Add realtime-grok-local-vad.py example
Grok Realtime supports manual mode (turn_detection=None) which disables its server-side VAD and lets local VAD drive turn boundaries — same pattern as OpenAI Realtime's turn_detection=False. Add the matching *-local-vad.py variant for parity, and point the Grok service docstring at it.
This commit is contained in:
Paul Kompfner
1
changelog/+realtime-grok-local-vad-example.added.md
Normal file
1
changelog/+realtime-grok-local-vad-example.added.md
Normal file
@@ -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.
|
||||
262
examples/realtime/realtime-grok-local-vad.py
Normal file
262
examples/realtime/realtime-grok-local-vad.py
Normal file
@@ -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()
|
||||
@@ -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
|
||||
|
||||
Reference in New Issue
Block a user