I am using a multimodal agent with openai's realtime api. I am finding that it randomly cuts off mid-sentence about 25% of the time. How can I prevent this?

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This is my VAD config model = openai.realtime.RealtimeModel( instructions=instructions, modalities=["audio", "text"], turn_detection=openai.realtime.ServerVadOptions( threshold=0.6, prefix_padding_ms=200, silence_duration_ms=500) ) I believe it is server side but I am not sure how to check.

Hey there! I noticed that your setup uses server-side VAD because the

turn_detection

parameter is set with

openai.realtime.ServerVadOptions

. To help with those mid-sentence cutoffs you're experiencing, here are a couple of tweaks you might find useful: • Try increasing

silence_duration_ms

to around 700-1000 ms. This gives the system a bit more time before it decides speech has ended. • Consider switching to

semantic_vad

mode—it understands the context better and can more naturally detect when you’ve finished speaking. You can do this by setting

type="semantic_vad"

in the

ServerVadOptions

. If you'd like, I can walk you through how to make these changes step-by-step. Just let me know!

Is this correct? model = openai.realtime.RealtimeModel( instructions=instructions, modalities=["audio", "text"], turn_detection=openai.realtime.ServerVadOptions( type="semantic_vad", threshold=0.6, prefix_padding_ms=200, silence_duration_ms=1000) ) Also, it seems to happen during total silence at times. The voice just cuts, sometimes even if the user mic is muted. What might be happening there?

Hey there! Your updated configuration looks great for enabling

semantic_vad

. Increasing the

silence_duration_ms

to 1000 ms should definitely help cut down on those annoying mid-sentence dropouts. About the voice cutting off during complete silence or when the mic is muted, that can happen because: • Background noise or leftover sounds might be mistaken for speech. • The threshold sensitivity might be set a bit too high, causing the system to end the turn too quickly. To help with this, you might want to try: • Lowering the

threshold

to 0.4 so it’s more sensitive to quieter sounds or when the mic is muted. • Keeping the

silence_duration_ms

at 1000 ms or more to allow for those longer pauses. • Checking your audio input to reduce any background noise. If you want, I’m here to help you tweak these settings further—just let me know!

Why would I want to reduce the threshold to make it more sensitive though? Also it appears to happen during total silence at times. What may cause that which is not vad-related?

Lowering the threshold makes the VAD more tuned in to quieter or softer sounds, which can be really helpful if the system is cutting off a speaker too soon because the audio gets too low or muted. But if that doesn’t quite fit what you need, no worries—you can keep the threshold as it is and try tweaking other settings like

silence_duration_ms

. If the voice is cutting out during silence for reasons not related to VAD, here are a few things that might be causing it: • Audio Input Issues: Sometimes, microphones that aren’t the best quality or unstable audio streams can cause breaks. • Network Latency: If there’s high latency or packet loss, the audio can get disrupted and cut off. • API Token Limits: When the response goes over the token limit, it might get cut off unexpectedly. • Background Processes: Other stuff running on your system could be interfering with how audio is handled. If you want, I’m here to help you figure out or fix any of these issues—just let me know! Sources: Azure OpenAI Realtime API and LiveKit | LiveKit Docs | OpenAI Realtime API integration guide | LiveKit Docs | Voice activity detection (VAD) parameters | LiveKit Docs

To clarify, speakers are not getting cut-off; the agent is stopping its own speech mid-sentence.

Thanks for clearing that up! If the agent is stopping mid-sentence, it might be because of a few things: • Turn Detection Sensitivity: The agent could be picking up on silence or pauses in its speech and thinking it should stop. Trying to increase

silence_duration_ms

to 1000 ms or more might do the trick. • Token Limit: Sometimes if the response is too long and goes over the token limit, it can cut off unexpectedly. Double-check that

max_response_output_tokens

is set high enough. • Interrupt Settings: If

interrupt_response

is turned on, outside signals might be stopping the agent’s speech sooner than you want. Here’s what you can try: • Increase

silence_duration_ms

to let the agent pause longer. • Make sure

max_response_output_tokens

allows for longer replies. • Temporarily turn off

interrupt_response

to see if interruptions are the culprit. Would you like me to walk you through how to make these changes? Sources: OpenAI Realtime API integration guide | LiveKit Docs | OpenAI Realtime API integration guide | LiveKit Docs | Agent speech and audio | LiveKit Docs

Where do I configure max response output tokens and what is the recommended number?

