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silero.VAD.load does not work with parameters
# ask-ai
n
silero.VAD.load does not work with parameters
t
Thanks for reaching out, Salman. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
The 
silero.VAD.load()
function should work with parameters in both Python and JavaScript implementations. Here are some key details to ensure correct usage: Python Implementation:
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vad = silero.VAD.load(
    min_speech_duration=0.05,
    min_silence_duration=0.55,
    prefix_padding_duration=0.5,
    max_buffered_speech=60.0,
    activation_threshold=0.5,
    sample_rate=16000,  # or 8000
    force_cpu=True
)
Note: Parameters must be passed as keyword arguments (not positional). JavaScript Implementation:
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const vad = await silero.VAD.load({
  minSpeechDuration: 50,        // in milliseconds
  minSilenceDuration: 550,      // in milliseconds  
  prefixPaddingDuration: 500,   // in milliseconds
  maxBufferedSpeech: 60000,     // in milliseconds
  activationThreshold: 0.5,
  sampleRate: 16000,            // or 8000
  forceCPU: true
});
Note: JavaScript uses camelCase for parameter names, and values are in milliseconds (unlike Python, which uses seconds). Common Issues: • Ensure parameter names match the required format (snake_case for Python, camelCase for JavaScript). • Use only supported sample rates (8000 or 16000 Hz). • In Python, parameters must be passed as keyword arguments. If you are still encountering issues, please provide the specific error message and confirm whether you are using Python or JavaScript. This will help in providing more targeted assistance. Sources: tests/test_vad.py | examples/src/outbound.ts | pipeline-llm/cerebras_llm.py
c
what is the optimal configuration of VAD parameters?
t
Hey there! Let's talk about tuning Silero VAD parameters to make your audio detection work like a charm. Think of it like finding the perfect volume for a great conversation. Core Parameters: • activation_threshold (default: 0.5): This is basically your speech detection "ears". Crank it up, and it gets more picky about what counts as speech. Lower it, and it'll catch even the quietest whispers. • min_speech_duration (default: 0.05s/50ms): Imagine this as a bouncer that only lets speech "conversations" longer than a quick mumble into the club. • min_silence_duration (default: 0.55s/550ms): This is like the pause between turns in a friendly chat - how long you wait before thinking someone's done talking. • prefix_padding_duration (default: 0.5s/500ms): Think of this as giving your speech a little runway to warm up before takeoff. • max_buffered_speech (default: 60.0s): Your memory bank for keeping track of what's been said. Configuration Examples: For testing scenarios:
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VAD = silero.VAD.load(
    min_speech_duration=0.5,
    min_silence_duration=0.75,
)
For real-time applications:
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vad = silero.VAD.load(
    min_speech_duration=0.2,
    min_silence_duration=0.6,
)
Optimization Considerations: • For conversational AI, keep those silences short (0.6-0.75s) so it feels like a natural back-and-forth. • A bit longer speech duration (0.2-0.5s) helps filter out those pesky background noises. • The default sensitivity usually hits the sweet spot for most situations. Pro tip: You can always tweak these settings on the fly with the 
update_options()
method. It's like adjusting your headphones until the sound is just right! Sources: livekit-plugins/livekit-plugins-silero/livekit/plugins/silero/onnx_model.py | basics/echo_transcriber_agent.py | Configuring turn detection and interruptions | LiveKit Docs
c
can you give me a set of starting values for each of these params for me to use in realtime conversational ai
t
Hey there! Let me help you get started with some friendly Silero VAD parameter recommendations for your conversational AI. These values are like a great starting recipe that you can totally tweak to fit your specific needs. • activation_threshold: 
0.5
(our default sweet spot that balances catching speech without going crazy with false alarms) • min_speech_duration: 
0.2
seconds (perfect for catching those quick little utterances) • min_silence_duration: 
0.6
seconds (helps make conversations feel natural and smooth) • prefix_padding_duration: 
0.5
seconds (catches the start of speech before the main detection kicks in) • max_buffered_speech: 
60.0
seconds (plenty of time for most chat scenarios) • sample_rate: 
16000
Hz (gives you nice, clear audio quality) • force_cpu: 
True
(super handy if you don't have a GPU or don't need one) Think of these as your starting point! If you notice any hiccups like background noise causing false positives, you can easily adjust things like 
activation_threshold
or 
min_speech_duration
. It's all about finding what works best for your specific setup.
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