[代码重构中]完善VADFunctor，测试持久化保存VAD片段的音频数据成功。

main.py

@@ -0,0 +1,34 @@
+from funasr import AutoModel
+
+chunk_size = 200 # ms
+model = AutoModel(
+    model="fsmn-vad", 
+    model_revision="v2.0.4",
+    disable_update=True
+)
+
+import soundfile
+
+wav_file = "tests/vad_example.wav"
+speech, sample_rate = soundfile.read(wav_file)
+chunk_stride = int(chunk_size * sample_rate / 1000)
+
+cache = {}
+total_chunk_num = int(len((speech)-1)/chunk_stride+1)
+for i in range(total_chunk_num):
+    speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
+    is_final = i == total_chunk_num - 1
+    res = model.generate(
+        input=speech_chunk, 
+        cache=cache, 
+        is_final=is_final, 
+        chunk_size=chunk_size,
+        disable_pbar=True
+    )
+    if len(res[0]["value"]):
+        print(res)
+
+print(f"len(speech): {len(speech)}")
+print(f"len(speech_chunk): {len(speech_chunk)}")
+print(f"total_chunk_num: {total_chunk_num}")
+print(f"generateconfig: chunk_size: {chunk_size}, chunk_stride: {chunk_stride}")

src/functor/vad_functor.py

@@ -1,13 +1,11 @@
 from funasr import AutoModel
-from typing import List, Dict, Any
-from src.models import VADResponse
-from src.models import AudioBinary_Config
-from src.models import AudioBinary_data_list
-from src.models import AudioBinary_Slice
+from typing import List, Dict, Any, Callable
+from src.models import VAD_Functor_result, _AudioBinary_data, AudioBinary_Config, AudioBinary_data_list
 from typing import Callable
 from src.functor.base import BaseFunctor
 import threading
 from queue import Empty, Queue
+import numpy
 
 # 日志
 from src.utils.logger import get_module_logger
@@ -20,15 +18,37 @@ class VADFunctor(BaseFunctor):
         self
     ):
         super().__init__()
-        self._model: dict = {}
-        self._callback: List[Callable] = []
-        self._status_lock: threading.Lock = threading.Lock()
-        self._input_queue: Queue = None
-        self._audio_config: AudioBinary_Config = None
+        # 资源与配置
+        self._model: dict = {} # 模型
+        self._callback: List[Callable] = [] # 回调函数
+        self._input_queue: Queue = None # 输入队列
+        self._audio_config: AudioBinary_Config = None # 音频配置
+        self._audio_binary_data_list: AudioBinary_data_list = None # 音频数据列表
+
+        # flag
+        # 此处用到两个锁，但都是为了截断_run线程，考虑后续优化
         self._is_running: bool = False
         self._stop_event: bool = False
-        self._audio_cache: bytes = b''
-        self._cache: dict = {}
+
+        # 状态锁
+        self._status_lock: threading.Lock = threading.Lock()
+
+        # 缓存
+        self._audio_cache: numpy.ndarray = None
+        self._audio_cache_preindex: int = 0
+        self._model_cache: dict = {}
+        self._cache_result_list = []
+        self._audiobinary_cache = None
+    
+    def reset_cache(self):
+        """
+        重置缓存, 用于任务完成后清理缓存数据, 准备下次任务
+        """
+        self._audio_cache = None
+        self._audio_cache_preindex = 0
+        self._model_cache = {}
+        self._cache_result_list = []
+        self._audiobinary_cache = None
 
     def set_input_queue(self, queue: Queue):
         self._input_queue = queue
@@ -38,32 +58,99 @@ class VADFunctor(BaseFunctor):
 
     def set_audio_config(self, audio_config: AudioBinary_Config):
         self._audio_config = audio_config
+        logger.info(f"VADFunctor设置音频配置: {self._audio_config}")
+
+    def set_audio_binary_data_list(self, audio_binary_data_list: AudioBinary_data_list):
+        self._audio_binary_data_list = audio_binary_data_list
 
     def add_callback(self, callback: Callable):
         if not isinstance(self._callback, list):
             self._callback = []
         self._callback.append(callback)
 
