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When the agent is listening to the user, and if th...
# ask-ai
c
When the agent is listening to the user, and if the user speaks for extended periods of time >20 seconds. The agent fails to respond when the user is done speaking.
t
Thanks for reaching out, Aaron. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
The issue where the agent fails to respond after a user speaks for extended periods (e.g., >20 seconds) is typically related to Voice Activity Detection (VAD) configuration. Root Cause: The VAD sensitivity settings, such as the 
min_silence_duration
threshold, may be set too high. This can prevent the agent from detecting appropriate turn-taking opportunities. Solution: • Adjust the VAD sensitivity parameters. For example, you can use 
VADStream(min_silence_duration=0.3)
to lower the silence duration threshold and improve responsiveness. • Ensure the 
min_endpointing_delay
parameter is appropriately configured. The default value is 500ms, which triggers an end-of-turn event after detecting silence for this duration. If you need to handle long user speech actively, you can implement interruption logic. For instance, you can track the number of sentences spoken and politely interrupt the user after a certain threshold:
Copy code
@session.on("user_input_transcribed")
def on_transcript(transcript):
    # Track transcript buffer and sentence count
    sentence_count = count_sentences(transcript_buffer)

    if sentence_count >= max_sentences:
        # Interrupt the user politely
        session.say("Sorry, can I pause you there?", allow_interruptions=False)
Balancing VAD sensitivity is key. Lowering the endpointing delay threshold makes the agent respond faster but may increase interruptions, while increasing it can make the agent feel unresponsive. Sources: Speech & Audio Processing | livekit/livekit_composite | DeepWiki | Improving voice AI's turn detection with transformers | pipeline-llm/interrupt_user.py
c
how can I use inference profiles for the aws bedrock plugin
t
Hey there! Let me walk you through using inference profiles with the AWS Bedrock plugin. It's actually pretty straightforward, and I'll help you set it up in two super simple ways. 1. Direct Parameter: Want the quickest route? Just pass the inference profile ARN directly to the 
model
parameter. It's as easy as:
Copy code
from livekit.plugins import aws

llm = aws.LLM(
    model="arn:aws:bedrock:us-east-1:123456789012:inference-profile/your-profile-name",
    temperature=0.8,
)
2. Environment Variable: If you prefer keeping things behind the scenes, you can set an environment variable instead. Just run this command:
Copy code
export BEDROCK_INFERENCE_PROFILE_ARN="arn:aws:bedrock:us-east-1:123456789012:inference-profile/your-profile-name"
Then create your LLM instance without mentioning the model:
Copy code
from livekit.plugins import aws

llm = aws.LLM(
    temperature=0.8,
)
The plugin's got your back and will automatically use the inference profile ARN from the environment variable if you don't specify a model. Error Handling: Just a heads up – if you forget to provide either a model or an inference profile ARN, the plugin will give you a friendly nudge with a 
ValueError
saying: "model or inference profile arn must be set using the argument or by setting the BEDROCK_INFERENCE_PROFILE_ARN environment variable." Sources: examples/voice_agents/realtime_joke_teller.py
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