#Recording

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rough quiver
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Hi i am using opena AI realtime API and i am using websocket and i need your help to implement call recording

restive oracleBOT
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Hi @rough quiver. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

#

To implement call recording using the OpenAI Realtime API over a WebSocket, you will need to handle audio input and output events. Here's a general approach based on the knowledge sources provided:

  1. Streaming Audio Input to the Server: You can stream audio input to the server using the input_audio_buffer.append client event. This involves sending chunks of Base64-encoded audio bytes to the Realtime API over the WebSocket. Each chunk should not exceed 15 MB in size. Here's a code snippet to help you get started:

    import base64
import json
import struct
import soundfile as sf
from websocket import create_connection

# ... create websocket-client named ws ...

def float_to_16bit_pcm(float32_array):
    clipped = [max(-1.0, min(1.0, x)) for x in float32_array]
    pcm16 = b''.join(struct.pack('<h', int(x * 32767)) for x in clipped)
    return pcm16

def base64_encode_audio(float32_array):
    pcm_bytes = float_to_16bit_pcm(float32_array)
    encoded = base64.b64encode(pcm_bytes).decode('ascii')
    return encoded

files = [
    './path/to/sample1.wav',
    './path/to/sample2.wav',
    './path/to/sample3.wav'
]

for filename in files:
    data, samplerate = sf.read(filename, dtype='float32')  
    channel_data = data[:, 0] if data.ndim > 1 else data
    base64_chunk = base64_encode_audio(channel_data)
    
    # Send the client event
    event = {
        "type": "input_audio_buffer.append",
        "audio": base64_chunk
    }
    ws.send(json.dumps(event))

    Source

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  1. Handling Audio Output from a WebSocket: To handle audio output, listen for response.audio.delta events, which contain Base64-encoded chunks of audio data from the model. You can buffer these chunks and write them to a file for recording purposes. Here's a code snippet for handling these events:

    def on_message(ws, message):
    server_event = json.loads(message)
    if server_event.type == "response.audio.delta":
        # Access Base64-encoded audio chunks:
        # print(server_event.delta)

    Source

By following these steps, you can implement call recording by capturing both the input and output audio streams. Make sure to handle the audio data appropriately, such as saving it to a file or processing it further as needed.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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@rough quiver Kapa.ai is still learning and improving, please let me know how I did by reacting below.

rough quiver
#

@restive oracle no i am using twilio recording twilio has recording i am plaining to use that only

restive oracleBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

If you are planning to use Twilio's recording capabilities in conjunction with the OpenAI Realtime API, you can integrate the two by streaming audio data between them. Here’s a general approach based on the knowledge sources:

  1. Using Twilio for Recording: Twilio provides robust APIs for handling telephony, including call recording. You can use Twilio's capabilities to record calls directly. This can be done by setting up a Twilio call and enabling recording through their API.

  2. Integrating with OpenAI Realtime API: You can stream audio data from Twilio to the OpenAI Realtime API using WebSockets. This involves capturing the audio from Twilio and sending it to the OpenAI API as Base64-encoded audio chunks.

  3. Handling Audio Output: For audio output from the OpenAI API, you can listen for response.audio.delta events over the WebSocket. These events contain Base64-encoded audio data, which you can then stream back to Twilio or handle as needed.

For more detailed integration, you might want to refer to Twilio's documentation on call recording and the OpenAI Realtime API documentation for handling audio input and output. Here are some relevant links from the knowledge sources:

By leveraging Twilio's recording capabilities and integrating with the OpenAI Realtime API, you can effectively manage call recordings while utilizing AI-driven interactions.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@rough quiver Kapa.ai is still learning and improving, please let me know how I did by reacting below.

#

Let me check the knowledge base for you again. One moment! :books:

#

Hmm, I don't know enough to give you a confident answer yet. However, you can refer to the following resources for more information on integrating Twilio with the OpenAI Realtime API and handling audio streams:

If you have specific code snippets or error messages, reviewing these resources might help you identify the issue. If the problem persists, consider reaching out to OpenAI support with detailed information about your setup and the issues you're encountering.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@rough quiver Kapa.ai is still learning and improving, please let me know how I did by reacting below.

rough quiver
#

@restive oracle can u provide me code for this response.record like i want reocrding for both my voice and AI response also

restive oracleBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

To record both your voice and the AI's response using the OpenAI Realtime API and Twilio, you can follow these general steps. While I don't have specific code for Twilio's recording setup, I can guide you on how to handle the audio streams with the OpenAI Realtime API.

Steps to Record Audio

  1. Capture Your Voice Input: Use Twilio's capabilities to capture your voice input during a call. Twilio can record calls directly, and you can configure it to save the recordings.

  2. Stream Audio to OpenAI: Use a WebSocket to send your audio input to the OpenAI Realtime API. You can use the input_audio_buffer.append event to stream audio data.

    import base64
import json
import struct
import soundfile as sf
from websocket import create_connection

# Create a WebSocket connection
ws = create_connection("wss://api.openai.com/v1/realtime")

def float_to_16bit_pcm(float32_array):
    clipped = [max(-1.0, min(1.0, x)) for x in float32_array]
    pcm16 = b''.join(struct.pack('<h', int(x * 32767)) for x in clipped)
    return pcm16

def base64_encode_audio(float32_array):
    pcm_bytes = float_to_16bit_pcm(float32_array)
    encoded = base64.b64encode(pcm_bytes).decode('ascii')
    return encoded

# Example of sending audio data
audio_data = ...  # Your audio data here
base64_chunk = base64_encode_audio(audio_data)
event = {
    "type": "input_audio_buffer.append",
    "audio": base64_chunk
}
ws.send(json.dumps(event))

  3. Receive and Record AI's Response: Listen for response.audio.delta events from the OpenAI API to capture the AI's audio response. You can buffer these audio chunks and save them to a file.

# 
   def on_message(ws, message):
       server_event = json.loads(message)
       if server_event['type'] == "response.audio.delta":
           # Access Base64-encoded audio chunks
           audio_chunk = server_event['delta']
           # Decode and save the audio chunk
           # Example: save to a file or stream to Twilio
  1. Integrate with Twilio: Use Twilio's API to manage call recordings. You can configure Twilio to record both the incoming and outgoing audio streams during a call.

Resources

By following these steps, you can record both your voice and the AI's response. Make sure to handle the audio data appropriately, such as saving it to a file or processing it further as needed.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: