> Try asking any of them “Am I speaking in a low voice or a high voice?” in a high-pitched voice, and they won’t be able to tell you.
I wonder how much of that is LLMs being bad, and how much is LLMs being (over) aligned not to do it.
AFAIK, Chat GPT Voice mode had to have a lot of safeguards put on it to prevent music generation, accent matching (if you sound Indian, it shouldn't also sound Indian), and assuming ethnicity / biasing based on accents.
It doesn't seem that impossible to me that some of these behaviors have been aligned out of these models out of an abundance of caution.
Author here. I think it's more of a capability issue than a safety issue. Since learning audio is still harder than learning text, audio models don't generalize as well. To fix that, audio models rely on combining information from text and audio (having a single model that consumes/produces both text and audio tokens) and the audio tokens basically end up being an integrated speech-to-text/text-to-speech. This reflects my colleagues' experience working on Moshi, and it seems to be the case for other models too, see the Conclusion section.
Part of the reason can also be synthetic data: if you fine-tune on data generated from text via a text-to-speech, the tone of the voice doesn't have any information, so the model learns to ignore it.
Did they respond differently depending on what race they thought you were? I'm surprised they would even do that honestly. I thought they were trained on text conversations which presumably wouldn't have any of that to learn from.
accent matching (if you sound Indian, it shouldn't also sound Indian)
Why not? I've found that it helps greatly with mutual intelligibility when both sides are speaking a similar dialect, and the one who can do this switching, switches to that of the one who can't.
(I wish I could also use an Indian accent bidirectionally; would definitely come in handy for those aggravating times I've had to talk to an outsourced customer service department.)
I don't think it's just safeguards; they really don't seem to understand pitch at all. I tried asking ChatGPT's advanced voice mode to recognize a tune I was humming, and it insisted it was Beethoven's 5th -- multiple times. I think it must have basically tokenized my humming to "dun dun dun duuun".
I wonder if a linear-space, constant-time model like RWKV or S4 would work better here. For audio, I wouldn't think you'd need long range context, and all-to-all mapping seems like overkill.
Maybe a transformer could be running in parallel, but much lower frequency, where the linear model feeds it "summary" tokens once per second, whose information would mostly be "text", but also some hint of emotion and other cues. Then the output of this could be fed back to the linear model so that it would know what it was saying and with what emotion. Basically the transformer would be the low frequency long range context thinker (and feeler), and the linear model would translate that to and from phonetics.
They'd be trained in parallel, so those transformer tokens would attain meaning at training time, not something that would have to be pre-defined. So it'd still be purely phonetic e2e, no direct translation to text. It could even end up being a good way to compress text for LLMs, since low-value words might have smaller representation in the token.
Probably would never reach the level of text based LLMs for logic and code and such, but that somewhat parallels humans anyway; it's pretty hard to explain an algorithm in detail in plain conversation.
I don't know about linear models, but this kind of hierarchical modelling is quite a common idea in speech research. For example, OpenAI's Jukebox (2020) [1], which uses a proto-neural audio codec, has three levels of encoding that get coarser and coarser. They use a language model to predict continuations in the coarsest level and then have models to upscale to the finer levels and finally back to audio.
The recent MiMo-audio bunches tokens into "patches" of four timesteps and has the model predict those. [2]
Why not normal audio codecs? How are JPEG and MP3 (i.e., DCT/MDCT) not a reasonable way to go about tokenizing spatial and time domain signals for these kinds of models?
Each MP3 frame is entirely self-contained and can completely reconstruct a few tens of milliseconds of original audio. It does not require other frames to do this. I think this is the most important element. At 128kbps CBR, each MP3 frame is ~418 bytes and covers ~26 milliseconds of time. This is a reduction of 10-11x over the raw PCM waveform. MP3 is also designed to eliminate the information that humans don't seem to care about.
I don't know if it's possible to use 400 byte tokens in a transformer model, but I would be very compelled to try.
Author here. There are a few reasons, but the biggest one is simply the compression ratio.
The OG neural audio codec SoundStream (whose first author is Neil, now at Kyutai) can sound decent at 3kbps, whereas MP3 typically has around 128kbps, as you say. Interestingly, it was originally developed for audio compression for Google Meet, not for LLMs. Today's neural codecs have even better compression.
The more modern MP3 alternative is Opus, which can work ok at 12kbps, but it's still less efficient than neural audio codecs. However, these traditional codecs are a lot less CPU-hungry, so they have that going for them.
