what's the difference between vad and turn detector?

Thanks for reaching out, affandy. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

is there cloud-based models for both VAD and turn detector? because I have a concern with latency. and using both seems will hurt the latency

Just a heads-up: LiveKit doesn’t currently offer cloud-based models for VAD or the Turn Detection model. These are designed to run locally to keep things speedy and reduce any lag: • VAD: Uses Silero VAD, which runs right on your device within the

AgentSession

or

VoicePipelineAgent

. • Turn Detection Model: This is a custom language model that works locally on your CPU and needs speech-to-text (STT) input. There are two versions: one for English only (~10ms inference time) and a multilingual one (~25ms inference time). If latency is something you’re thinking about, you can tweak the

min_endpointing_delay

and

max_endpointing_delay

settings to find the sweet spot between speed and accuracy. For instance, lowering the maximum delay might make things feel more responsive. Also, if you’re working with a realtime LLM like OpenAI’s Realtime API, you can tap into their built-in VAD and turn detection features for cloud-based processing, which might make your life a bit easier. Sources: AI voice agents | LiveKit Docs | LiveKit turn detector plugin | LiveKit Docs | Turn detection and interruptions | LiveKit Docs | Turn detection and interruptions | LiveKit Docs | How does end-of-utterance detection work in conversations?

explain what are min and max endpointing delay

Hey there! Just wanted to explain a bit about the min_endpointing_delay and max_endpointing_delay settings—they basically control how long the system waits before wrapping up when a user finishes talking: • min_endpointing_delay: This is the shortest time (in seconds) the system waits after it thinks you’ve stopped speaking before it officially ends your turn. It kicks in when the system is pretty sure you’re done. The default is 0.5 seconds. • max_endpointing_delay: This is the longest time (in seconds) the system will wait if it suspects you might keep talking. It helps avoid cutting you off too soon. The default here is 6.0 seconds. You can tweak these in the

AgentSession

setup to find the right balance between being quick and sounding natural. Here’s a quick example:

session = AgentSession(
    min_endpointing_delay=0.5,
    max_endpointing_delay=5.0,
)

If you want me to walk you through it more or give some other examples, just let me know—I’m happy to help! Sources: livekit-plugins/livekit-plugins-speechmatics/README.md | Turn detection and interruptions | LiveKit Docs | LiveKit turn detector plugin | LiveKit Docs | LiveKit turn detector plugin | LiveKit Docs

ok then between vad and turn detector, if I'm about to use only one of it, which one do you suggest for me to use. remember I want the lowest latency

If you're aiming for the lowest latency possible, I'd suggest going with VAD (Voice Activity Detection). It works by processing speech and silence straight from the audio signals, so it’s naturally quicker than the Turn Detector model, which needs some extra language model processing (adding about ~10ms for English or ~25ms for multilingual setups). On the other hand, the Turn Detector model shines when you want conversations to feel more natural and smooth, especially if avoiding interruptions during pauses is a priority over having the absolute lowest latency. Sources: Agents Core Architecture | livekit/livekit_composite | DeepWiki | Silero VAD plugin | LiveKit Docs | Turn detection and interruptions | LiveKit Docs | livekit-plugins/livekit-plugins-turn-detector/README.md

what is recommended system requirement to run agent with VAD in production mode with low latency?

Hey there! If you're looking to run a LiveKit agent with VAD in production mode for low latency, here are some friendly recommendations for your system setup: • CPU and Memory: It’s a good idea to start with 4 cores and 8GB of memory per worker. To keep things smooth during busy times, plan on 4 cores for every 25 voice agents. • Instance Type: Go for compute-optimized instances like AWS c6i or c7i instead of burstable ones — this helps avoid those pesky CPU credit limits. • Storage: Around 10GB of ephemeral storage should do the trick, covering the Docker image (~1GB) and any temporary files. • Networking: For the best performance in Dockerized setups, using host networking is the way to go. Following these tips will help keep latency low and performance steady when things get busy. If you want to dive deeper or have any questions, just let me know — I’m here to help! Sources: Silero VAD plugin | LiveKit Docs | Silero VAD plugin | LiveKit Docs | Deploying LiveKit | LiveKit Docs | Deploying to production | LiveKit Docs | Deployment and scaling | LiveKit Docs

does loading VAD at prewarm stage can help reduce latency?

