[唤醒灵敏度与端点恢复]：完成唤醒提示顺序和Hybrid VAD优化，包含阈值默认值、缓冲时序和测试覆盖

src/owner_voice_pet/__init__.py

@@ -17,7 +17,7 @@ from .models import (
 )
 from .transport import AudioRingBuffer, FileReplayTransport, MemoryAudioTransport
 from .wakeword import KeywordWakeWordProvider, SherpaOnnxKeywordWakeWordProvider
-from .vad import EnergyVadProvider, VadRecorder
+from .vad import EnergyVadProvider, HybridVadProvider, VadRecorder
 from .stt import CloudAsrSttProvider, MetadataSttProvider, SherpaOnnxSttProvider, is_valid_transcript_text
 from .conversation import ConversationContext
 from .llm import MockLlmProvider, OpenAICompatibleLlmProvider
@@ -38,6 +38,7 @@ __all__ = [
     "KeywordWakeWordProvider",
     "SherpaOnnxKeywordWakeWordProvider",
     "EnergyVadProvider",
+    "HybridVadProvider",
     "VadRecorder",
     "CloudAsrSttProvider",
     "MetadataSttProvider",

src/owner_voice_pet/config.py

@@ -22,11 +22,11 @@ class AppConfig:
     log_dir: Path = Path("logs")
     wake_provider: str = "local_kws"
     wake_keywords_file: Path | None = None
-    wake_kws_threshold: float = 0.25
+    wake_kws_threshold: float = 0.15
     wake_kws_score: float = 1.0
     wake_ack_text: str = "我在"
-    post_playback_drain_ms: int = 250
-    vad_provider: str = "local"
+    post_playback_drain_ms: int = 50
+    vad_provider: str = "hybrid"
     vad_threshold: float = 0.5
     vad_min_duration_ms: int = 250
     vad_end_silence_ms: int = 350
@@ -63,11 +63,11 @@ class AppConfig:
             log_dir=Path(get("LOG_DIR", "logs") or "logs"),
             wake_provider=(get("WAKE_PROVIDER", "local_kws") or "local_kws").lower(),
             wake_keywords_file=Path(value) if (value := get("WAKE_KEYWORDS_FILE")) else None,
-            wake_kws_threshold=float(get("WAKE_KWS_THRESHOLD", "0.25") or "0.25"),
+            wake_kws_threshold=float(get("WAKE_KWS_THRESHOLD", "0.15") or "0.15"),
             wake_kws_score=float(get("WAKE_KWS_SCORE", "1.0") or "1.0"),
             wake_ack_text=get("WAKE_ACK_TEXT", "我在") or "我在",
-            post_playback_drain_ms=int(get("POST_PLAYBACK_DRAIN_MS", "250") or "250"),
-            vad_provider=(get("VAD_PROVIDER", "local") or "local").lower(),
+            post_playback_drain_ms=int(get("POST_PLAYBACK_DRAIN_MS", "50") or "50"),
+            vad_provider=(get("VAD_PROVIDER", "hybrid") or "hybrid").lower(),
             vad_threshold=float(get("VAD_THRESHOLD", "0.5") or "0.5"),
             vad_min_duration_ms=int(get("VAD_MIN_DURATION_MS", "250") or "250"),
             vad_end_silence_ms=int(get("VAD_END_SILENCE_MS", "350") or "350"),
@@ -178,11 +178,11 @@ class AppConfig:
                     "startup",
                 )
             )
-        if self.vad_provider not in {"local", "energy"}:
+        if self.vad_provider not in {"hybrid", "local", "energy"}:
             errors.append(
                 ProviderError(
                     ErrorCode.CONFIG_MISSING_VALUE,
-                    "OWNER_VAD_PROVIDER must be local or energy",
+                    "OWNER_VAD_PROVIDER must be hybrid, local, or energy",
                     False,
                     "config",
                     "startup",