-    def _process(self, data: bytes):
+    def _do_callback(self, result: List[List[int]], audio_cache: AudioBinary_data_list):
+        """
+        回调函数
+        VADFunctor包装结果, 存储到AudioBinary中, 并向队列中添加AudioBinary_Slice
+
+        输入:
+            result: List[[start,end]] 处理所得VAD端点
+            其中若start==-1, 则表示前无端点, 若end==-1, 则表示后无端点
+            当处理得到一个完成片段时, 存入AudioBinary中, 并向队列中添加AudioBinary_Slice
+        输出:
+            None
+        """
+        # 持久化缓存结果队列
+        for pair in result:
+            [start, end] = pair
+            # 若无前端点, 则向缓存队列中合并
+            if start == -1:
+                self._cache_result_list[-1][1] = end
+            else:
+                self._cache_result_list.append(pair)
+        while len(self._cache_result_list) > 1:
+            # 创建VAD片段
+            # 计算开始帧
+            start_frame = self._audio_config.ms2frame(self._cache_result_list[0][0]) 
+            start_frame -= self._audio_cache_preindex
+            # 计算结束帧
+            end_frame = self._audio_config.ms2frame(self._cache_result_list[0][1])
+            end_frame -= self._audio_cache_preindex
+            # 计算开始时间
+            vad_result = VAD_Functor_result.create_from_push_data(
+                audiobinary_data_list=self._audio_binary_data_list,
+                data=self._audiobinary_cache[start_frame:end_frame],
+                start_time=self._cache_result_list[0][0],
+                end_time=self._cache_result_list[0][1]
+            )
+            self._audio_cache_preindex += end_frame
+            self._audiobinary_cache = self._audiobinary_cache[end_frame:]
+            for callback in self._callback:
+                callback(vad_result)
+            self._cache_result_list.pop(0)
+
+    def _predeal_data(self, data: Any):
+        """
+        预处理数据
+        """
+        if self._audio_cache is None:
+            self._audio_cache = data
+        else:
+            # 拼接音频数据
+            if isinstance(self._audio_cache, numpy.ndarray):
+                self._audio_cache = numpy.concatenate((self._audio_cache, data))
+            elif isinstance(self._audio_cache, list):
+                self._audio_cache.append(data)
+        if self._audiobinary_cache is None:
+            self._audiobinary_cache = data
+        else:
+            # 拼接音频数据
+            if isinstance(self._audiobinary_cache, numpy.ndarray):
+                self._audiobinary_cache = numpy.concatenate((self._audiobinary_cache, data))
+            elif isinstance(self._audiobinary_cache, list):
+                self._audiobinary_cache.append(data)
+    def _process(self, data: Any):
         """
         处理数据
         """
-        self._audio_cache += data
-        if len(self._audio_cache) >= self._audio_config.chunk_size*100:
+        self._predeal_data(data)
+        if len(self._audio_cache) >= self._audio_config.chunk_stride:
             result = self._model['vad'].generate(
                 input=self._audio_cache,
-                cache=self._cache,
+                cache=self._model_cache,
                 chunk_size=self._audio_config.chunk_size,
                 is_final=False,
             )
-            logger.info(f"VADFunctor处理数据: {len(self._audio_cache)}, {result}")
-            self._audio_cache = b''
+            if (len(result[0]['value']) > 0):
+                self._do_callback(result[0]['value'], self._audio_cache)
+                logger.debug(f"VADFunctor结果: {result[0]['value']}")
+            self._audio_cache = None
         
