回声待处理
This commit is contained in:
朱潮
parent
3f70062c2b
commit
9871b445f0
@ -114,7 +114,7 @@ class InputProcess:
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self.tts_access_key = "ynJMX-5ix1FsJvswC9KTNlGUdubcchqc"
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self.tts_resource_id = "volc.service_type.10029"
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self.tts_app_key = "aGjiRDfUWi"
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self.tts_speaker = "zh_female_wanqudashu_moon_bigtts"
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self.tts_speaker = config.get('tts_speaker', "zh_female_wanqudashu_moon_bigtts") if config else "zh_female_wanqudashu_moon_bigtts"
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# 启动 TTS 工作线程
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self._start_tts_worker()
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@ -448,8 +448,6 @@ class InputProcess:
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# 生成音频数据
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self._generate_tts_audio(content)
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self.tts_task_queue.task_done()
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except queue.Empty:
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continue
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except Exception as e:
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@ -591,10 +589,19 @@ class OutputProcess:
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self.playback_completed = False # 播放完成标志
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self.end_signal_received = False # 结束信号接收标志
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# 智能缓冲系统 - 借鉴 recorder.py 的缓冲策略
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self.preload_buffer = [] # 预加载缓冲区
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# 智能缓冲系统 - 借鉴 recorder.py 的智能句子累积策略
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self.preload_buffer = [] # 预加载缓冲区（保留用于音频块）
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self.preload_size = 3 # 预加载3个音频块
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# 智能句子缓冲系统 - 从 recorder.py 借鉴的核心机制
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self.tts_buffer = [] # 智能句子缓冲区
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self.tts_buffer_max_size = 3 # 最多缓冲3个句子
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self.tts_buffer_min_size = 1 # 最少1个句子
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self.tts_accumulation_time = 0.2 # 200ms积累窗口
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self.tts_last_trigger_time = 0 # 上次触发TTS的时间
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self.tts_pending_sentences = [] # 待处理的句子
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self.min_buffer_size = 1 # 最小缓冲区大小
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self.audio_device_healthy = True # 音频设备健康状态
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# 统一播放工作线程 - 核心改进
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@ -606,6 +613,14 @@ class OutputProcess:
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self.playback_timeout = 5.0 # 播放超时时间（秒）
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self.completion_sent = False # 防止重复发送完成事件
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# 增强的播放完成检测状态
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self.llm_generation_complete = False # LLM生成是否完成
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self.tts_generation_complete = False # TTS生成是否完成
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self.all_audio_received = False # 所有音频数据是否已接收
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self.pre_buffer_empty = False # 预缓冲区是否为空
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self.playback_buffer_empty = False # 播放缓冲区是否为空
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self.no_active_playback = False # 是否没有活跃的播放
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# PyAudio实例
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self.audio = None
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self.output_stream = None
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@ -624,7 +639,7 @@ class OutputProcess:
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self.tts_access_key = "ynJMX-5ix1FsJvswC9KTNlGUdubcchqc"
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self.tts_resource_id = "volc.service_type.10029"
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self.tts_app_key = "aGjiRDfUWi"
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self.tts_speaker = "zh_female_wanqudashu_moon_bigtts"
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self.tts_speaker = config.get('tts_speaker', "zh_female_wanqudashu_moon_bigtts") if config else "zh_female_wanqudashu_moon_bigtts"
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# 启动工作线程 - 先启动播放线程，再启动TTS线程
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print("🔊 准备启动播放工作线程...")