The simple, elegant approach of training convolutional neural networks (CNNs) directly from RGB pixels has enjoyed overwhelming empirical success. But can more performance be squeezed out of networks by using different input representations? In this paper we propose and explore a simple idea: train CNNs directly on the blockwise discrete cosine transform (DCT) coefficients computed and available in the middle of the JPEG codec. Intuitively, when processing JPEG images using CNNs, it seems unnecessary to decompress a blockwise frequency representation to an expanded pixel representation, shuffle it from CPU to GPU, and then process it with a CNN that will learn something similar to a transform back to frequency representation in its first layers. Why not skip both steps and feed the frequency domain into the network directly? In this paper we modify \libjpeg to produce DCT coefficients directly, modify a ResNet-50 network to accommodate the differently sized and strided input, and evaluate performance on ImageNet. We find networks that are both faster and more accurate, as well as networks with about the same accuracy but 1.77x faster than ResNet-50.
Human audio perception is based on detecting the frequency components, which we detect via what amounts to a filter bank in the inner ear (different length hairs with different resonant frequencies).
Speech perception builds upon frequencies and is based on "formants" - the frequency bands that are attentuated via the vocal tract resonances created by articulation when the speech was generated. More specifically, most speech information is contained in formant changes since these correspond to articulatory changes. There are also other articulatory artifacts in speech such as the onsets of speech energy corresponding to plosives ("puh", "buh"), and the high frequencies generated by fricatives like "sss".
One problem with embedding MP3 frames as audio tokens would be that although MP3 compression is based on frequency representation, you've then got quantization, huffman encoding and the MP3 frame structure all on top of that, so the frame as a whole is going to be more of a black box. Presumably a transformer could still use MP3 frames to predict the text transcription, or any arbitrary encoding of speech audio for that matter (similar to how an LLM can predict text from Base64 representation, or vice versa), but it's certainly not making it easier if the input is obfuscating the frequency components and formants etc that correspond to the generating process.
Not having direct access to the frequency/formant information is also going to make generalization more difficult since that is based around formant structure and changes. When articulating the same word, the specific formant frequencies will differ between individuals, primarily based on vocal tract length, but humans have no problem generalizing across these and understanding speech from different individuals. I'm not sure if an LLM only trained to predict MP3 speech from, say, male adults, would necessarily have generalized enough to also be able to recognize child speech or that from a speech synthesizer.
You can try to train an adapter from a raw 400-byte MP3 frame to an embedding for a given LLM (4096+ floating point numbers, exact precision varies).
But you'd need that information to be digestible for a neural network. Otherwise, you'll have a very hard time getting that adapter to work.
As a rule: neural networks love highly redundant data, and hate highly compressed data at their inputs. Tokenized text good, GZIP compressed bytestream bad. But who knows, really. It's a rule of thumb, not a mathematical law. So you could have some success getting that MP3-based adapter to work. I've seen weirder shit work.
The approach in TFA encodes into a 32 dimensional space. I suspect this is significantly more dimensions than any psycho-acoustic compression algorithm uses. Also, throwing away information that our hearing systems can't process very well is not particularly useful if your goal is speech (or more generally, audio) synthesis from scratch.
I believe language models usually use 2-byte (16 bit) tokens, which corresponds to an embedding dimension of 2^16=65536. With 400 bytes per token this would be 2^(400*8), which is an extremely large number. Way too large to be practical, I assume.
This has got to be one of the most visually pleasing explanations I have seen of these concepts. Congrats!
I attempted some similar VQ-VAE work instead trying to tokenize rendered text. I was curious if I could make a visual llm working on 10 pt rendered font, but I also tried using PDF sources. The basic idea was to do what more advanced diffusion image models can do where they generate images of text. Make a specific image text diffusion model to do completions. Further I wondered if I could embed things like document type and language so you could have a latent representation of text more abstracted than current dictionary tokenizers. Learned a lot and thought it was all beautifully displayed in this post.
An ongoing question I have is why effort wasn't put into tokenising speech (instead of transcribed words) and then making an LLM out of that. There are huge amounts of speech available to train on.
The article is talking about doing exactly that. The key question is how to convert an inherently continuous signal (speech/audio) into a discrete set of tokens. A single window of audio is usually somewhere between 10ms and 100ms. It's difficult to squish all that information down to a single "token" that represents the semantic and acoustic content for that window.