 
     def _run(self):
         """
         线程运行逻辑
-        监听输入队列，当有数据时，处理数据
+        监听输入队列, 当有数据时, 处理数据
         当输入队列为空时, 间隔1s检测是否进入停止事件。
         """
         # 刷新运行状态
@@ -76,7 +163,7 @@ class VADFunctor(BaseFunctor):
                 data = self._input_queue.get(True, timeout=1)
                 self._process(data)
                 self._input_queue.task_done()
-            # 当队列为空时，间隔1s检测是否进入停止事件。
+            # 当队列为空时, 间隔1s检测是否进入停止事件。
             except Empty:
                 if self._stop_event:
                     break
@@ -90,12 +177,26 @@ class VADFunctor(BaseFunctor):
         """
         启动 _run 线程, 并返回线程对象
         """
+        self._pre_check()
         self._thread = threading.Thread(target=self._run, daemon=True)
         self._thread.start()
         return self._thread
 
-    def _pre_check(self):
-        pass
+    def _pre_check(self) -> bool:
+        """
+        检测硬性资源是否都已设置
+        """
+        if self._model is None:
+            raise ValueError("模型未设置")
+        if self._audio_config is None:
+            raise ValueError("音频配置未设置")
+        if self._audio_binary_data_list is None:
+            raise ValueError("音频数据列表未设置")
+        if self._input_queue is None:
+            raise ValueError("输入队列未设置")
+        if self._callback is None:
+            raise ValueError("回调函数未设置")
+        return True
 
     def stop(self):
         with self._status_lock:

src/models/__init__.py

@@ -1,3 +1,3 @@
-from .audio import AudioBinary_Config, AudioBinary_data_list, AudioBinary_Slice
-from .vad import VADResponse
-__all__ = ["AudioBinary_Config", "AudioBinary_data_list", "AudioBinary_Slice", "VADResponse"]
+from .audio import AudioBinary_Config, AudioBinary_data_list, _AudioBinary_data
+from .vad import VAD_Functor_result
+__all__ = ["AudioBinary_Config", "AudioBinary_data_list", "_AudioBinary_data", "VAD_Functor_result"]

src/models/audio.py

@@ -1,9 +1,19 @@
-from pydantic import BaseModel, Field
-from typing import List
+from pydantic import BaseModel, Field, validator
+from typing import List, Any
+import numpy
+
+from src.utils import get_module_logger
+
+logger = get_module_logger(__name__)
+
+binary_data_types = (bytes, numpy.ndarray)
 
 class AudioBinary_Config(BaseModel):
     """二进制音频块配置信息"""
-    audio_data: bytes = Field(description="音频数据", default=None)
+    class Config:
+        arbitrary_types_allowed = True
+
+    audio_data: binary_data_types = Field(description="音频数据", default=None)
     chunk_size: int = Field(description="块大小", default=100)
     chunk_stride: int = Field(description="块步长", default=1600)
     sample_rate: int = Field(description="采样率", default=16000)
@@ -15,23 +25,117 @@ class AudioBinary_Config(BaseModel):
     def AudioBinary_Config_from_dict(cls, data: dict):
         return cls(**data)
 
+    def ms2frame(self, ms: int) -> int:
+        """
+        将毫秒转换为帧
+        """
+        return int(ms * self.sample_rate / 1000)
+
+    def frame2ms(self, frame: int) -> int:
+        """
+        将帧转换为毫秒
+        """
+        return int(frame * 1000 / self.sample_rate)
 
