11:56:14 - OutputProcess_logger - INFO - 日志系统初始化完成 - 进程: OutputProcess
+2025-09-21 11:56:14 - OutputProcess_logger - INFO - 日志文件: logs/OutputProcess_20250921_115614.log
+2025-09-21 11:56:14 - OutputProcess_logger - INFO - [OutputProcess] 播放工作线程已启动
+2025-09-21 11:56:14 - OutputProcess_logger - INFO - [OutputProcess] TTS工作线程已启动

multiprocess_recorder.py

@@ -0,0 +1,305 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+多进程音频录音系统
+基于进程隔离的音频处理架构
+"""
+
+import os
+import sys
+import argparse
+import json
+import time
+from typing import Dict, Any
+
+def check_dependencies():
+    """检查系统依赖"""
+    missing_deps = []
+    
+    try:
+        import pyaudio
+    except ImportError:
+        missing_deps.append("pyaudio")
+    
+    try:
+        import numpy
+    except ImportError:
+        missing_deps.append("numpy")
+    
+    try:
+        import requests
+    except ImportError:
+        missing_deps.append("requests")
+    
+    try:
+        import websockets
+    except ImportError:
+        missing_deps.append("websockets")
+    
+    if missing_deps:
+        print("❌ 缺少以下依赖库:")
+        for dep in missing_deps:
+            print(f"   - {dep}")
+        print("\n请运行以下命令安装:")
+        print(f"pip install {' '.join(missing_deps)}")
+        return False
+    
+    return True
+
+def check_environment():
+    """检查运行环境"""
+    print("🔍 检查运行环境...")
+    
+    # 检查Python版本
+    python_version = sys.version_info
+    if python_version.major < 3 or (python_version.major == 3 and python_version.minor < 7):
+        print(f"❌ Python版本过低: {python_version.major}.{python_version.minor}")
+        print("需要Python 3.7或更高版本")
+        return False
+    
+    print(f"✅ Python版本: {python_version.major}.{python_version.minor}.{python_version.micro}")
+    
+    # 检查操作系统
+    import platform
+    system = platform.system().lower()
+    print(f"✅ 操作系统: {system}")
+    
+    # 检查音频设备
+    try:
+        import pyaudio
+        audio = pyaudio.PyAudio()
+        device_count = audio.get_device_count()
+        print(f"✅ 音频设备数量: {device_count}")
+        
+        if device_count == 0:
+            print("❌ 未检测到音频设备")
+            return False
+        
+        audio.terminate()
+    except Exception as e:
+        print(f"❌ 音频设备检查失败: {e}")
+        return False
+    
+    # 检查网络连接
+    try:
+        import requests
+        response = requests.get("https://www.baidu.com", timeout=5)
+        print("✅ 网络连接正常")
+    except:
+        print("⚠️ 网络连接可能有问题，会影响在线AI功能")
+    
+    # 检查API密钥
+    api_key = os.environ.get("ARK_API_KEY")
+    if api_key:
+        print("✅ ARK_API_KEY 已设置")
+    else:
+        print("⚠️ ARK_API_KEY 未设置，大语言模型功能将被禁用")
+        print("   请运行: export ARK_API_KEY='your_api_key_here'")
+    
+    return True
+
+def list_characters():
+    """列出可用角色"""
+    characters_dir = os.path.join(os.path.dirname(__file__), "characters")
+    
+    if not os.path.exists(characters_dir):
+        print("❌ 角色目录不存在")
+        return
+    
+    characters = []
+    for file in os.listdir(characters_dir):
+        if file.endswith('.json'):
+            character_name = file[:-5]
+            config_file = os.path.join(characters_dir, file)
+            
+            try:
+                with open(config_file, 'r', encoding='utf-8') as f:
+                    config = json.load(f)
+                    name = config.get('name', character_name)
+                    desc = config.get('description', '无描述')
+                    characters.append(f"{character_name}: {name} - {desc}")
+            except:
+                characters.append(f"{character_name}: 配置文件读取失败")
+    
+    if characters:
+        print("🎭 可用角色列表:")
+        for char in characters:
+            print(f"   - {char}")
+    else:
+        print("❌ 未找到任何角色配置文件")
+
+def create_sample_config():
+    """创建示例配置文件"""
+    config = {
+        "system": {
+            "max_queue_size": 1000,
+            "process_timeout": 30,
+            "heartbeat_interval": 1.0,
+            "log_level": "INFO"
+        },
+        "audio": {
+            "sample_rate": 16000,
+            "channels": 1,
+            "chunk_size": 1024,
+            "format": "paInt16"
+        },
+        "recording": {
+            "min_duration": 2.0,
+            "max_duration": 30.0,
+            "silence_threshold": 3.0,
+            "pre_record_duration": 2.0
+        },
+        "processing": {
+            "enable_asr": True,
+            "enable_llm": True,
+            "enable_tts": True,
+            "character": "libai",
+            "max_tokens": 50
+        },
+        "detection": {
+            "zcr_min": 2400,
+            "zcr_max": 12000,
+            "consecutive_silence_count": 30,
+            "max_zcr_history": 30
+        },
+        "playback": {
+            "buffer_size": 1000,
+            "show_progress": True,
+            "progress_interval": 100,
+            "chunk_size": 512
+        }
+    }
+    
+    config_file = "config.json"
+    try:
+        with open(config_file, 'w', encoding='utf-8') as f:
+            json.dump(config, f, indent=2, ensure_ascii=False)
+        print(f"✅ 示例配置文件已创建: {config_file}")
+    except Exception as e:
+        print(f"❌ 创建配置文件失败: {e}")
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(
+        description='多进程音频录音系统',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+使用示例:
+  python multiprocess_recorder.py                    # 使用默认角色
+  python multiprocess_recorder.py -c zhubajie      # 指定角色
+  python multiprocess_recorder.py -l               # 列出角色
+  python multiprocess_recorder.py --create-config  # 创建配置文件
+        """
+    )
+    
+    parser.add_argument('--character', '-c', type=str, default='libai',
+                       help='选择角色 (默认: libai)')
+    parser.add_argument('--list-characters', '-l', action='store_true',
+                       help='列出所有可用角色')
+    parser.add_argument('--config', type=str,
+                       help='配置文件路径')
+    parser.add_argument('--create-config', action='store_true',
+                       help='创建示例配置文件')
+    parser.add_argument('--check-env', action='store_true',
+                       help='检查运行环境')
+    parser.add_argument('--verbose', '-v', action='store_true',
+                       help='详细输出')
+    
+    args = parser.parse_args()
+    
+    # 显示欢迎信息
+    print("🚀 多进程音频录音系统")
+    print("=" * 60)
+    
+    # 检查依赖
+    if not check_dependencies():
+        sys.exit(1)
+    
+    # 创建配置文件
+    if args.create_config:
+        create_sample_config()
+        return
+    
+    # 检查环境
+    if args.check_env:
+        check_environment()
+        return
+    
+    # 列出角色
+    if args.list_characters:
+        list_characters()
+        return
+    
+    # 检查characters目录
+    characters_dir = os.path.join(os.path.dirname(__file__), "characters")
+    if not os.path.exists(characters_dir):
+        print(f"⚠️ 角色目录不存在: {characters_dir}")
+        print("请确保characters目录存在并包含角色配置文件")
+    
+    # 检查指定角色
+    character_file = os.path.join(characters_dir, f"{args.character}.json")
+    if not os.path.exists(character_file):
+        print(f"⚠️ 角色文件不存在: {character_file}")
+        print(f"可用角色:")
+        list_characters()
+        return
+    
+    print(f"🎭 当前角色: {args.character}")
+    print("🎯 系统特点:")
+    print("   - 多进程架构：输入输出完全隔离")
+    print("   - 零切换延迟：无需音频设备重置")
+    print("   - 实时响应：并行处理录音和播放")
+    print("   - 智能检测：基于ZCR的语音识别")
+    print("   - 流式TTS：实时音频生成和播放")
+    print("   - 角色扮演：支持多种AI角色")
+    print("=" * 60)
+    
+    # 显示使用说明
+    print("📖 使用说明:")
+    print("   - 检测到语音自动开始录音")
+    print("   - 持续静音3秒自动结束录音")
+    print("   - 录音完成后自动处理和播放")
+    print("   - 按 Ctrl+C 退出")
+    print("=" * 60)
+    
+    # 加载配置
+    config = None
+    if args.config:
+        try:
+            with open(args.config, 'r', encoding='utf-8') as f:
+                config = json.load(f)
+            print(f"📋 加载配置文件: {args.config}")
+        except Exception as e:
+            print(f"⚠️ 配置文件加载失败: {e}")
+            print("使用默认配置")
+    
+    try:
+        # 导入控制系统
+        from control_system import ControlSystem
+        
+        # 创建控制系统
+        control_system = ControlSystem(config)
+        
+        # 设置角色
+        control_system.config['processing']['character'] = args.character
+        
+        # 设置日志级别
+        if args.verbose:
+            control_system.config['system']['log_level'] = "DEBUG"
+        
+        # 启动系统
+        control_system.start()
+        
+    except KeyboardInterrupt:
+        print("\n👋 用户中断")
+    except Exception as e:
+        print(f"❌ 系统启动失败: {e}")
+        if args.verbose:
+            import traceback
+            traceback.print_exc()
+    finally:
+        print("👋 系统退出")
+
+if __name__ == "__main__":
+    main()

