must you have a vad configured when you are using openAIs realtime model?

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

okey but if i am using only open AI realtime, multilingual model, and I want to make sure to use the best VAD,we are also using Norwegian as the primary language, which may be defecautl for the context

Thanks — quick clarification: are you currently running only OpenAI Realtime + LiveKit (no external STT), or do you have a separate STT provider available (Deepgram, AssemblyAI, etc.)? Short guidance while I wait: • If you only run OpenAI Realtime: use OpenAI’s built-in server-side VAD (

server_vad

) and tune parameters like

silence_duration_ms

and

threshold

for Norwegian. This is the best option for Norwegian. • If you can add an STT that streams interim transcripts: you could add a VAD (e.g., Silero) + a turn detector, but LiveKit’s Multilingual turn detector does NOT support Norwegian for semantic turn detection — it will fall back to VAD-style endpointing, so you’ll get little benefit over

server_vad

. Tell me which setup you have (only Realtime, or Realtime + STT), and I’ll suggest exact

server_vad

parameter values to try for Norwegian. Sources: Gemini Live API plugin | LiveKit docs | LiveKit turn detector plugin | LiveKit docs | OpenAI Realtime API plugin guide | LiveKit docs | Azure OpenAI Realtime API and LiveKit | LiveKit docs | Turn detection and interruptions | LiveKit Docs

yes we only use openAI realtime + Livekit. Is using the built in server vad better than the sementic vad

Since you’re only using OpenAI Realtime + LiveKit and your primary language is Norwegian: • Use OpenAI’s built-in server-side VAD (

server_vad

). It’s the better choice here because it’s fully supported with Realtime, provides low-latency turn/endpointing, and doesn’t require a separate STT stream. • LiveKit’s semantic (Multilingual) turn detector relies on interim transcripts and does not support Norwegian for semantic detection. That means it will effectively fall back to VAD-style endpointing and won’t give meaningful semantic improvements over

server_vad

. Recommendation: stick with

server_vad

and tune parameters like

silence_duration_ms

,

threshold

, and

prefix_padding_ms

to match Norwegian speech patterns (e.g., slightly longer silence thresholds if speakers pause more). If you’d like, tell me your target latency and typical speaker behavior and I’ll suggest concrete parameter values.

how would I change this section appropriately then? turn_detection=TurnDetection( type="semantic_vad", eagerness="auto", create_response=True, interrupt_response=True, ),

Replace the

semantic_vad

block with

server_vad

and add tuning parameters. Example:

turn_detection=TurnDetection(
    type="server_vad",
    eagerness="auto",
    create_response=True,
    interrupt_response=True,
    # tuning options for Norwegian — adjust values to match your latency/pauses
    threshold=0.5,
    silence_duration_ms=600,
    prefix_padding_ms=300,
)

Suggested starting values: • `silence_duration_ms`: 500–800 (increase if speakers pause longer) • `threshold`: 0.4–0.6 (lower = more sensitive) • `prefix_padding_ms`: 200–400 (helps include leading audio) Tell me your target latency and typical speaker pause length and I’ll recommend exact numbers.

turn_detection=TurnDetection( type="server_vad", eagerness="auto", create_response=True, interrupt_response=True, # tuning options for Norwegian — adjust values to match your latency/pauses threshold=0.5, silence_duration_ms=600, prefix_padding_ms=300, ) input_audio_transcription=InputAudioTranscription(model="whisper-1", language="no"), ), (voice-ai-agent) mariusrodderbjornoy@X-JFX09Q5GKY voice-ai-agent % uv run voice_agent/agent.py start File "/Users/mariusrodderbjornoy/Documents/GMG/voice-ai-agent/voice_agent/agent.py", line 167 turn_detection=TurnDetection( ^^^^^^^^^^^^^^ SyntaxError: invalid syntax. Perhaps you forgot a comma?

Thanks — the SyntaxError is caused by misplaced commas/parentheses and an extra trailing comma/parenthesis placement. Your snippet looks like part of a larger call; ensure each argument is separated by commas and parentheses close in the right order. Here’s a corrected example assuming this is inside the session/agent constructor:

turn_detection=TurnDetection(
    type="server_vad",
    eagerness="auto",
    create_response=True,
    interrupt_response=True,
    threshold=0.5,
    silence_duration_ms=600,
    prefix_padding_ms=300,
),
input_audio_transcription=InputAudioTranscription(model="whisper-1", language="no"),

Or if

turn_detection

and

input_audio_transcription

are the last arguments in a function call, remove the extra comma after the final argument:

turn_detection=TurnDetection(
    type="server_vad",
    eagerness="auto",
    create_response=True,
    interrupt_response=True,
    threshold=0.5,
    silence_duration_ms=600,
    prefix_padding_ms=300
),
input_audio_transcription=InputAudioTranscription(model="whisper-1", language="no")

If you’re only using OpenAI Realtime (no external STT), you can omit

input_audio_transcription

entirely. Paste the surrounding lines (a few lines before and after the block) if you want me to spot the exact mismatch.

what does threshold do here?