 class _AudioBinary_data(BaseModel):
-    """音频二进制数据"""
-    binary_data: bytes = Field(description="音频二进制数据", default=None)
+    """音频数据"""
+    binary_data: binary_data_types = Field(description="音频二进制数据", default=None)
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    @validator('binary_data')
+    def validate_binary_data(cls, v):
+        """
+        验证音频数据
+        Args:
+            v: 音频数据
+        Returns:
+            binary_data_types: 音频数据
+        """
+        if not isinstance(v, (bytes, numpy.ndarray)):
+            logger.warning("[%s]binary_data不是bytes, numpy.ndarray类型, 而是%s类型, 请检查", cls.__class__.__name__, type(v))
+        return v
+
+    def __len__(self):
+        """
+        获取音频数据长度
+        Returns:
+            int: 音频数据长度
+        """
+        return len(self.binary_data)
+    
+    def __init__(self, binary_data: binary_data_types):
+        """
+        初始化音频数据
+        Args:
+            binary_data: 音频数据
+        """
+        logger.debug("[%s]初始化音频数据, 数据类型为%s", self.__class__.__name__, type(binary_data))
+        super().__init__(binary_data=binary_data)
+
+    def __getitem__(self):
+        """
+        当获取数据时, 直接返回binary_data
+        Returns:
+            binary_data_types: 音频数据
+        """
+        return self.binary_data
 
 class AudioBinary_data_list(BaseModel):
-    """音频二进制数据列表"""
-    binary_data_list: List[_AudioBinary_data] = Field(description="音频二进制数据列表", default=[])
+    """音频数据列表"""
+    binary_data_list: List[_AudioBinary_data] = Field(description="音频数据列表", default=[])
 
-    def __call__(self):
-        return self.binary_data_list
+    class Config:
+        arbitrary_types_allowed = True
 
-class AudioBinary_Slice(BaseModel):
-    """音频块切片"""
-    target_Binary: AudioBinary_data_list = Field(description="目标音频块", default=None)
-    start_index: int = Field(description="开始位置", default=0)
-    end_index: int = Field(description="结束位置", default=0)
+    def push_data(self, data: binary_data_types) -> int:
+        """
+        添加音频数据
+        Args:
+            data: 音频数据
+        Returns:
+            int: 数据在binary_data_list中的索引
+        """
+        self.binary_data_list.append(_AudioBinary_data(binary_data=data))
+        return len(self.binary_data_list) - 1
 
-    def __call__(self):
-        return self.target_Binary(self.start_index, self.end_index)
+    def __getitem__(self, index: int):
+        """
+        获取音频数据
+        Args:
+            index: 音频数据在binary_data_list中的索引
+        Returns:
+            _AudioBinary_data: 音频数据
+        """
+        return self.binary_data_list[index]
+
+    def __len__(self):
+        """
+        获取音频数据列表长度
+        Returns:
+            int: 音频数据列表长度
+        """
+        return len(self.binary_data_list)
+
+# class AudioBinary_Slice(BaseModel):
+#     """音频块切片"""
+#     target_Binary: AudioBinary_data_list = Field(description="目标音频块", default=None)
+#     start_index: int = Field(description="开始位置", default=0)
+#     end_index: int = Field(description="结束位置", default=-1)
+
+#     @validator('start_index')
+#     def validate_start_index(cls, v):
+#         if v < 0:
+#             raise ValueError("start_index必须大于0")
+#         return v
+
+#     @validator('end_index')
+#     def validate_end_index(cls, v):
+#         if v < cls.start_index:
+#             logger.debug("[%s]end_index小于start_index, 将end_index设置为start_index", cls.__class__.__name__)
+#             v = cls.start_index
+#         return v
+
+#     def __call__(self):
+#         return self.target_Binary(self.start_index, self.end_index)

src/models/vad.py

@@ -1,143 +1,88 @@
 from pydantic import BaseModel, Field, validator
-from typing import List, Optional, Callable
+from typing import List, Optional, Callable, Any
+from .audio import AudioBinary_data_list, _AudioBinary_data
 
-class VADSegment(BaseModel):
-    """VAD片段"""
-    start: int = Field(description="开始时间(ms)")
-    end: int = Field(description="结束时间(ms)")
+class VAD_Functor_result(BaseModel):
+    """
+    VADFunctor结果
+    """
+    audiobinary_data_list: AudioBinary_data_list = Field(description="音频数据列表")
+    audiobinary_index: int = Field(description="音频数据索引")
+    audiobinary_data: _AudioBinary_data = Field(description="音频数据, 指向AudioBinary_data")
+    start_time: int = Field(description="开始时间", is_required=True)
+    end_time: int = Field(description="结束时间", is_required=True)
 
