Is it possible to record agent and user speech without egress? If all audio is already coming through agent.py it should be doable to put all audio in a file and upload to s3? @millions-hair-24956

It seems likely that recording agent and user speech without using LiveKit's Egress service is possible, but it may require more manual handling. According to a Slack conversation, you can record audio data directly from the agent itself. However, this approach might involve mixing all audio tracks manually, which can be complex compared to using Egress. If you want to proceed without Egress, you would need to capture the audio streams from the agent, mix them if necessary, and then save them to a file. This file can then be uploaded to S3 using a library like

boto3

in Python. Would you like more detailed guidance on how to implement this? Sources: - Slack conversation on recording audio 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 yes. I can't open the link, provide info from that discussion here

My understanding is that you can record audio directly from the agent by capturing the audio data as it passes through the agent. This involves manually handling the audio streams, which can be more complex than using LiveKit's Egress service. Here's a general approach you might consider: 1. Capture Audio Streams: Use the LiveKit SDK to subscribe to the audio tracks of both the agent and the user. This can be done by accessing the audio frames as they are received in the agent's code. 2. Mix Audio Tracks: If you need to combine multiple audio tracks into a single file, you can use a library like

numpy

to mix the audio frames. This involves summing the audio samples from each track, ensuring they are properly synchronized. 3. Save to File: Once you have the mixed audio data, you can write it to a file using a library like

wave

or

pydub

in Python. This file can be in a format like WAV or MP3. 4. Upload to S3: Use the

boto3

library to upload the audio file to an S3 bucket. You'll need to configure your AWS credentials and specify the bucket name and file path. Here's a basic example of how you might handle the audio data:

import numpy as np
import wave
import boto3

# Example function to mix audio frames
def mix_audio(frames):
    mixed = np.sum(frames, axis=0)
    return mixed

# Example function to save audio to a WAV file
def save_to_wav(filename, audio_data, sample_rate):
    with wave.open(filename, 'wb') as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(sample_rate)
        wf.writeframes(audio_data.tobytes())

# Example function to upload file to S3
def upload_to_s3(filename, bucket_name, s3_key):
    s3 = boto3.client('s3')
    s3.upload_file(filename, bucket_name, s3_key)

# Capture, mix, save, and upload audio
audio_frames = []  # Collect audio frames from agent
mixed_audio = mix_audio(audio_frames)
save_to_wav('output.wav', mixed_audio, 24000)
upload_to_s3('output.wav', 'your-bucket-name', 'path/to/output.wav')

This is a simplified example, and the actual implementation will depend on how you capture and process the audio frames in your agent. Let me know if you need further details on any specific part of this process. Sources: - https://livekit-users.slack.com/archives/C07FVFARRK7/p1732199530.298559 - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1730476092.019429 👍 0 button 👎 1 button 💡 Hint: Mention @millions-hair-24956 in the thread for followups.