My understanding is that this is purely a strategic choice by the bigger labs. When OpenAI released Whisper, it was by far best-in-class, and they haven't released any major upgrades since then. It's been 3.5 years... Whisper is older than ChatGPT.
Gemini 3 Pro Preview has superlative audio listening comprehension. If I send it a recording of myself in a car, with me talking, and another passenger talking to the driver, and the radio playing, me in English, the radio in Portuguese, and the driver+passenger in Spanish, Gemini can parse all 4 audio streams as well as other background noises and give a translation for each one, including figuring out which voice belongs to which person, and what everyone's names are (if it's possible to figure that out from the conversation).
I'm sure it would have superlative audio generation capabilities too, if such a feature were enabled.
OpenAI and google are too scared of music industry lawyers to tackle this. Internally they without a doubt have models that would crush these startups over night if they chose to release them.
Is your claim that music industry lawyers are that much scarier than movie industry lawyers? Because the big labs don't seem to have any problem releasing models that create (possibly infringing) video.
> Is your claim that music industry lawyers are that much scarier than movie industry lawyers?
Not qoez:
You have to balance market opportunities with the risk of reputational damage and litigation risk.
Video will probably make a lot more money than audio, so you are willing to take a bigger risk. Additionally, at least for Google there exists a strong synergy between their video generation models and YouTube, which makes it even more sensible for Google to make video models available to the public despite these risks.
I'm not sure it's just fear of lawyers, although that's definitely part of it. Big companies have way more to lose reputationally and legally, so the bar for releasing something is much higher
Most current "voice assistants" still feel like glorified walkie-talkies... you talk, pause awkwardly, they respond, and any interruption breaks the flow
Moshi was an amazing tech demo, building the entire stack from scratch in 6 months with a small team was an amazing show of skill: 7B text LLM data + training, emotive TTS for synth data generation (again model + data collection), synth data pipeline, novel speech codec, rust inference stack for low latency, audio LLM architecture incl. text "thoughts" stream which was novel.
But, this piece is a fluff piece: "underfunded" means a total of around $400 million ($330 million in the initial round, $70 million for Gradium). Compare to Elevenlabs who used a $2 million pre-seed for creating their initial product.
A bunch of other stuff there is disingenuous, like comparing their 7B model to Llama-3 405B (hint: the 7B model is a _lot_ dumber). There's also the outright lie: team of 4 made Moshi, which is corrected _in the same piece_ to 8 if you read enough.
This matches my experience. In Kaggle audio competitions, I've seen many competitors struggle with basics like proper PCM filtering - anti-aliasing before downsampling, handling spectral leakage, etc.
Audio really is a blue ocean compared to text/image ML. The barriers aren't primarily compute or data - they're knowledge. You can't scale your way out of bad preprocessing or codec choices.
When 4 researchers can build Moshi from scratch in 6 months while big labs consider voice "solved," it shows we're still in a phase where domain expertise matters more than scale. There's an enormous opportunity here for teams who understand both ML and signal processing fundamentals.
Also, while the author complains that there is not a lot of high quality data around [0], you do not need a lot of data to train small models. Depending on the problem you are trying to solve, you can do a lot with single-digit gigabytes of audio data. See, e.g., https://jmvalin.ca/demo/rnnoise/
[0] Which I do agree with, particularly if you need it to be higher quality or labeled in a particular way: the Fisher database mentioned is narrowband and 8-bit mu-law quantized, and while there are timestamps, they are not accurate enough for millisecond-level active speech determination. It is also less than 6000 conversations totaling less than 1000 hours (x2 speakers, but each is silent over half the time, a fact that can also throw a wrench in some standard algorithms, like volume normalization). It is also English-only.
Wrt data, it's not like there is a shortage of transcribed audio in the form of music sheets, lyrics and subtitles.
If one asks ~~nice~~ expensive enough they can even get isolated multitracks or teleprompter feeds together with the audiovisual tracks. Heck, if they wanted they could set up dedicated transcription teams for the plethora of podcasts with the costs somewhere in the rounding error range. But you can't siphon that off of torrents and paying for training material goes against the core ethics of the big players.
Too bad you can't really scrape tiktok/instagram reels with subtitles... Oh no, oh no, oh no no no no
I refuse to believe that none of these people ever heard of Nyquist, and that noone was able to come up with "ayyy lmao let's put a low pass on this before downsampling".
Edit: 2 day old account posting stuff that doesn't pass the sniff test. Hmmmm... baited by a bot?
in ~30 years of my work in DSP domain, I've seen insane amount of ways to do signal processing wrong even for simplest things like passing a buffer and doing resampling.
