Hey HN! We’re Toby and Jay, creators of Dia. Dia is 1.6B parameter open-weights model that generates dialogue directly from a transcript.
Unlike TTS models that generate each speaker turn and stitch them together, Dia generates the entire conversation in a single pass. This makes it faster, more natural, and easier to use for dialogue generation.
It also supports audio prompts — you can condition the output on a specific voice/emotion and it will continue in that style.
We started this project after falling in love with NotebookLM’s podcast feature. But over time, the voices and content started to feel repetitive. We tried to replicate the podcast-feel with APIs but it did not sound like human conversations.
So we decided to train a model ourselves. We had no prior experience with speech models and had to learn everything from scratch — from large-scale training, to audio tokenization. It took us a bit over 3 months.
Our work is heavily inspired by SoundStorm and Parakeet. We plan to release a lightweight technical report to share what we learned and accelerate research.
We’d love to hear what you think! We are a tiny team, so open source contributions are extra-welcomed. Please feel free to check out the code, and share any thoughts or suggestions with us.
Easily 10 times better than recent OpenAI voice model. I don't like robotic voices.
Example voices seems like over loud, over excitement like Andrew Tate, Speed or advertisement. It's lacking calm, normal conversation or normal podcast like interaction.
+1 to this, amazing how you managed to deliver this, and iff you're willing to share i'd be most interested in learning what you did in terms of train data..!
Could one usecase be generating an audiobook with this from existing books? I wonder if I could fine-tune the "characters" that speak these lines since you said it's a single pass whole the whole convo. Wonder if that's a limitation for this kind of a usecase (where speed is not imperative).
Yes! But you would need to put together a LLM system that created scripts from the book content. There is an open source project called OpenNotebookLM (https://github.com/gabrielchua/open-notebooklm) that does something similar. If you hook the Dia model to that kind of system, it will be very possible :) Thanks for the interest!
Hi! This is awesome for size and quality. I want to see a book reading example or try it myself.
This is a tangent point but it would have been nicer if it wasn't a notion site. You could put the same page on github pages and it will be much lighter to open, navigate and link (like people trying to link some audio)
Thanks for the kind words!
You can try it now on https://huggingface.co/spaces/nari-labs/Dia-1.6B
Also, we'll try to update the Demo Page to something lighter when we have time. Thanks for the feedback :))
I know it’s taboo to ask, but I must: where’s the dataset from? Very eager to play around with audio models myself, but I find existing datasets limiting
Why would that be a taboo question to ask? It should be the question we always ask, when presented with a model and in some cases we should probably reject the model, based on that information.
No. It's for giving credit where credit is due. And yes, that includes the question if the people who generated the training data in the first place have given their consent that this can be used for AI training.
It's quite concerning that the community around here is usually livid about FOSS license violations, which typically use copyright law as leverage, but somehow is perfectly OK with training models on copyrighted work and just labels that as "fair use".
Ah, that's a classic. "How can you criticize Big Oil and at the same time drive a car!" and voila, the case is closed.
I am allowed to criticize things without having to live like a hermit. I make moderate use of ChatGPT, yet at the same time I think that its training does not fall under fair use, and that creators should get compensated. If OpenAI's business model does not allow for this, then it should fail, and that's fine by me. I lived without ChatGPT, and I can live without it again.
Well presumably since they're individuals and not a business the consequences are much less severe legally - but public opinion still won't be great, but since when was it ever, for any new thing?
If I cut up a song or TV show & put it on Youtube (and screech about fair use/parody law) then that's fine, but people will balk at something like this.
1. What GPU did you use to train the model? I'd love to train a model like this, but currently, I only have a 16GB MacBook. Thinking about buying a 5090 if it's worth.
2. Is it possible to use this for real time audio generation, similar to the demo on the Sesame website?
Its really amazing cant wait to play with it some, the samples are great... but oddly all seem... really fast, like they'd be perfect but they feel like they're playing at 1.2x speed or is that just me?