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@ -825,7 +840,10 @@ class OutputProcess:
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# 关键修复：即使正在播放，也要检查播放完成
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if self.end_signal_received:
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self._check_playback_completion()
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# 使用增强的播放完成检测
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if self._check_enhanced_playback_completion():
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print(f"📥 播放状态时增强播放完成检测通过，处理结束信号")
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self._finish_playback()
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time.sleep(0.1) # 播放时增加延迟减少CPU使用
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continue
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@ -846,7 +864,10 @@ class OutputProcess:
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# 5. 主动检查播放完成（无论什么状态都要检查）
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if self.end_signal_received:
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self._check_playback_completion()
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# 使用增强的播放完成检测
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if self._check_enhanced_playback_completion():
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print(f"📥 主循环增强播放完成检测通过，处理结束信号")
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self._finish_playback()
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# 6. 借鉴 recorder.py: 根据播放状态调整休眠时间，优化性能
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if self.is_playing and (self.playback_buffer or self.preload_buffer):
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@ -907,6 +928,7 @@ class OutputProcess:
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print(f"📥 输出进程收到结束信号")
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end_signal_received = True
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self.end_signal_received = True
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self.all_audio_received = True
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# 重置完成事件标记
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self.completion_sent = False
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@ -914,18 +936,13 @@ class OutputProcess:
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self.playback_completed = False
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print(f"📥 已重置所有播放完成相关标志")
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# 检查是否应该立即结束
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tts_queue_size = self.tts_task_queue.qsize()
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playback_queue_size = len(self.playback_buffer) + len(self.preload_buffer)
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if tts_queue_size == 0 and playback_queue_size == 0 and not self.currently_playing:
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# 双重确认机制：所有队列已清空且播放工作线程已停止
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print(f"📥 双重确认通过：TTS队列={tts_queue_size}, 播放缓冲={len(self.playback_buffer)}, 预加载={len(self.preload_buffer)}, 正在播放={self.currently_playing}")
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print(f"📥 所有音频已播放完成，处理结束信号")
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# 使用增强的播放完成检测
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if self._check_enhanced_playback_completion():
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print(f"📥 增强播放完成检测通过，处理结束信号")
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self._finish_playback()
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return
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else:
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print(f"📥 延迟处理结束信号 - TTS队列: {tts_queue_size}, 播放缓冲: {playback_queue_size}, 正在播放: {self.currently_playing}")
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print(f"📥 延迟处理结束信号 - 等待LLM、TTS和播放完成")
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# 重新放回队列，稍后重试
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self.audio_queue.put(None)
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time.sleep(0.05)
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@ -937,6 +954,39 @@ class OutputProcess:
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print(f"📥 输出进程收到元数据: {metadata}")
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continue
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# 处理来自ControlSystem的流式文本命令
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if isinstance(audio_data, str) and audio_data.startswith("STREAMING_TEXT:"):
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# 流式文本处理 - 智能缓冲
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streaming_text = audio_data[15:] # 移除 "STREAMING_TEXT:" 前缀
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print(f"📥 输出进程收到流式文本: {streaming_text}")
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self.process_streaming_text(streaming_text)
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continue
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if isinstance(audio_data, str) and audio_data.startswith("COMPLETE_TEXT:"):
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# 完整文本处理 - 强制刷新缓冲区
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complete_text = audio_data[14:] # 移除 "COMPLETE_TEXT:" 前缀
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print(f"📥 输出进程收到完整文本: {complete_text}")
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self.process_complete_text(complete_text)
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continue
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if isinstance(audio_data, str) and audio_data.startswith("FLUSH_TTS_BUFFER:"):
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# 刷新TTS缓冲区 - 确保所有内容都被处理
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print(f"📥 输出进程收到刷新缓冲区命令")
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self._flush_tts_buffer()
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continue
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if isinstance(audio_data, str) and audio_data.startswith("LLM_COMPLETE:"):
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# LLM生成完成信号
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print(f"📥 输出进程收到LLM生成完成信号")
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self.llm_generation_complete = True
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continue
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if isinstance(audio_data, str) and audio_data.startswith("TTS_COMPLETE:"):
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# TTS生成完成信号
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print(f"📥 输出进程收到TTS生成完成信号")
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self.tts_generation_complete = True
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continue
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# 音频数据处理
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if isinstance(audio_data, bytes):
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# 更新最后收到音频数据的时间
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@ -985,44 +1035,28 @@ class OutputProcess:
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except queue.Empty:
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# 队列为空时的处理
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if end_signal_received:
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tts_queue_size = self.tts_task_queue.qsize()
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playback_queue_size = len(self.playback_buffer) + len(self.preload_buffer)
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print(f"📥 队列空时检查播放完成: TTS={tts_queue_size}, 播放缓冲={len(self.playback_buffer)}, 预加载={len(self.preload_buffer)}, 正在播放={self.currently_playing}")
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if tts_queue_size == 0 and playback_queue_size == 0:
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# 三重确认机制：所有缓冲区都空了，检查播放状态
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if not self.currently_playing:
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print(f"📥 三重确认通过：TTS队列=0, 播放缓冲=0, 预加载=0, 播放状态=False")
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print(f"📥 调用 _finish_playback() 前，completion_sent={self.completion_sent}")
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self._finish_playback()
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print(f"📥 调用 _finish_playback() 后，completion_sent={self.completion_sent}")
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return
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else:
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# 播放工作线程可能还在播放最后一个块，等待一下
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print(f"📥 等待播放工作线程完成...")
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time.sleep(0.3) # 增加等待时间，确保播放完成
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if not self.currently_playing:
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print(f"📥 三重确认通过（等待后）：TTS队列=0, 播放缓冲=0, 预加载=0, 播放状态=False")
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print(f"📥 调用 _finish_playback() 前，completion_sent={self.completion_sent}")
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self._finish_playback()
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print(f"📥 调用 _finish_playback() 后，completion_sent={self.completion_sent}")
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return
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else:
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print(f"📥 播放工作线程仍在播放，继续等待...")