You're asking what latency parameters do in the

server_vad

turn detection. Short answer: they control how quickly the model decides a speaker stopped and when to emit a turn — trading responsiveness for completeness. Key parameters and their effect: •

silence_duration_ms

— how long silence must be observed before the VAD declares the end of a turn. Lower = faster turn end (lower latency) but risk of cutting off speakers who pause; higher = slower but safer (captures longer pauses). •

prefix_padding_ms

— how much audio to include before the detected turn start when emitting the turn. Larger padding preserves leading words that might be clipped, but increases perceived latency slightly because the system buffers that padding when forming the turn. •

threshold

— sensitivity for detecting voice vs silence. Lower threshold = more sensitive (fewer missed starts, may trigger on noise); higher = less sensitive (less noise, may miss quiet speech). Indirectly affects latency because missed starts or re-detections can delay stable turn boundaries. •

eagerness

(if used) — controls how aggressively the system tries to start creating a response before turn end; more eager reduces response latency but can increase interruptions or partial responses. Practical trade-offs: • For low-latency interactive apps, use smaller

silence_duration_ms

(e.g., 200–400 ms), moderate

prefix_padding_ms

(100–300 ms), and a slightly lower

threshold

to avoid clipping quiet speech. • For accuracy with speakers who pause a lot (or noisy environments), increase

silence_duration_ms

(500–800+ ms) and

prefix_padding_ms

(200–400 ms). • Always test with real Norwegian audio and adjust: Norwegian prosody may require slightly longer silence thresholds than English. If you tell me your target response latency (e.g., <500 ms, ~1s) and typical speaker pause lengths, I’ll suggest specific values to try.

what does threshold do here? (edited) LiveKit Assistant

Good question — here's a concise explanation. The

threshold

sets the voice/silence sensitivity for

server_vad

. Lower values make VAD more sensitive (it treats quieter sounds as speech), and higher values make it stricter (ignores low-volume sounds/noise). Effects: • Lower

threshold

→ fewer missed speech starts, but more false positives from noise and potential spurious turns. • Higher

threshold

→ fewer false triggers from noise, but risk of missing quiet/soft speech or delaying detection. • Indirectly impacts latency: if threshold is too high, the system may take longer to detect speech start and end; if too low, you may get earlier but noisier detections. Suggested tuning starting point for Norwegian speech: try 0.4–0.6 and adjust up if you see noise-triggered turns, or down if quiet speech is missed.

is eagerness, used when not semantic

Yes —

eagerness

still applies when using

server_vad

(non-semantic). It controls how early the system begins generating a response relative to turn end or ongoing speech. What it does: • Higher eagerness → the model will start forming a response sooner (even before the speaker finishes). This lowers perceived response latency but increases chances of interruptions or partial responses. • Lower eagerness → waits longer or until clearer end-of-turn signals. This reduces interruptions and gives more complete context, but increases latency. Practical guidance: • For interactive, low-latency apps: try

eagerness="high"

or

"auto"

with smaller

silence_duration_ms

(e.g., 200–400 ms). • For accuracy and fewer interruptions (helpful with Norwegian pauses): use

eagerness="low"

or

"auto"

with larger

silence_duration_ms

(500–800 ms). • Keep `create_response=True`/`interrupt_response=True` if you want responses to be created and interruptible; tune eagerness together with

threshold

and

silence_duration_ms

. If you tell me your target latency and whether you prefer fewer interruptions or faster replies, I’ll recommend specific

eagerness

plus exact parameter values.

does this look good?

You're almost there — a couple quick notes on the values and a small recommendation: •

threshold=0.1

is very low (very sensitive). That will pick up quiet sounds/noise and may create false turns. For Norwegian start with 0.4–0.6 and lower only if you miss quiet speakers. •

silence_duration_ms=800

is conservative (waits longer before ending turns). Good if speakers pause a lot, but increases latency. If you want faster replies, try 400–600 ms. •

prefix_padding_ms=300

is reasonable to avoid clipping starts. If you prefer fewer interruptions and more complete turns: keep

threshold

~0.5 and

silence_duration_ms

600–800. If you prefer lower latency: use

threshold

0.4 and

silence_duration_ms

200–400 with

eagerness="high"

. If you'd like, tell me your target response latency (e.g., <500 ms or ~1s) and typical pause behavior and I’ll propose one exact config to try.