That's why residual vector quantization is a useful technique - using multiple dictionaries to quantize a single timeslice, each conditioned on the previous residual level. You can also quantize a signal at different frequencies.
There are samples towards the end of the post of their LLM trained on their Mimi audio codec.
There is data but nowhere near the amount of written language that is fairly normalized and without the need to account for additional features such as language, dialect, intonation, facial expression, hand gestures. Speech to text is used as the translation layer as it throws many of those other features away and contextualizes it into a set of tokens that are much more efficient to map between languages.
It costs more to train on audio tokens but I'm sure we will get there. Training a model on transcript of a lecture on YouTube vs. training on audio of it will make a difference.
Audio tokenization consumes at least 4x tokens versus text. So there is an efficiency problem to start with. Then is there enough audio data to train a LLM from scratch?
Obviously working directly with audio is vastly more complex than with text.
But it is very exciting to see how part of making LLMs work natively with speech, is finding a codec that is maximally efficient at encoding speech.
I even have to wonder if, at some point, we ultimately create a popular voice codec usable with LLMs based not on the Fourier transform or similar, but rather on some kind of set of physical parameters describing vocal cord shape, tongue position, throat/chest/mouth shape, etc.
I can imagine such a model being arrived at statistically (determining the necessary number of parameters), and then almost becoming "hard-coded" as a standard since human anatomy doesn't change much there, beyond certain ranges.
I think it's called formant speech encoding, and it would be interesting if LLMs wind up massively advancing that field. Since I think historically it's had to do more with speech synthesis than audio compression.
Author here, thanks for the kind words! I think such a physics-based codec is unlikely to happen: in general, machine learning is always moving from handcrafted domain-specific assumptions to leaving as much as possible to the model. The more assumptions you bake in, the smaller the space of sounds you can model, so the quality is capped. Basically, modern ML is just about putting the right data into transformers.
That being said, having a more constrained model can also lead to some really cool stuff. The DDSP paper learns how to control a synthesizer to mimic instruments: https://arxiv.org/abs/2001.04643
You could probably do something similar for a speech model. The result would not sound as good but you could get away with much fewer parameters, because much of the modelling work is done by the assumptions you put in.
Compare also KokoroTTS, a tiny TTS that's so tiny because it uses a handcrafted system to turn text into phonemes, and then just synthesizes from those phonemes: https://huggingface.co/spaces/hexgrad/Kokoro-TTS
In speech coding/synthesis this called a "source-filter" model (decompose speech production into a sound generator in the vocal folds and filter in the vocal tract, and parameterize them) and it's actually older than Tukey and Cooley's rediscovery of the FFT.
There’s a long history of attempts at artificial speech that take this approach, recreating mouth parts and vibrating air. They are all pretty silly, like this work, which fails to understand how writing isn’t just a derivative of speech.
Thanks for sharing this well written post that I will share with my team; we just recently started using audio/voice in our AI suite and the details herein will be helpful and informative.
I've been messing around with Higgs Audio that actually uses the delay pattern. It has to apply it and then unapply it after the generation. I noticed it's actually really hard to chunk and stream audio correctly when you need to apply and reapply these patterns essentially to the "entire" output.
I can't wait for LLMs to actually understand how they and you are speaking. It's going to be so cool when an AI can correct your second language pronunciation or laugh at you for making a silly sound. The usecases and value will explode when that happens 100%
I wouldn't mind so much if they cheat on the way back but listen in earnest. There are use cases like teaching language where having the AI understand the sounds carefully matters a ton.
Out of curiosity, would it be possible to attach pitch, emotion, tone info as text-based metadata to each word during ASR, so that the asr output retains these metadata?
> Many LLMs have voice interfaces, but they usually work by transcribing your speech, generating the answer in text, and using text-to-speech to read the response out loud. That’s perfectly fine in many cases (...), but it’s a wrapper, not real speech understanding.
But I can say the same about tokenization. LLMs first convert groups of characters to tokens, then use that to generate tokens, and then convert the tokens back to characters. That's not real understanding! If LLMs are so smart, we should be able to skip the tokenization step.
Nothing is real understanding because we have no benchmark for understanding because we don't mechanistically know what understanding is. The best we have is people "vibe knowing" a benchmark that they made up on the spot.
There's a great blog post from Sander Dieleman about exactly this - why do we need a two step pipeline, in particular for images and audio?
https://sander.ai/2025/04/15/latents.html
For text, there are a few papers that train the tokenization and language model end-to-end, see: https://arxiv.org/abs/2305.07185
I train for 1M steps (batch size 64, block size 2048), which is enough for the model to more-or-less converge.
