Your Voice Agent is Just a Walkie Talkie — Neil Zeghidour, Gradium
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Your Voice Agent Is Just a Walkie-Talkie
Neil Zeghidour traces voice interfaces from rigid assistants to tool-using agents, then shows why fast speech-to-speech still feels unnatural—and how audio tokens, simultaneous streams, and a hybrid voice-plus-text architecture could fix it.
From a talk by Neil Zeghidour
At a glance
Ideas worth remembering
Open-ended dialogue, external action, expressive speech, low latency, and simultaneous conversation are distinct capabilities; improving one does not guarantee the others.
Speech-to-speech can remove the explicit text cascade, but a half-duplex system still alternates between listening and speaking. Backchannels therefore become unwanted turn changes.
Neural codecs and multi-stream modeling solve different problems: codecs compress long waveforms into audio tokens, while two streams represent simultaneous speech and silence for both participants.
Full duplex makes overlap resilient but does not guarantee good timing or strong reasoning. The early system could interrupt too often while remaining less capable than cascaded agents.
The hybrid proposal gives a small full-duplex model responsibility for conversational flow and delegates difficult reasoning and tools to a replaceable text backend. Its success depends on reliable delegation and asynchronous coordination.
Voice assistants first traded agency for conversational range
Gradium co-founder and CEO Neil Zeghidour frames voice-agent progress around capabilities that often get conflated: open-ended conversation, useful action, natural vocal expression, low latency, and the ability to listen while speaking. Gradium grew from voice research at Kyutai and trains audio foundation models for tasks including speech recognition, synthesis, translation, and spoken dialogue. The history matters because each generation improved a different part of the experience rather than advancing all of them together.
The 2011 Siri demonstration already contained real agency. A person could ask for the weather or the Nasdaq, and the assistant connected that utterance to an application action. Behind the voice, speech recognition produced a transcript; natural-language understanding classified the request, selected the application, and triggered a supported operation. This was a complex, closed-ended pipeline: useful inside its predefined territory, unable to roam far beyond it.
OpenAI’s original voice mode reversed that balance. An LLM replaced much of the hand-built dialogue logic, so a user could request a bedtime story about Larry, a hedgehog with sunflower petals instead of spines, and receive an improvised response. The system gained conversational breadth and more natural speech, but in Zeghidour’s comparison it lacked Siri’s ability to retrieve weather or market information and still took several seconds to answer. Open-ended dialogue and agency turned out to be separate features.
Classifies a transcript, selects an application, and triggers a predefined action.
The early systems did not improve along one simple axis: action, conversational range, and natural timing arrived through different architectures.
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A drive-through order shows what tool use adds—and what text removes
The drive-through demonstration develops the next step through one concrete order. The customer asks for a chicken sandwich, selects the classic option, asks what else is available, and adds mac and cheese. The observable change is persistent task state: after several exchanges, the agent can report that the order contains both items. Zeghidour says payment would follow, although the shown exchange stops before completing it.
The causal chain is straightforward: the system interprets each utterance, consults the menu, records the chosen sandwich, answers a category question, adds the side, and computes the current order. An LLM is no longer merely generating a conversational response; it participates in an agent equipped with tool calling, reasoning, planning, and task state. That recovers agency in a more general form than the original closed assistant.
The remaining weakness sits in the cascade: speech-to-text converts the voice into words, a text model reasons over those words, and text-to-speech generates a new voice. This architecture can use a strong text model and dependable tools, but every component adds delay. More subtly, the transcript flattens information that was present in the input audio—tone, emotion, hesitation, and other nonlinguistic cues. Speech-to-speech promises to preserve that information while reducing latency.
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Backchanneling exposes the walkie-talkie underneath
A single speech-to-speech model can absorb the explicit speech-recognition, text-model, and speech-synthesis stages. In the advanced voice example, a person says he is nervous during a live demonstration and receives an immediate spoken response. Direct audio modeling can retain nonlinguistic information and deliver latency that Zeghidour considers already good enough. The cost, in his qualitative assessment, is that these speech-to-speech systems remain less capable than their text-based, cascaded counterparts; the talk does not present a benchmark for the size of that gap.
Speech-to-speech still does not imply full duplex. A half-duplex system divides the exchange into two states: the model speaks, or the model listens. That works until the listener contributes a brief “mm-hmm,” “yeah,” or “sure.” Humans use these backchannels to signal attention without asking for the floor, but a turn-taking system can interpret any incoming speech as an interruption.