-class VADResult(BaseModel):
-    """VAD结果"""
-    key: str = Field(description="音频标识")
-    value: List[VADSegment] = Field(description="VAD片段列表")
-
-class VADResponse(BaseModel):
-    """VAD响应"""
-    results: List[VADResult] = Field(description="VAD结果列表", default_factory=list)
-    time_chunk: List[VADSegment] = Field(description="时间块", default_factory=list)
-    time_chunk_index: int = Field(description="当前处理时间块索引", default=0)
-    time_chunk_index_callback: Optional[Callable[[int], None]] = Field(
-        description="时间块索引回调函数", 
-        default=None
-    )
-    
-    @validator('time_chunk')
-    def validate_time_chunk(cls, v):
-        """验证时间块的有效性"""
-        if not v:
-            return v
-            
-        # 检查时间顺序
-        for i in range(len(v) - 1):
-            if v[i].end >= v[i + 1].start:
-                raise ValueError(f"时间块{i}的结束时间({v[i].end})大于等于下一个时间块的开始时间({v[i + 1].start})")
+    @validator('audiobinary_data_list')
+    def validate_audiobinary_data_list(cls, v):
+        if not isinstance(v, AudioBinary_data_list):
+            raise ValueError("audiobinary_data_list必须是AudioBinary_data_list类型")
         return v
 
-    # 回调未处理的时间块
-    def process_time_chunk(self, callback: Callable[[int], None] = None) -> None:
-        """处理时间块"""
-        # print("Enter process_time_chunk", self.time_chunk_index, len(self.time_chunk))
-        while self.time_chunk_index < len(self.time_chunk) - 1:
-            index = self.time_chunk_index
-            if self.time_chunk[index].end != -1:
-                x = {
-                    "start_time": self.time_chunk[index].start, 
-                    "end_time": self.time_chunk[index].end
-                }
-                if callback is not None:
-                    callback(x)
-                elif self.time_chunk_index_callback is not None:
-                    self.time_chunk_index_callback(x)
-                else:
-                    print("[Warning] No callback available")
-                self.time_chunk_index += 1
+    @validator('audiobinary_index')
+    def validate_audiobinary_index(cls, v):
+        if not isinstance(v, int):
+            raise ValueError("audiobinary_index必须是int类型")
+        if v < 0:
+            raise ValueError("audiobinary_index必须大于0")
+        return v
 
-    def __add__(self, other: 'VADResponse') -> 'VADResponse':
-        """合并两个VADResponse"""
-        if not self.results:
-            self.results = other.results
-            self.time_chunk = other.time_chunk
-            return self
+    @validator('audiobinary_data')
+    def validate_audiobinary_data(cls, v):
+        if not isinstance(v, _AudioBinary_data):
+            raise ValueError("audiobinary_data必须是AudioBinary_data类型")
+        return v
 
-        # 检查是否可以合并最后一个结果
-        last_result = self.results[-1]
-        first_other = other.results[0]
+    @validator('start_time')
+    def validate_start_time(cls, v):
+        if not isinstance(v, int):
+            raise ValueError("start_time必须是int类型")
+        if v < 0:
+            raise ValueError("start_time必须大于0")
+        return v
     
-        if last_result.value[-1].end == first_other.value[0].start:
-            # 合并相邻的时间段
-            last_result.value[-1].end = first_other.value[0].end
-            first_other.value.pop(0)
-            
-            # 更新time_chunk
-            self.time_chunk[-1].end = other.time_chunk[0].end
-            other.time_chunk.pop(0)
-            
-            # 添加剩余的结果
-            if first_other.value:
-                self.results.extend(other.results)
-                self.time_chunk.extend(other.time_chunk)
-        else:
-            # 直接添加所有结果
-            self.results.extend(other.results)
-            self.time_chunk.extend(other.time_chunk)
-        
-        return self
+    @validator('end_time')
+    def validate_end_time(cls, v, values):
+        if not isinstance(v, int):
+            raise ValueError("end_time必须是int类型")
+        if 'start_time' in values and v <= values['start_time']:
+            raise ValueError("end_time必须大于start_time")
+        return v
     