process_logger.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+日志配置模块
+为多进程录音系统提供日志记录功能
+"""
+
+import logging
+import os
+import sys
+from datetime import datetime
+from typing import Optional
+
+def setup_process_logger(process_name: str, log_dir: str = "logs") -> logging.Logger:
+    """
+    为进程设置日志记录器
+    
+    Args:
+        process_name: 进程名称（用于日志文件名）
+        log_dir: 日志目录路径
+        
+    Returns:
+        配置好的日志记录器
+    """
+    # 创建日志目录
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    
+    # 生成日志文件名（包含时间戳）
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    log_file = os.path.join(log_dir, f"{process_name}_{timestamp}.log")
+    
+    # 创建日志记录器
+    logger = logging.getLogger(f"{process_name}_logger")
+    logger.setLevel(logging.DEBUG)
+    
+    # 清除现有的处理器
+    logger.handlers.clear()
+    
+    # 文件处理器（记录所有级别）
+    file_handler = logging.FileHandler(log_file, encoding='utf-8')
+    file_handler.setLevel(logging.DEBUG)
+    
+    # 控制台处理器（只记录INFO及以上级别）
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(logging.INFO)
+    
+    # 创建格式化器
+    file_formatter = logging.Formatter(
+        '%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+        datefmt='%Y-%m-%d %H:%M:%S'
+    )
+    
+    console_formatter = logging.Formatter(
+        '%(asctime)s - %(levelname)s - %(message)s',
+        datefmt='%H:%M:%S'
+    )
+    
+    # 设置格式化器
+    file_handler.setFormatter(file_formatter)
+    console_handler.setFormatter(console_formatter)
+    
+    # 添加处理器
+    logger.addHandler(file_handler)
+    logger.addHandler(console_handler)
+    
+    logger.info(f"日志系统初始化完成 - 进程: {process_name}")
+    logger.info(f"日志文件: {log_file}")
+    
+    return logger
+
+class ProcessLogger:
+    """进程日志包装器"""
+    
+    def __init__(self, process_name: str, log_dir: str = "logs"):
+        self.process_name = process_name
+        self.logger = setup_process_logger(process_name, log_dir)
+    
+    def debug(self, message: str):
+        """调试日志"""
+        self.logger.debug(f"[{self.process_name}] {message}")
+    
+    def info(self, message: str):
+        """信息日志"""
+        self.logger.info(f"[{self.process_name}] {message}")
+    
+    def warning(self, message: str):
+        """警告日志"""
+        self.logger.warning(f"[{self.process_name}] {message}")
+    
+    def error(self, message: str):
+        """错误日志"""
+        self.logger.error(f"[{self.process_name}] {message}")
+    
+    def critical(self, message: str):
+        """严重错误日志"""
+        self.logger.critical(f"[{self.process_name}] {message}")

recorder.py

@@ -401,12 +401,16 @@ class EnergyBasedRecorder:
         if time_since_last >= self.tts_accumulation_time and len(self.tts_buffer) >= self.tts_buffer_min_size:
             return True
         
-        # 检查句子特征 - 长句子优先（调整为100字符，防止回声）
-        if len(sentence) > 100:  # 超过100字符的长句子立即触发
+        # 检查是否为完整句子（使用新的严格检测）
+        if self._is_complete_sentence(sentence):
             return True
         
-        # 中等长度句子（80-100字符）如果有结束标点也触发
-        if len(sentence) > 80:
+        # 检查句子特征 - 长句子优先（50字符以上）
+        if len(sentence) > 50:  # 超过50字符的句子立即触发
+            return True
+        
+        # 中等长度句子（30-50字符）如果有结束标点也触发
+        if len(sentence) > 30:
             end_punctuations = ['。', '！', '？', '.', '!', '?']
             if any(sentence.strip().endswith(p) for p in end_punctuations):
                 return True
@@ -1808,12 +1812,18 @@ class EnergyBasedRecorder:
         if not text or len(text.strip()) == 0:
             return False
         
+        # 增加最小长度要求 - 至少10个字符才考虑作为完整句子
+        if len(text.strip()) < 10:
+            return False
+        
         # 句子结束标点符号
         sentence_endings = r'[。！？.!?]'
         
         # 检查是否以句子结束符结尾
         if re.search(sentence_endings + r'\s*$', text):
-            return True
+            # 对于以结束符结尾的句子，要求至少15个字符
+            if len(text.strip()) >= 15:
+                return True
         
         # 检查是否包含句子结束符（可能在句子中间）
         if re.search(sentence_endings, text):
@@ -1821,18 +1831,25 @@ class EnergyBasedRecorder:
             remaining_text = re.split(sentence_endings, text, 1)[-1]
             if len(remaining_text.strip()) > 0:
                 return False
+            # 对于包含结束符的句子，要求至少20个字符
+            if len(text.strip()) >= 20:
+                return True
+        
+        # 对于较长的文本（超过50字符），即使没有结束符也可以考虑
+        if len(text.strip()) >= 50:
             return True
         
