> …but as a user, I would much rather wait an extra 200ms for my slow/expensive prompt to be accurate
This is the opposite of the feedback I get. Users want instant responses. If you have delay in generating responses/interruptions it kills the magic. You also don't want to send faster than real-time. If the user interrupts the model you just wasted a bunch of bandwidth sending 3 minutes of audio (but only played 10 seconds)
> TTS is faster than real-time
https://research.nvidia.com/labs/adlr/personaplex/ Voice AI for the latest/aspirational is moving away from what the author describes. It is trickled in/out at 20ms
> We really hope the user’s source IP/port never changes, because we broke that functionality.
That is supported. When new IP for ufrag comes in its supported
> It takes a minimum of 8* round trips (RTT)
That's wrong. https://datatracker.ietf.org/doc/draft-hancke-webrtc-sped/
> I’d just stream audio over WebSockets
You lose stuff like AEC. You also push complexity on clients. The simplicity of WebRTC (createOffer -> setRemoteDescription) is what lets people onboard easily. Lots of developers struggled with Realtime API + web sockets (lots of code and having to do stuff by hand)
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I think if I had my choice I would pick Offer/Answer model and then doing QUIC instead of DTLS+SCTP. Maybe do RTP over QUIC? I personally don't feel strongly about the protocol itself. I don't know how to ship code to multiple clients (and customers clients) with a much large code footprint.
1. Of course users want lower latency, but they also want fewer instances where the LLM "misheard" them. It would be amazing to run A/B experiments on the trade-off between latency vs quality, but WebRTC makes that knob difficult to turn.
2. I'm obviously not an TTS expert, but what benefit is there to trickling out the result? The silicon doesn't care how quickly the time number increments?
3. Yeah, sometimes the client is aware when their IP changes and can do an ICE renegotiation. But often they aren't aware, and normally would rely on the server detecting the change, but that's not possible with your LB setup. It's not a big deal, just unfortunate given how many hoops you have to jump through already.
4. Okay, that draft means 7 RTTs instead of 8 RTTs? Again some can be pipelined so the real number is a bit lower. But like the real issue is the mandatory signaling server which causes a double TLS handshake just in case P2P is being used.
5. Of course WebRTC is easier for a new developer because it's a black box conferencing app. But for a large company like OpenAI, that black box starts to cause problems that really could be fixed with lower level primitives.
I absolutely think you should mess around with RTP over QUIC and would love to help. If you're worried about code size, the browser (and one day the OS) provides the QUIC library. And if you switch to something closer to MoQ, QUIC handles fragmentation, retransmissions, congestion control, etc. Your application ends up being surprisingly small.
The main shortcoming with RoQ/MoQ is that we can't implement GCC because QUIC is congestion controlled (including datagrams). We're stuck with cubic/BBR when sending from the browser for now.
2.) The model isn't processing just text anymore. Also taking into account breathing/emotion etc... not just spitting out big responses anymore. As it generates them it is taking into account the users response.
3.) It works with the LB setup today. Clients are sending ICE traffic, if it roams we lookup the ufrag and route appropriately.
4.) With DTLS 1.3 it is 1 RTT with SNAP[0] for WebRTC session. SCTP info goes in Offer/Answer, DTLS is packed into ICE. You are totally right about signaling though! [1] was my answer for doing WebRTC without signaling, couldn't get anyone to care though.
5.) I don't have anything that I need to tune. If I want to increase (or decrease) latency [3] is something I put into Transceiver. Otherwise I can't think of any 'change this WebRTC behavior' that has been asked by users/developers.
[0] https://datatracker.ietf.org/doc/draft-hancke-tsvwg-snap/
[1] https://github.com/pion/offline-browser-communication
[3] https://webrtc.googlesource.com/src/+/refs/heads/main/docs/n...
People can tolerate missing words surprisingly well. If a phrase is slightly clipped, masked by noise, or dropped, the listener can often infer it from context. That happens constantly in real speech.
But pauses and stalls are much more damaging. A sudden freeze in the middle of speech breaks turn-taking, timing, and attention. It feels like the speaker stopped thinking, the connection died, or the system got stuck.
For voice UX, a tiny omission is often less harmful than a perfectly complete sentence that freezes halfway.
LLMs are surprisingly good at this, too.
This entire blog post is based on assumptions that
1) WebRTC garbling is common
2) LLMs fall apart if there are any audio glitches
I would bet money that OpenAI explored and has statistics on both of those and how it impacts service. More than this blogger heaping snark upon snark to avoid having a realistic conversation about pros and cons
If I'm talking to a friend or peer and I'm on a crappy link, we can probably work it out. If I'm calling my lawyer from prison with my "one call" I really want my lawyer to get my instructions clearly and correctly, ideally the first time without a lot of coaching.
Where on this scale does "person talking to LLM" fit?
I believe there's a ton of research into the shannon limit and human speech. You can trivially observe how much redundancy there is by listening to a podcast at 1x, 1.2x, 1.5x, 2x, etc, and when you can't follow what's going on, you've found the "redundancy" built into that language. This number falls way off when you're listening to a person with an accent or when the recording is noisy or whatever.
You'll also find that your tolerance for lossy media is radically different based on latency and echos and jitter in the audio (which I believe is the point of the original "don't use webrtc" article...)
Finally, people may tolerate this, but the "phonem to token" thinger may be less tolerant, and will certainly not be able to magic correct meaning from lost packets, and if the resulting exchange is extremely expensive or important (from the lawyer and the "I'm in jail in poughkeepsie; I need bail!" exchange) you really want to take the time to get it right, not make things guess.
> This is the opposite of the feedback I get. Users want instant responses.
I am skeptical that you are getting feedback that users prefer instant wrong results to 200ms-lag correct results.