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elif tts_queue_size == 0 and playback_queue_size > 0:
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# 还有数据要播放，继续等待
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print(f"📥 还有 {playback_queue_size} 个音频块待播放，等待播放完成")
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time.sleep(0.2) # 增加等待时间
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elif tts_queue_size == 0 and len(self.playback_buffer) == 0 and len(self.preload_buffer) > 0:
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# 关键修复：播放缓冲区为空但预加载缓冲区还有数据，需要转移数据
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print(f"📥 播放缓冲区为空但预加载缓冲区有 {len(self.preload_buffer)} 个数据块，转移数据到播放缓冲区")
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transfer_count = min(len(self.preload_buffer), 3) # 一次转移最多3个块
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for _ in range(transfer_count):
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if self.preload_buffer:
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self.playback_buffer.append(self.preload_buffer.pop(0))
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print(f"📥 已转移 {transfer_count} 个数据块到播放缓冲区")
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time.sleep(0.2) # 增加等待时间
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# 使用增强的播放完成检测
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if self._check_enhanced_playback_completion():
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print(f"📥 队列空时增强播放完成检测通过，处理结束信号")
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self._finish_playback()
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return
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else:
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print(f"📥 队列空时增强播放完成检测未通过，继续等待")
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# 如果还有数据要播放，继续等待
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tts_queue_size = self.tts_task_queue.qsize()
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playback_queue_size = len(self.playback_buffer) + len(self.preload_buffer)
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if playback_queue_size > 0:
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print(f"📥 还有 {playback_queue_size} 个音频块待播放，等待播放完成")
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time.sleep(0.2) # 增加等待时间
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elif tts_queue_size == 0 and len(self.playback_buffer) == 0 and len(self.preload_buffer) > 0:
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# 关键修复：播放缓冲区为空但预加载缓冲区还有数据，需要转移数据
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print(f"📥 播放缓冲区为空但预加载缓冲区有 {len(self.preload_buffer)} 个数据块，转移数据到播放缓冲区")
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transfer_count = min(len(self.preload_buffer), 3) # 一次转移最多3个块
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for _ in range(transfer_count):
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if self.preload_buffer:
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self.playback_buffer.append(self.preload_buffer.pop(0))
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print(f"📥 已转移 {transfer_count} 个数据块到播放缓冲区")
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time.sleep(0.2) # 增加等待时间
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# 检查是否应该补充播放缓冲区的数据
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if not self.is_playing and len(self.preload_buffer) >= self.min_buffer_size:
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@ -1047,7 +1081,10 @@ class OutputProcess:
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else:
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# 关键修复：即使没有处理数据，也要检查播放完成
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if self.end_signal_received:
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self._check_playback_completion()
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# 使用增强的播放完成检测
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if self._check_enhanced_playback_completion():
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print(f"📥 无数据处理时增强播放完成检测通过，处理结束信号")
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self._finish_playback()
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time.sleep(0.01)
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@ -1105,6 +1142,18 @@ class OutputProcess:
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self.preload_buffer.clear()
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self.last_playback_time = 0
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# 重置所有增强的状态标志
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self.llm_generation_complete = False
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self.tts_generation_complete = False
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self.all_audio_received = False
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self.pre_buffer_empty = False
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self.playback_buffer_empty = False
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self.no_active_playback = False
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self.end_signal_received = False
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self.playback_completed = False
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print("📡 输出进程：已重置所有播放完成状态标志")
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# 通知主进程播放完成
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if self.event_queue:
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try:
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@ -1128,6 +1177,79 @@ class OutputProcess:
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time.sleep(0.5)
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print("📡 输出进程：播放完成逻辑执行完毕")
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def _check_enhanced_playback_completion(self):
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"""增强的播放完成检测 - 考虑LLM、TTS和缓冲区状态"""
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if not self.end_signal_received:
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return False
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# 更新状态变量
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self.pre_buffer_empty = (len(self.preload_buffer) == 0)
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self.playback_buffer_empty = (len(self.playback_buffer) == 0)
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self.no_active_playback = (not self.currently_playing) # 同步更新状态变量
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tts_queue_size = self.tts_task_queue.qsize()
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print(f"🔍 增强播放完成检查:")
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print(f" - LLM生成完成: {self.llm_generation_complete}")
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print(f" - TTS生成完成: {self.tts_generation_complete}")
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print(f" - 所有音频已接收: {self.all_audio_received}")
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print(f" - 预缓冲区为空: {self.pre_buffer_empty}")
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print(f" - 播放缓冲区为空: {self.playback_buffer_empty}")
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print(f" - 无活跃播放: {self.no_active_playback}")
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print(f" - TTS队列大小: {tts_queue_size}")
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# 检查是否所有条件都满足
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all_conditions_met = (
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self.llm_generation_complete and
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self.tts_generation_complete and
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self.all_audio_received and
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self.pre_buffer_empty and
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self.playback_buffer_empty and
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not self.currently_playing and # 直接检查当前播放状态
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tts_queue_size == 0
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)
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if all_conditions_met:
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print(f"✅ 所有播放完成条件已满足，可以结束播放")
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return True
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# 如果LLM和TTS都完成了，但还有音频数据，等待播放完成
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if (self.llm_generation_complete and
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self.tts_generation_complete and
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self.all_audio_received and
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tts_queue_size == 0):
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if self.pre_buffer_empty and self.playback_buffer_empty:
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if not self.currently_playing: # 直接检查当前播放状态
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print(f"✅ LLM和TTS完成，所有缓冲区已清空，播放器空闲")
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return True
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else:
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print(f"⏳ 等待最后的音频播放完成...")