It's also a tiny model for LLM standards, with 150M parameters. The goal wasn't really to reach state of the art but to show how the performance of a single language model architecture can be vastly different when you just change the tokenizer.
the OP is quite an interesting team to watch regarding open-weights* voice-related efforts. this is a nice read to understand the core of their approach.
quite unfortunate, however, their approach to accessibility. unmute [1], which uses the approach discussed in this post, runs quite well with claimed feature of adapting to any voice provided you have a 10 second recording. this is not made available to public at all, despite an issue raised since july. [2]
given the pace of the industry, it is a shame that we need to look elsewhere for using an otherwise well-designed tooling.
Another interesting thing here is that the model presumably has some understanding of the passage of time. That's one thing that can be odd about chat models, in that they will respond the same no matter whether you respond a second later or a month later.
I think even for text models, "streams" could be useful. Perhaps if the LLM sees too long of a pause after explaining something and asking a question, they could interject a "do you need help?" or something. Pure chat GPTs don't have that ability.
Indeed, the title undersells it and I'm glad I didn't skip over it, the article is basically an information-dense but approachable summary of audio generation.
Man, one of the best uses of all those AI algorithms based around finding similarities between stuff, would be to give you actually relevant recommendations for music.
All the streaming services are shit at it. They can't do much beyond shallow similarities or hardcoded recommendations that are probably just based on manually-entered keywords like the genre etc.
Has that already been done?
Or is it yet another of those what-could-have-been utopian things that got crippled before it was born because of corporate overcontrolling/overcautiousness (not being able to train on copyrighted music)
Maybe some open-source project could do it?
(I don't even feel confident in asking AI if a music-recc AI exists because ChatGPT 5 didn't know ChatGPT 5 was out, and Claude still thinks iOS 26 isn't out yet..sigh)
Y’all need to learn about the history and development of spoken language and writing. Writing isn’t just a copy or derivation of writing. LLMs work because of the conceptual characteristics of writing (consider the distinctions between ideographic, logographic, alphabetical…). What a sloppy mess!
Read some Wittgenstein and Goodman, but especially Derrida who calls this logocentrism.
miki123211|4 months ago
I wonder how much of that is LLMs being bad, and how much is LLMs being (over) aligned not to do it.
AFAIK, Chat GPT Voice mode had to have a lot of safeguards put on it to prevent music generation, accent matching (if you sound Indian, it shouldn't also sound Indian), and assuming ethnicity / biasing based on accents.
It doesn't seem that impossible to me that some of these behaviors have been aligned out of these models out of an abundance of caution.
vvolhejn|4 months ago
Part of the reason can also be synthetic data: if you fine-tune on data generated from text via a text-to-speech, the tone of the voice doesn't have any information, so the model learns to ignore it.
tsol|4 months ago
idonotknowwhy|4 months ago
userbinator|4 months ago
Why not? I've found that it helps greatly with mutual intelligibility when both sides are speaking a similar dialect, and the one who can do this switching, switches to that of the one who can't.
(I wish I could also use an Indian accent bidirectionally; would definitely come in handy for those aggravating times I've had to talk to an outsourced customer service department.)
sbrother|4 months ago
bongodongobob|4 months ago
unknown|4 months ago
[deleted]
daxfohl|4 months ago
Maybe a transformer could be running in parallel, but much lower frequency, where the linear model feeds it "summary" tokens once per second, whose information would mostly be "text", but also some hint of emotion and other cues. Then the output of this could be fed back to the linear model so that it would know what it was saying and with what emotion. Basically the transformer would be the low frequency long range context thinker (and feeler), and the linear model would translate that to and from phonetics.
They'd be trained in parallel, so those transformer tokens would attain meaning at training time, not something that would have to be pre-defined. So it'd still be purely phonetic e2e, no direct translation to text. It could even end up being a good way to compress text for LLMs, since low-value words might have smaller representation in the token.