Deeply skeptical!
The blog post glosses over the details and implies that 200ms of latency would be a magic solution. They do admit that WebRTC already has provisions for up to 200ms, so I guess they’re really implying that 400ms would be the happy case path for their alternative buffering, which is starting to get in the range where users would probably be annoyed.
Have you tried having conversational speech over a link with almost half a second of delay? It’s bad. You have to work hard to establish a turn taking routine with the other party and do extra mental work to identify your slot to talk.
The other half of this problem requires acknowledging that LLMs are actually pretty decent at interpreting input with gaps. You can drop words or even letters from LLM input and still get surprisingly decent results back. This post acts like a dropped packet means your response is going to send the LLM off on a wrong response or something.
Sure, but I am skeptical that users are actually saying "I prefer wrong answers over lag", which is what the post I responded to implied.
This is different to user's saying "I prefer quick answers to laggy answers", which is what I presume they may have said.
To actually settle this, the feedback must answer the question "Do you want wrong answers quickly or correct answers with an added 0.2 second delay?" because, well, those are the only two options right now.
My desires are pretty different in the two scenarios. Q&A mode if it's not quick to respond I'll think something is wrong with my phone.
Deep think mode I'm honestly kind of pissed off at how fast it tries to respond. I want it to slow down and give me a chance to process and use extra compute on its side (including newer models) so it doesn't just spew low thought bullshit at me.
It seems like the system could detect which of these two modes was happening and adapt, including protocol.
I haven't tried the voice mode since the new model updates, maybe it's gotten better.
Counter to everything I just said though and germain to the topic at hand, when I'm in q&a mode that's probably the worst time for it to drop audio as it changes the query significantly. vs when I'm talking at it for 2 minutes it could probably throw half away.
You are skeptical that people would prefer instant responses with 99.99% accuracy to waiting noticeably longer for a higher-accuracy rate?
The Internet, over its entire history, suggests otherwise.
A single dropped or missed word in a sentence can reverse the meaning.
I am skeptical that people would rather have wrong answers than lag. I am not claiming what the percentage is and neither are you, because no one measured it at the low lag.
WebRTC is complex, even if it's a library (even if it's a library built into the browser they're already using). For a client/server voice interaction, I don't see why you would willingly use it. Ship voice samples over something else; maybe borrow some jitter buffer logic for playback.
My job currently involves voice and video conferencing and 1:1 calls, and WebRTC is so much complexity... it got our product going quickly, but when it does unreasonable things, it's a challenge to fix it; even though we fork it for our clients.
I could write an enormous rant about TURN [1]. But all of the webrtc protocol suite is designed for an internet that doesn't exist.
[1] Turn should allocate a rendesvous id rather than an ephemeral port when the turn client requests an allocation. Then their peer would connect to the turn server on the service port and request a connection to the rendesvous id, without needing the client to know the peer address and add a permission. It would require less communication to get to an end to end relayed connection. Advanced clusters could encode stuff in the id so the client and peer could each contact a turn server local to them and the servers could hook things up; less advanced clusters would need to share the turn server ip and service port(s) with the id.
This is closer to being the real problem with WebRTC than the whole "it's making decisions about latency that I disagree with".
If you had a way to setup the tracks/channels over UDP connections that didn't involve P2P/STUN/TURN etc. but got to keep all the codec negotiation and things like AEC that would be awesome. MoQ isn't that though, because it's by people that don't actually see the whole problem end-to-end; just their little piece of it in the middle.
You only need to send ~1 second at a time. There's no reason to send 20ms or 10 min at a time. Both are stupid.
I disagree with this SO strongly. I find the conversational voice mode to be a game changer because you can actually have an almost normal conversation with it. I'd be thrilled if they could shave off another 50-100ms of latency, and I might stop using it if they added 200ms. If I want deep research I'll use text and carefully compose my prompt; when I'm out and about I want to have a conversation with the Star Trek computer.
Interestingly I'm involved with a related effort at a different tech company and when I voiced this opinion it was clear that there was plenty of disagreement. This still surprises me since it seems so obvious to me that conversational fluidity is the number one most important feature.
I prompt orchestrations most of the day, and am very particular about the fidelity of my context stack.
Yet I’ve used advanced voice mode on ChatGPT via the iOS app a lot. And I have not had a problem with it understanding my requests or my side of the conversation.
I have looked at the dictation of my side and seen it has blatant mistakes, but I think the models have overcome that the same way they do conference audio stt transcripts.
I have had times where the ~sandbox of those conversations and their far more limited ability to build useful corpus of context via web searches or by accessing prior conversation content.
The biggest problem I have had with adv voice was when I accidentally set the personality to some kind of non emotional setting. (The current config seems much more nuanced)
The AI who normally speaks with relative warmth and easy going nature turned into an emotionless and detached entity.
It was unable to explain why it was acting this way. I suspect the low latency did disservice there because when it is paired with something adversarial it was deeply troubling.
But you’re not. And you won’t. You’ll never have a conversation with the Star Trek computer while you continue to place anything else above accuracy. Every time I see someone comparing LLMs to the Star Trek computers, it seems to be someone who doesn’t understand that correctness was their most important feature. I’m starting to get the feeling people making that comparison never actually watched or understood Star Trek.
A computer which gives you constant bullshit is something only the lowest of the Ferengi would try to sell.
> This still surprises me since it seems so obvious to me that conversational fluidity is the number one most important feature.
It’s not. It absolutely is not and will never be. Not unless all you’re looking for is affirmation, companionship, titillation. I suggest looking for that outside chat bots.
If the connection is truly bad, upload your voice and quantify emotional payload.
Did they really say they prefer fast response over accurate repsonse?