     @classmethod
-    def from_raw(cls, raw_data: List[dict]) -> "VADResponse":
+    def create_from_push_data(
+        cls,
+        audiobinary_data_list: AudioBinary_data_list,
+        data: Any,
+        start_time: int,
+        end_time: int
+    ):
         """
-        从原始数据创建VADResponse
-        
-        参数:
-            raw_data: 原始数据，格式如 [{'key': 'xxx', 'value': [[-1, 59540], [59820, -1]]}]
-            
-        返回:
-            VADResponse: 解析后的VAD响应
+        创建VAD片段
         """
-        results = []
-        time_chunk = []
-        for item in raw_data:
-            segments = [
-                VADSegment(start=seg[0], end=seg[1])
-                for seg in item['value']
-            ]
-            results.append(VADResult(
-                key=item['key'],
-                value=segments
-            ))
-            time_chunk.extend(segments)
-        return cls(results=results, time_chunk=time_chunk)
+        index = audiobinary_data_list.push_data(data)
 
-    def to_raw(self) -> List[dict]:
-        """
-        转换为原始数据格式
-        
-        返回:
-            List[dict]: 原始数据格式
-        """
-        return [
-            {
-                'key': result.key,
-                'value': [[seg.start, seg.end] for seg in result.value]
-            }
-            for result in self.results
-        ]
-
-    def __str__(self):
-        result_str = "VADResponse:\n"
-        for result in self.results:
-            for value_item in result.value:
-                result_str += f"[{value_item.start}:{value_item.end}]\n"
-        return result_str
-
-    def __iter__(self):
-        return iter(self.time_chunk)
-    
-    def __next__(self):
-        return next(self.time_chunk)
+        return cls(
+            audiobinary_data_list=audiobinary_data_list,
+            audiobinary_index=index,
+            audiobinary_data=audiobinary_data_list[index],
+            start_time=start_time,
+            end_time=end_time)
 
     def __len__(self):
-        return len(self.time_chunk)
+        """
+        获取音频数据长度
+        """
+        return len(self.audiobinary_data.binary_data)
+
+    def __str__(self):
+        """
+        字符串展示内容
+        """
+        tostr = f'audiobinary_data_index: {self.audiobinary_index}\n'
+        tostr += f'start_time: {self.start_time}\n'
+        tostr += f'end_time: {self.end_time}\n'
+        tostr += f'data_length: {len(self.audiobinary_data.binary_data)}\n'
+        tostr += f'data_type: {type(self.audiobinary_data.binary_data)}\n'
+        return tostr
+    
     
-    def __getitem__(self, index):
-        return self.time_chunk[index]

src/pipeline/ASRpipeline.py

@@ -16,7 +16,7 @@ class ASRPipeline(PipelineBase):
         """
         super().__init__(*args, **kwargs)
         self._config: Dict[str, Any] = {}
-        self._funtor_dict: Dict[str, Any] = {}
+        self._functor_dict: Dict[str, Any] = {}
         self._subqueue_dict: Dict[str, Any] = {}
         self._is_baked: bool = False
 
@@ -46,28 +46,28 @@ class ASRPipeline(PipelineBase):
         """
         烘焙管道
         """
-        self._init_funtor()
+        self._init_functor()
         self._is_baked = True
 