The demonstration makes the failure visible. The model starts brainstorming, the user says “mm-hmm,” and the response stops. After the user explains that the acknowledgment was not an interruption, the model resumes—only to stop again at the next “sure.” Even the explicit lesson about backchanneling does not fix the exchange, because the failure is structural rather than instructional. The system must switch turns whenever it detects user speech.
That is the walkie-talkie problem. Faster turn changes can make half duplex feel responsive, but they cannot represent both sides speaking at once. Human calls include acknowledgments, interruptions, overlap, and moments when neither person speaks. Zeghidour says calls with a relative can contain simultaneous speech for up to 20% of the time; the talk supplies no measurement behind that figure, so it serves as an illustration of normal overlap rather than a quantified general rule.
The model accepts user audio but does not speak.
The key difference is not response speed. It is whether both streams can remain active at the same time.
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Raw waveforms are too long, so codecs turn audio into tokens
A text language model predicts the next token from earlier tokens. A speech model needs an analogous prediction target for audio, but a raw waveform is far longer than a sentence’s word sequence. Zeghidour’s example takes about three seconds to say and contains eight words. At 24 kHz, those three seconds contain 72,000 waveform samples: 3 × 24,000.
That length is prohibitive for ordinary transformer attention. Under the quadratic-cost relationship used in the talk, multiplying sequence length by 10,000 multiplies attention cost by 100 million. The 10,000 factor is a rounded illustration rather than the exact ratio between 72,000 samples and eight words, but it captures the scale problem: treating every audio sample like a language token makes the sequence astronomically expensive.
A neural codec, also called an audio tokenizer here, changes the representation. An encoder compresses the waveform into a dense sequence of learned audio tokens; the language model predicts over those tokens; a decoder reconstructs high-quality audio. Tokenization makes speech manageable for language-model training, but it does not solve duplex behavior. If user and system tokens are still placed one after another in a single sequence, the conversation remains turn-based.
Three seconds at 24 kHz contains 72,000 samples.
Compression solves sequence length; it does not by itself allow simultaneous conversation.
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Full duplex requires two timelines, not one alternating sequence
Full duplex adds a second representation change. Instead of one token stream alternating between user and system, a multi-stream model represents both participants independently. At any instant, both may speak, both may be silent, or either one may speak alone. Overlap is no longer an exceptional event that forces a turn transition; it is an ordinary state in the model’s input and output.
The Moshi demonstration makes that relationship audible through a fictional spacecraft exchange. A person asks for a route, travel time, and mission readiness. The model sometimes anticipates the end of a question and begins answering before the speaker finishes. Crucially, simultaneous speech does not terminate the interaction. The demonstration establishes overlap handling and conversational timing, not actual trajectory calculation or verification of the ship’s supplies.
Full duplex brings its own behavioral problem: the early model interrupted too often. Zeghidour describes that as irritating, even while reporting that the conversation remained continuous through noise and coughing. Two streams make overlap survivable; they do not teach politeness or determine when anticipating a speaker is appropriate.
Natural flow also did not make the model capable enough for serious agent work. Zeghidour bluntly describes the early full-duplex system as less intelligent than speech-to-speech models that were already behind cascaded agents. Casual conversation may tolerate that. An agent entrusted with tool calls, planning, or transactions cannot give up reasoning merely to sound more human.
His explanation is finite model capacity. A text model adapted to speech must understand language, interpret audio, and generate audio using a fixed collection of weights. Those extra modality responsibilities consume capacity that could otherwise support reasoning. This is a proposed explanation for the observed tradeoff, not a measured law quantifying how many reasoning capabilities each audio feature costs.
Contributions occupy one sequence, so the conversation alternates turns.
The multi-stream representation makes silence and overlap explicit for each participant.
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Two paths forward: scale everything, or separate conversation from intelligence
The first path is to keep the system end to end and scale it: use a larger speech-to-speech model, better pretraining, and stronger post-training until its intelligence becomes sufficient for more tasks. The operational advantage is simplicity—a single model handles the interaction. The development disadvantage is that adapting a frontier text model to understand and produce audio is complex, slow, and expensive. Zeghidour speculates that a major provider follows this path but explicitly says he does not know its internal architecture.