Hey there! Loading the VAD model during the prewarm stage is a great way to cut down on latency. By preloading it, you skip that initial wait time when a new session kicks off, so the VAD is all set and ready to go right away. Here’s a quick example in TypeScript:

import { defineAgent, JobProcess } from '@livekit/agents';
import * as silero from '@livekit/agents-plugin-silero';

export default defineAgent({
  prewarm: async (proc: JobProcess) => {
    proc.userData.vad = await silero.VAD.load();
  },
  entry: async (ctx) => {
    const vad = ctx.proc.userData.vad;
    // Use the preloaded VAD model here
  },
});

And if you’re working with Python, here’s how you can do it:

from livekit.agents import JobProcess
from livekit.plugins import silero

def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()

async def entrypoint(ctx):
    vad = ctx.proc.userdata["vad"]
    # Use the preloaded VAD model here

This little trick helps make voice activity detection quicker right from the get-go. If you have any questions or want to dive deeper, just let me know—I’m here to help! Sources: livekit-plugins/livekit-plugins-silero/livekit/plugins/silero/vad.py | plugins/silero/src/vad.ts | packages/basics/src/uninterruptable.ts | agent/main.py | VAD | LiveKit Agents

when I want to use VAD alone, should I set turn detection to vad as well? or can I just leave it blank?

Just a quick tip when using VAD on its own: it’s a good idea to explicitly set

turn_detection="vad"

in your configuration. This way, the system knows to rely on VAD data (like speech and silence) to figure out where turns start and end. If you don’t set it, LiveKit will pick the best option automatically based on what’s available, but setting it to VAD yourself helps keep things consistent and avoids any surprises with fallback modes. Feel free to reach out if you want me to walk you through it or share some examples! Sources: Turn detection and interruptions | LiveKit Docs | VoicePipelineAgent | LiveKit Docs | How does end-of-utterance detection work in conversations?

ok let's go back to tweaking the vad in agent session. explain to me each parameter in agent session that could help activate vad without sacrifice the latency

Hey there! To help you activate and fine-tune VAD in

AgentSession

without slowing things down, here are some key parameters you can play around with: • For Local VAD (Silero): ◦ `activation_threshold`: This is set to 0.5 by default. You can bump it up (like to 0.6) if you want fewer false alarms, or lower it if you want it to catch more. ◦ `min_silence_duration`: Default is 0.55s. Bringing it down (say to 0.3s) helps detect turns faster. ◦ `prefix_padding_duration`: Usually 0.5s. Dropping it to around 0.2–0.3s can help cut down latency. • Endpointing Parameters: ◦ `min_endpointing_delay`: Default is 0.4s. You can reduce it to 0.3–0.5s for quicker responses. ◦ `max_endpointing_delay`: Default is 6.0s. Feel free to tweak it based on how your users behave—for example, lowering it to 4.0s. • Prewarm: Don’t forget to preload the VAD model during the prewarm stage to skip any startup delays. Here’s a quick example to get you started:

from livekit.plugins import silero

session = AgentSession(
    vad=silero.VAD.load(
        activation_threshold=0.5,
        min_silence_duration=0.3,
        prefix_padding_duration=0.3,
    ),
    min_endpointing_delay=0.3,
    max_endpointing_delay=4.0,
)

If you want me to walk you through anything else or need more examples, just give me a shout! Sources: Azure OpenAI Realtime API and LiveKit | LiveKit Docs | Silero VAD plugin | LiveKit Docs | Azure OpenAI Realtime API and LiveKit | LiveKit Docs | livekit-plugins/livekit-plugins-silero/livekit/plugins/silero/vad.py | OpenAI Realtime API integration guide | LiveKit Docs

then, what is

min_consecutive_speech_delay

? also, what's the difference between VAD and endpointing?

min_consecutive_speech_delay: This parameter sets the minimum delay (default: 0.0 seconds) between back-to-back speech segments. It helps keep things sounding natural by controlling how quickly one speech response can follow another. Difference between VAD and Endpointing: • VAD (Voice Activity Detection): This listens in real-time to spot when someone is speaking or silent by analyzing the audio. It figures out when speech starts and stops based on voice activity or silence. • Endpointing: Think of this as VAD’s helpful sidekick—it adds timing rules (like

min_endpointing_delay

and

max_endpointing_delay

) to decide when a speaker has really finished their turn. This way, it avoids cutting someone off during short pauses. So, to sum it up: VAD picks up on speech activity, and endpointing makes sure the conversation flows smoothly by knowing when a turn is truly done. Sources: livekit-agents/livekit/agents/voice/agent.py | Improving voice AI's turn detection with transformers | AI voice agents | LiveKit Docs | How does end-of-utterance detection work in conversations?

then, what's the difference between min and max endpointing delay?