Probably would never reach the level of text based LLMs for logic and code and such, but that somewhat parallels humans anyway; it's pretty hard to explain an algorithm in detail in plain conversation.
vvolhejn|4 months ago
The recent MiMo-audio bunches tokens into "patches" of four timesteps and has the model predict those. [2]
[1] https://arxiv.org/abs/2005.00341
[2] https://github.com/XiaomiMiMo/MiMo-Audio/blob/main/MiMo-Audi...
ketothekingdom|4 months ago
tehnub|4 months ago
bob1029|4 months ago
Each MP3 frame is entirely self-contained and can completely reconstruct a few tens of milliseconds of original audio. It does not require other frames to do this. I think this is the most important element. At 128kbps CBR, each MP3 frame is ~418 bytes and covers ~26 milliseconds of time. This is a reduction of 10-11x over the raw PCM waveform. MP3 is also designed to eliminate the information that humans don't seem to care about.
I don't know if it's possible to use 400 byte tokens in a transformer model, but I would be very compelled to try.
vvolhejn|4 months ago
The OG neural audio codec SoundStream (whose first author is Neil, now at Kyutai) can sound decent at 3kbps, whereas MP3 typically has around 128kbps, as you say. Interestingly, it was originally developed for audio compression for Google Meet, not for LLMs. Today's neural codecs have even better compression.
The more modern MP3 alternative is Opus, which can work ok at 12kbps, but it's still less efficient than neural audio codecs. However, these traditional codecs are a lot less CPU-hungry, so they have that going for them.
WithinReason|4 months ago
The simple, elegant approach of training convolutional neural networks (CNNs) directly from RGB pixels has enjoyed overwhelming empirical success. But can more performance be squeezed out of networks by using different input representations? In this paper we propose and explore a simple idea: train CNNs directly on the blockwise discrete cosine transform (DCT) coefficients computed and available in the middle of the JPEG codec. Intuitively, when processing JPEG images using CNNs, it seems unnecessary to decompress a blockwise frequency representation to an expanded pixel representation, shuffle it from CPU to GPU, and then process it with a CNN that will learn something similar to a transform back to frequency representation in its first layers. Why not skip both steps and feed the frequency domain into the network directly? In this paper we modify \libjpeg to produce DCT coefficients directly, modify a ResNet-50 network to accommodate the differently sized and strided input, and evaluate performance on ImageNet. We find networks that are both faster and more accurate, as well as networks with about the same accuracy but 1.77x faster than ResNet-50.
https://proceedings.neurips.cc/paper_files/paper/2018/file/7...
I suspect mp3 is also a good idea
HarHarVeryFunny|4 months ago
Speech perception builds upon frequencies and is based on "formants" - the frequency bands that are attentuated via the vocal tract resonances created by articulation when the speech was generated. More specifically, most speech information is contained in formant changes since these correspond to articulatory changes. There are also other articulatory artifacts in speech such as the onsets of speech energy corresponding to plosives ("puh", "buh"), and the high frequencies generated by fricatives like "sss".
One problem with embedding MP3 frames as audio tokens would be that although MP3 compression is based on frequency representation, you've then got quantization, huffman encoding and the MP3 frame structure all on top of that, so the frame as a whole is going to be more of a black box. Presumably a transformer could still use MP3 frames to predict the text transcription, or any arbitrary encoding of speech audio for that matter (similar to how an LLM can predict text from Base64 representation, or vice versa), but it's certainly not making it easier if the input is obfuscating the frequency components and formants etc that correspond to the generating process.
Not having direct access to the frequency/formant information is also going to make generalization more difficult since that is based around formant structure and changes. When articulating the same word, the specific formant frequencies will differ between individuals, primarily based on vocal tract length, but humans have no problem generalizing across these and understanding speech from different individuals. I'm not sure if an LLM only trained to predict MP3 speech from, say, male adults, would necessarily have generalized enough to also be able to recognize child speech or that from a speech synthesizer.
ACCount37|4 months ago
But you'd need that information to be digestible for a neural network. Otherwise, you'll have a very hard time getting that adapter to work.
As a rule: neural networks love highly redundant data, and hate highly compressed data at their inputs. Tokenized text good, GZIP compressed bytestream bad. But who knows, really. It's a rule of thumb, not a mathematical law. So you could have some success getting that MP3-based adapter to work. I've seen weirder shit work.
PaulDavisThe1st|4 months ago
cubefox|4 months ago
robviren|4 months ago
I attempted some similar VQ-VAE work instead trying to tokenize rendered text. I was curious if I could make a visual llm working on 10 pt rendered font, but I also tried using PDF sources. The basic idea was to do what more advanced diffusion image models can do where they generate images of text. Make a specific image text diffusion model to do completions. Further I wondered if I could embed things like document type and language so you could have a latent representation of text more abstracted than current dictionary tokenizers. Learned a lot and thought it was all beautifully displayed in this post.
trollbridge|4 months ago
nmfisher|4 months ago
That's why residual vector quantization is a useful technique - using multiple dictionaries to quantize a single timeslice, each conditioned on the previous residual level. You can also quantize a signal at different frequencies.