The second path separates the voice interface from the brain. A small, potentially on-device full-duplex model maintains timing, overlap, and natural speech. When the conversation requires reasoning, tools, planning, or external knowledge, it delegates asynchronously to a background text model. A shared backend could serve many small voice interfaces and return text for them to express conversationally.
This hybrid depends on a difficult handoff: the small interface must know when it does not know. The talk identifies that requirement without explaining how uncertainty is detected, how delegation is trained, or how a delayed answer is reconciled with a conversation that may have moved on. Those coordination details determine whether the split feels natural rather than like a cascade wearing a better voice.
Zeghidour favors the split for two practical reasons:
- Cost: Ordinary small talk does not need a gigantic multimodal model capable of solving differential equations. A small interface can handle routine interaction and reserve expensive reasoning for requests that need it.
- Backend choice: Developers can replace the text model as better reasoning systems appear. A unified speech-to-speech product ties conversational behavior and intelligence to one provider and to a costly audio-adaptation cycle.
The closing bet is deliberately more demanding than an impressive demo. A viable voice agent must match the natural overlap of full duplex while remaining economically competitive and capable of the tool use available in today’s cascaded systems. The hybrid architecture is presented as Gradium’s route toward that combination, not as proof that the handoff, economics, and agent reliability have already been solved.
Includes words, timing, acknowledgments, and overlap.
A lightweight full-duplex interface handles the live conversation and delegates difficult work to a replaceable text backend.
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Resources
From the talk
The official AI Engineer page provides the recording, chapter navigation, and complete timestamped transcript for following the demonstrations.
Further reading
- Gradium: Solving voiceArticle
Background on Gradium’s audio-model approach, research lineage, supported speech tasks, and intended production applications.
Related talks
- Neil Zeghidour - Voice AI: when is the "Her" moment?
Extends the same full-duplex argument into latency, paralinguistic understanding, production controls, cost, privacy, and on-device synthesis.
- Beyond Transcription: Building Voice AI That Actually Understands Conversations
Explains speaker diarization and overlapping speech, adding another view of why a transcript alone cannot represent a real conversation.
- Why ChatGPT Keeps Interrupting You
Examines interruption and premature turn detection from the voice-agent orchestration side, complementing the talk’s model-architecture explanation.
Read the complete timestamped transcript
- 0:01
[music]
- 0:12
>> Okay, hi everyone.
- 0:14
I'm Nel
- 0:15
co-founder and CEO of of Gradio.
- 0:18
So Gradio is a startup based in Paris.
- 0:22
Most of our background is from research.
- 0:24
In particular, we have invented
- 0:26
algorithms such as audio LLMs, speech
- 0:28
speech-to-speech models, neural codex,
- 0:31
and so on and so forth.
- 0:32
And
- 0:33
basically
- 0:35
we started from a research project
- 0:37
called QTI, a non-profit research lab
- 0:39
that has been focusing on voice since
- 0:41
day one. So in particular, we released
- 0:43
in 2024 the first
- 0:45
full duplex speech-to-speech model
- 0:46
called Moshi, the first real-time
- 0:48
speech-to-speech translation system
- 0:50
called Hibiki, and the first
- 0:52
TTS model that can run locally on a on a
- 0:54
smartphone.
- 0:56
And basically I will just say a few
- 0:58
words about what we do, but we are
- 1:00
a model company that trains models for
- 1:02
building voice agents and voice
- 1:04
applications. So we do TTS, API, and
- 1:06
on-device speech-to-text,
- 1:08
speech-to-speech translation, and much
- 1:09
more to come. What we do is that we
- 1:12
train foundation models for audio, and
- 1:13
then we can apply them for a lot of
- 1:15
different tasks.
- 1:17
So I go quickly on voice agents because
- 1:19
it's the fourth talk
- 1:21
about the topic, but basically now we
- 1:24
have these
- 1:26
voice interfaces that we can use to do a
- 1:28
lot of things across a variety of
- 1:30
products and types of interactions with
- 1:32
NPCs, with customer agents, language
- 1:35
learners, coach, and so on and so forth.
- 1:37
And in this talk I tried to go through
- 1:40
the history of this technology and
- 1:44
where I see it going in the next years.
- 1:46
And maybe to start, I think we can take
- 1:48
a look at the announcement of Siri back
- 1:50
in 2011.
- 1:52
And you'll see that it's actually, you
- 1:55
know, I think it it aged pretty well.
- 2:00
>> What is the weather like today?
- 2:06
>> Here's the forecast for today.
