what is the equivalent of this in the new livekit sdk version if participant_data.get("turn_detection"): turn_detection_json = json.loads(participant_data.get("turn_detection")) turn_detection = openai.realtime.ServerVadOptions( threshold=turn_detection_json.get("threshold", 0.5), prefix_padding_ms=turn_detection_json.get("prefix_padding_ms", 200), silence_duration_ms=turn_detection_json.get("silence_duration_ms", 300), ) else: turn_detection = openai.realtime.DEFAULT_SERVER_VAD_OPTIONS

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fix this from openai.types.beta.assistant import TurnDetection in forget how to import TurnDetection

To import

TurnDetection

in the LiveKit SDK, you can use one of the following options depending on the model you want to use: 1. For the English model:

from livekit.plugins.turn_detector.english import EnglishModel

2. For the multilingual model:

from livekit.plugins.turn_detector.multilingual import MultilingualModel

After importing, you can initialize the agent session with the turn detector like this:

session = AgentSession(
    ...
    turn_detection=EnglishModel(),
    # or for multilingual model:
    # turn_detection=MultilingualModel(),
)

Sources: Turn detection and interruptions | LiveKit Docs | Tree View: | dse-workbench

i mean TurnDetection class so i can create instance

To import the

TurnDetection

class and create an instance in the LiveKit SDK, you can use the following options depending on the model you need: For Python:

# For English model
from livekit.plugins.turn_detector.english import EnglishModel

# For multilingual model
from livekit.plugins.turn_detector.multilingual import MultilingualModel

You can then create an instance like this:

turn_detection = EnglishModel()
# or for multilingual:
turn_detection = MultilingualModel()

For JavaScript:

// Import EOUModel from the turnDetector module
import { turnDetector } from '@livekit/agents-plugin-silero';
const { EOUModel } = turnDetector;

// Create an instance
const turnDetection = new EOUModel({ unlikelyThreshold: 0.15 });

Let me know if you need further assistance! Sources: Turn detection and interruptions | LiveKit Docs | EOUModel | LiveKit Agents

i want to manually create turn detection config in python3 sdk

To manually create a turn detection configuration in the Python3 LiveKit SDK, you can set up the

AgentSession

with the

"manual"

turn detection option and control turns programmatically. Here's an example:

session = AgentSession(
    # other configurations...
    turn_detection="manual",
)

With manual turn detection, you can control when turns start and end using methods like: •

session.interrupt()

- Interrupts the agent's speech and switches to listening mode •

session.input.set_audio_enabled(True/False)

- Enables or disables audio input •

session.generate_reply()

- Generates a response after a user turn ends This is particularly useful for implementing push-to-talk functionality. Here's an example using RPC calls:

@ctx.room.local_participant.register_rpc_method("start_turn")
async def start_turn(data: rtc.RpcInvocationData):
    session.interrupt()
    
    # listen to the caller if multi-user
    room_io.set_participant(data.caller_identity)
    session.input.set_audio_enabled(True)

@ctx.room.local_participant.register_rpc_method("end_turn")
async def end_turn(data: rtc.RpcInvocationData):
    session.input.set_audio_enabled(False)
    session.generate_reply()

Let me know if you need further assistance! Sources: Turn detection and interruptions | LiveKit Docs | Turn detection and interruptions | LiveKit Docs

what is the possible values of turn_detection in llm=openai.realtime.RealtimeModel( api_key=OPENAI_API_KEY, temperature=self.get_generated_session_config().temperature, turn_detection=self.get_generated_session_config().turn_detection,

The possible values for

turn_detection

in

RealtimeModel

are: 1. Server VAD (default): Uses periods of silence to automatically chunk the audio. Example configuration:

turn_detection=TurnDetection(
  type="server_vad",
  threshold=0.5,
  prefix_padding_ms=300,
  silence_duration_ms=500,
  create_response=True,
  interrupt_response=True,
)

2. Semantic VAD: Uses a semantic classifier to detect when the user has finished speaking based on their words. Example configuration:

turn_detection=TurnDetection(
  type="semantic_vad",
  eagerness="medium",
  create_response=True,
  interrupt_response=True,
)

These options are specifically for the OpenAI Realtime API integration with LiveKit Agents. Let me know if you need further clarification! Sources: OpenAI Realtime API integration guide | LiveKit Docs | Turn detection and interruptions | LiveKit Docs