Migrate realtime examples to RealtimeServiceModeConfig

Pass realtime_service_mode=RealtimeServiceModeConfig() through every
realtime LLM service example (base, async-tool, video, text-output,
persistent-context, update-settings, MCP) so context aggregation uses
the new realtime-mode semantics instead of relying on local VAD as a
workaround.

Where examples previously wired SileroVADAnalyzer into
LLMUserAggregatorParams to coax turn frames out of services that don't
emit them server-side (AWS Nova Sonic, Ultravox, Gemini Live), the local
VAD is now removed. realtime_service_mode keeps context writes correct
without it, and the Phase 1.5 server-side InterruptionFrame fixes for
Nova Sonic and Ultravox keep the bot from talking past the user when
they barge in.

Transcript-logging event handlers move from on_user_turn_stopped /
on_assistant_turn_stopped to on_user_message_added /
on_assistant_message_added, which carry the finalized text in realtime
mode (the turn-stopped events fire before the message is finalized, so
their `content` is None in that mode).

For services that don't emit user-turn frames (Gemini Live, AWS Nova
Sonic, Ultravox) the example now carries a Tier 1 comment block that
spells out which downstream processors won't activate, how to add local
VAD if needed, and the caveat that locally-generated turn boundaries
are a heuristic that may diverge from server-side ground truth.

Adds examples/realtime/realtime-openai-local-vad.py, a new variant of
the OpenAI Realtime example that disables OpenAI's server-side turn
detection and drives turn boundaries locally — useful when you want a
turn analyzer like LocalSmartTurnV3 to decide when the user is done
speaking. Server-emitted turn frames are still preferred when available.

The Gemini Live local-VAD variant already existed; it's been updated in
place rather than rewritten.

examples/realtime/realtime-openai.py

@@ -14,7 +14,6 @@ from loguru import logger
 
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, LLMSetToolsFrame
 from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
 from pipecat.pipeline.pipeline import Pipeline
@@ -24,7 +23,7 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     AssistantTurnStoppedMessage,
     LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
+    RealtimeServiceModeConfig,
     UserTurnStoppedMessage,
 )
 from pipecat.runner.types import RunnerArguments
@@ -187,7 +186,13 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
 
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+        # OpenAI Realtime drives the conversation server-side and emits its
+        # own UserStarted/StoppedSpeakingFrame from server VAD events, so
+        # local VAD on the aggregator is unnecessary. realtime_service_mode
+        # decouples context writes from turn frames and transcript-bound
+        # turn-end. See `realtime-openai-local-vad.py` for the variant
+        # that disables server VAD and drives turn detection locally.
+        realtime_service_mode=RealtimeServiceModeConfig(),
     )
 
     pipeline = Pipeline(
@@ -251,15 +256,19 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # Log transcript updates
-    @user_aggregator.event_handler("on_user_turn_stopped")
-    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
+    # Log transcript updates. In realtime mode the turn-stopped events
+    # fire before the message text is finalized (UserTurnStoppedMessage
+    # content is None), so subscribe to the *_message_added events
+    # instead — they fire when the message is written to context and
+    # carry the finalized content.
+    @user_aggregator.event_handler("on_user_message_added")
+    async def on_user_message_added(aggregator, message: UserTurnStoppedMessage):
         timestamp = f"[{message.timestamp}] " if message.timestamp else ""
         line = f"{timestamp}user: {message.content}"
         logger.info(f"Transcript: {line}")
 
-    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
-    async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
+    @assistant_aggregator.event_handler("on_assistant_message_added")
+    async def on_assistant_message_added(aggregator, message: AssistantTurnStoppedMessage):
         timestamp = f"[{message.timestamp}] " if message.timestamp else ""
         line = f"{timestamp}assistant: {message.content}"
         logger.info(f"Transcript: {line}")