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time.sleep(0.5)
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if not self.currently_playing:
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print(f"✅ 最后的音频播放完成")
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return True
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else:
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print(f"⏳ 等待缓冲区数据播放完成 - 预缓冲: {len(self.preload_buffer)}, 播放缓冲: {len(self.playback_buffer)}")
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return False
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# 如果LLM还未完成，但其他条件满足，等待LLM完成
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if not self.llm_generation_complete:
|
||||
print(f"⏳ 等待LLM生成完成...")
|
||||
return False
|
||||
|
||||
# 如果TTS还未完成，但LLM已完成，等待TTS完成
|
||||
if not self.tts_generation_complete:
|
||||
print(f"⏳ 等待TTS生成完成...")
|
||||
return False
|
||||
|
||||
# 如果音频还未完全接收，等待接收完成
|
||||
if not self.all_audio_received:
|
||||
print(f"⏳ 等待所有音频接收完成...")
|
||||
return False
|
||||
|
||||
return False
|
||||
|
||||
def _check_playback_completion(self):
|
||||
"""检查播放完成状态 - 独立的播放完成检测方法"""
|
||||
if not self.end_signal_received:
|
||||
@ -1235,8 +1357,6 @@ class OutputProcess:
|
||||
# 生成音频数据并发送到统一播放队列
|
||||
self._generate_tts_audio(content)
|
||||
|
||||
self.tts_task_queue.task_done()
|
||||
|
||||
except queue.Empty:
|
||||
continue
|
||||
except Exception as e:
|
||||
@ -1370,6 +1490,10 @@ class OutputProcess:
|
||||
success_rate = (success_count / chunk_count * 100) if chunk_count > 0 else 0
|
||||
self.logger.info(f"TTS音频生成完成: {chunk_count} 块, 成功率 {success_rate:.1f}% | 总大小: {total_audio_size / 1024:.1f} KB")
|
||||
|
||||
# 通知自己TTS生成已完成
|
||||
self.tts_generation_complete = True
|
||||
print(f"🎵 OutputProcess TTS生成已完成")
|
||||
|
||||
# 等待播放完成
|
||||
if success_count > 0:
|
||||
self.logger.info("等待TTS音频播放完成...")
|
||||
@ -1406,9 +1530,170 @@ class OutputProcess:
|
||||
# 即使超时也要调用播放完成处理
|
||||
self._finish_playback()
|
||||
|
||||
# ========== 智能句子缓冲系统 - 从 recorder.py 借鉴 ==========
|
||||
|
||||
def _should_trigger_tts(self, sentence):
|
||||
"""智能判断是否应该触发TTS - 借鉴 recorder.py 的策略"""
|
||||
current_time = time.time()
|
||||
|
||||
# 检查缓冲区大小
|
||||
if len(self.tts_buffer) >= self.tts_buffer_max_size:
|
||||
return True
|
||||
|
||||
# 检查时间窗口
|
||||
time_since_last = current_time - self.tts_last_trigger_time
|
||||
if time_since_last >= self.tts_accumulation_time and len(self.tts_buffer) >= self.tts_buffer_min_size:
|
||||
return True
|
||||
|
||||
# 检查是否为完整句子（使用严格的检测）
|
||||
if self._is_complete_sentence(sentence):
|
||||
return True
|
||||
|
||||
# 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
|
||||
|
||||
# 短句子只在缓冲区较多或时间窗口到期时触发
|
||||
return False
|
||||
|
||||
def _process_tts_buffer(self):
|
||||
"""处理TTS缓冲区 - 发送累积的句子到TTS"""
|
||||
if not self.tts_buffer:
|
||||
return
|
||||
|
||||
# 合并缓冲区的句子
|
||||
combined_text = ''.join(self.tts_buffer)
|
||||
|
||||
# 添加到TTS任务队列
|
||||
if self._add_tts_task(combined_text):
|
||||
print(f"🎵 触发TTS: {combined_text[:50]}...")