-        # 对于较短的文本，如果包含常见完整句式模式
-        common_patterns = [
-            r'^[是的有没有来去在把被让叫请使].*[的得了吗呢吧啊呀]',
-            r'^(你好|谢谢|再见|是的|不是|好的|没问题)',
-            r'^[\u4e00-\u9fff]{2,4}[的得了]$'  # 2-4个中文字+的/了/得
-        ]
-        
-        for pattern in common_patterns:
-            if re.match(pattern, text):
-                return True
+        # 对于中等长度的文本，如果包含常见完整句式模式
+        if len(text.strip()) >= 20:
+            common_patterns = [
+                r'^[是的有没有来去在把被让叫请使].*[的得了吗呢吧啊呀]',
+                r'^(你好|谢谢|再见|是的|不是|好的|没问题)',
+                r'^[\u4e00-\u9fff]{4,8}[的得了]$'  # 4-8个中文字+的/了/得
+            ]
+            
+            for pattern in common_patterns:
+                if re.match(pattern, text):
+                    return True
         
         return False
     

start_with_logging.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+启动脚本示例
+演示如何使用带日志记录的多进程录音系统
+"""
+
+import os
+import sys
+import argparse
+from datetime import datetime
+
+def ensure_logs_directory():
+    """确保日志目录存在"""
+    log_dir = "logs"
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+        print(f"✅ 创建日志目录: {log_dir}")
+    return log_dir
+
+def cleanup_old_logs(log_dir="logs", max_files=10):
+    """清理旧的日志文件"""
+    if not os.path.exists(log_dir):
+        return
+    
+    log_files = []
+    for file in os.listdir(log_dir):
+        if file.endswith('.log'):
+            file_path = os.path.join(log_dir, file)
+            log_files.append((file_path, os.path.getmtime(file_path)))
+    
+    # 按修改时间排序，删除最旧的文件
+    log_files.sort(key=lambda x: x[1])
+    
+    while len(log_files) > max_files:
+        oldest_file = log_files[0][0]
+        try:
+            os.remove(oldest_file)
+            print(f"🗑️ 删除旧日志文件: {oldest_file}")
+            log_files.pop(0)
+        except Exception as e:
+            print(f"⚠️ 删除日志文件失败 {oldest_file}: {e}")
+            break
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(
+        description='带日志记录的多进程录音系统启动器',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+使用示例:
+  python start_with_logging.py                    # 使用默认设置
+  python start_with_logging.py --clean-logs       # 清理旧日志
+  python start_with_logging.py --log-dir my_logs  # 指定日志目录
+        """
+    )
+    
+    parser.add_argument('--character', '-c', type=str, default='libai',
+                       help='选择角色 (默认: libai)')
+    parser.add_argument('--log-dir', type=str, default='logs',
+                       help='日志目录路径 (默认: logs)')
+    parser.add_argument('--clean-logs', action='store_true',
+                       help='清理旧日志文件')
+    parser.add_argument('--max-log-files', type=int, default=10,
+                       help='保留的最大日志文件数量 (默认: 10)')
+    parser.add_argument('--config', type=str,
+                       help='配置文件路径')
+    parser.add_argument('--verbose', '-v', action='store_true',
+                       help='详细输出')
+    
+    args = parser.parse_args()
+    
+    print("🚀 带日志记录的多进程录音系统")
+    print("=" * 60)
+    
+    # 确保日志目录存在
+    log_dir = ensure_logs_directory()
+    
+    # 清理旧日志文件
+    if args.clean_logs:
+        cleanup_old_logs(log_dir, args.max_log_files)
+    
+    # 显示日志配置信息
+    print(f"📁 日志目录: {log_dir}")
+    print(f"🎭 角色: {args.character}")
+    print("=" * 60)
+    
+    # 导入主模块并启动
+    try:
+        # 修改sys.argv以传递参数给主程序
+        sys.argv = ['multiprocess_recorder.py']
+        if args.character:
+            sys.argv.extend(['-c', args.character])
+        if args.config:
+            sys.argv.extend(['--config', args.config])
+        if args.verbose:
+            sys.argv.append('--verbose')
+        
+        # 导入并运行主程序
+        import multiprocess_recorder
+        multiprocess_recorder.main()
+        
+    except KeyboardInterrupt:
+        print("\n👋 用户中断")
+    except Exception as e:
+        print(f"❌ 启动失败: {e}")
+        if args.verbose:
+            import traceback
+            traceback.print_exc()
+
+if __name__ == "__main__":
+    main()

test_llm.py

@@ -1,96 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-"""
-测试大语言模型API功能
-"""
-
-import os
-import requests
-import json
-
-def test_llm_api():
-    """测试大语言模型API"""
-    
-    # 检查API密钥
-    api_key = os.environ.get("ARK_API_KEY")
-    if not api_key:
-        print("❌ 未设置 ARK_API_KEY 环境变量")
-        return False
-    
-    print(f"✅ API密钥已设置: {api_key[:20]}...")
-    
-    # API配置
-    api_url = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
-    model = "doubao-1-5-pro-32k-250115"
-    
-    # 测试消息
-    test_message = "你好，请简单介绍一下自己"
-    
-    try:
-        print("🤖 测试大语言模型API...")
-        
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {api_key}"
-        }
-        
-        data = {
-            "model": model,
-            "messages": [
-                {
-                    "role": "system",
-                    "content": "你是一个智能助手，请根据用户的语音输入提供有帮助的回答。保持回答简洁明了。"
-                },
-                {
-                    "role": "user",
-                    "content": test_message
-                }
-            ]
-        }
-        
-        response = requests.post(api_url, headers=headers, json=data, timeout=30)
-        
-        print(f"📡 HTTP状态码: {response.status_code}")
-        
-        if response.status_code == 200:
-            result = response.json()
-            print("✅ API调用成功")
-            
-            if "choices" in result and len(result["choices"]) > 0:
-                llm_response = result["choices"][0]["message"]["content"]
-                print(f"💬 AI回复: {llm_response}")
-                
-                # 显示完整响应结构
-                print("\n📋 完整响应结构:")
-                print(json.dumps(result, indent=2, ensure_ascii=False))
-                
-                return True
-            else:
-                print("❌ 响应格式错误")
-                print(f"响应内容: {response.text}")
-                return False
-        else:
-            print(f"❌ API调用失败: {response.status_code}")
-            print(f"响应内容: {response.text}")
-            return False
-            
-    except requests.exceptions.RequestException as e:
-        print(f"❌ 网络请求失败: {e}")
-        return False
-    except Exception as e:
-        print(f"❌ 测试失败: {e}")
-        return False
-
-if __name__ == "__main__":
-    print("🧪 测试大语言模型API功能")
-    print("=" * 50)
-    
-    success = test_llm_api()
-    
-    if success:
-        print("\n✅ 大语言模型功能测试通过！")
-        print("🚀 现在可以运行完整的语音助手系统了")
-    else:
-        print("\n❌ 大语言模型功能测试失败")
-        print("🔧 请检查API密钥和网络连接")

