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Do you know for a fact that new GPT models include native voice modality, versus some sort of Whisper preprocessing stage? I’m asking, because a couple of days ago I was trying to explain to /u/jkf that this is most definitely within the potential range of capabilities of frontier models, with him being skeptical.
https://community.openai.com/t/advanced-voice-mode-limited/959015
"The GPT-4o model used in Advanced Voice Mode is multimodal and directly receives audio."
Thanks!
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I don't think it's not within their potential capabilities, I just don't see any reason why this would be added -- if you were to do so, it clearly would need to be specifically trained on audio tokens which seems to me to amount to embedding a Whisper model into your LLM. I just don't see any reason to do that? Wouldn't it make more sense to just call a transcription model (which as you know are pretty good these days) and throw the resulting text at your LLM?
Think of it this way: the point of even purely text-based LLMs is not to understand text per se, but rather to understand concepts, ideas, and meaning. The text itself is just a medium through which these are conveyed. The same is true about voice modality: we do not care about the pressure waveforms, but rather about what is being conveyed by these. Transcribing them to words and digesting them as word tokens is lossy, you lose tone, tempo, background, etc. Training on audio is going to make your model perform better, not only on audio, but likely also when dealing with just text. It's similar to how models pretrained on lots of computer code work much better at non code related tasks.
But audio tokens are not intercompatible with text tokens, for obvious reasons -- if you were to train your model on a corpus of audio tokens intermingled with the text ones, wouldn't it tend to respond differently depending on the form of your input?
I don’t understand what you mean, eg
What exactly do you mean by “not intercompatible”?
A sound is not a word? (or chunk thereof)
There also is not a 1:1 mapping of sounds to tokens in English. (or most languages really, but English is particularly bad that way)
LLMs are trained on text corpora -- how do you map those text tokens to sound data?
You seem to fundamentally misunderstand what multi modality is.
The LLMs are not trained on text corpora. They are trained on tokens. These tokens are just numbers. The training process is good at finding patterns in the numbers they are fed, and predicting subsequent numbers.
Where do the numbers come from? Well, in text only LLMs, there is a tokenizer stage that takes text input and translates it into string of numbers. These numbers don’t directly correspond to individual letters or words, and so LLMs never really see those. This is why, for example, they had trouble telling how many rs are in strawberry, because what you’re asking them is how many rs are in 2645, 675, 15717 (this is the tokenization of Strawberry). It’s actually pretty magical that they manage to figure out that there are any rs in these numbers at all, that they are able to learn detokenization.
So how about different modalities? Well, these are represented as just different tokens, different numbers. For us humans some numbers represent image fragments, some represent sound snippets, and some represent pieces of text. For a multimodal LLM though, it’s all the same, it’s all just numbers. When you train them, you feed text, images, and sounds directly into the model (after tokenizing), you don’t do anything like “first transcribe sound to text through some different model and then feed it as text”. The sounds are never mapped 1-1 to English tokens, they are mapped 1-1 to sound tokens, which is just a different set of numbers than those representing Unicode text.
Because of this, for an LLM, not only sounds and words are intercompatible, but in fact there is literally no difference between the two on a technical level. There is of course a difference on a semantic level: for example, it learns quickly that text and sound tokens never mix, because it’s never fed training data that’s intermingled sound and text. Instead, after sound comes another sound (eg response to a dialogue) or a bunch of text (a transcription). But, to a model, there is no fundamental difference between text and sound and image.
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Just feed the text into a text-to-speech device or software, then use the output from that for the mapping.
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Direct audio input is better than a transcript because it can capture things like tone.
It could (maybe), but this is drifting even further from how current LLMs work -- the previous discussion on this was around an example where (if the model were doing this rather than working from a transcript and hallucinating) it completely failed to account for pronunciation, much less tone.
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