|
||||
self.tts_last_trigger_time = time.time()
|
||||
|
||||
# 清空缓冲区
|
||||
self.tts_buffer.clear()
|
||||
|
||||
def _add_sentence_to_buffer(self, sentence):
|
||||
"""添加句子到智能缓冲区 - 核心方法"""
|
||||
if not sentence.strip():
|
||||
return
|
||||
|
||||
self.tts_buffer.append(sentence)
|
||||
|
||||
# 检查是否应该触发TTS
|
||||
if self._should_trigger_tts(sentence):
|
||||
self._process_tts_buffer()
|
||||
|
||||
def _flush_tts_buffer(self):
|
||||
"""强制刷新TTS缓冲区 - 确保所有内容都被处理"""
|
||||
if self.tts_buffer:
|
||||
self._process_tts_buffer()
|
||||
|
||||
def _is_complete_sentence(self, text):
|
||||
"""检测是否为完整句子 - 从 recorder.py 完全移植"""
|
||||
import re
|
||||
|
||||
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*$', text):
|
||||
# 对于以结束符结尾的句子，要求至少15个字符
|
||||
if len(text.strip()) >= 15:
|
||||
return True
|
||||
|
||||
# 检查是否包含句子结束符（可能在句子中间）
|
||||
if re.search(sentence_endings, text):
|
||||
# 如果后面有其他标点或字符，可能不是完整句子
|
||||
remaining_text = re.split(sentence_endings, text, 1)[-1]
|
||||
if len(remaining_text.strip()) > 0:
|
||||
return False
|
||||
# 对于包含结束符的句子，要求至少20个字符
|
||||
if len(text.strip()) >= 20:
|
||||
return True
|
||||
|
||||
# 对于较长的文本（超过50字符），即使没有结束符也可以考虑
|
||||
if len(text.strip()) >= 50:
|
||||
return True
|
||||
|
||||
# 对于中等长度的文本，如果包含常见完整句式模式
|
||||
if len(text.strip()) >= 20:
|
||||
common_patterns = [
|
||||
r'^[是的有没有来去在把被让叫请使].*[的得了吗呢吧啊呀]',
|
||||
r'^(你好|谢谢|再见|是的|不是|好的|没问题)',
|
||||
r'^[\u4e00-\u9fff]{4,8}[的得了]$' # 4-8个中文字+的/了/得
|
||||
]
|
||||
|
||||
for pattern in common_patterns:
|
||||
if re.match(pattern, text):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def _filter_parentheses_content(self, text):
|
||||
"""过滤文本中的括号内容（包括中文和英文括号）- 从 recorder.py 移植"""
|
||||
import re
|
||||
|
||||
# 移除中文括号内容：（内容）
|
||||
filtered_text = re.sub(r'（[^）]*）', '', text)
|
||||
# 移除英文括号内容：(content)
|
||||
filtered_text = re.sub(r'\([^)]*\)', '', filtered_text)
|
||||
# 移除方括号内容：【内容】
|
||||
filtered_text = re.sub(r'【[^】]*】', '', filtered_text)
|
||||
# 移除方括号内容：[content]
|
||||
filtered_text = re.sub(r'\[[^\]]*\]', '', filtered_text)
|
||||
# 移除书名号内容：「内容」
|
||||
filtered_text = re.sub(r'「[^」]*」', '', filtered_text)
|
||||
|
||||
# 清理多余空格
|
||||
filtered_text = re.sub(r'\s+', ' ', filtered_text).strip()
|
||||
|
||||
return filtered_text
|
||||
|
||||
def process_streaming_text(self, text):
|
||||
"""处理流式文本 - 新增的公共接口，用于与LLM流式输出集成"""
|
||||
if not text.strip():
|
||||
return
|
||||
|
||||
# 过滤括号内容
|
||||
filtered_text = self._filter_parentheses_content(text.strip())
|
||||
|
||||
if filtered_text:
|
||||
# 使用智能句子缓冲系统
|
||||
self._add_sentence_to_buffer(filtered_text)
|
||||
|
||||
def process_complete_text(self, text):
|
||||
"""处理完整文本 - 强制刷新缓冲区"""
|
||||
if not text.strip():
|
||||
return
|
||||
|
||||
# 过滤括号内容
|
||||
filtered_text = self._filter_parentheses_content(text.strip())
|
||||
|
||||
if filtered_text:
|
||||
# 直接添加到缓冲区并强制处理
|
||||
self.tts_buffer.append(filtered_text)
|
||||
self._process_tts_buffer()
|
||||
|
||||
# ========== 原有方法保持不变 ==========
|
||||
|
||||
def process_tts_request(self, text):
|
||||
"""处理TTS请求的公共接口"""
|
||||
return self._add_tts_task(text)
|
||||
"""处理TTS请求的公共接口 - 兼容原有接口"""
|
||||
# 使用新的智能缓冲系统
|
||||
self.process_complete_text(text)