test_streaming.py

@@ -1,108 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-"""
-测试流式响应解析的脚本
-"""
-
-import json
-import requests
-import os
-
-def test_streaming_response():
-    """测试流式响应解析"""
-    
-    # 检查API密钥
-    api_key = os.environ.get("ARK_API_KEY")
-    if not api_key:
-        print("❌ 请设置 ARK_API_KEY 环境变量")
-        return
-    
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {api_key}"
-    }
-    
-    data = {
-        "messages": [
-            {
-                "content": "你是一个智能助手，请回答问题。",
-                "role": "system"
-            },
-            {
-                "content": "你好，请简单介绍一下自己",
-                "role": "user"
-            }
-        ],
-        "model": "doubao-1-5-pro-32k-250115",
-        "stream": True
-    }
-    
-    print("🚀 开始测试流式响应...")
-    
-    try:
-        response = requests.post(
-            "https://ark.cn-beijing.volces.com/api/v3/chat/completions",
-            headers=headers,
-            json=data,
-            stream=True,
-            timeout=30
-        )
-        
-        print(f"📊 响应状态: {response.status_code}")
-        
-        if response.status_code != 200:
-            print(f"❌ 请求失败: {response.text}")
-            return
-        
-        print("🔍 开始解析流式响应...")
-        
-        accumulated_text = ""
-        line_count = 0
-        
-        for line in response.iter_lines(decode_unicode=True):
-            line_count += 1
-            
-            if not line or not line.strip():
-                continue
-            
-            # 预处理
-            line = line.strip()
-            
-            print(f"\n--- 第{line_count}行 ---")
-            print(f"原始内容: {repr(line)}")
-            
-            if line.startswith("data: "):
-                data_str = line[6:]  # 移除 "data: " 前缀
-                print(f"处理后: {repr(data_str)}")
-                
-                if data_str == "[DONE]":
-                    print("✅ 流结束")
-                    break
-                
-                try:
-                    chunk_data = json.loads(data_str)
-                    print(f"✅ JSON解析成功: {chunk_data}")
-                    
-                    if "choices" in chunk_data and len(chunk_data["choices"]) > 0:
-                        delta = chunk_data["choices"][0].get("delta", {})
-                        content = delta.get("content", "")
-                        
-                        if content:
-                            accumulated_text += content
-                            print(f"💬 累计内容: {accumulated_text}")
-                        
-                except json.JSONDecodeError as e:
-                    print(f"❌ JSON解析失败: {e}")
-                    print(f"🔍 问题数据: {repr(data_str)}")
-                except Exception as e:
-                    print(f"❌ 其他错误: {e}")
-        
-        print(f"\n✅ 测试完成，总共处理了 {line_count} 行")
-        print(f"📝 最终内容: {accumulated_text}")
-        
-    except Exception as e:
-        print(f"❌ 测试失败: {e}")
-
-if __name__ == "__main__":
-    test_streaming_response()