-    def _init_funtor(self) -> None:
+    def _init_functor(self) -> None:
         """
         初始化函数
         """
         try:
-            from src.funtor import FuntorFactory
-            # 加载VAD、asr、spk funtor
-            self._funtor_dict["vad"] = FuntorFactory.make_funtor(
-                funtor_name = "vad", 
+            from src.functor import functorFactory
+            # 加载VAD、asr、spk functor
+            self._functor_dict["vad"] = functorFactory.make_functor(
+                functor_name = "vad", 
                 config = self._config, 
                 models = self._models
             )
-            self._funtor_dict["asr"] = FuntorFactory.make_funtor(
-                funtor_name = "asr", 
+            self._functor_dict["asr"] = functorFactory.make_functor(
+                functor_name = "asr", 
                 config = self._config, 
                 models = self._models
             )
-            self._funtor_dict["spk"] = FuntorFactory.make_funtor(
-                funtor_name = "spk", 
+            self._functor_dict["spk"] = functorFactory.make_functor(
+                functor_name = "spk", 
                 config = self._config, 
                 models = self._models
             )
@@ -79,13 +79,13 @@ class ASRPipeline(PipelineBase):
             self._subqueue_dict["spkend"] = Queue()
 
             # 设置子队列的输入队列
-            self._funtor_dict["vad"].set_input_queue(self._input_queue)
-            self._funtor_dict["asr"].set_input_queue(self._subqueue_dict["vad2asr"])
-            self._funtor_dict["spk"].set_input_queue(self._subqueue_dict["vad2spk"])
+            self._functor_dict["vad"].set_input_queue(self._input_queue)
+            self._functor_dict["asr"].set_input_queue(self._subqueue_dict["vad2asr"])
+            self._functor_dict["spk"].set_input_queue(self._subqueue_dict["vad2spk"])
 
             # 设置回调函数——放置到对应队列中
-            self._funtor_dict["vad"].add_callback(self._subqueue_dict["vad2asr"].put)
-            self._funtor_dict["vad"].add_callback(self._subqueue_dict["vad2spk"].put)
+            self._functor_dict["vad"].add_callback(self._subqueue_dict["vad2asr"].put)
+            self._functor_dict["vad"].add_callback(self._subqueue_dict["vad2spk"].put)
 
             # 构造带回调函数的put
             def put_with_checkcallback(queue: Queue, callback: Callable) -> None:
@@ -97,11 +97,11 @@ class ASRPipeline(PipelineBase):
                     callback()
                 return put_with_check
 
-            self._funtor_dict["asr"].add_callback(put_with_checkcallback(self._subqueue_dict["asrend"], self._check_result))
-            self._funtor_dict["spk"].add_callback(put_with_checkcallback(self._subqueue_dict["spkend"], self._check_result))
+            self._functor_dict["asr"].add_callback(put_with_checkcallback(self._subqueue_dict["asrend"], self._check_result))
+            self._functor_dict["spk"].add_callback(put_with_checkcallback(self._subqueue_dict["spkend"], self._check_result))
 
         except ImportError:
-            raise ImportError("FuntorFactory引入失败,ASRPipeline无法完成初始化") 
+            raise ImportError("functorFactory引入失败,ASRPipeline无法完成初始化") 
 
     def get_config(self) -> Dict[str, Any]:
         """
@@ -129,10 +129,10 @@ class ASRPipeline(PipelineBase):
         if not self._is_baked:
             raise RuntimeError("管道未烘焙，无法运行")
 
-        # 运行所有funtor
-        for funtor_name, funtor in self._funtor_dict.items():
-            logger.info(f"运行{funtor_name}funtor")
-            funtor.run()
+        # 运行所有functor
+        for functor_name, functor in self._functor_dict.items():
+            logger.info(f"运行{functor_name}functor")
+            self._functor_dict[functor_name].run()
 
         # 运行管道
         if not self._input_queue:
@@ -152,6 +152,7 @@ class ASRPipeline(PipelineBase):
                     # 检查是否是结束信号
                     if data is None:
                         logger.info("收到结束信号，管道准备停止")
+                        self._stop()
                         self._input_queue.task_done()  # 标记结束信号已处理
                         break
                     
@@ -187,3 +188,13 @@ class ASRPipeline(PipelineBase):
             # 通知回调函数
             self._notify_callbacks(result)
 