|
||||
return True
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试代码
|
||||
|
||||
@ -222,7 +222,8 @@ class ControlSystem:
|
||||
output_config = {
|
||||
'buffer_size': 1000,
|
||||
'show_progress': True,
|
||||
'progress_interval': 100
|
||||
'progress_interval': 100,
|
||||
'tts_speaker': self.api_config['tts']['speaker']
|
||||
}
|
||||
|
||||
self.output_process = mp.Process(
|
||||
@ -397,29 +398,26 @@ class ControlSystem:
|
||||
else:
|
||||
text = "语音识别功能已禁用"
|
||||
|
||||
# 2. 大语言模型 (LLM)
|
||||
# 2. 大语言模型 (LLM) - 使用流式处理支持智能缓冲
|
||||
if self.config['processing']['enable_llm']:
|
||||
response = self._call_llm(text)
|
||||
if not response:
|
||||
success = self._call_llm_streaming(text)
|
||||
if not success:
|
||||
print("❌ 大语言模型调用失败")
|
||||
self._handle_processing_failure()
|
||||
return
|
||||
|
||||
print(f"💬 AI回复: {response}")
|
||||
else:
|
||||
# 如果禁用LLM，直接发送文本到TTS
|
||||
response = "大语言模型功能已禁用"
|
||||
|
||||
# 3. 文本转语音 (TTS)
|
||||
if self.config['processing']['enable_tts']:
|
||||
success = self._text_to_speech_streaming(response)
|
||||
if not success:
|
||||
print("❌ 文本转语音失败")
|
||||
self._handle_processing_failure()
|
||||
return
|
||||
else:
|
||||
print("ℹ️ 文本转语音功能已禁用")
|
||||
# 直接发送结束信号
|
||||
self.output_audio_queue.put(None)
|
||||
if self.config['processing']['enable_tts']:
|
||||
success = self._send_text_to_output_process(response)
|
||||
if not success:
|
||||
print("❌ 文本转语音失败")
|
||||
self._handle_processing_failure()
|
||||
return
|
||||
else:
|
||||
print("ℹ️ 文本转语音功能已禁用")
|
||||
# 直接发送结束信号
|
||||
self.output_audio_queue.put(None)
|
||||
|
||||
# 标记处理完成
|
||||
self.processing_complete = True
|
||||
@ -814,6 +812,287 @@ class ControlSystem:
|
||||
print(f"❌ 大语言模型调用失败: {e}")
|
||||
return None
|
||||
|
||||
def _call_llm_streaming(self, text: str) -> bool:
|
||||
"""流式调用大语言模型并实时处理响应"""
|
||||
if not self.config['processing']['enable_llm']:
|
||||
return False
|
||||
|
||||
try:
|
||||
print("🤖 调用大语言模型（流式输出）...")
|
||||
|
||||
# 获取角色配置
|
||||
character_config = self._load_character_config(self.config['processing']['character'])
|
||||
if character_config and "system_prompt" in character_config:
|
||||
system_prompt = character_config["system_prompt"]
|
||||
else:
|
||||
system_prompt = "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。"
|
||||
|
||||
# 获取角色配置中的最大token数
|
||||
max_tokens = 50
|
||||
if character_config and "max_tokens" in character_config:
|
||||
max_tokens = character_config["max_tokens"]
|
||||
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {self.api_config['llm']['api_key']}"
|
||||
}
|
||||
|
||||
# 构建消息列表
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": system_prompt
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": text
|
||||
}
|
||||
]
|
||||
|
||||
data = {
|
||||
"model": self.api_config['llm']['model'],
|
||||
"messages": messages,
|
||||
"max_tokens": max_tokens,
|
||||
"stream": True # 启用流式输出
|
||||
}
|
||||
|
||||
# 使用流式请求
|
||||
response = requests.post(
|
||||
self.api_config['llm']['api_url'],
|
||||
headers=headers,
|
||||
json=data,
|
||||
stream=True,
|
||||
timeout=30
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
print("🔄 开始接收流式响应...")