voice_chat.py

@@ -1,840 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-"""
-语音交互聊天系统 - 集成豆包AI
-基于能量检测的录音 + 豆包语音识别 + TTS回复
-"""
-
-import sys
-import os
-import time
-import threading
-import asyncio
-import subprocess
-import wave
-import struct
-import json
-import gzip
-import uuid
-from typing import Dict, Any, Optional
-import pyaudio
-import numpy as np
-import websockets
-
-# 豆包协议常量
-PROTOCOL_VERSION = 0b0001
-CLIENT_FULL_REQUEST = 0b0001
-CLIENT_AUDIO_ONLY_REQUEST = 0b0010
-SERVER_FULL_RESPONSE = 0b1001
-SERVER_ACK = 0b1011
-SERVER_ERROR_RESPONSE = 0b1111
-NO_SEQUENCE = 0b0000
-MSG_WITH_EVENT = 0b0100
-NO_SERIALIZATION = 0b0000
-JSON = 0b0001
-GZIP = 0b0001
-
-class DoubaoClient:
-    """豆包音频处理客户端"""
-    
-    def __init__(self):
-        self.base_url = "wss://openspeech.bytedance.com/api/v3/realtime/dialogue"
-        self.app_id = "8718217928"
-        self.access_key = "ynJMX-5ix1FsJvswC9KTNlGUdubcchqc"
-        self.app_key = "PlgvMymc7f3tQnJ6"
-        self.resource_id = "volc.speech.dialog"
-        self.session_id = str(uuid.uuid4())
-        self.ws = None
-        self.log_id = ""
-        
-    def get_headers(self) -> Dict[str, str]:
-        """获取请求头"""
-        return {
-            "X-Api-App-ID": self.app_id,
-            "X-Api-Access-Key": self.access_key,
-            "X-Api-Resource-Id": self.resource_id,
-            "X-Api-App-Key": self.app_key,
-            "X-Api-Connect-Id": str(uuid.uuid4()),
-        }
-    
-    def generate_header(self, message_type=CLIENT_FULL_REQUEST, 
-                       message_type_specific_flags=MSG_WITH_EVENT,
-                       serial_method=JSON, compression_type=GZIP) -> bytes:
-        """生成协议头"""
-        header = bytearray()
-        header.append((PROTOCOL_VERSION << 4) | 1)  # version + header_size
-        header.append((message_type << 4) | message_type_specific_flags)
-        header.append((serial_method << 4) | compression_type)
-        header.append(0x00)  # reserved
-        return bytes(header)
-    
-    async def connect(self) -> None:
-        """建立WebSocket连接"""
-        print(f"🔗 连接豆包服务器...")
-        try:
-            self.ws = await websockets.connect(
-                self.base_url,
-                additional_headers=self.get_headers(),
-                ping_interval=None
-            )
-            
-            # 获取log_id
-            if hasattr(self.ws, 'response_headers'):
-                self.log_id = self.ws.response_headers.get("X-Tt-Logid")
-            elif hasattr(self.ws, 'headers'):
-                self.log_id = self.ws.headers.get("X-Tt-Logid")
-            
-            print(f"✅ 连接成功, log_id: {self.log_id}")
-            
-            # 发送StartConnection请求
-            await self._send_start_connection()
-            
-            # 发送StartSession请求
-            await self._send_start_session()
-            
-        except Exception as e:
-            print(f"❌ 连接失败: {e}")
-            raise
-    
-    def parse_response(self, response):
-        """解析响应"""
-        if len(response) < 4:
-            return None
-            
-        protocol_version = response[0] >> 4
-        header_size = response[0] & 0x0f
-        message_type = response[1] >> 4
-        flags = response[1] & 0x0f
-        
-        payload_start = header_size * 4
-        payload = response[payload_start:]
-        
-        result = {
-            'protocol_version': protocol_version,
-            'header_size': header_size,
-            'message_type': message_type,
-            'flags': flags,
-            'payload': payload,
-            'payload_size': len(payload)
-        }
-        
-        # 解析payload
-        if len(payload) >= 4:
-            result['event'] = int.from_bytes(payload[:4], 'big')
-            
-            if len(payload) >= 8:
-                session_id_len = int.from_bytes(payload[4:8], 'big')
-                if len(payload) >= 8 + session_id_len:
-                    result['session_id'] = payload[8:8+session_id_len].decode()
-                    
-                    if len(payload) >= 12 + session_id_len:
-                        data_size = int.from_bytes(payload[8+session_id_len:12+session_id_len], 'big')
-                        result['data_size'] = data_size
-                        result['data'] = payload[12+session_id_len:12+session_id_len+data_size]
-                        
-                        # 尝试解析JSON数据
-                        try:
-                            result['json_data'] = json.loads(result['data'].decode('utf-8'))
-                        except:
-                            pass
-        
-        return result
-    
-    async def _send_start_connection(self) -> None:
-        """发送StartConnection请求"""
-        request = bytearray(self.generate_header())
-        request.extend(int(1).to_bytes(4, 'big'))
-        
-        payload_bytes = b"{}"
-        payload_bytes = gzip.compress(payload_bytes)
-        request.extend(len(payload_bytes).to_bytes(4, 'big'))
-        request.extend(payload_bytes)
-        
-        await self.ws.send(request)
-        response = await self.ws.recv()
-    
-    async def _send_start_session(self) -> None:
-        """发送StartSession请求"""
-        session_config = {
-            "asr": {"extra": {"end_smooth_window_ms": 1500}},
-            "tts": {
-                "speaker": "zh_female_vv_jupiter_bigtts",
-                "audio_config": {"channel": 1, "format": "pcm", "sample_rate": 24000}
-            },
-            "dialog": {
-                "bot_name": "豆包",
-                "system_role": "你使用活泼灵动的女声，性格开朗，热爱生活。",
-                "speaking_style": "你的说话风格简洁明了，语速适中，语调自然。",
-                "location": {"city": "北京"},
-                "extra": {
-                    "strict_audit": False,
-                    "audit_response": "支持客户自定义安全审核回复话术。",
-                    "recv_timeout": 30,
-                    "input_mod": "audio",
-                },
-            },
-        }
-        
-        request = bytearray(self.generate_header())
-        request.extend(int(100).to_bytes(4, 'big'))
-        request.extend(len(self.session_id).to_bytes(4, 'big'))
-        request.extend(self.session_id.encode())
-        
-        payload_bytes = json.dumps(session_config).encode()
-        payload_bytes = gzip.compress(payload_bytes)
-        request.extend(len(payload_bytes).to_bytes(4, 'big'))
-        request.extend(payload_bytes)
-        
-        await self.ws.send(request)
-        response = await self.ws.recv()
-        await asyncio.sleep(1.0)
-    
-    async def process_audio(self, audio_data: bytes) -> tuple[str, bytes]:
-        """处理音频并返回(识别文本, TTS音频)"""
-        try:
-            # 发送音频数据 - 使用与doubao_simple.py相同的格式
-            task_request = bytearray(
-                self.generate_header(message_type=CLIENT_AUDIO_ONLY_REQUEST,
-                                 serial_method=NO_SERIALIZATION))
-            task_request.extend(int(200).to_bytes(4, 'big'))
-            task_request.extend(len(self.session_id).to_bytes(4, 'big'))
-            task_request.extend(self.session_id.encode())
-            payload_bytes = gzip.compress(audio_data)
-            task_request.extend(len(payload_bytes).to_bytes(4, 'big'))
-            task_request.extend(payload_bytes)
-            await self.ws.send(task_request)
-            print("📤 音频数据已发送")
-            
-            recognized_text = ""
-            tts_audio = b""
-            response_count = 0
-            
-            # 接收响应 - 使用与doubao_simple.py相同的解析逻辑
-            audio_chunks = []
-            max_responses = 30
-            
-            while response_count < max_responses:
-                try:
-                    response = await asyncio.wait_for(self.ws.recv(), timeout=30.0)
-                    response_count += 1
-                    
-                    parsed = self.parse_response(response)
-                    if not parsed:
-                        continue
-                        
-                    print(f"📥 响应 {response_count}: message_type={parsed['message_type']}, event={parsed.get('event', 'N/A')}, size={parsed['payload_size']}")
-                    
-                    # 处理不同类型的响应
-                    if parsed['message_type'] == 11:  # SERVER_ACK - 可能包含音频
-                        if 'data' in parsed and parsed['data_size'] > 0:
-                            audio_chunks.append(parsed['data'])
-                            print(f"收集到音频块: {parsed['data_size']} 字节")
-                    
-                    elif parsed['message_type'] == 9:  # SERVER_FULL_RESPONSE
-                        event = parsed.get('event', 0)
-                        
-                        if event == 450:  # ASR开始
-                            print("🎤 ASR处理开始")
-                        elif event == 451:  # ASR结果
-                            if 'json_data' in parsed and 'results' in parsed['json_data']:
-                                text = parsed['json_data']['results'][0].get('text', '')
-                                recognized_text = text
-                                print(f"🧠 识别结果: {text}")
-                        elif event == 459:  # ASR结束
-                            print("✅ ASR处理结束")
-                        elif event == 350:  # TTS开始
-                            print("🎵 TTS生成开始")
-                        elif event == 359:  # TTS结束
-                            print("✅ TTS生成结束")
-                            break
-                        elif event == 550:  # TTS音频数据
-                            if 'data' in parsed and parsed['data_size'] > 0:
-                                # 检查是否是JSON（音频元数据）还是实际音频数据
-                                try:
-                                    json.loads(parsed['data'].decode('utf-8'))
-                                    print("收到TTS音频元数据")
-                                except:
-                                    # 不是JSON，可能是音频数据
-                                    audio_chunks.append(parsed['data'])
-                                    print(f"收集到TTS音频块: {parsed['data_size']} 字节")
-                
-                except asyncio.TimeoutError:
-                    print(f"⏰ 等待响应 {response_count + 1} 超时")
-                    break
-                except websockets.exceptions.ConnectionClosed:
-                    print("🔌 连接已关闭")
-                    break
-            
-            print(f"共收到 {response_count} 个响应，收集到 {len(audio_chunks)} 个音频块")
-            
-            # 合并音频数据
-            if audio_chunks:
-                tts_audio = b''.join(audio_chunks)
-                print(f"合并后的音频数据: {len(tts_audio)} 字节")
-            
-            # 转换TTS音频格式（32位浮点 -> 16位整数）
-            if tts_audio:
-                # 检查是否是GZIP压缩数据
-                try:
-                    decompressed = gzip.decompress(tts_audio)
-                    print(f"解压缩后音频数据: {len(decompressed)} 字节")
-                    audio_to_write = decompressed
-                except:
-                    print("音频数据不是GZIP压缩格式，直接使用原始数据")
-                    audio_to_write = tts_audio
-                
-                # 检查音频数据长度是否是4的倍数（32位浮点）
-                if len(audio_to_write) % 4 != 0:
-                    print(f"警告：音频数据长度 {len(audio_to_write)} 不是4的倍数，截断到最近的倍数")
-                    audio_to_write = audio_to_write[:len(audio_to_write) // 4 * 4]
-                
-                # 将32位浮点转换为16位整数
-                float_count = len(audio_to_write) // 4
-                int16_data = bytearray(float_count * 2)
-                
-                for i in range(float_count):
-                    # 读取32位浮点数（小端序）
-                    float_value = struct.unpack('<f', audio_to_write[i*4:i*4+4])[0]
-                    
-                    # 将浮点数限制在[-1.0, 1.0]范围内
-                    float_value = max(-1.0, min(1.0, float_value))
-                    
-                    # 转换为16位整数
-                    int16_value = int(float_value * 32767)
-                    
-                    # 写入16位整数（小端序）
-                    int16_data[i*2:i*2+2] = struct.pack('<h', int16_value)
-                
-                tts_audio = bytes(int16_data)
-                print(f"✅ 音频转换完成: {len(tts_audio)} 字节")
-            
-            return recognized_text, tts_audio
-            
-        except Exception as e:
-            print(f"❌ 处理失败: {e}")
-            import traceback
-            traceback.print_exc()
-            return "", b""
-    
-    async def send_silence_data(self, duration_ms=100) -> None:
-        """发送静音数据保持连接活跃"""
-        try:
-            # 生成静音音频数据
-            samples = int(16000 * duration_ms / 1000)  # 16kHz采样率
-            silence_data = bytes(samples * 2)  # 16位PCM
-            
-            # 发送静音数据
-            task_request = bytearray(
-                self.generate_header(message_type=CLIENT_AUDIO_ONLY_REQUEST,
-                                 serial_method=NO_SERIALIZATION))
-            task_request.extend(int(200).to_bytes(4, 'big'))
-            task_request.extend(len(self.session_id).to_bytes(4, 'big'))
-            task_request.extend(self.session_id.encode())
-            payload_bytes = gzip.compress(silence_data)
-            task_request.extend(len(payload_bytes).to_bytes(4, 'big'))
-            task_request.extend(payload_bytes)
-            await self.ws.send(task_request)
-            print("💓 发送心跳数据保持连接")
-            
-            # 简单处理响应（不等待完整响应）
-            try:
-                response = await asyncio.wait_for(self.ws.recv(), timeout=5.0)
-                # 只确认收到响应，不处理内容
-            except asyncio.TimeoutError:
-                print("⚠️ 心跳响应超时")
-            except websockets.exceptions.ConnectionClosed:
-                print("❌ 心跳时连接已关闭")
-                raise
-                
-        except Exception as e:
-            print(f"❌ 发送心跳数据失败: {e}")
-    
-    async def close(self) -> None:
-        """关闭连接"""
-        if self.ws:
-            try:
-                await self.ws.close()
-            except:
-                pass
-            print("🔌 连接已关闭")
-
-class VoiceChatRecorder:
-    """语音聊天录音系统"""
-    
-    def __init__(self, enable_ai_chat=True):
-        # 音频参数
-        self.FORMAT = pyaudio.paInt16
-        self.CHANNELS = 1
-        self.RATE = 16000
-        self.CHUNK_SIZE = 1024
-        
-        # 能量检测参数
-        self.energy_threshold = 500
-        self.silence_threshold = 2.0
-        self.min_recording_time = 1.0
-        self.max_recording_time = 20.0
-        
-        # 状态变量
-        self.audio = None
-        self.stream = None
-        self.running = False
-        self.recording = False
-        self.recorded_frames = []
-        self.recording_start_time = None
-        self.last_sound_time = None
-        self.energy_history = []
-        self.zcr_history = []
-        
-        # AI聊天功能
-        self.enable_ai_chat = enable_ai_chat
-        self.doubao_client = None
-        self.is_processing_ai = False
-        self.heartbeat_thread = None
-        self.last_heartbeat_time = time.time()
-        self.heartbeat_interval = 10.0  # 每10秒发送一次心跳
-        
-        # 预录音缓冲区
-        self.pre_record_buffer = []
-        self.pre_record_max_frames = int(2.0 * self.RATE / self.CHUNK_SIZE)
-        
-        # 播放状态
-        self.is_playing = False
-        
-        # ZCR检测参数
-        self.consecutive_low_zcr_count = 0
-        self.low_zcr_threshold_count = 15
-        self.voice_activity_history = []
-        
-        self._setup_audio()
-    
-    def _setup_audio(self):
-        """设置音频设备"""
-        try:
-            self.audio = pyaudio.PyAudio()
-            self.stream = self.audio.open(
-                format=self.FORMAT,
-                channels=self.CHANNELS,
-                rate=self.RATE,
-                input=True,
-                frames_per_buffer=self.CHUNK_SIZE
-            )
-            print("✅ 音频设备初始化成功")
-        except Exception as e:
-            print(f"❌ 音频设备初始化失败: {e}")
-    
-    def generate_silence_audio(self, duration_ms=100):
-        """生成静音音频数据"""
-        # 生成指定时长的静音音频（16位PCM，值为0）
-        samples = int(self.RATE * duration_ms / 1000)
-        silence_data = bytes(samples * 2)  # 16位 = 2字节每样本
-        return silence_data
-    
-    def calculate_energy(self, audio_data):
-        """计算音频能量"""
-        if len(audio_data) == 0:
-            return 0
-        
-        audio_array = np.frombuffer(audio_data, dtype=np.int16)
-        rms = np.sqrt(np.mean(audio_array ** 2))
-        
-        if not self.recording:
-            self.energy_history.append(rms)
-            if len(self.energy_history) > 50:
-                self.energy_history.pop(0)
-        
-        return rms
-    
-    def calculate_zero_crossing_rate(self, audio_data):
-        """计算零交叉率"""
-        if len(audio_data) == 0:
-            return 0
-        
-        audio_array = np.frombuffer(audio_data, dtype=np.int16)
-        zero_crossings = np.sum(np.diff(np.sign(audio_array)) != 0)
-        zcr = zero_crossings / len(audio_array) * self.RATE
-        
-        self.zcr_history.append(zcr)
-        if len(self.zcr_history) > 30:
-            self.zcr_history.pop(0)
-        
-        return zcr
-    
-    def is_voice_active(self, energy, zcr):
-        """使用ZCR进行语音活动检测"""
-        # 16000Hz采样率下的语音ZCR范围
-        zcr_condition = 2400 < zcr < 12000
-        return zcr_condition
-    
-    def save_recording(self, audio_data, filename=None):
-        """保存录音"""
-        if filename is None:
-            timestamp = time.strftime("%Y%m%d_%H%M%S")
-            filename = f"recording_{timestamp}.wav"
-        
-        try:
-            with wave.open(filename, 'wb') as wf:
-                wf.setnchannels(self.CHANNELS)
-                wf.setsampwidth(self.audio.get_sample_size(self.FORMAT))
-                wf.setframerate(self.RATE)
-                wf.writeframes(audio_data)
-            
-            print(f"✅ 录音已保存: {filename}")
-            return True, filename
-        except Exception as e:
-            print(f"❌ 保存录音失败: {e}")
-            return False, None
-    
-    def play_audio(self, filename):
-        """播放音频文件"""
-        try:
-            # 停止当前录音
-            if self.recording:
-                self.recording = False
-                self.recorded_frames = []
-            
-            # 关闭输入流
-            if self.stream:
-                self.stream.stop_stream()
-                self.stream.close()
-                self.stream = None
-            
-            self.is_playing = True
-            time.sleep(0.2)
-            
-            # 使用系统播放器
-            print(f"🔊 播放: {filename}")
-            subprocess.run(['aplay', filename], check=True)
-            print("✅ 播放完成")
-            
-        except Exception as e:
-            print(f"❌ 播放失败: {e}")
-        finally:
-            self.is_playing = False
-            time.sleep(0.2)
-            self._setup_audio()
-    
-    def update_pre_record_buffer(self, audio_data):
-        """更新预录音缓冲区"""
-        self.pre_record_buffer.append(audio_data)
-        if len(self.pre_record_buffer) > self.pre_record_max_frames:
-            self.pre_record_buffer.pop(0)
-    
-    def start_recording(self):
-        """开始录音"""
-        print("🎙️ 检测到声音，开始录音...")
-        self.recording = True
-        self.recorded_frames = []
-        self.recorded_frames.extend(self.pre_record_buffer)
-        self.pre_record_buffer = []
-        self.recording_start_time = time.time()
-        self.last_sound_time = time.time()
-        self.consecutive_low_zcr_count = 0
-    
-    def stop_recording(self):
-        """停止录音"""
-        if len(self.recorded_frames) > 0:
-            audio_data = b''.join(self.recorded_frames)
-            duration = len(audio_data) / (self.RATE * 2)
-            
-            print(f"📝 录音完成，时长: {duration:.2f}秒")
-            
-            if self.enable_ai_chat:
-                # AI聊天模式
-                self.process_with_ai(audio_data)
-            else:
-                # 普通录音模式
-                success, filename = self.save_recording(audio_data)
-                if success and filename:
-                    print("=" * 50)
-                    print("🔊 播放刚才录制的音频...")
-                    self.play_audio(filename)
-                    print("=" * 50)