There are samples towards the end of the post of their LLM trained on their Mimi audio codec.
ca_tech|4 months ago
mohsen1|4 months ago
benob|4 months ago
MichealCodes|4 months ago
mmaunder|4 months ago
crazygringo|4 months ago
Obviously working directly with audio is vastly more complex than with text.
But it is very exciting to see how part of making LLMs work natively with speech, is finding a codec that is maximally efficient at encoding speech.
I even have to wonder if, at some point, we ultimately create a popular voice codec usable with LLMs based not on the Fourier transform or similar, but rather on some kind of set of physical parameters describing vocal cord shape, tongue position, throat/chest/mouth shape, etc.
I can imagine such a model being arrived at statistically (determining the necessary number of parameters), and then almost becoming "hard-coded" as a standard since human anatomy doesn't change much there, beyond certain ranges.
I think it's called formant speech encoding, and it would be interesting if LLMs wind up massively advancing that field. Since I think historically it's had to do more with speech synthesis than audio compression.
vvolhejn|4 months ago
That being said, having a more constrained model can also lead to some really cool stuff. The DDSP paper learns how to control a synthesizer to mimic instruments: https://arxiv.org/abs/2001.04643
You could probably do something similar for a speech model. The result would not sound as good but you could get away with much fewer parameters, because much of the modelling work is done by the assumptions you put in.
Compare also KokoroTTS, a tiny TTS that's so tiny because it uses a handcrafted system to turn text into phonemes, and then just synthesizes from those phonemes: https://huggingface.co/spaces/hexgrad/Kokoro-TTS
duped|4 months ago
quinndupont|4 months ago
unknown|4 months ago
[deleted]
mondainx|4 months ago
lxe|4 months ago
casey2|4 months ago
Rickasaurus|4 months ago
liqilin1567|4 months ago
amelius|4 months ago
But I can say the same about tokenization. LLMs first convert groups of characters to tokens, then use that to generate tokens, and then convert the tokens back to characters. That's not real understanding! If LLMs are so smart, we should be able to skip the tokenization step.
Workaccount2|4 months ago
vvolhejn|4 months ago
For text, there are a few papers that train the tokenization and language model end-to-end, see: https://arxiv.org/abs/2305.07185
croemer|4 months ago
vvolhejn|4 months ago
orena|4 months ago
vvolhejn|4 months ago
It's also a tiny model for LLM standards, with 150M parameters. The goal wasn't really to reach state of the art but to show how the performance of a single language model architecture can be vastly different when you just change the tokenizer.
rldjbpin|4 months ago
quite unfortunate, however, their approach to accessibility. unmute [1], which uses the approach discussed in this post, runs quite well with claimed feature of adapting to any voice provided you have a 10 second recording. this is not made available to public at all, despite an issue raised since july. [2]
given the pace of the industry, it is a shame that we need to look elsewhere for using an otherwise well-designed tooling.
[1] https://news.ycombinator.com/item?id=44109610 [2] https://github.com/kyutai-labs/unmute/issues/99
daxfohl|4 months ago
I think even for text models, "streams" could be useful. Perhaps if the LLM sees too long of a pause after explaining something and asking a question, they could interject a "do you need help?" or something. Pure chat GPTs don't have that ability.
bkitano19|4 months ago
krackers|4 months ago
unknown|4 months ago
[deleted]
unknown|4 months ago
[deleted]
Razengan|4 months ago
All the streaming services are shit at it. They can't do much beyond shallow similarities or hardcoded recommendations that are probably just based on manually-entered keywords like the genre etc.
Has that already been done?
Or is it yet another of those what-could-have-been utopian things that got crippled before it was born because of corporate overcontrolling/overcautiousness (not being able to train on copyrighted music)
Maybe some open-source project could do it?
(I don't even feel confident in asking AI if a music-recc AI exists because ChatGPT 5 didn't know ChatGPT 5 was out, and Claude still thinks iOS 26 isn't out yet..sigh)
quinndupont|4 months ago
Read some Wittgenstein and Goodman, but especially Derrida who calls this logocentrism.