turn detection model on VPA with nodejs?

To implement turn detection in a VoicePipelineAgent (VPA) using Node.js, you can utilize the Voice Activity Detection (VAD) parameters provided by LiveKit. The VAD helps in detecting when a user has finished speaking, which is crucial for maintaining a natural conversation flow. You can adjust parameters like

threshold

,

prefix_padding_ms

, and

silence_duration_ms

to fine-tune the sensitivity and behavior of the VAD. For example, you can set up a VoicePipelineAgent with custom VAD settings as follows:

const { VoicePipelineAgent } = require('livekit-agents');
const vad = require('silero-vad'); // Example VAD library

const agent = new VoicePipelineAgent({
  vad: vad.load(),
  stt: new DeepgramSTT({ model: 'nova-2-general' }),
  llm: new OpenAILLM(),
  tts: new CartesiaTTS(),
  min_endpointing_delay: 0.5, // Adjust as needed
  allow_interruptions: true,
  interrupt_speech_duration: 0.5,
  interrupt_min_words: 0,
  before_llm_cb: null,
  before_tts_cb: null,
});

agent.start(room, participant);

Would you like more details on configuring these parameters or handling specific events? Sources: - VoicePipelineAgent - Turn Detection 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 can you give me detail explanation about this option?

Certainly! Let's break down the configuration options for turn detection in the VoicePipelineAgent using Node.js: 1. Voice Activity Detection (VAD): This is a crucial component for detecting when a user has stopped speaking. The VAD can be fine-tuned using several parameters: - Threshold: This parameter controls the sensitivity of the VAD. A lower threshold makes the VAD more sensitive to quieter sounds, while a higher threshold makes it less sensitive. This is useful for environments with background noise. - Prefix Padding (prefix_padding_ms): This parameter specifies the minimum duration of speech required to start a new speech chunk. It helps prevent very short sounds from triggering speech detection. - Silence Duration (silence_duration_ms): This parameter defines the minimum duration of silence required at the end of speech before considering the speech segment finished. It ensures that brief pauses do not prematurely end a speech segment. 2. Interruption Handling: These settings determine how the agent handles interruptions during speech: - Allow Interruptions: A boolean that specifies whether the user can interrupt the agent while it is speaking. - Interrupt Speech Duration: The minimum duration of speech required to consider it an interruption. - Interrupt Min Words: The minimum number of words required to consider an interruption valid. 3. Endpointing Delay (min_endpointing_delay): This parameter defines the minimum silence duration to detect the end of a turn. Increasing this value allows for longer pauses before the agent assumes the user has finished speaking. 4. Callbacks: - Before LLM Callback (before_llm_cb): A function that can be used to modify the chat context before it is sent to the LLM model. This is useful for adding extra context or adjusting the context based on the conversation. - Before TTS Callback (before_tts_cb): A function that allows you to modify the text before it is sent to the TTS model, useful for customizing pronunciation or adding extra context to the text. These options allow you to customize the behavior of the VoicePipelineAgent to suit your application's needs, ensuring smooth and natural conversation flow. Would you like more information on any specific parameter or how to implement these in your application? Sources: - Turn Detection - VoicePipelineAgent 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.