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Best Silero VAD configuration for a voice agent
# ask-ai
m
Best Silero VAD configuration for a voice agent
t
Thanks for reaching out, Can. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Choosing the best Silero VAD configuration for your voice agent really depends on what you need, like how quickly you want it to respond, the noise around, and how it fits with your other systems. Here are some friendly suggestions to help you get started: • General Purpose: The default settings with 
min_silence_duration=0.55
seconds and 
min_speech_duration=0.05
seconds usually offer a nice balance. • Fast Response: If you want quicker turn detection, try lowering 
min_silence_duration
to between 0.4 and 0.5 seconds. • Noisy Environments: For those tricky noisy places, increasing 
min_silence_duration
to 0.75 seconds or more and setting 
activation_threshold=0.6
can really help manage the noise better. • Non-Streaming STT: When using non-streaming speech-to-text, pairing with a 
StreamAdapter
and setting 
min_silence_duration=0.5
seconds works well. To get the best out of Silero VAD, combining it with a contextual turn detection model like 
MultilingualModel
can make the system smarter and more aware of context. If you'd like, I’m here to provide more details or examples—just let me know!
m
How does the multilingual turn detection work? is it just semantic?
t
I'm happy to share more about LiveKit's multilingual turn detection with you! It’s not just about the words spoken; it uses a transformer-based model that thoughtfully looks at both what’s being said and the flow of the conversation to predict when someone has finished speaking. This helps prevent those awkward moments of cutting in too soon, especially with phrases like "I understand your point, but..." where context really matters. The model is quite versatile—it supports 13 languages and can even handle conversations where multiple languages are mixed. It also takes into account the full conversation history from the 
llm.ChatContext
, including several back-and-forth exchanges, so it makes predictions that feel natural and context-aware instead of just reacting to the last thing said. What’s really great is that it works efficiently in real-time, completing its analysis in under 25ms for a 100-token context while using less than 500MB of RAM. Plus, it can provide real-time End-of-Utterance (EOU) probability updates, which is perfect if you want to show visual feedback in your frontend apps. If you’d like, I’d be more than happy to dive deeper or share some examples with you!
m
So it does not help with not being interrupted by outside voices or understanding better. It is to prevent the agent from interrupting while the person has not finished their sentence?
t
You're absolutely right! The multilingual turn detection model is thoughtfully designed to help the agent avoid interrupting while you're still speaking. It works by carefully analyzing the conversation and the transcribed speech to understand if you've finished your thought, even when there are natural pauses. However, it’s important to know that this model doesn’t filter out interruptions from outside voices or enhance the overall speech understanding. For managing background noise or external voices, LiveKit provides separate noise cancellation features, which work independently from the turn detection. If you'd like, I’d be happy to share more details about these features or assist with any other questions you have!
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