update:server连接api (#747)

* update:server连接manager-api

* update:读取智能体模型配置

* update:添加默认模型的按钮

* update:优化配置读取方式

* update：server兼容manager接口改造

* update:优化私有配置加载

* update:加载私有模型配置

main/xiaozhi-server/core/utils/vad.py

@@ -1,77 +1,19 @@
-from abc import ABC, abstractmethod
+import importlib
+import os
+import sys
+from core.providers.vad.base import VADProviderBase
 from config.logger import setup_logging
-import opuslib_next
-import time
-import numpy as np
-import torch
 
 TAG = __name__
 logger = setup_logging()
 
-class VAD(ABC):
-    @abstractmethod
-    def is_vad(self, conn, data):
-        """检测音频数据中的语音活动"""
-        pass
 
+def create_instance(class_name: str, *args, **kwargs) -> VADProviderBase:
+    """工厂方法创建VAD实例"""
+    if os.path.exists(os.path.join("core", "providers", "vad", f"{class_name}.py")):
+        lib_name = f"core.providers.vad.{class_name}"
+        if lib_name not in sys.modules:
+            sys.modules[lib_name] = importlib.import_module(f"{lib_name}")
+        return sys.modules[lib_name].VADProvider(*args, **kwargs)
 
-class SileroVAD(VAD):
-    def __init__(self, config):
-        logger.bind(tag=TAG).info("SileroVAD", config)
-        self.model, self.utils = torch.hub.load(repo_or_dir=config["model_dir"],
-                                                source='local',
-                                                model='silero_vad',
-                                                force_reload=False)
-        (get_speech_timestamps, _, _, _, _) = self.utils
-
-        self.decoder = opuslib_next.Decoder(16000, 1)
-        self.vad_threshold = config.get("threshold")
-        self.silence_threshold_ms = config.get("min_silence_duration_ms")
-
-    def is_vad(self, conn, opus_packet):
-        try:
-            pcm_frame = self.decoder.decode(opus_packet, 960)
-            conn.client_audio_buffer.extend(pcm_frame)  # 将新数据加入缓冲区
-
-            # 处理缓冲区中的完整帧（每次处理512采样点）
-            client_have_voice = False
-            while len(conn.client_audio_buffer) >= 512 * 2:
-                # 提取前512个采样点（1024字节）
-                chunk = conn.client_audio_buffer[:512 * 2]
-                conn.client_audio_buffer = conn.client_audio_buffer[512 * 2:]
-
-                # 转换为模型需要的张量格式
-                audio_int16 = np.frombuffer(chunk, dtype=np.int16)
-                audio_float32 = audio_int16.astype(np.float32) / 32768.0
-                audio_tensor = torch.from_numpy(audio_float32)
-
-                # 检测语音活动
-                speech_prob = self.model(audio_tensor, 16000).item()
-                client_have_voice = speech_prob >= self.vad_threshold
-
-                # 如果之前有声音，但本次没有声音，且与上次有声音的时间查已经超过了静默阈值，则认为已经说完一句话
-                if conn.client_have_voice and not client_have_voice:
-                    stop_duration = time.time() * 1000 - conn.client_have_voice_last_time
-                    if stop_duration >= self.silence_threshold_ms:
-                        conn.client_voice_stop = True
-                if client_have_voice:
-                    conn.client_have_voice = True
-                    conn.client_have_voice_last_time = time.time() * 1000
-
-            return client_have_voice
-        except opuslib_next.OpusError as e:
-            logger.bind(tag=TAG).info(f"解码错误: {e}")
-        except Exception as e:
-            logger.bind(tag=TAG).error(f"Error processing audio packet: {e}")
-
-
-def create_instance(class_name, *args, **kwargs) -> VAD:
-    # 获取类对象
-    cls_map = {
-        "SileroVAD": SileroVAD,
-        # 可扩展其他SileroVAD实现
-    }
-
-    if cls := cls_map.get(class_name):
-        return cls(*args, **kwargs)
-    raise ValueError(f"不支持的SileroVAD类型: {class_name}")
+    raise ValueError(f"不支持的VAD类型: {class_name}，请检查该配置的type是否设置正确")