src/owner_voice_pet/runtime.py

@@ -12,7 +12,7 @@ from .protocols import AudioTransport, LlmProvider, SttProvider, TtsProvider, Wa
 from .stt import CloudAsrSttProvider, SherpaOnnxSttProvider, is_valid_transcript_text
 from .transport import SoundDeviceAudioTransport
 from .tts import CloudTtsProvider, MacSayTtsProvider, SentenceBuffer
-from .vad import EnergyVadProvider, SherpaOnnxVadProvider, VadRecorder
+from .vad import EnergyVadProvider, HybridVadProvider, SherpaOnnxVadProvider, VadRecorder
 from .wakeword import SherpaOnnxKeywordWakeWordProvider
 
 
@@ -145,10 +145,11 @@ class LiveVoiceRuntime:
         wake_error = self._wait_for_local_wake(turn_id)
         if wake_error is not None:
             return wake_error
-        self._state(PipelineState.SPEECH_DETECTING, "唤醒命中：请说出问题", turn_id=turn_id)
+        self._state(PipelineState.SPEECH_DETECTING, "唤醒命中", turn_id=turn_id)
         ack_error = self._acknowledge_wake(turn_id)
         if ack_error is not None:
             return ack_error
+        self._state(PipelineState.SPEECH_DETECTING, "请说出问题", turn_id=turn_id)
         user_segment = self._capture_segment(turn_id, state_message="录音中：正在听取问题")
         if isinstance(user_segment, ProviderError):
             return user_segment
@@ -277,7 +278,12 @@ def build_live_runtime(config: AppConfig, reporter: RuntimeReporter | None = Non
     else:
         stt = SherpaOnnxSttProvider(str(config.speech_models_dir))
         tts = MacSayTtsProvider()
-    if config.vad_provider == "local":
+    if config.vad_provider == "hybrid":
+        vad_provider = HybridVadProvider(
+            SherpaOnnxVadProvider(config.speech_models_dir, threshold=config.vad_threshold),
+            EnergyVadProvider(),
+        )
+    elif config.vad_provider == "local":
         vad_provider = SherpaOnnxVadProvider(config.speech_models_dir, threshold=config.vad_threshold)
     else:
         vad_provider = EnergyVadProvider()

src/owner_voice_pet/vad.py

@@ -157,6 +157,53 @@ class SherpaOnnxVadProvider:
             self._model.reset()
 
 
+class HybridVadProvider:
+    """Combine local model VAD with energy fallback for live microphone variance."""
+
+    def __init__(self, primary: Any, fallback: Any) -> None:
+        self.primary = primary
+        self.fallback = fallback
+        self.loaded = False
+        self._speech_ms = 0
+        self._silence_ms = 0
+
+    def load(self) -> None:
+        self.primary.load()
+        self.fallback.load()
+        self.loaded = True
+
+    def analyze(self, frame: AudioFrame) -> VadResult:
+        if not self.loaded:
+            raise ProviderError(
+                ErrorCode.VAD_MODEL_LOAD_FAILED,
+                "hybrid VAD provider is not loaded",
+                False,
+                "hybrid-vad",
+                "vad",
+            )
+        primary = self.primary.analyze(frame)
+        fallback = self.fallback.analyze(frame)
+        is_speech = primary.is_speech or fallback.is_speech
+        frame_ms = int(frame.metadata.get("duration_ms", 20))
+        if is_speech:
+            self._speech_ms += frame_ms
+            self._silence_ms = 0
+        else:
+            self._silence_ms += frame_ms
+        return VadResult(
+            is_speech=is_speech,
+            confidence=max(primary.confidence, fallback.confidence),
+            speech_ms=self._speech_ms,
+            silence_ms=self._silence_ms,
+        )
+
+    def reset(self) -> None:
+        self._speech_ms = 0
+        self._silence_ms = 0
+        self.primary.reset()
+        self.fallback.reset()
+
+
 @dataclass(slots=True)
 class VadRecorder:
     provider: Any