The last example I've seen in one large company, done by a developer lacking audio/DSP experience: they used ffmpeg's resampling lib, but, after every 10ms audio frame processed by resampler, they'd invoke flush(), just for the sake of convenience of having the same number of input and output buffers ... :)
What's the point of saying that without backing it up? Either you think it's so obvious it doesn't need backing up (in which case you don't need to say it), or ...?
The reason it matters is that soon, any time somebody sees a comment they don't like or think is stupid, they'll just say, "eh a bot said that," and totally dilute the rest of the discussion, even if the comment was real.
Good article and I agree with everything in there. For my own voice agent I decided to make him PTT by default as the problems of the model accurately guessing the end of utterance are just too great. I think it can be solved in the future but, I haven't seen a really good example of it being done with modern day tech including this labs. Fundamentally it all comes down to the fact that different humans have different ways of speaking, and the human listening to them updates their own internal model of the speech pattern. Adjusting their own model after a couple of interactions and arriving at the proper way of speaking with said person. Something very similar will need to be done and at very fast latency's for it to succeed in the audio ml world. But I don't think we have anything like that yet. It seems currently best you can do is tune the model on a generic speech pattern that you expect to fit over a larger percentage of the human population and that's about the best you can do, anyone who falls outside of that will feel the pain of getting interrupted every time.
Can someone reccomend to me: a service that will generate a loopable engine drone for a "WWII Plane Japan Kawasaki Ki-61"? It doesn't have to be perfect, just convincing in a hollywood blockbuster context, and not just a warmed over clone of a Merlin engine sound. Turns out Suno will make whatever background music I need, but I want a "unique sound effect on demand" service. I'm not convinced voice AI stuff is sustainable
With the prompt "WWII Plane Japan Kawasaki Ki-61 flying by, propeller airplane" and setting looping on and 30 sec duration manually instead of auto (the duration predictor fails pretty bad at this prompt, you need to be logged in to set duration manually) it works pretty well. No idea if it's close to that specific airplane though it sounds like a ww2 plane to me though.
Why would they do that? Once they have their win-condition, there's no reason to innovate. Only to reduce the costs of existing solutions. I expect that unless voice becomes a parameter which drives competition and first-choice for adoption, it will never become a focus of the frontier orgs. Which is curious to me, as almost the opposite of how I'm reading your comment.
i buy the thesis that audio is a wedge because latency/streaming constraints are brutal, but i wonder if it's also just that evaluation is easier. with vision, it's hard to say if a model is 'right' without human taste, but with speech you can measure wer, speaker similarity, diarization errors, and stream jitter. do you think the real moat is infra (real-time) or data (voices / conversational corpora)?
OpenAI being the death star and audio AI being the rebels is such a weird comparison, like what? Wouldn't the real rebels be the ones running their own models locally?
True, but there's a fun irony: the Rebels' X-Wings are powered by GPUs from a company that's... checks relationships ...also supplying the Empire.
NVIDIA's basically the galaxy's most successful arms dealer, selling to both sides while convincing everyone they're just "enabling innovation." The real rebels would be training audio models on potato-patched RP2040s. Brave souls, if they exist.
The company behind the T-65B X-Wing, Incom Corporation, did supply the Empire, as they did the Republic Navy before. By 0 BBY, Incom was nationalized by the Imperials. The X-Wing became the mainstay Alliance fighter because the plans were stolen by some defecting Incom engineers.
not sure about the irony - you can't really expect rebels to start their own weapons manufacturing lab right from converting ore into steel... these things are often supplied by a large manufacturer (which is often a monopoly)
why is it any different for a startup to tap into nvidia's proverbial shovel in order to start digging for gold?
I was suspicious that they are not mentioned, but then I realized this is a VC opinion piece and the first company mentioned joined their portfolio last year.
Transcription providers like wisprflow and willow voice are
typically providing nice UI/UX around open source models.
Wisprflow does not create it's own models but i know willow voice did do extensive finetuning to improve the quality and speed of their transcription models so you may count them.
Gemini 3 Pro Preview has superlative audio listening comprehension. If I send it a recording of myself in a car, with me talking, and another passenger talking to the driver, and the radio playing, me in English, the radio in Portuguese, and the driver+passenger in Spanish, Gemini can parse all 4 audio streams as well as other background noises and give a translation for each one, including figuring out which voice belongs to which person, and what everyone's names are (if it's possible to figure that out from the conversation).
I'm sure it would have superlative audio generation capabilities too, if such a feature were enabled.
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