[独立唤醒与转写显示]：完成运行时本地唤醒分离，包含wake注入、转写输出和污染回归测试

tests/test_live_runtime.py

@@ -10,6 +10,7 @@ from owner_voice_pet.runtime import LiveVoiceRuntime
 from owner_voice_pet.transport import MemoryAudioTransport
 from owner_voice_pet.tts import SineTtsProvider
 from owner_voice_pet.vad import EnergyVadProvider, VadRecorder
+from owner_voice_pet.wakeword import KeywordWakeWordProvider
 
 
 def segment_frames(start_id: int, start_ms: int) -> list[AudioFrame]:
@@ -21,6 +22,17 @@ def segment_frames(start_id: int, start_ms: int) -> list[AudioFrame]:
     ]
 
 
+def wake_frame(frame_id: int, timestamp_ms: int) -> AudioFrame:
+    return AudioFrame(
+        b"\xff\x7f",
+        16000,
+        1,
+        timestamp_ms,
+        frame_id,
+        {"duration_ms": 20, "wake_word": "小杰小杰", "wake_confidence": 0.95},
+    )
+
+
 class QueueSttProvider:
     def __init__(self, texts: list[str]) -> None:
         self.texts = list(texts)
@@ -39,10 +51,17 @@ class QueueSttProvider:
 class RecordingReporter:
     def __init__(self) -> None:
         self.statuses: list[str] = []
+        self.transcripts: list[str] = []
         self.errors: list[str] = []
+        self.events: list[str] = []
 
     def status(self, state: str, message: str, *, turn_id: int | None = None) -> None:
         self.statuses.append(message)
+        self.events.append(f"status:{message}")
+
+    def transcript(self, text: str, *, final: bool, turn_id: int | None = None) -> None:
+        self.transcripts.append(text)
+        self.events.append(f"transcript:{text}")
 
     def error(self, stage: str, code: str, message: str, *, turn_id: int | None = None) -> None:
         self.errors.append(f"{stage}:{code}:{message}")
@@ -50,8 +69,11 @@ class RecordingReporter:
 
 def make_runtime(texts: list[str], context: ConversationContext | None = None) -> tuple[LiveVoiceRuntime, QueueSttProvider, MockLlmProvider, MemoryAudioTransport, RecordingReporter]:
     frames = []
-    for idx in range(4):
-        frames.extend(segment_frames(idx * 4, idx * 80))
+    for idx, _text in enumerate(texts):
+        base_id = idx * 5
+        base_ms = idx * 120
+        frames.append(wake_frame(base_id, base_ms))
+        frames.extend(segment_frames(base_id + 1, base_ms + 20))
     transport = MemoryAudioTransport(frames)
     stt = QueueSttProvider(texts)
     llm = MockLlmProvider(["这是答复。"])
@@ -60,6 +82,7 @@ def make_runtime(texts: list[str], context: ConversationContext | None = None) -
     runtime = LiveVoiceRuntime(
         config=AppConfig(llm_api_key="secret", speech_provider="cloud"),
         transport=transport,
+        wakeword=KeywordWakeWordProvider(),
         vad_recorder=VadRecorder(EnergyVadProvider(), min_duration_ms=40, end_silence_ms=40),
         stt=stt,
         llm=llm,
@@ -72,19 +95,18 @@ def make_runtime(texts: list[str], context: ConversationContext | None = None) -
 
 class LiveRuntimeTests(unittest.TestCase):
     def test_repeated_runtime_runs_two_turns_and_returns_to_standby(self) -> None:
-        runtime, stt, llm, transport, reporter = make_runtime(
-            ["小杰小杰", "第一问", "小杰小杰", "第二问"]
-        )
+        runtime, stt, llm, transport, reporter = make_runtime(["第一问", "第二问"])
         summary = runtime.run(max_turns=2)
 
         self.assertEqual(summary.completed_turns, 2)
-        self.assertEqual(len(stt.calls), 4)
+        self.assertEqual(len(stt.calls), 2)
         self.assertEqual(len(llm.calls), 2)
         self.assertEqual(len(transport.played_segments), 2)
+        self.assertEqual(reporter.transcripts, ["第一问", "第二问"])
         self.assertIn("恢复待机：可继续唤醒", reporter.statuses[-1])
 
     def test_temporary_context_is_sent_to_second_llm_call(self) -> None:
-        runtime, _, llm, _, _ = make_runtime(["小杰小杰", "第一问", "小杰小杰", "第二问"])
+        runtime, _, llm, _, _ = make_runtime(["第一问", "第二问"])
         runtime.run(max_turns=2)
 
         second_call_text = [message.content for message in llm.calls[1]]
@@ -94,14 +116,31 @@ class LiveRuntimeTests(unittest.TestCase):
 
     def test_new_runtime_context_starts_empty(self) -> None:
         first_context = ConversationContext()
-        first_runtime, _, _, _, _ = make_runtime(["小杰小杰", "第一问"], context=first_context)
+        first_runtime, _, _, _, _ = make_runtime(["第一问"], context=first_context)
         first_runtime.run(max_turns=1)
         self.assertGreater(len(first_context.messages()), 0)
 
         second_context = ConversationContext()
-        make_runtime(["小杰小杰", "第二问"], context=second_context)
+        make_runtime(["第二问"], context=second_context)
         self.assertEqual(second_context.messages(), ())
 
+    def test_transcript_is_reported_before_llm_thinking(self) -> None:
+        runtime, _, _, _, reporter = make_runtime(["第一问"])
+        runtime.run(max_turns=1)
+
+        transcript_index = reporter.events.index("transcript:第一问")
+        thinking_index = next(
+            index for index, event in enumerate(reporter.events) if event == "status:思考中：正在生成回复"
+        )
+        self.assertLess(transcript_index, thinking_index)
+
+    def test_wake_keyword_does_not_pollute_llm_user_message(self) -> None:
+        runtime, _, llm, _, _ = make_runtime(["第一问"])
+        runtime.run(max_turns=1)
+
+        self.assertEqual(llm.calls[0][-1].content, "第一问")
+        self.assertNotIn("小杰小杰", llm.calls[0][-1].content)
+
 
 if __name__ == "__main__":
     unittest.main()