|
||||
|
||||
# 处理流式响应
|
||||
accumulated_text = ""
|
||||
sentence_buffer = ""
|
||||
|
||||
# 使用行缓冲区处理多字节字符
|
||||
buffer = ""
|
||||
for line_bytes in response.iter_lines():
|
||||
if not line_bytes:
|
||||
continue
|
||||
|
||||
# 解码为字符串，处理可能的编码问题
|
||||
try:
|
||||
line = line_bytes.decode('utf-8')
|
||||
except UnicodeDecodeError:
|
||||
try:
|
||||
line = line_bytes.decode('latin-1')
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
# 添加到缓冲区
|
||||
buffer += line
|
||||
|
||||
# 检查是否包含完整的数据行
|
||||
while buffer:
|
||||
# 查找完整的数据行
|
||||
if '\n' in buffer:
|
||||
line, buffer = buffer.split('\n', 1)
|
||||
else:
|
||||
line = buffer
|
||||
buffer = ""
|
||||
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
try:
|
||||
# 解析SSE格式的响应
|
||||
if line.startswith("data: "):
|
||||
data_str = line[6:] # 移除 "data: " 前缀
|
||||
|
||||
if data_str == "[DONE]":
|
||||
break
|
||||
|
||||
# 尝试解析JSON
|
||||
try:
|
||||
chunk_data = json.loads(data_str)
|
||||
except json.JSONDecodeError as e:
|
||||
# 尝试修复JSON：检查是否是不完整的字符串
|
||||
if not data_str.endswith('}'):
|
||||
# 这可能是一个不完整的JSON，等待下一行
|
||||
buffer = data_str + buffer
|
||||
continue
|
||||
|
||||
print(f"⚠️ JSON解析错误: {e}")
|
||||
continue
|
||||
|
||||
# 处理解析后的数据
|
||||
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
|
||||
sentence_buffer += content
|
||||
|
||||
# 检查是否有完整句子
|
||||
if self._is_complete_sentence(sentence_buffer):
|
||||
print(f"📝 检测到完整句子: {sentence_buffer}")
|
||||
|
||||
# 过滤括号内容
|
||||
filtered_sentence = self._filter_parentheses_content(sentence_buffer.strip())
|
||||
|
||||
if filtered_sentence:
|
||||
print(f"🎵 发送到智能缓冲区: {filtered_sentence}")
|
||||
# 发送到输出进程的智能缓冲系统
|
||||
self._send_streaming_text_to_output_process(filtered_sentence)
|
||||
|
||||
# 重置句子缓冲区
|
||||
sentence_buffer = ""
|
||||
|
||||
# 显示进度
|
||||
print(f"\r💬 已生成: {accumulated_text}", end='', flush=True)
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ 处理流式响应时出错: {e}")
|
||||
continue
|
||||
|
||||
print(f"\n✅ 流式响应完成: {accumulated_text}")
|
||||
|
||||
# 强制刷新缓冲区，确保所有内容都被处理
|
||||
if sentence_buffer:
|
||||
filtered_sentence = self._filter_parentheses_content(sentence_buffer.strip())
|
||||
if filtered_sentence:
|
||||
print(f"🎵 发送剩余句子: {filtered_sentence}")
|
||||
self._send_streaming_text_to_output_process(filtered_sentence)
|
||||
|
||||
# 通知输出进程LLM生成已完成
|
||||
self._notify_llm_complete()
|
||||
|
||||
# 通知输出进程所有文本已发送完成
|
||||
self._flush_output_process_tts_buffer()
|
||||
|
||||
return accumulated_text != ""
|
||||
|
||||
else:
|
||||
print(f"❌ LLM API调用失败: {response.status_code}")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 流式LLM调用失败: {e}")
|
||||
return False
|
||||
|
||||
def _send_streaming_text_to_output_process(self, text: str):
|
||||
"""发送流式文本到输出进程的智能缓冲系统"""
|
||||
try:
|
||||
# 发送特殊命令到输出进程，指示这是流式文本
|
||||
streaming_command = f"STREAMING_TEXT:{text}"
|
||||
self.output_audio_queue.put(streaming_command)
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"❌ 发送流式文本失败: {e}")
|
||||
return False
|
||||
|
||||
def _send_text_to_output_process(self, text: str) -> bool:
|
||||
"""发送完整文本到输出进程"""
|
||||
try:
|
||||
# 发送特殊命令到输出进程，指示这是完整文本
|
||||
complete_command = f"COMPLETE_TEXT:{text}"
|
||||
self.output_audio_queue.put(complete_command)
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"❌ 发送文本失败: {e}")
|
||||
return False
|
||||
|
||||