-        
-        self.recording = False
-        self.recorded_frames = []
-        self.recording_start_time = None
-        self.last_sound_time = None
-    
-    def process_with_ai(self, audio_data):
-        """使用AI处理录音"""
-        if self.is_processing_ai:
-            print("⏳ AI正在处理中，请稍候...")
-            return
-        
-        self.is_processing_ai = True
-        
-        # 在新线程中处理AI
-        ai_thread = threading.Thread(target=self._ai_processing_thread, args=(audio_data,))
-        ai_thread.daemon = True
-        ai_thread.start()
-    
-    def _heartbeat_thread(self):
-        """心跳线程 - 定期发送静音数据保持连接活跃"""
-        while self.running and self.doubao_client and self.doubao_client.ws:
-            current_time = time.time()
-            if current_time - self.last_heartbeat_time >= self.heartbeat_interval:
-                try:
-                    # 异步发送心跳数据
-                    loop = asyncio.new_event_loop()
-                    asyncio.set_event_loop(loop)
-                    try:
-                        loop.run_until_complete(self.doubao_client.send_silence_data())
-                        self.last_heartbeat_time = current_time
-                    except Exception as e:
-                        print(f"❌ 心跳失败: {e}")
-                        # 如果心跳失败，可能需要重新连接
-                        break
-                    finally:
-                        loop.close()
-                except Exception as e:
-                    print(f"❌ 心跳线程异常: {e}")
-                    break
-            
-            # 睡眠一段时间
-            time.sleep(1.0)
-        
-        print("📡 心跳线程结束")
-    
-    def _ai_processing_thread(self, audio_data):
-        """AI处理线程"""
-        try:
-            print("🤖 开始AI处理...")
-            print("🧠 正在进行语音识别...")
-            
-            # 异步处理
-            loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(loop)
-            
-            try:
-                # 连接豆包
-                self.doubao_client = DoubaoClient()
-                loop.run_until_complete(self.doubao_client.connect())
-                
-                # 启动心跳线程
-                self.last_heartbeat_time = time.time()
-                self.heartbeat_thread = threading.Thread(target=self._heartbeat_thread)
-                self.heartbeat_thread.daemon = True
-                self.heartbeat_thread.start()
-                print("💓 心跳线程已启动")
-                
-                # 语音识别和TTS回复
-                recognized_text, tts_audio = loop.run_until_complete(
-                    self.doubao_client.process_audio(audio_data)
-                )
-                
-                if recognized_text:
-                    print(f"🗣️ 你说: {recognized_text}")
-                
-                if tts_audio:
-                    # 保存TTS音频
-                    tts_filename = "ai_response.wav"
-                    with wave.open(tts_filename, 'wb') as wav_file:
-                        wav_file.setnchannels(1)
-                        wav_file.setsampwidth(2)
-                        wav_file.setframerate(24000)
-                        wav_file.writeframes(tts_audio)
-                    
-                    print("🎵 AI回复生成完成")
-                    print("=" * 50)
-                    print("🔊 播放AI回复...")
-                    self.play_audio(tts_filename)
-                    print("=" * 50)
-                else:
-                    print("❌ 未收到AI回复")
-                
-                # 等待一段时间再关闭连接，以便心跳继续工作
-                print("⏳ 等待5秒后关闭连接...")
-                time.sleep(5)
-                
-            except Exception as e:
-                print(f"❌ AI处理失败: {e}")
-            finally:
-                # 停止心跳线程
-                if self.heartbeat_thread and self.heartbeat_thread.is_alive():
-                    print("🛑 停止心跳线程")
-                    self.heartbeat_thread = None
-                
-                # 关闭连接
-                if self.doubao_client:
-                    loop.run_until_complete(self.doubao_client.close())
-                
-                loop.close()
-            
-        except Exception as e:
-            print(f"❌ AI处理线程失败: {e}")
-        finally:
-            self.is_processing_ai = False
-    
-    def run(self):
-        """运行语音聊天系统"""
-        if not self.stream:
-            print("❌ 音频设备未初始化")
-            return
-        
-        self.running = True
-        
-        if self.enable_ai_chat:
-            print("🤖 语音聊天AI助手")
-            print("=" * 50)
-            print("🎯 功能特点:")
-            print("- 🎙️ 智能语音检测")
-            print("- 🧠 豆包AI语音识别")
-            print("- 🗣️ AI智能回复")
-            print("- 🔊 TTS语音播放")
-            print("- 🔄 实时对话")
-            print("=" * 50)
-            print("📖 使用说明:")
-            print("- 说话自动录音")
-            print("- 静音2秒结束录音")
-            print("- AI自动识别并回复")
-            print("- 按 Ctrl+C 退出")
-            print("=" * 50)
-        else:
-            print("🎙️ 智能录音系统")
-            print("=" * 50)
-            print("📖 使用说明:")
-            print("- 说话自动录音")
-            print("- 静音2秒结束录音")
-            print("- 录音完成后自动播放")
-            print("- 按 Ctrl+C 退出")
-            print("=" * 50)
-        
-        try:
-            while self.running:
-                # 如果正在播放AI回复，跳过音频处理
-                if self.is_playing or self.is_processing_ai:
-                    status = "🤖 AI处理中..."
-                    print(f"\r{status}", end='', flush=True)
-                    time.sleep(0.1)
-                    continue
-                
-                # 读取音频数据
-                data = self.stream.read(self.CHUNK_SIZE, exception_on_overflow=False)
-                
-                if len(data) == 0:
-                    continue
-                
-                # 计算能量和ZCR
-                energy = self.calculate_energy(data)
-                zcr = self.calculate_zero_crossing_rate(data)
-                
-                if self.recording:
-                    # 录音模式
-                    self.recorded_frames.append(data)
-                    recording_duration = time.time() - self.recording_start_time
-                    
-                    # 检测语音活动
-                    if self.is_voice_active(energy, zcr):
-                        self.last_sound_time = time.time()
-                        self.consecutive_low_zcr_count = 0
-                    else:
-                        self.consecutive_low_zcr_count += 1
-                    
-                    # 检查是否应该结束录音
-                    should_stop = False
-                    
-                    # ZCR静音检测
-                    if self.consecutive_low_zcr_count >= self.low_zcr_threshold_count:
-                        should_stop = True
-                    
-                    # 时间静音检测
-                    if not should_stop and time.time() - self.last_sound_time > self.silence_threshold:
-                        should_stop = True
-                    
-                    # 执行停止录音
-                    if should_stop and recording_duration >= self.min_recording_time:
-                        print(f"\n🔇 检测到静音，结束录音")
-                        self.stop_recording()
-                    
-                    # 检查最大录音时间
-                    if recording_duration > self.max_recording_time:
-                        print(f"\n⏰ 达到最大录音时间")
-                        self.stop_recording()
-                    
-                    # 显示录音状态
-                    is_voice = self.is_voice_active(energy, zcr)
-                    zcr_count = f"{self.consecutive_low_zcr_count}/{self.low_zcr_threshold_count}"
-                    status = f"录音中... {recording_duration:.1f}s | ZCR: {zcr:.0f} | 语音: {is_voice} | 静音计数: {zcr_count}"
-                    print(f"\r{status}", end='', flush=True)
-                
-                else:
-                    # 监听模式
-                    self.update_pre_record_buffer(data)
-                    
-                    if self.is_voice_active(energy, zcr):
-                        # 检测到声音，开始录音
-                        self.start_recording()
-                    else:
-                        # 显示监听状态
-                        is_voice = self.is_voice_active(energy, zcr)
-                        buffer_usage = len(self.pre_record_buffer) / self.pre_record_max_frames * 100
-                        status = f"监听中... ZCR: {zcr:.0f} | 语音: {is_voice} | 缓冲: {buffer_usage:.0f}%"
-                        print(f"\r{status}", end='', flush=True)
-                
-                time.sleep(0.01)
-                
-        except KeyboardInterrupt:
-            print("\n👋 退出")
-        except Exception as e:
-            print(f"❌ 错误: {e}")
-        finally:
-            self.stop()
-    
-    def stop(self):
-        """停止系统"""
-        self.running = False
-        
-        # 停止心跳线程
-        if self.heartbeat_thread and self.heartbeat_thread.is_alive():
-            print("🛑 停止心跳线程")
-            self.heartbeat_thread = None
-        
-        if self.recording:
-            self.stop_recording()
-        
-        if self.stream:
-            self.stream.stop_stream()
-            self.stream.close()
-        
-        if self.audio:
-            self.audio.terminate()
-        
-        # 关闭AI连接
-        if self.doubao_client and self.doubao_client.ws:
-            try:
-                loop = asyncio.new_event_loop()
-                asyncio.set_event_loop(loop)
-                loop.run_until_complete(self.doubao_client.close())
-                loop.close()
-            except:
-                pass
-
-def main():
-    """主函数"""
-    import argparse
-    
-    parser = argparse.ArgumentParser(description='语音聊天AI助手')
-    parser.add_argument('--no-ai', action='store_true', help='禁用AI功能，仅录音')
-    args = parser.parse_args()
-    
-    enable_ai = not args.no_ai
-    
-    if enable_ai:
-        print("🚀 语音聊天AI助手")
-    else:
-        print("🚀 智能录音系统")
-    
-    print("=" * 50)
-    
-    # 创建语音聊天系统
-    recorder = VoiceChatRecorder(enable_ai_chat=enable_ai)
-    
-    print("✅ 系统初始化成功")
-    print("=" * 50)
-    
-    # 开始运行
-    recorder.run()
-
-if __name__ == "__main__":
-    main()