- 2:09
>> It is THAT EASY.
- 2:11
>> [cheering]
- 2:12
[applause]
- 2:13
>> LOTS OF THINGS. We've integrated with
- 2:14
the stocks. So, you can ask it about the
- 2:16
stock market. Something like
- 2:18
How is the NASDAQ doing today?
- 2:23
>> NASDAQ composite is down right now at
- 2:26
2,321.70.
- 2:29
>> Again, you can ask this from the lock
- 2:30
screen anywhere. Just press the button
- 2:32
and ask. You can ask about, you know,
- 2:33
the NASDAQ, the Dow.
- 2:35
>> So,
- 2:36
what you just saw is what kind of a
- 2:38
voice agent. It was a bit constrained,
- 2:39
but it was technically a voice agent.
- 2:41
And the architecture behind it, so you
- 2:43
have seen a thousand times today the STT
- 2:45
LLM TTS. Back then, it was even worse,
- 2:47
right? So, there was no LLM, obviously.
- 2:50
So, there was what was called natural
- 2:52
language understanding. So, you would go
- 2:53
from the transcript and uh try to do
- 2:57
basic classification of what is
- 2:58
expected, uh what is the app that is
- 3:00
supposed to be uh controlled, what is
- 3:03
the action to trigger, and so on. So, it
- 3:05
was a very complex pipeline and very
- 3:09
constrained to very specific use cases.
- 3:11
So, it was what was called close
- 3:14
uh ended um
- 3:16
uh dialogue back then.
- 3:18
Then, the next generation of models, uh
- 3:21
I could say, I think was
- 3:24
is illustrated pretty well by the OpenAI
- 3:26
voice mode, not the advanced voice mode,
- 3:28
the original one. In which case, you
- 3:29
just have um a conversational experience
- 3:32
that is open-ended. So, it's much less
- 3:34
constrained than Siri. You can talk
- 3:35
about pretty much anything. At the same
- 3:37
time, you don't have um
- 3:39
as the same agentic capabilities, let's
- 3:42
say, in the sense that it cannot really
- 3:43
give you the NASDAQ or uh give you the
- 3:46
weather.
- 3:51
>> We want to hear a bedtime story.
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Tell us a story about the super-duper
- 3:54
sunflower hedgehog named Larry.
- 3:57
Start with telling us a little bit about
- 3:58
him.
- 4:03
Larry was a unique hedgehog unlike any
- 4:05
other.
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He had bright sunflower petals instead
- 4:08
of spines. Residing in Meadowville,
- 4:11
Larry spread joy and color wherever he
- 4:13
rolled.
- 4:15
>> So, in that context, basically that was,
- 4:17
you know, kind of second generation of
- 4:18
models where all the logic, the very
- 4:20
complex logic was replaced
- 4:22
uh by an LLM.
- 4:24
And
- 4:25
if we compare Siri and the voice mode
- 4:27
from OpenAI, uh
- 4:29
not the Ad Ons one, Ad Ons one, the
- 4:31
original one,
- 4:32
uh
- 4:33
the big strength is opening the
- 4:35
dialogue, but the agentic capabilities
- 4:37
were inexistent. The naturalness was,
- 4:39
obviously, much better than with Siri.
- 4:41
The latency was still pretty bad. So, we
- 4:43
are talking about several seconds, and
- 4:44
that was documented in the in the
- 4:46
previous presentation.
- 4:48
And so, then, you know, we're in this
- 4:51
third phase, which is a voice, what we
- 4:54
call voice agents. Uh and here I'm going
- 4:56
to show a quick demo of uh of a
- 4:58
drive-thru bot uh live-coded with our
- 5:01
with our voice models.
- 5:03
>> Hi there. What can I get started for you
- 5:05
today?
- 5:06
>> Hi, I'm actually in a hurry and I'd like
- 5:08
to order a chicken sandwich quickly.
- 5:11
>> We have the classic, spicy, deluxe, or
- 5:14
grilled chicken sandwich. Which one
- 5:16
would you like?
- 5:17
>> Uh let's go with the classic chicken
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sandwich.
- 5:19
>> Anything else for you?
- 5:21
>> Uh what else do you have on the menu?
- 5:24
>> We have sandwiches, sides, drinks, and
- 5:27
desserts. What can I get for you?
- 5:30
>> Can I get some mac and cheese, maybe?
- 5:32
>> You have a classic chicken sandwich and
- 5:34
mac and cheese.
- 5:36
Your total comes to eight.
- 5:38
>> So, now you know, unlike what we just
- 5:40
saw, it's an actual agent. It's taking
- 5:42
actions. It's keeping track of the
- 5:44
order. It's then going to make you pay.
- 5:46
So, it's it's an actual voice agent that
- 5:48
can do uh real tasks. So, here instead
- 5:51
of having an LLM that is just a
- 5:53
conversational interface,
- 5:54
we have a real agent that is empowered
- 5:56
with tool call, reasoning, planning, and
- 5:58
and all this stuff.
- 6:00
So,
- 6:02
what we see now is we have gained back
- 6:05
agentic capabilities, and actually they
- 6:07
are much more
- 6:09
uh powerful and generic than before,
- 6:12
while keeping a very good level of uh of
- 6:15
naturalness.
- 6:16
And
- 6:17
that's where speech-to-speech LLM came.
- 6:19
In particular, what we could see here is
- 6:22
the latency, it's better with cascaded
- 6:24
system, but it's still higher than you
- 6:27
will have with human conversation. And
- 6:29
as also was explained before, the
- 6:31
naturalness is fundamentally limited by
- 6:32
the fact that you go through text, so
- 6:34
you lose a lot of information about what
- 6:37
uh is said, the tone, the emotion of the
- 6:39
user, and so on and so forth.
- 6:41
So, now that we have tackled
- 6:42
intelligence and agentic capabilities,
- 6:44
speech-to-speech seems like a natural
- 6:46
next step for naturalness and latency.
- 6:48
And so here it's the announcement from
- 6:49
the uh OpenAI advanced voice mode.
- 6:53
>> [clears throat]
- 6:53
>> Hey, ChatGPT. I'm Mark. How are you?
- 6:56
>> Oh, Mark.
- 6:58
I'm doing great. Thanks for asking. How
- 7:00
about you?
- 7:02
>> Hey, so I'm on stage right now. I'm
- 7:03
doing a live demo, and frankly I'm
- 7:05
feeling a little bit nervous. Can you
- 7:07
help me calm my nerves a little bit?
- 7:09
>> Oh, you're doing a live demo right now?
- 7:12
That's awesome.
- 7:13
Just
- 7:14
>> I think we all remember it was very
- 7:16
impressive very impressive release.
- 7:18
And in that context now, all the steps
- 7:21
of STT, LLM, and TTS have been absorbed
- 7:24
into a a single one.
- 7:27
And so now,
- 7:28
intelligence, you know, like naturalness
- 7:30
is
- 7:32
still very good. Uh
- 7:34
actually it can be better because it can
- 7:36
understand non-linguistic information.
- 7:37
Latency is really, really nice.
- 7:40
Honestly, it doesn't make sense to go uh
- 7:42
better than that.
- 7:44
Interestingly and everyone was used any
- 7:46
uh speech-to-speech model can uh attest
- 7:48
that
- 7:50
the intelligence
- 7:51
is still much more limited in that
- 7:54
context than uh the cascaded
- 7:56
counterpart. So, the speech-to-speech
- 7:58
models are fundamentally still limited
- 8:00
compared to the textual models.
- 8:03
Another limitation is turn-taking. So,
- 8:07
people tend to
- 8:08
mix speech-to-speech and full duplex.
- 8:11
But basically,
- 8:13
when you do have a speech-to-speech
- 8:14
model like GPT-3 time, it's still based
- 8:16
on fundamental turn-taking. In the sense
- 8:18
that it's going to segment the
- 8:19
conversation into as long as the model
- 8:21
is speaking or the model is listening.
- 8:23
And to give to show you how this can
- 8:26
make an interaction unnatural, I'm going
- 8:28
to show a a small demo with what is
- 8:30
called backchanneling, which is this
- 8:32
very human thing that you do when
- 8:33
someone talks to you is that you say,
- 8:34
"Mhm, yeah." and so on.
- 8:37
>> Hey, how's it going? Just like to
- 8:39
brainstorm a bit about it with you.
- 8:42
>> Oh, that's a great topic. Yeah, I'd love
- 8:44
to help you brainstorm. Are you thinking
- 8:45
what
- 8:47
Exactly. Yeah, I was thinking
- 8:48
>> No, no, I didn't mean to interrupt, you
- 8:50
know, I was just saying, "Yeah." like
- 8:51
that. You can just keep going. I know,
- 8:53
don't mind me. It's just something I
- 8:54
typically do.
