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-gemini-live.py

@@ -21,6 +21,7 @@ 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
@@ -130,8 +131,23 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
     context = LLMContext()
-    # Server-side VAD is enabled by default; no local VAD is added.
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
+    # Gemini Live drives the conversation server-side. It does NOT emit
+    # UserStartedSpeakingFrame / UserStoppedSpeakingFrame, so pipeline
+    # processors that depend on those frames — RTVI client speech events,
+    # TurnTrackingObserver, AudioBufferProcessor turn recording,
+    # UserIdleController, user mute strategies, voicemail detector — won't
+    # activate with the default server-VAD-only setup. Context aggregation
+    # still works with realtime_service_mode.
+    #
+    # To produce these frames locally, see `realtime-gemini-live-local-vad.py`.
+    # Caveat: locally-generated turn boundaries are a heuristic and may not
+    # match Gemini Live's server-side turn decisions, which is what drives the
+    # conversation; the two can drift apart in subtle ways especially around
+    # interruptions and overlapping speech.
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        realtime_service_mode=RealtimeServiceModeConfig(),
+    )
 
     pipeline = Pipeline(
         [
@@ -166,14 +182,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    @user_aggregator.event_handler("on_user_turn_stopped")
-    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
+    # Gemini Live doesn't emit user-turn frames so on_user_turn_stopped
+    # would never fire. The *_message_added events fire when messages are
+    # written to context and carry the finalized content; use those for
+    # transcript logging regardless of whether the service emits turn
+    # frames.
+    @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}")