Let’s chat about the difference between min_endpointing_delay and *max_endpointing_delay*—they both help manage when the system decides you’ve finished speaking, but in slightly different ways: • min_endpointing_delay: Think of this as the shortest pause (default: 0.5 seconds) the system waits after it notices silence before it decides you’re done talking. It’s there to make sure the system doesn’t jump in too quickly during those natural little pauses. • max_endpointing_delay: This one’s like the longest wait (default: 6.0 seconds) the system allows before it wraps up your turn, even if it thinks you might say more. It stops things from dragging on if you take a longer pause. So, to put it simply,

min_endpointing_delay

helps keep things snappy by setting the shortest wait time, while

max_endpointing_delay

makes sure the system doesn’t hang around forever waiting for you to keep talking.

ok, now what about noise cancellation? is there any alternative for self host?

Hey there! For self-hosted LiveKit deployments, the enhanced noise cancellation using Krisp models isn’t available. But don’t worry—you can still count on: • WebRTC's native noise suppression: This is turned on by default and offers basic noise and echo cancellation. You can tweak it using

AudioCaptureOptions

in the client SDKs. • Custom third-party noise cancellation libraries: Although not built-in, you have the option to add external solutions if you need stronger noise suppression. If you want, I’m here to help you set up WebRTC noise suppression or explore those custom options—just let me know! Sources: Voice AI quickstart | LiveKit Docs | Voice AI quickstart | LiveKit Docs | SIP overview | LiveKit Docs | Knowledge Management System | livekit/livekit_composite | DeepWiki | Enhanced noise cancellation | LiveKit Docs

yeah, show me how to setup the WebRTC's based solution. additionally, it would be great if you can help me looking for 3rd party libraries for noise cancellation

To get WebRTC-based noise suppression up and running in LiveKit, you can tweak the

AudioCaptureOptions

in the client SDKs. Here are some examples for different platforms to help you out: • JavaScript/TypeScript:

const audioOptions = {
  noiseSuppression: true,
  echoCancellation: true,
  autoGainControl: true
};
await room.localParticipant.setMicrophoneEnabled(true, audioOptions);

• Python:

apm = AudioProcessingModule(noise_suppression=True)

• Swift:

let audioOptions = AudioCaptureOptions(
  noiseSuppression: true,
  echoCancellation: true
)

• Android:

val audioOptions = LocalAudioTrackOptions(
  noiseSuppression = true,
  echoCancellation = true
)

These settings turn on WebRTC's built-in noise suppression and echo cancellation to help improve your audio quality. I'll also check out some third-party noise cancellation libraries and get back to you soon with what I find. Sources: src/room/track/options.ts | lib/src/track/options.dart | Runtime/Scripts/Proto/AudioFrame.cs | Noise & echo cancellation | LiveKit Docs | Noise & echo cancellation | LiveKit Docs

hey, I'm about to setup the vad on my agent. and you were said by loading it at prewarm stage can cut latency. does it happen in the same way if I load stt and tts at prewarm stage?

Hey! Loading your STT and TTS models during the prewarm stage is a great way to cut down on latency, just like we do with VAD. Prewarming gets these models all set up and ready to go before any job kicks off, so you won’t face any delays during that first interaction. For example, you can preload your STT and TTS models right in the

prewarm

function like this:

def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()
    proc.userdata["stt"] = deepgram.STT()  # or openai.STT(), groq.STT()
    proc.userdata["tts"] = openai.TTS(model="tts-1", voice="alloy")

Then, you can easily reuse them in your agent like so:

async def entrypoint(ctx: JobContext):
    agent = VoicePipelineAgent(
        vad=ctx.proc.userdata["vad"],
        stt=ctx.proc.userdata["stt"],
        tts=ctx.proc.userdata["tts"],
        llm=openai.LLM(model="gpt-4o-mini"),
    )
    agent.start(ctx.room)

If you want, I’d be happy to help you set up prewarm for a specific STT or TTS provider—just let me know! Sources: agent/main.py | agent/main.py | examples/other/kokoro_tts.py | chain-of-thought-tts/agent.py | Live Conversations with AI using ChatGPT and WebRTC

how I can load turn detector model at prewarm stage?