zcr_monitor.py

@@ -0,0 +1,198 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+实时ZCR监控工具
+用于观察实际的ZCR值和测试语音检测
+"""
+
+import threading
+import time
+
+import numpy as np
+import pyaudio
+
+
+class ZCRMonitor:
+    """ZCR实时监控器"""
+    
+    def __init__(self):
+        self.FORMAT = pyaudio.paInt16
+        self.CHANNELS = 1
+        self.RATE = 16000
+        self.CHUNK_SIZE = 1024
+        
+        # 监控参数
+        self.running = False
+        self.zcr_history = []
+        self.max_history = 100
+        
+        # 音频设备
+        self.audio = None
+        self.stream = None
+        
+        # 检测阈值（匹配recorder.py的设置）
+        self.zcr_min = 2400
+        self.zcr_max = 12000
+        
+    def setup_audio(self):
+        """设置音频设备"""
+        try:
+            self.audio = pyaudio.PyAudio()
+            self.stream = self.audio.open(
+                format=self.FORMAT,
+                channels=self.CHANNELS,
+                rate=self.RATE,
+                input=True,
+                frames_per_buffer=self.CHUNK_SIZE
+            )
+            return True
+        except Exception as e:
+            print(f"❌ 音频设备初始化失败: {e}")
+            return False
+    
+    def calculate_zcr(self, audio_data):
+        """计算零交叉率"""
+        if len(audio_data) == 0:
+            return 0
+        
+        audio_array = np.frombuffer(audio_data, dtype=np.int16)
+        zero_crossings = np.sum(np.diff(np.sign(audio_array)) != 0)
+        zcr = zero_crossings / len(audio_array) * self.RATE
+        return zcr
+    
+    def is_voice(self, zcr):
+        """简单的语音检测"""
+        return self.zcr_min < zcr < self.zcr_max
+    
+    def monitor_callback(self, in_data, frame_count, time_info, status):
+        """音频回调函数"""
+        zcr = self.calculate_zcr(in_data)
+        
+        # 更新历史
+        self.zcr_history.append(zcr)
+        if len(self.zcr_history) > self.max_history:
+            self.zcr_history.pop(0)
+        
+        # 计算统计信息
+        if len(self.zcr_history) > 10:
+            avg_zcr = np.mean(self.zcr_history[-10:])  # 最近10个值的平均
+            std_zcr = np.std(self.zcr_history[-10:])
+        else:
+            avg_zcr = zcr
+            std_zcr = 0
+        
+        # 判断是否为语音
+        voice_detected = self.is_voice(zcr)
+        
+        # 实时显示
+        status = "🎤" if voice_detected else "🔇"
+        color = "\033[92m" if voice_detected else "\033[90m"  # 绿色或灰色
+        reset = "\033[0m"
+        
+        # 显示信息
+        info = (f"{color}{status} ZCR: {zcr:.0f} | "
+               f"阈值: {self.zcr_min}-{self.zcr_max} | "
+               f"平均: {avg_zcr:.0f}±{std_zcr:.0f}{reset}")
+        
+        print(f"\r{info}", end='', flush=True)
+        
+        return (in_data, pyaudio.paContinue)
+    
+    def start_monitoring(self):
+        """开始监控"""
+        print("🎙️ ZCR实时监控工具")
+        print("=" * 50)
+        print("📊 当前检测阈值:")
+        print(f"   ZCR范围: {self.zcr_min} - {self.zcr_max}")
+        print("💡 请说话测试语音检测...")
+        print("🛑 按 Ctrl+C 停止监控")
+        print("=" * 50)
+        
+        try:
+            # 使用回调模式
+            self.stream = self.audio.open(
+                format=self.FORMAT,
+                channels=self.CHANNELS,
+                rate=self.RATE,
+                input=True,
+                frames_per_buffer=self.CHUNK_SIZE,
+                stream_callback=self.monitor_callback
+            )
+            
+            self.stream.start_stream()
+            self.running = True
+            
+            # 主循环
+            while self.running:
+                time.sleep(0.1)
+                
+        except KeyboardInterrupt:
+            print("\n🛑 监控停止")
+        finally:
+            self.cleanup()
+    
+    def show_statistics(self):
+        """显示统计信息"""
+        if not self.zcr_history:
+            return
+        
+        print("\n📊 ZCR统计信息:")
+        print(f"   样本数量: {len(self.zcr_history)}")
+        print(f"   最小值: {min(self.zcr_history):.0f}")
+        print(f"   最大值: {max(self.zcr_history):.0f}")
+        print(f"   平均值: {np.mean(self.zcr_history):.0f}")
+        print(f"   标准差: {np.std(self.zcr_history):.0f}")
+        
+        # 分析语音检测
+        voice_count = sum(1 for zcr in self.zcr_history if self.is_voice(zcr))
+        voice_percentage = voice_count / len(self.zcr_history) * 100
+        print(f"   语音检测: {voice_count}/{len(self.zcr_history)} ({voice_percentage:.1f}%)")
+        
+        # 建议新的阈值
+        avg_zcr = np.mean(self.zcr_history)
+        std_zcr = np.std(self.zcr_history)
+        suggested_min = max(800, avg_zcr + std_zcr)
+        suggested_max = min(8000, avg_zcr + 4 * std_zcr)
+        
+        print(f"\n🎯 建议的检测阈值:")
+        print(f"   最小值: {suggested_min:.0f}")
+        print(f"   最大值: {suggested_max:.0f}")
+    
+    def cleanup(self):
+        """清理资源"""
+        self.running = False
+        
+        if self.stream:
+            try:
+                self.stream.stop_stream()
+                self.stream.close()
+            except:
+                pass
+        
+        if self.audio:
+            try:
+                self.audio.terminate()
+            except:
+                pass
+        
+        # 显示最终统计
+        self.show_statistics()
+
+def main():
+    """主函数"""
+    monitor = ZCRMonitor()
+    
+    if not monitor.setup_audio():
+        print("❌ 无法初始化音频设备")
+        return
+    
+    try:
+        monitor.start_monitoring()
+    except Exception as e:
+        print(f"❌ 监控过程中出错: {e}")
+    finally:
+        monitor.cleanup()
+
+if __name__ == "__main__":
+    main()