if i capture the sip_dtmf_received event in the ro...
# ask-ai
a
if i capture the sip_dtmf_received event in the room, and then I want to send it to the agent posing as the user has said it, how would that be done? we also want to interrupt speech while we're at it
t
Thanks for reaching out, Isaam. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
To handle the
sip_dtmf_received
event and simulate it as user speech while interrupting ongoing agent speech, here’s a friendly step-by-step guide to help you through it: 1. Set up a DTMF event listener: Start by using the room's event system to register a handler for the
sip_dtmf_received
event. This way, DTMF tones are caught and processed smoothly, without any delays. 2. Interrupt agent speech: When you detect the event, use
session.interrupt()
to gently pause any ongoing agent speech and switch the session into listening mode. This helps keep the conversation natural and responsive. 3. Simulate user speech: Next, you can use
session.generate_reply(user_input=...)
to feed a text message into the agent’s conversation flow, making it feel like the user just spoke. You can map those DTMF digits to specific user intents or responses to keep things clear. 4. Integrate with agent lifecycle: Make sure your agent is properly initialized with the right session and userdata, and that it gracefully handles important room events like disconnections. This keeps everything running smoothly behind the scenes. 5. Optional - Handle call transfers: If your DTMF input indicates a department choice, you can update the user state accordingly and use the LiveKit SIP API to transfer calls seamlessly. This adds a nice touch of flexibility! If you’d like, I’m here to provide detailed code examples or walk you through any part of this. Just let me know—I’m happy to help! Sources: VoicePipelineAgent | LiveKit Docs | Turn detection and interruptions | LiveKit Docs | Company directory phone assistant | LiveKit Docs | README.md | Handling DTMF | LiveKit Docs
a
gimme some alternatives to session.generate_reply because this: def reset_dtmf_timer(ev_participant): nonlocal dtmf_timer, dtmf_buffer, participant_has_left, dtmf_participant if participant_has_left: logger.debug(f"[Room -> {ctx.room.name}] Skipping DTMF timer reset - participant has already left") return # Store the participant who sent the DTMF dtmf_participant = ev_participant if dtmf_timer and not dtmf_timer.done(): logger.debug(f"[Room -> {ctx.room.name}] Cancelling previous DTMF timer") dtmf_timer.cancel() async def timer(): nonlocal dtmf_buffer try: # Mute participant at start of DTMF collection await mute_participant_for_dtmf(dtmf_participant) await asyncio.sleep(dtmf_delay) logger.info(f"[Room -> {ctx.room.name}] DTMF input timeout reached. Processing buffer: '{dtmf_buffer}'") # Process the buffered DTMF input if dtmf_buffer and session: try: await session.interrupt() await session.generate_reply( instructions=f"The user pressed the following DTMF digits on their phone keypad: {dtmf_buffer}. Please proceed with the conversation accordingly.", allow_interruptions=True ) logger.info(f"[Room -> {ctx.room.name}] Sent DTMF buffer to agent: '{dtmf_buffer}'") except Exception as e: logger.error(f"[Room -> {ctx.room.name}] Error processing DTMF buffer: {e}") # Clear the buffer after processing dtmf_buffer = "" except asyncio.CancelledError: logger.debug(f"[Room -> {ctx.room.name}] DTMF timer cancelled") raise except Exception as e: logger.error(f"[Room -> {ctx.room.name}] Error in DTMF timer: {e}") finally: # ALWAYS unmute participant, regardless of what happened try: await unmute_participant_after_dtmf(dtmf_participant) except Exception as e: logger.error(f"[Room -> {ctx.room.name}] Critical error unmuting participant: {e}") dtmf_timer = asyncio.create_task(timer()) whenever the timer reaches the dtmf_delay, i realise the agent does recognize an input as it generates a reply but it doesn't acknowledge that the dtmf_buffer was passed to it (i've tried telling it that i did send you the digits but it sitll doesn't recognize it) i feel like there should be an alternative maybe to session.generate_reply or something which im deffo missing here
t
I'm here to help you with the issue you're facing where
session.generate_reply
isn't properly recognizing the DTMF buffer. Let's explore some alternatives and best practices that might make things smoother for you: 1. Use
session.say()
for predefined responses:
Instead of generating replies dynamically, you might find it easier to use
session.say()
to deliver a fixed response based on the DTMF input. For example:
await session.say("You pressed the following digits: {dtmf_buffer}. Please hold while we process your request.")
2. Handle DTMF input directly with event-driven logic: Consider using the
sip_dtmf_received
event to manage the input and connect it to specific actions or responses. This approach helps avoid relying on
generate_reply
for processing input. 3. Maintain proper state management: It’s helpful to use a shared state object (like
userdata
) to keep track of the DTMF buffer, making sure the agent has the right context when handling it. 4. Interrupt ongoing speech when needed: Before processing DTMF input, calling
session.interrupt()
ensures the agent is ready and focused on the new input. 5. Debugging tip: Logging the DTMF buffer and agent responses can give you insight into whether the input is processed correctly. For example:
<http://logger.info|logger.info>(f"DTMF buffer processed: {dtmf_buffer}")
If any of this feels unclear or if you'd like me to walk you through some more examples, just let me know—I'm happy to help! Sources: Company directory phone assistant | LiveKit Docs | README.md | Docs::Recipes | Handling DTMF | LiveKit Docs | Agent speech and audio | LiveKit Docs
a
it cant be session.say it needs to be a way for me to mimic that the user/participant has said something - please find a solid way to do that
t
I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!