- 8:56
>> Uh no worries at all. Yeah, I was just
- 8:58
going to say we could break it down into
- 8:59
a few aspects. Like
- 9:01
Yeah, exactly.
- 9:02
>> please stop stop interrupting. You know,
- 9:03
it's called backchanneling. Humans do it
- 9:05
all the time. It shows that you're just
- 9:06
following the conversation. That you
- 9:08
don't,
- 9:09
you know, like interrupt you in your
- 9:10
flow. Just just going.
- 9:12
>> Ah, got it. Thanks for letting me know.
- 9:14
>> No problem.
- 9:16
Oh, come on.
- 9:17
>> Yeah, so you see, you know, it's it's
- 9:18
still very annoying. Uh you can have
- 9:20
lightning speed latency. Fundamentally,
- 9:23
this is
- 9:24
uh an issue that can not be resolved
- 9:27
when you're using turn-taking. So, here
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that's the walkie-talkie.
- 9:30
Um any real-time model today, I mean,
- 9:33
now there is a bidirectional one that
- 9:35
will come from OpenAI, but it's called
- 9:37
half duplex. So, the model is listening
- 9:39
or speaking. A human conversation
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has a constant flow between two people.
- 9:45
People do back channeling. People
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interrupt one another, talk on one
- 9:48
another, and so on.
- 9:50
If you have If you're having a relative
- 9:52
on the phone, there is up to 20% of the
- 9:54
time where you are both speaking at the
- 9:56
same time.
- 9:57
And that makes, you know, this very
- 10:00
flexible dynamics in the conversation
- 10:02
makes it much more comfortable for
- 10:03
humans.
- 10:04
And so, to understand how
- 10:07
we can make a model full duplex, I'll
- 10:09
give a very short
- 10:12
presentation of how we train such
- 10:13
models. So, the way you create a
- 10:15
speech-to-speech model half duplex or
- 10:16
full duplex is the following one. So,
- 10:18
you you start from a text LLM, which is
- 10:20
a probabilistic models over over words.
- 10:23
And instead of predicting the next word
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based on the past,
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what you want to do is rather predict
- 10:28
the next audio based based on the past
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audio.
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The issue now is that if you pass a raw
- 10:33
audio to your model, which is, you know,
- 10:35
a waveform, it's
- 10:37
air pressure variations.
- 10:39
Uh
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basically, you take this sentence, it's
- 10:42
eight words.
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It takes around 3 seconds to pronounce
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it. And so, at 24 kHz audio, instead of
- 10:49
having eight words, the audio form is
- 10:51
72,000 time steps that you would need to
- 10:53
feed to your LLM. Given that LLMs have
- 10:56
quadratic complexity with sequence
- 10:58
length, so the complexity is the square
- 11:00
of the sequence length. A 10,000 times
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longer sequence is 100 million times
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more expensive to to process. So, there
- 11:06
is no way you can train an LLM on raw
- 11:08
audio. So, the way you address it is by
- 11:10
creating neural codecs, or you can also
- 11:12
call them audio tokenizers. And
- 11:14
basically, it's an encoder that takes an
- 11:16
audio and compresses it in a very dense
- 11:19
compressed representation, a bit similar
- 11:21
to text. And then you have a decoder
- 11:23
that can reconstruct high-quality audio
- 11:25
from it. So, now
- 11:27
you have gone from the audio domain into
- 11:29
a abstract representation domain, where
- 11:31
you can train an LLM exactly like you
- 11:33
would train it on text.
- 11:35
And the speech-to-speech model from
- 11:37
ElevenLabs, as I was showing before,
- 11:38
works in this fashion. So, instead of
- 11:40
having text tokens into your model, you
- 11:43
have audio tokens that represent either
- 11:45
the LLM or the user, and you put them
- 11:47
one after the other, and the model
- 11:49
predicts the audio tokens uh that should
- 11:52
be said by the model, being given the
- 11:53
context from both sides of the
- 11:55
conversation.
- 11:56
However, you can see that it's still a
- 11:58
sequence between user and system, which
- 12:01
is still half duplex. So, how did we
- 12:04
make the first full duplex model ever?
