| import asyncio | |
| import multiprocessing | |
| import threading | |
| from collections import OrderedDict | |
| from voice_dialogue.utils.cache import LRUCacheDict | |
| from .session_manager import SessionIdManager | |
| from .state_manager import VoiceStateManager | |
| # ======================= 音频配置常量 ======================= | |
| # 音频采样率与窗口大小映射配置 | |
| SAMPLE_RATE_WINDOW_SIZE_MAPPING = { | |
| # 电话语音常用采样率，窗口大小选择512便于在较低采样率下进行相对精细的短时分析 | |
| 8000: 512, | |
| # 常见的语音处理采样率，例如语音识别等场景，512的窗口大小在这个采样率下能较好地平衡频率分辨率和时间分辨率 | |
| 16000: 512, | |
| # 一些音频录制设备的标准采样率，1024的窗口大小可以获取更宽的频率范围信息，适合音频分析等应用 | |
| 44100: 1024, | |
| # 专业音频领域常用采样率，更高的采样率需要适当增大窗口大小以充分利用高分辨率优势，2048的窗口大小有助于提取更丰富的音频特征 | |
| 48000: 2048, | |
| # 高清音频采样率，对于这样高的采样率，更大的窗口大小可以让我们在频域分析时有更好的表现，这里选择4096作为窗口大小 | |
| 96000: 4096, | |
| # 超高清音频采样率，对应更大的窗口尺寸便于在处理这种高质量音频时获得更精准的频谱等信息 | |
| 192000: 8192 | |
| } | |
| # 默认音频配置 | |
| DEFAULT_SAMPLE_RATE = 16000 | |
| DEFAULT_WINDOW_SIZE = 512 | |
| # ======================= 队列变量 ======================= | |
| # 音频处理相关队列 | |
| audio_frames_queue = multiprocessing.Queue() | |
| user_voice_queue = multiprocessing.Queue() | |
| transcribed_text_queue = multiprocessing.Queue() | |
| text_input_queue = multiprocessing.Queue() | |
| audio_output_queue = multiprocessing.Queue() | |
| websocket_message_queue = asyncio.Queue() | |
| # ======================= 全局状态实例 ======================= | |
| # 语音状态管理器实例 | |
| voice_state_manager = VoiceStateManager() | |
| # 会话缓存 | |
| chat_history_cache: dict[str, OrderedDict] = {} | |
| session_manager: SessionIdManager = SessionIdManager() | |
| dropped_audio_cache = LRUCacheDict(maxsize=50) | |
| # ======================= 线程事件对象 ======================= | |
| # 音频播放相关事件 | |
| audio_playing_event = threading.Event() | |
| silence_over_threshold_event = threading.Event() | |
| user_still_speaking_event = threading.Event() | |
| user_interrupting_playback_event = threading.Event() | |
| # 中断任务ID | |
| interrupt_task_id = '' | |