move vad config to json

engine/pipeline.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
     LLMRunFrame,
     OutputTransportMessageUrgentFrame,
@@ -24,6 +25,10 @@ from pipecat.transports.websocket.fastapi import (
     FastAPIWebsocketParams,
     FastAPIWebsocketTransport,
 )
+from pipecat.turns.user_stop.speech_timeout_user_turn_stop_strategy import (
+    SpeechTimeoutUserTurnStopStrategy,
+)
+from pipecat.turns.user_turn_strategies import UserTurnStrategies
 
 from .config import EngineConfig
 from .product_protocol import ProductWebsocketSerializer
@@ -84,9 +89,33 @@ async def run_pipeline_with_serializer(
         messages.append({"role": "system", "content": config.agent.greeting})
 
     context = LLMContext(messages)
+
+    vad_params = VADParams(
+        confidence=config.turn.vad.confidence,
+        start_secs=config.turn.vad.start_secs,
+        stop_secs=config.turn.vad.stop_secs,
+        min_volume=config.turn.vad.min_volume,
+    )
+    # Replace pipecat's default stop strategy (Smart Turn v3) with a simple
+    # silence-timeout strategy. Smart Turn v3 was finalizing every short
+    # Chinese phrase as a complete turn, which caused one logical utterance
+    # to become several LLM calls and several user bubbles in the UI. The
+    # timeout strategy waits for `user_speech_timeout_sec` of silence
+    # (re-armed every time the user resumes speaking) before declaring the
+    # turn finished — which is what we actually want for streaming ASRs.
+    user_turn_strategies = UserTurnStrategies(
+        stop=[
+            SpeechTimeoutUserTurnStopStrategy(
+                user_speech_timeout=config.turn.user_speech_timeout_sec,
+            ),
+        ],
+    )
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+        user_params=LLMUserAggregatorParams(
+            vad_analyzer=SileroVADAnalyzer(params=vad_params),
+            user_turn_strategies=user_turn_strategies,
+        ),
     )
 
     pipeline = Pipeline(