142 lines
5.3 KiB
Python
142 lines
5.3 KiB
Python
# -*- coding: utf-8 -*-
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"""
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全局VAD/PUNC模型管理模块
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提供线程安全的全局模型实例管理
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"""
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import logging
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import threading
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from funasr import AutoModel
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from app.core.config import settings
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from app.infrastructure import resolve_model_path
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logger = logging.getLogger(__name__)
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# 全局语音活动检测(VAD)模型缓存（避免重复加载）
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_global_vad_model = None
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_vad_model_lock = threading.Lock()
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_vad_inference_lock = threading.Lock() # 推理互斥锁，防止并发状态混乱
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# 全局标点符号模型缓存（避免重复加载）
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_global_punc_model = None
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_punc_model_lock = threading.Lock()
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_punc_inference_lock = threading.Lock() # 推理互斥锁，防止并发状态混乱
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# 全局实时标点符号模型缓存（避免重复加载）
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_global_punc_realtime_model = None
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_punc_realtime_model_lock = threading.Lock()
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_punc_realtime_inference_lock = threading.Lock() # 推理互斥锁，防止并发状态混乱
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def _resolve_device(device: str) -> str:
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"""解析设备字符串，将 auto 转换为实际的设备"""
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from app.core.device import detect_device
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return detect_device(device)
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def get_global_vad_model(device: str):
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"""获取全局语音活动检测(VAD)模型实例（线程安全，双重检查锁定）"""
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global _global_vad_model
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if _global_vad_model is None:
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with _vad_model_lock:
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if _global_vad_model is None:
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try:
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# 解析模型路径：优先使用本地缓存
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resolved_vad_path = resolve_model_path(settings.VAD_MODEL)
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logger.info(f"正在加载全局语音活动检测(VAD)模型: {resolved_vad_path}")
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# 解析 auto 设备
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resolved_device = _resolve_device(device)
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_global_vad_model = AutoModel(
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model=resolved_vad_path,
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device=resolved_device,
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speech_noise_thres=0.6, # VAD 语音噪声阈值（FunASR默认0.6，设为0.7稍微严格一些，分段更碎）
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**settings.FUNASR_AUTOMODEL_KWARGS,
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)
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logger.info("全局语音活动检测(VAD)模型加载成功 (speech_noise_thres=0.6)")
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except Exception as e:
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logger.error(f"全局语音活动检测(VAD)模型加载失败: {str(e)}")
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_global_vad_model = None
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raise
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return _global_vad_model
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def get_vad_inference_lock():
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"""获取VAD模型推理锁（线程安全）"""
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return _vad_inference_lock
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def get_global_punc_model(device: str):
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"""获取全局标点符号模型实例（离线版，线程安全，双重检查锁定）"""
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global _global_punc_model
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if _global_punc_model is None:
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with _punc_model_lock:
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if _global_punc_model is None:
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try:
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# 解析模型路径：优先使用本地缓存
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resolved_punc_path = resolve_model_path(settings.PUNC_MODEL)
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logger.info(f"正在加载全局标点符号模型（离线）: {resolved_punc_path}")
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# 解析 auto 设备
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resolved_device = _resolve_device(device)
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_global_punc_model = AutoModel(
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model=resolved_punc_path,
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device=resolved_device,
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**settings.FUNASR_AUTOMODEL_KWARGS,
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)
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logger.info("全局标点符号模型（离线）加载成功")
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except Exception as e:
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logger.error(f"全局标点符号模型（离线）加载失败: {str(e)}")
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_global_punc_model = None
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raise
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return _global_punc_model
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def get_punc_inference_lock():
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"""获取PUNC模型推理锁（线程安全）"""
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return _punc_inference_lock
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def get_global_punc_realtime_model(device: str):
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"""获取全局实时标点符号模型实例（线程安全，双重检查锁定）"""
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global _global_punc_realtime_model
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if _global_punc_realtime_model is None:
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with _punc_realtime_model_lock:
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if _global_punc_realtime_model is None:
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try:
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# 解析模型路径：优先使用本地缓存
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resolved_punc_realtime_path = resolve_model_path(settings.PUNC_REALTIME_MODEL)
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logger.info(f"正在加载全局标点符号模型（实时）: {resolved_punc_realtime_path}")
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# 解析 auto 设备
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resolved_device = _resolve_device(device)
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_global_punc_realtime_model = AutoModel(
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model=resolved_punc_realtime_path,
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device=resolved_device,
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**settings.FUNASR_AUTOMODEL_KWARGS,
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)
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logger.info("全局标点符号模型（实时）加载成功")
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except Exception as e:
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logger.error(f"全局标点符号模型（实时）加载失败: {str(e)}")
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_global_punc_realtime_model = None
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raise
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return _global_punc_realtime_model
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def get_punc_realtime_inference_lock():
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"""获取实时PUNC模型推理锁（线程安全）"""
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return _punc_realtime_inference_lock
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