diff --git a/changelog/+realtime-grok-local-vad-example.added.md b/changelog/+realtime-grok-local-vad-example.added.md new file mode 100644 index 000000000..b0092d7b8 --- /dev/null +++ b/changelog/+realtime-grok-local-vad-example.added.md @@ -0,0 +1 @@ +- Added `examples/realtime/realtime-grok-local-vad.py`, a variant of the base Grok Realtime example that disables Grok's server-side turn detection (`turn_detection=None`, manual mode) and instead drives turn boundaries locally with `SileroVADAnalyzer` wired into the user aggregator. Mirrors the OpenAI Realtime local-VAD variant. Server-emitted turn frames are preferred when available. diff --git a/examples/realtime/realtime-grok-local-vad.py b/examples/realtime/realtime-grok-local-vad.py new file mode 100644 index 000000000..b5de8927e --- /dev/null +++ b/examples/realtime/realtime-grok-local-vad.py @@ -0,0 +1,262 @@ +# +# Copyright (c) 2024-2026, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +"""Grok Realtime with locally-driven turn detection. + +By default Grok Realtime drives the conversation with its own server-side +VAD (see `realtime-grok.py`). This variant disables server-side turn +detection (``turn_detection=None``, the "manual" mode in Grok's session +properties) and instead drives turn boundaries locally with +``SileroVADAnalyzer`` wired into the user aggregator. Use this variant if +you want a turn analyzer like ``LocalSmartTurnV3`` to decide when the user +is done speaking, or if you need ``UserStartedSpeakingFrame`` / +``UserStoppedSpeakingFrame`` to fire from the same source as +``InterruptionFrame``. + +Caveat: locally-generated turn boundaries are a heuristic and may not match +the provider's actual server-side turn decisions. Prefer server-emitted +turn frames (i.e. the base `realtime-grok.py` example) unless you have a +specific reason to drive turn detection locally. +""" + +import os +from datetime import datetime + +from dotenv import load_dotenv +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 +from pipecat.observers.loggers.transcription_log_observer import ( + TranscriptionLogObserver, +) +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +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 +from pipecat.runner.utils import create_transport +from pipecat.services.llm_service import FunctionCallParams +from pipecat.services.xai.realtime.events import SessionProperties +from pipecat.services.xai.realtime.llm import GrokRealtimeLLMService +from pipecat.transports.base_transport import BaseTransport, TransportParams +from pipecat.transports.daily.transport import DailyParams +from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams + +load_dotenv(override=True) + + +async def fetch_weather_from_api(params: FunctionCallParams): + """Handle weather function calls.""" + temperature = 75 if params.arguments.get("format") == "fahrenheit" else 24 + await params.result_callback( + { + "conditions": "nice", + "temperature": temperature, + "format": params.arguments.get("format", "celsius"), + "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"), + } + ) + + +async def get_current_time(params: FunctionCallParams): + """Handle time function calls.""" + await params.result_callback( + { + "time": datetime.now().strftime("%H:%M:%S"), + "date": datetime.now().strftime("%Y-%m-%d"), + "timezone": "local", + } + ) + + +async def get_restaurant_recommendation(params: FunctionCallParams): + """Handle restaurant recommendation function calls.""" + location = params.arguments.get("location", "unknown") + await params.result_callback( + { + "name": "The Golden Dragon", + "cuisine": "Chinese", + "location": location, + "rating": 4.5, + } + ) + + +weather_function = FunctionSchema( + name="get_current_weather", + description="Get the current weather for a location", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use.", + }, + }, + required=["location", "format"], +) + +time_function = FunctionSchema( + name="get_current_time", + description="Get the current time and date", + properties={}, + required=[], +) + +restaurant_function = FunctionSchema( + name="get_restaurant_recommendation", + description="Get a restaurant recommendation for a location", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + }, + required=["location"], +) + +tools = ToolsSchema(standard_tools=[weather_function, time_function, restaurant_function]) + + +transport_params = { + "daily": lambda: DailyParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), + "twilio": lambda: FastAPIWebsocketParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), + "webrtc": lambda: TransportParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), +} + + +async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): + logger.info("Starting Grok Voice Agent bot") + + session_properties = SessionProperties( + voice="Ara", + # Disable Grok's server-side turn detection (manual mode). This + # example drives turn boundaries locally via the SileroVADAnalyzer + # wired into the user aggregator below. + turn_detection=None, + ) + + llm = GrokRealtimeLLMService( + api_key=os.environ["XAI_API_KEY"], + settings=GrokRealtimeLLMService.Settings( + system_instruction="""You are a helpful and friendly AI assistant powered by Grok. + + You have access to several tools: + - Weather information + - Current time + - Restaurant recommendations + - Web search (built-in) + - X/Twitter search (built-in) + + Your voice and personality should be warm and engaging. Keep your responses + concise and conversational since this is a voice interaction. + + If the user asks about current events or news, use web search. + If they ask about what people are saying on social media, use X search. + + Always be helpful and proactive in offering assistance.""", + session_properties=session_properties, + ), + ) + + llm.register_function("get_current_weather", fetch_weather_from_api) + llm.register_function("get_current_time", get_current_time) + llm.register_function("get_restaurant_recommendation", get_restaurant_recommendation) + + context = LLMContext( + [{"role": "developer", "content": "Say hello and introduce yourself!"}], + tools, + ) + + user_aggregator, assistant_aggregator = LLMContextAggregatorPair( + context, + # Drive turn detection locally via SileroVAD wired into the user + # aggregator. realtime_service_mode keeps context-write semantics + # correct and (by default) drops the transcript wait on turn-end so + # local VAD can drive turn boundaries on the latency critical path. + realtime_service_mode=RealtimeServiceModeConfig(), + user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()), + ) + + pipeline = Pipeline( + [ + transport.input(), + user_aggregator, + llm, + transport.output(), + assistant_aggregator, + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + enable_metrics=True, + enable_usage_metrics=True, + ), + idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, + observers=[TranscriptionLogObserver()], + ) + + @transport.event_handler("on_client_connected") + async def on_client_connected(transport, client): + logger.info("Client connected") + await task.queue_frames([LLMRunFrame()]) + + @transport.event_handler("on_client_disconnected") + async def on_client_disconnected(transport, client): + logger.info("Client disconnected") + await task.cancel() + + @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_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}") + + runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) + + await runner.run(task) + + +async def bot(runner_args: RunnerArguments): + """Main bot entry point compatible with Pipecat Cloud.""" + transport = await create_transport(runner_args, transport_params) + await run_bot(transport, runner_args) + + +if __name__ == "__main__": + from pipecat.runner.run import main + + main() diff --git a/src/pipecat/services/xai/realtime/llm.py b/src/pipecat/services/xai/realtime/llm.py index 6f60e6c2e..87073ff9e 100644 --- a/src/pipecat/services/xai/realtime/llm.py +++ b/src/pipecat/services/xai/realtime/llm.py @@ -201,8 +201,10 @@ class GrokRealtimeLLMService(LLMService[GrokRealtimeLLMAdapter]): ``LLMContextAggregatorPair(..., realtime_service_mode=RealtimeServiceModeConfig())`` so context writes are decoupled from those frames. If you wire local VAD (``LLMUserAggregatorParams.vad_analyzer``) on top of this - service, disable Grok's server-side turn detection first; otherwise - both sources broadcast duplicate user-turn frames. + service, disable Grok's server-side turn detection first via + ``turn_detection=None`` (manual mode); otherwise both sources + broadcast duplicate user-turn frames. See + ``examples/realtime/realtime-grok-local-vad.py``. """ Settings = GrokRealtimeLLMSettings