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-inworld.py

@@ -47,6 +47,7 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     AssistantTurnStoppedMessage,
     LLMContextAggregatorPair,
+    RealtimeServiceModeConfig,
     UserTurnStoppedMessage,
 )
 from pipecat.runner.types import RunnerArguments
@@ -149,7 +150,10 @@ Always be helpful and proactive in offering assistance.""",
         tools,
     )
 
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        realtime_service_mode=RealtimeServiceModeConfig(),
+    )
 
     # Build the pipeline
     pipeline = Pipeline(
@@ -182,13 +186,16 @@ Always be helpful and proactive in offering assistance.""",
         logger.info("Client disconnected")
         await task.cancel()
 
-    @user_aggregator.event_handler("on_user_turn_stopped")
-    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
+    # In realtime mode the turn-stopped events fire before the message
+    # text is finalized; subscribe to the *_message_added events for 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 ""
         logger.info(f"Transcript: {timestamp}user: {message.content}")
 
-    @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 ""
         logger.info(f"Transcript: {timestamp}assistant: {message.content}")