Hey there! Just a quick heads-up about the turn detector model in LiveKit—it’s totally handled by the framework for you, so you don’t need to worry about loading it manually during the

prewarm

stage. It gets loaded automatically the first time

AgentSession

kicks in, as long as the model weights are already downloaded. To keep things running smoothly and reduce any lag: • Make sure to pre-download the model weights by running:

python agent.py download-files

• And you can preload the VAD model during

prewarm

to speed things up even more:

def prewarm(proc: JobProcess):\n    proc.userdata[\"vad\"] = silero.VAD.load()

The great news is the turn detector model initializes and optimizes itself automatically while you’re running, so no extra preloading steps needed on your end. If you have any questions or want to chat more about this, just let me know—I’m here to help! Sources: Silero VAD plugin | LiveKit Docs | Improving voice AI's turn detection with transformers | plugins/livekit/src/turn_detector.ts | Gemini Live API integration guide | LiveKit Docs | LiveKit turn detector plugin | LiveKit Docs

why loading stt, tts, and llm module at prewarm will cut the latency?

Hey there! Loading the STT, TTS, and LLM modules during the prewarm stage is a great way to cut down on wait times because it skips the usual setup that happens during the very first interaction. Here’s the scoop: • Model Initialization: These modules need to load model weights, connect to APIs, or set up their runtime environments. Preloading takes care of all that before the first request, so you don’t have to wait later on. • First-Use Overhead: If you don’t prewarm, the first time you use these modules, they have to load everything right then and there, which can slow things down—especially with big models or calls to external APIs. • Resource Allocation: Prewarming also makes sure all the necessary resources like memory and compute power are ready to go upfront, so the system can jump into action immediately. So, by preloading these modules, everything’s set and ready to roll as soon as a request comes in, making the whole experience smoother and quicker. If you want me to dive deeper or share some examples, just let me know—I’m here to help!

could you please explain again how VAD and endpointing works especially with the interruption thing?

Let me walk you through how VAD and endpointing work, especially when it comes to handling interruptions: • VAD (Voice Activity Detection): VAD listens for speech by analyzing audio signals, figuring out when someone starts and stops talking based on silence or voice activity. But here’s the thing—VAD doesn’t understand context, so it might jump in during natural pauses. For example, if you pause mid-sentence, VAD could mistakenly think you’re done speaking. • Endpointing: Endpointing takes VAD a step further by adding some smart timing rules and, if you want, a bit of meaning analysis to really nail down when someone’s finished talking. It uses settings like

min_endpointing_delay

(how long to wait after silence) and

max_endpointing_delay

(the max time before deciding the turn is over). This way, the system won’t cut you off during brief pauses but also won’t keep waiting forever. So, to sum it up: VAD spots when speech happens, and endpointing helps keep the conversation smooth by figuring out the right moment to end a turn, avoiding those awkward interruptions. If anything’s unclear or you want to dive deeper, just let me know—I’m here to help!

and which are parameters I should tune to avoid false interrutpion and handle the correct one properly?

Hey there! To help you avoid false interruptions and handle the real ones smoothly, you can tweak these parameters in LiveKit: • allow_interruptions: Set this to

False

if you want to block interruptions during those important moments when the agent is speaking. • min_interruption_duration: This is the minimum speech length (default: 0.5s) needed to count as an interruption. You can raise this to filter out brief noise bursts. • min_interruption_words: The minimum number of words transcribed (default: 0) to consider it an interruption. Setting it to 2–3 helps avoid interruptions from just a single word. • agent_false_interruption_timeout: This timeout (default: 4.0s) helps detect false interruptions. It’s useful to resume the agent if no meaningful user input comes after an interruption. • min_endpointing_delay: The minimum wait time (default: 0.4s) after silence before ending a turn. You can increase this to prevent ending turns too soon. • max_endpointing_delay: The maximum wait time (default: 6.0s) before ending a turn. Feel free to adjust this based on how your users behave. Here’s a quick example to help reduce false interruptions:

session = AgentSession(\n    allow_interruptions=True,\n    min_interruption_duration=0.8,\n    min_interruption_words=3,\n    agent_false_interruption_timeout=3.0\n)

If you want me to walk you through any of these or need some tailored examples, just let me know — I’m here to help! Sources: Agent speech and audio | LiveKit Docs | AI voice agents | LiveKit Docs | VoicePipelineAgent | LiveKit Docs | Voice activity detection (VAD) parameters | LiveKit Docs | Turn detection and interruptions | LiveKit Docs

what about the parameters inside VAD itself?

min_speech_duration: float = 0.05,
        min_silence_duration: float = 0.4,
        prefix_padding_duration: float = 0.5,
        max_buffered_speech: float = 60.0,
        activation_threshold: float = 0.5,
        sample_rate: Literal[8000, 16000] = 16000,
        force_cpu: bool = True,