def _flush_output_process_tts_buffer(self):
|
||||
"""通知输出进程刷新TTS缓冲区"""
|
||||
try:
|
||||
# 发送刷新缓冲区命令
|
||||
flush_command = "FLUSH_TTS_BUFFER:"
|
||||
self.output_audio_queue.put(flush_command)
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"❌ 刷新缓冲区失败: {e}")
|
||||
return False
|
||||
|
||||
def _notify_llm_complete(self):
|
||||
"""通知输出进程LLM生成已完成"""
|
||||
try:
|
||||
# 发送LLM完成信号
|
||||
llm_complete_command = "LLM_COMPLETE:"
|
||||
self.output_audio_queue.put(llm_complete_command)
|
||||
print("📡 主控制：已发送LLM生成完成信号")
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"❌ 发送LLM完成信号失败: {e}")
|
||||
return False
|
||||
|
||||
def _is_complete_sentence(self, text):
|
||||
"""检测是否为完整句子 - 从 recorder.py 移植"""
|
||||
import re
|
||||
|
||||
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*$', text):
|
||||
# 对于以结束符结尾的句子，要求至少15个字符
|
||||
if len(text.strip()) >= 15:
|
||||
return True
|
||||
|
||||
# 检查是否包含句子结束符（可能在句子中间）
|
||||
if re.search(sentence_endings, text):
|
||||
# 如果后面有其他标点或字符，可能不是完整句子
|
||||
remaining_text = re.split(sentence_endings, text, 1)[-1]
|
||||
if len(remaining_text.strip()) > 0:
|
||||
return False
|
||||
# 对于包含结束符的句子，要求至少20个字符
|
||||
if len(text.strip()) >= 20:
|
||||
return True
|
||||
|
||||
# 对于较长的文本（超过50字符），即使没有结束符也可以考虑
|
||||
if len(text.strip()) >= 50:
|
||||
return True
|
||||
|
||||
# 对于中等长度的文本，如果包含常见完整句式模式
|
||||
if len(text.strip()) >= 20:
|
||||
common_patterns = [
|
||||
r'^[是的有没有来去在把被让叫请使].*[的得了吗呢吧啊呀]',
|
||||
r'^(你好|谢谢|再见|是的|不是|好的|没问题)',
|
||||
r'^[\u4e00-\u9fff]{4,8}[的得了]$' # 4-8个中文字+的/了/得
|
||||
]
|
||||
|
||||
for pattern in common_patterns:
|
||||
if re.match(pattern, text):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def _filter_parentheses_content(self, text):
|
||||
"""过滤文本中的括号内容（包括中文和英文括号）- 从 recorder.py 移植"""
|
||||
import re
|
||||
|
||||
# 移除中文括号内容：（内容）
|
||||
filtered_text = re.sub(r'（[^）]*）', '', text)
|
||||
# 移除英文括号内容：(content)
|
||||
filtered_text = re.sub(r'\([^)]*\)', '', filtered_text)
|
||||
# 移除方括号内容：【内容】
|
||||
filtered_text = re.sub(r'【[^】]*】', '', filtered_text)
|
||||
# 移除方括号内容：[content]
|
||||
filtered_text = re.sub(r'\[[^\]]*\]', '', filtered_text)
|
||||
# 移除书名号内容：「内容」
|
||||
filtered_text = re.sub(r'「[^」]*」', '', filtered_text)
|
||||
|
||||
# 清理多余空格
|
||||
filtered_text = re.sub(r'\s+', ' ', filtered_text).strip()
|
||||
|
||||
return filtered_text
|
||||
|
||||
def _text_to_speech_streaming(self, text: str) -> bool:
|
||||
"""文本转语音（流式）"""
|
||||
if not self.config['processing']['enable_tts']:
|
||||
@ -949,6 +1228,15 @@ class ControlSystem:
|
||||
|
||||
time.sleep(0.5)
|
||||
|
||||
# 发送TTS完成信号
|
||||
print(f"📦 发送TTS完成信号到输出进程")
|
||||
try:
|
||||
tts_complete_command = "TTS_COMPLETE:"
|
||||
self.output_audio_queue.put(tts_complete_command)
|
||||
print(f"📡 已发送TTS完成信号到输出进程")
|
||||
except Exception as e:
|
||||
print(f"❌ 发送TTS完成信号失败: {e}")
|
||||
|
||||
# 发送结束信号，通知输出进程所有音频已发送完成
|
||||
print(f"📦 发送结束信号到输出进程")
|
||||
print(f"📊 音频队列当前大小: {self.output_audio_queue.qsize()}")
|
||||
|
||||
6
logs/InputProcess_20250921_115617.log
Normal file
6
logs/InputProcess_20250921_115617.log
Normal file
@ -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
|
||||
8
logs/OutputProcess_20250921_115614.log
Normal file
8
logs/OutputProcess_20250921_115614.log
Normal file
@ -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
|
||||
Loading…
Reference in New Issue
Block a user