166 lines
4.9 KiB
Python
166 lines
4.9 KiB
Python
"""
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ResultBinderFunctor
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负责聚合结果，将所有input_queue中的结果进行聚合，并进行callback
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"""
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from src.functor.base import BaseFunctor
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from src.models import AudioBinary_Config, VAD_Functor_result
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from typing import Callable, List, Dict, Any
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from queue import Queue, Empty
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import threading
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import time
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# 日志
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from src.utils.logger import get_module_logger
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logger = get_module_logger(__name__)
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class ResultBinderFunctor(BaseFunctor):
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"""
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ResultBinderFunctor
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负责聚合结果，将所有input_queue中的结果进行聚合，并进行callback
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"""
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def __init__(self) -> None:
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super().__init__()
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# 资源与配置
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self._callback: List[Callable] = [] # 回调函数
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self._input_queue: Dict[str, Queue] = {} # 输入队列
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self._audio_config: AudioBinary_Config = None # 音频配置
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def reset_cache(self) -> None:
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"""
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重置缓存, 用于任务完成后清理缓存数据, 准备下次任务
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"""
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pass
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def add_input_queue(self,
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name: str,
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queue: Queue,
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) -> None:
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"""
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设置监听的输入消息队列
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"""
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self._input_queue[name] = queue
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def set_model(self, model: dict) -> None:
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"""
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设置推理模型
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resultbinder_functor 不应设置模型
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"""
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logger.warning("ResultBinderFunctor不应设置模型")
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self._model = model
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def set_audio_config(self, audio_config: AudioBinary_Config) -> None:
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"""
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设置音频配置
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resultbinder_functor 不应设置音频配置
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"""
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logger.warning("ResultBinderFunctor不应设置音频配置")
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self._audio_config = audio_config
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def add_callback(self, callback: Callable) -> None:
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"""
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向自身的_callback: List[Callable]回调函数列表中添加回调函数
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"""
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if not isinstance(self._callback, list):
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self._callback = []
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self._callback.append(callback)
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def _do_callback(self, result: List[str]) -> None:
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"""
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回调函数
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"""
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for callback in self._callback:
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callback(result)
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def _process(self, data: Dict[str, Any]) -> None:
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"""
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处理数据
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{
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"is_final": false,
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"mode": "2pass-offline",
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"text": "，等一下我回一下，ok你看这里就有了",
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"wav_name": "h5",
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"speaker_id":
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}
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"""
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logger.debug("ResultBinderFunctor处理数据: %s", data)
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# 将data中的result进行聚合
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# 此步暂时无意义，预留
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results = {
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"is_final": False,
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"mode": "2pass-offline",
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"text": data["asr"],
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"wav_name": "h5",
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"speaker_id": data["spk"]["speaker_id"]
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}
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# for name, result in data.items():
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# results[name] = result
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self._do_callback(results)
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def _run(self) -> None:
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"""
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线程运行逻辑
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"""
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with self._status_lock:
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self._is_running = True
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self._stop_event = False
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# 运行逻辑
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while self._is_running:
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try:
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# 若有队列为空，则等待0.1s
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for name, queue in self._input_queue.items():
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if queue.empty():
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time.sleep(0.1)
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raise Empty
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data = {}
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for name, queue in self._input_queue.items():
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data[name] = queue.get(True, timeout=1)
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queue.task_done()
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self._process(data)
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# 当队列为空时, 检测是否进入停止事件。
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except Empty:
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if self._stop_event:
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break
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continue
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# 其他异常
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except Exception as e:
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logger.error("SpkFunctor运行时发生错误: %s", e)
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raise e
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def run(self) -> threading.Thread:
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"""
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启动线程
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Returns:
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threading.Thread: 返回已运行线程实例
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"""
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self._pre_check()
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self._thread = threading.Thread(target=self._run, daemon=True)
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self._thread.start()
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return self._thread
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def _pre_check(self) -> bool:
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"""
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预检查
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"""
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if self._model is None:
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raise ValueError("模型未设置")
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if self._input_queue is None:
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raise ValueError("输入队列未设置")
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if self._callback is None:
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raise ValueError("回调函数未设置")
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return True
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def stop(self) -> bool:
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"""
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停止线程
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"""
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with self._status_lock:
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self._stop_event = True
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self._thread.join()
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with self._status_lock:
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self._is_running = False
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return not self._thread.is_alive()
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