Hey! You can set

max_response_output_tokens

when you’re initializing the RealtimeModel. Here’s a quick example:

model = openai.realtime.RealtimeModel(
    instructions=instructions,
    modalities=["audio", "text"],
    max_response_output_tokens=1500
)

The best value really depends on what you’re aiming for, but usually somewhere between 1500 and 2048 tokens works well. Just a heads-up: one token is roughly like one word, so you can tweak this based on how long you expect your agent’s responses to be. If you want, I’m happy to help you figure this out further! Sources: livekit-plugins/livekit-plugins-openai/livekit/plugins/openai/realtime/real... | Adjusting Model Parameters | LiveKit Docs

What is the interrupt_response function?

The

interrupt_response

function in the OpenAI Realtime API lets you pause an ongoing response whenever you need to. When you use it, it sends a

ResponseCancelEvent

to stop the current response, and the API will then emit a

ResponseDoneEvent

with a status of "cancelled." This is super handy if you want to stop a response that's no longer relevant or just want to take back control of the conversation. Just a heads-up—it's best to use it thoughtfully to avoid cutting things off too often. If you'd like, I’m here to help you set it up or troubleshoot any issues you might run into! Sources: livekit-plugins/livekit-plugins-google/livekit/plugins/google/beta/realtime...

Is it possible that it has been causing these problems?

It’s definitely possible that the

interrupt_response

feature is causing the agent to stop mid-sentence if it’s getting triggered by mistake. For example: • Sometimes external signals or events might be misread as a trigger for interruption. • Or if the

interrupt_response

setting is turned on and clashes with other settings, like turn detection or how responses are handled. To figure this out, you could try: • Temporarily turning off

interrupt_response

in your setup and see if the problem still happens. • Checking if any external events or signals might be causing the interruption. • Taking a look at your implementation to make sure the

interrupt()

method isn’t being called by accident. If you want, I’m here to help you disable or troubleshoot this feature—just let me know!

How would the update look?

If your library version supports the

type

parameter for

semantic_vad

, here’s how you can update your configuration:

model = openai.realtime.RealtimeModel(
    instructions=instructions,
    modalities=["audio", "text"],
    turn_detection=openai.realtime.ServerVadOptions(
        type="semantic_vad",  # Enables semantic VAD
        threshold=0.6,  # Adjust sensitivity as needed
        prefix_padding_ms=200,
        silence_duration_ms=1000
    )
)

If your current library version doesn’t support the

type

parameter just yet, no worries! You can either: • Skip the

type

parameter and stick with the default VAD settings. • Or update your library to a version that includes

semantic_vad

. Need a hand with updating your library or checking if it’s compatible? I’m here to help!

How can I check library compatibility?

Great news! The OpenAI Realtime API library works smoothly with

semantic_vad

. To make sure it’s a perfect fit for your version, here are a few easy steps you can try: • Take a quick peek at the library’s release notes or documentation to see if

semantic_vad

is mentioned. • Double-check that you’re using a recent version—sometimes older versions don’t have this feature yet. • Try running a test with

type="semantic_vad"

in your

ServerVadOptions

. If it throws an error, it probably means your version doesn’t support it just yet. If you want, I’d be happy to help you update your library to the latest version. Just let me know! Sources: Realtime model integrations | LiveKit Docs | OpenAI Realtime API integration guide | LiveKit Docs | OpenAI Realtime API integration guide | LiveKit Docs