- 12:06
Very simple. We call it multi-stream
- 12:08
language models. That's the technology
- 12:10
now used also by Thinking Machines for
- 12:12
their interaction model, and most likely
- 12:14
by the for the by the directional model
- 12:16
of OpenAI. Is that instead of having a
- 12:18
transformer that models one sequence of
- 12:20
tokens, it models two of them, so that
- 12:23
both parties can be active at the same
- 12:25
time, inactive at the same time, one
- 12:27
active and one inactive. And
- 12:30
I just show a very quick demo of uh of
- 12:32
how it sounds [snorts] like, but that's
- 12:34
the release of machine August 2024,
- 12:37
uh where we did an announcement live on
- 12:39
stage talking to it for the first time.
- 12:41
And you'll see that the model often
- 12:43
guesses the end of the question, answers
- 12:46
over the speaker,
- 12:48
and both speaking at the same time is
- 12:50
not breaking the flow like we saw with
- 12:52
GPT. The whole thing is just extremely
- 12:54
resilient to the most chaotic uh
- 12:56
situations.
- 12:57
>> So, the planet is serious 22. Can you
- 13:00
plot a trajectory course to it, please?
- 13:02
>> Yes, sir.
- 13:03
>> Okay. How long is it going to take us to
- 13:05
get there?
- 13:06
>> it out. It's approximately 5 months to
- 13:08
get there.
- 13:09
>> Okay, that's that's not too bad. Uh do
- 13:11
you think we have all we need on board
- 13:13
the ship to start the mission?
- 13:14
>> We have everything we need.
- 13:16
>> So, back then it was even a bit
- 13:18
irritating to people because it was
- 13:19
interrupting you all the time. But the
- 13:21
thing is that you can use you could
- 13:23
still use it in extremely noisy
- 13:24
environments with a lot of noise, people
- 13:26
coughing, and so on. And you know, the
- 13:28
flow is just constant. You don't get
- 13:30
this very irritating break of the
- 13:32
conversational flow. So,
- 13:35
these full duplex models, they are the
- 13:37
highest level of naturalness you can
- 13:38
expect. That's the same conversation
- 13:40
with a human.
- 13:42
The thing is, in with our models, it was
- 13:44
even more stupid than
- 13:46
speech-to-speech models that were
- 13:47
already less intelligent than cascaded
- 13:49
systems.
- 13:50
It's probably fine for some use cases if
- 13:52
you just want to have a chit-chat. You
- 13:53
know, the model doesn't need to be very
- 13:55
intelligent. But make an actual full
- 13:57
duplex voice agent,
- 13:58
there is no way we can give up on on
- 14:00
intelligence just to gain uh
- 14:01
speech-to-speech abilities.
- 14:04
So, how do we finally make models that
- 14:06
tackle all these aspects jointly?
- 14:08
And I think interestingly, if you if you
- 14:11
look at the history I showed, there is a
- 14:12
tension between naturalness and
- 14:14
intelligence. So, every time we improve
- 14:16
naturalness or humanness of the of the
- 14:18
models, they were less intelligent than
- 14:20
the cascaded system. The cascaded
- 14:22
agents, they are basically as smart as
- 14:24
the best text models. So, if you have a
- 14:26
voice agent that is powered by the
- 14:28
latest model from Anthropic or OpenAI,
- 14:29
it's going to be extremely smart, have
- 14:31
all the same reliability for tool call,
- 14:33
and so on. Speech-to-speech has this
- 14:35
naturalness
- 14:36
aspect. However, you give up
- 14:38
intelligence to get that. And the reason
- 14:40
why you give up intelligence is remember
- 14:43
that the LLM is a model that has a
- 14:45
certain number of weights that we call
- 14:47
the capacity.
- 14:48
And if you take a text model and now it
- 14:51
not only has to handle text, but it also
- 14:53
needs to understand speech and produce
- 14:54
speech,
- 14:55
it's taking some of its capacity, and
- 14:58
this capacity now
- 15:00
is taken from the intelligence. So,
- 15:01
fundamentally, there is a cost of adding
- 15:04
a new modality to a text model that is
- 15:06
going to be paid in in intelligence.
- 15:08
So, where do we go from here? There are
- 15:10
two paths that are in front of us, and
- 15:12
both are going to be explored at the
- 15:14
same time. The first one is scaling the
- 15:16
model. So,
- 15:18
making your speech-to-speech model
- 15:19
bigger, better pre-trained, better
- 15:22
post-trained, and so on. We likely
- 15:24
progressively increase its intelligence
- 15:26
until it it's good enough for a lot of
- 15:28
use cases.