+    def stop(self) -> None:
+        """
+        停止管道
+        """
+        self._is_running = False
+        self._stop_event = True
+        for functor_name, functor in self._functor_dict.items():
+            logger.info(f"停止{functor_name}functor")
+            functor.stop()
+        logger.info("子Functor停止")

tests/functor/vad_test.py

@@ -5,10 +5,15 @@ VAD测试
 from src.functor.vad_functor import VADFunctor
 from queue import Queue, Empty
 from src.model_loader import ModelLoader
-from src.models import AudioBinary_Config
+from src.models import AudioBinary_Config, AudioBinary_data_list
 from src.utils.data_format import wav_to_bytes
 import time
 from src.utils.logger import get_module_logger
+from pydub import AudioSegment
+import soundfile
+
+# 观察参数
+OVERWATCH = False
 
 logger = get_module_logger(__name__)
 
@@ -22,37 +27,54 @@ def test_vad_functor():
         "auto_update": False,
     }
     model_loader.load_models(args)
-    # 创建VAD函数器
-    vad_functor = VADFunctor()
+    # 加载数据
+    f_data, sample_rate = soundfile.read("tests/vad_example.wav")
+    audio_config = AudioBinary_Config(
+        chunk_size=200,
+        chunk_stride=1600,
+        sample_rate=sample_rate,
+        sample_width=16,
+        channels=1
+    )
+    chunk_stride = int(audio_config.chunk_size*sample_rate/1000)
+    audio_config.chunk_stride = chunk_stride
     # 创建输入队列
     input_queue = Queue()
+    # 创建音频数据列表
+    audio_binary_data_list = AudioBinary_data_list()
+
+    # 创建VAD函数器
+    vad_functor = VADFunctor()
     # 设置输入队列
     vad_functor.set_input_queue(input_queue)
     # 设置音频配置
-    vad_functor.set_audio_config(AudioBinary_Config(
-        chunk_size=960,
-        chunk_stride=480,
-        sample_rate=16000,
-        sample_width=2,
-        channels=1
-    ))
+    vad_functor.set_audio_config(audio_config)
+    # 设置音频数据列表
+    vad_functor.set_audio_binary_data_list(audio_binary_data_list)
     # 设置回调函数
-    vad_functor.add_callback(lambda x: print(x))
+    vad_functor.add_callback(lambda x: print(f"callback: {x}"))
     # 设置模型
     vad_functor.set_model({
         'vad': model_loader.models['vad']
     })
+
     # 启动VAD函数器
     vad_functor.run()
-
-    # 加载数据
-    f_binary = wav_to_bytes("tests/vad_example.wav")
-    chunk_size = 960000
-    # chunk_size = len(f_binary)
-    print(f"f_binary: {len(f_binary)}, chunk_size: {chunk_size}, clip_num: {len(f_binary) // chunk_size}")
-    for i in range(0, len(f_binary), chunk_size):
-        binary_data = f_binary[i:i+chunk_size]
+    f_binary = f_data
+    audio_clip_len = 200
+    print(f"f_binary: {len(f_binary)}, audio_clip_len: {audio_clip_len}, clip_num: {len(f_binary) // audio_clip_len}")
+    for i in range(0, len(f_binary), audio_clip_len):
+        binary_data = f_binary[i:i+audio_clip_len]
         input_queue.put(binary_data)
     # 等待VAD函数器结束
-    time.sleep(10)
+    print("[vad_test] 等待input_queue为空")
+    input_queue.join()
+    print("[vad_test] input_queue为空")
     vad_functor.stop()
+    print("[vad_test] VAD函数器结束")
+
+    # 保存音频数据
+    if OVERWATCH:
+        for index in range(len(audio_binary_data_list)):
+            save_path = f"tests/vad_test_output_{index}.wav"
+            soundfile.write(save_path, audio_binary_data_list[index].binary_data, sample_rate)