Let me walk you through the VAD parameters and what they do: • min_speech_duration (default: 0.05s): This sets the shortest length of detected speech to count as actual speech. If you bump this up a bit, it can help avoid false alarms from quick noises. • min_silence_duration (default: 0.4s): This is how long silence needs to be before the system decides speech has ended. Making this longer can reduce interruptions but might slow down responses a little. • prefix_padding_duration (default: 0.5s): Adds a little extra time at the start of speech chunks so you don’t miss any words. • max_buffered_speech (default: 60.0s): This caps how much speech is stored in the buffer to keep memory use in check. • activation_threshold (default: 0.5): This sets the sensitivity for detecting speech. Lowering it makes the system more sensitive but might pick up some background noise as speech. • sample_rate (default: 16000): You can use either 8kHz or 16kHz, but 16kHz usually gives you better accuracy. • force_cpu (default: True): This makes sure the processing runs on the CPU, which works just fine for VAD. You can tweak these settings depending on your setup and what you need. If you want, I’m here to help you fine-tune them! Sources: Voice activity detection (VAD) parameters | LiveKit Docs | Azure OpenAI Realtime API and LiveKit | LiveKit Docs | Silero VAD plugin | LiveKit Docs | Improving voice AI's turn detection with transformers | VoicePipelineAgent | LiveKit Docs

hey, currently I have the following setup for VAD

MAX_ENDPOINTING_DELAY: "2"
  MIN_ENDPOINTING_DELAY: "0.5"
  MIN_INTERRUPTION_DURATION: "0.5"
  MIN_CONSECUTIVE_SPEECH_DELAY: "0"
  ACTIVATION_THRESHOLD: "0.5"
  MIN_SILENCE_DURATION: "0.3"
  MIN_SPEECH_DURATION: "0.05"
  ENABLE_VAD: "true"

however, upon our testing the VAD seems dont take much effect. for example, when we are trying to interrupt, it was stopped, but it didn't wait up to the max_enpointing_delay before it start to speak again. can you help figure out what causing it? and how I should setup the configuration?

Hey there! The issue you're running into happens because

max_endpointing_delay

is mainly meant to be used with a turn detection model, not just basic VAD alone. In your current setup, when you're using VAD-only mode, it applies

min_endpointing_delay

(which defaults to 0.5s) after it detects silence, but

max_endpointing_delay

doesn’t have any effect without a turn detection model. To fix this and make sure

max_endpointing_delay

works as expected, you'll want to set up a turn detection model. Here’s an example:

from livekit.plugins.turn_detector.multilingual import MultilingualModel\n\nsession = AgentSession(\n    turn_detection=MultilingualModel(),\n    vad=ctx.proc.userdata[\"vad\"],\n    min_endpointing_delay=0.5,\n    max_endpointing_delay=2.0,\n)

This way, the model helps decide whether to use

min_endpointing_delay

or

max_endpointing_delay

based on how likely it is that the user has finished speaking. If you’d rather stick with just VAD, you can try increasing

min_endpointing_delay

to make the agent less likely to jump in too quickly, but just a heads-up—`max_endpointing_delay` won’t come into play in that case. Feel free to reach out if you want a hand setting up the turn detection model or tweaking your VAD settings—I’m here to help! Sources: Speech & Audio Processing | livekit/livekit_composite | DeepWiki | How does end-of-utterance detection work in conversations? | Improving voice AI's turn detection with transformers | LiveKit turn detector plugin | LiveKit Docs | livekit-agents/livekit/agents/voice/agent_session.py

I mean obviously I put both values into parameters lol. can you help me how to balance between both values?

To find a good balance between

min_endpointing_delay

and

max_endpointing_delay

, here are some friendly guidelines to keep in mind: • min_endpointing_delay: This sets the shortest wait time after silence before the system decides the user’s turn is over. If you want things to feel snappy, try a lower value like 0.5s. But if your users tend to pause a bit while speaking, bumping it up to somewhere between 0.7 and 1.0 seconds can help avoid cutting them off. • max_endpointing_delay: This is the longest the system waits before ending a turn when it’s unsure if the user is finished. A higher value, like 2.0 to 4.0 seconds, gives users room to think or gather their thoughts without feeling rushed. Some tips to get you started: • If you need your system to be quick and responsive, try

min_endpointing_delay = 0.5s

and

max_endpointing_delay = 2.0s

. • For a more relaxed, natural flow—especially in deeper conversations—go for

min_endpointing_delay = 0.7–1.0s

and

max_endpointing_delay = 3.0–4.0s

. • The best way is always to test with real users to discover what feels just right, since everyone’s speaking style and pauses can be quite different. If you want, I’m here to help you fine-tune these settings to fit your specific needs—just let me know!