- 15:29
The second one is splitting the model
- 15:32
between naturalness and intelligence.
- 15:35
The first one,
- 15:36
I'm not at OpenAI, so I don't know
- 15:37
because they don't release their model.
- 15:39
I guess OpenAI is the path one, so it's
- 15:42
a frontier text LLM with a lot of
- 15:44
science around post-training, in-
- 15:46
instruct tuning to fine-tune it on
- 15:48
audio, and teaching it to be quite smart
- 15:51
while using audio.
- 15:54
The nice thing about that is you have a
- 15:55
single model to orchestrate, so it's
- 15:57
quite easy to deploy.
- 15:59
Um
- 16:00
and
- 16:02
one
- 16:03
big aspect, however, is that it's a
- 16:06
extremely complex and costly process to
- 16:08
go from the text model to the
- 16:09
speech-to-speech model.
- 16:11
The second path is to split it. It's an
- 16:13
approach that we introduced in one of
- 16:14
our recent papers called Moushiraq, and
- 16:16
that has been reused by, in particular,
- 16:18
the Thinking Machine Interaction models.
- 16:21
Where, basically, the idea is that now
- 16:23
you have two models. The first one is a
- 16:25
small, maybe even on-device,
- 16:27
full-duplex, extremely natural
- 16:29
speech-to-speech interface. And its only
- 16:31
role
- 16:32
is to keep a very natural
- 16:34
conversation and be able to delegate
- 16:37
all the thinking, tool calling,
- 16:40
reasoning, agentic capabilities to a
- 16:42
background text model. And so, the way
- 16:44
to see it is you have a background text
- 16:46
LLM
- 16:47
that receives asynchronously queries
- 16:49
from hundreds to thousands of small
- 16:51
voice interfaces and just give them
- 16:53
their text, you know? And, basically,
- 16:55
what we did is um
- 16:57
very small full-duplex model that just
- 16:59
needs to know when it doesn't know, so
- 17:01
that it can delegate to the background
- 17:03
model.
- 17:04
And the reason why we believe mostly in
- 17:06
this approach,
- 17:07
um
- 17:08
and I go back to to it later, it's a
- 17:12
our
- 17:13
uh
- 17:14
let's say our culture is more of first
- 17:16
one, the bitter lesson. So, every time
- 17:18
we've been pushing for end-to-end
- 17:19
systems and so on. But now I think the
- 17:22
hybrid approach has two main
- 17:24
advantages. The The first one is cost.
- 17:26
So, speech-to-speech models are
- 17:28
notoriously quite expensive.
- 17:30
And when you think about it,
- 17:32
it's a loss of money to do chit-chat
- 17:35
with gigantic speech-to-speech models
- 17:37
that can resolve differential equations
- 17:39
and so on. So, it doesn't really
- 17:42
make sense economically to get all your
- 17:44
workflow through this gigantic
- 17:46
multimodal mixture of experts.
- 17:48
At the same time, we see that people are
- 17:50
very attached to their ability to
- 17:51
control the backend, to be able to
- 17:53
switch So they So they 5 was released a
- 17:56
few minutes ago. People want to switch
- 17:58
the backend and the intelligence and get
- 17:59
a lot of optionality on that, right?
- 18:02
When you're using a speech-to-speech
- 18:03
model, your your hands are a bit tied
- 18:06
with this model provider. And to give
- 18:08
you an idea of that,
- 18:09
until recently the AdSense voice mode
- 18:11
from OpenAI was powered by GPT-4o,
- 18:13
despite the fact that there have been
- 18:15
several generations of the text model
- 18:17
since then, because this process is so
- 18:19
expensive and so long.
- 18:21
For this reason, we rather bet on the
- 18:24
hybrid approach because that will give
- 18:26
something that is not only very natural
- 18:28
and very nice for demos and impressive,
- 18:31
but also will be a viable alternative
- 18:33
from a economic point of view and
- 18:35
agentic capabilities point of view
- 18:38
to the best cascaded systems that are
- 18:40
still most of the market today in voice.
- 18:43
So,
- 18:44
what now?
- 18:45
Uh you can use our models on gradium.ai.
- 18:48
You can apply to gradium. We are
- 18:50
recruiting research scientists and
- 18:51
engineers. And thanks for your
- 18:53
attention.
- 18:56
>> [applause]
- 19:10
[music]