It’s not just you. The speedup is an artefact of the CFG (Classifier-Free Guidance) the model uses. The other problem is the speedup isn’t constant—it actually accelerates as the generation progresses. The Parakeet paper [1] (which OP lifted their model architecture almost directly from [2]) gives a fairly robust treatment to the matter:
> When we apply CFG to Parakeet sampling, quality is significantly improved. However, on inspecting generations, there tends to be a dramatic speed-up over the duration of the sample (i.e. the rate of speaking increases significantly over time). Our intuition for this problem is as follows: Say that is our model is (at some level) predicting phonemes and the ground truth distribution for the next phoneme occuring is 25% at a given timestep. Our conditional model may predict 20%, but because our uncondtional model cannot see the text transcription, its prediction for the correct next phoneme will be much lower, say 5%. With a reasonable level of CFG, because [the logit delta] will be large for the correct next phoneme, we’ll obtain a much higher final probability, say 50%, which biases our generation towards faster speech. [emphasis mine]
Parakeet details a solution to this, though this was not adopted (yet?) by Dia:
> To address this, we introduce CFG-filter, a modification to CFG that mitigates the speed drift. The idea is to first apply the CFG calculation to obtain a new set of logits as before, but rather than use these logits to sample, we use these logits to obtain a top-k mask to apply to our original conditional logits. Intuitively, this serves to constrict the space of possible “phonemes” to text-aligned phonemes without heavily biasing the relative probabilities of these phonemes (or for example, start next word vs pause more). [emphasis mine]
The paper contains audio samples with ablations you can listen to.
In terms of guiding voice and expression, audio prompts are promising but I believe text instructions serve different experiences as well. Will there be support for that as well?
I know it's a bit ridiculous to see that as some kind of conspiracy, but I have seen a very long list of AI-related projects that got the same name as a famous open-source project, as if they wanted to hijack the popularity of those projects, and Dia is yet another example. It was relatively famous a few years ago and you cannot have forgotten it if you used Linux for more than a few weeks. It's almost done on purpose.
The generous interpretation is that the AI hype people just didn’t know about those other projects, i.e. that they are neither open source developers, nor users.
Fun, I can't get to it because I can't get past the "Making sure you're not a bot!" page. It's just stuck at "calculating...". I understand the desire to slow down AI bots, but . If all the gnome apps are now behind this, they just completely shut down a small-time contributor. I love to play with Gnome apps and help out with things here and there, but I'm not going to fight with this damn thing to do so.
Impressive project! We'd love to use something like this over at Delfa (https://delfa.ai). How does this hold up from the perspective of stability? I've spoken to various folks working on voice models, and one thing that has consistently held Eleven Labs ahead of the pack from my experience is that their models seem to mostly avoid (while albeit not being immune to) accent shifts and distortions when confronted with unfamiliar medical terminology.
A high quality, affordable TTS model that can consistently nail medical terminology while maintaining an American accent has been frustratingly elusive.
Interesting. I haven't thought of that problem before. I'm guessing a large enough audio dataset for medical terminology does not exist publicly.
But AFAIK, even if you have just a few hours of audio containing specific terminology (and correct pronunciation), fine-tuning on that data will significantly improve performance.
We're envisioning a platform with a social aspect, so that is the biggest difference. Also, bigger models!
We are aware of the fact that you do not need to create a venv when using pre-existing uv. Just added it for people spinning up new GPUs on cloud. But I'll update the README to make that a bit clearer. Thanks for the feedback :)
The full version of Dia requires around 10GB of VRAM to run.
If you have a 16gb of VRAM, I guess you could pair this with a 3B param model along side it, or really probably only 1B param with reasonable context window.
Sounds awesome!
I think it won't be very hard to run it using output streaming, although that might require beefier GPUs. Give us an email and we can talk more - nari.ai.contact at gmail dot com.
It's way past bedtime where I live, so will be able to get back to you after a few hours. Thanks for the interest :)
You can add an audio prompt and prepend text corresponding to it in the script. You can get a feel for it by trying the second example in the Gradio interface!
This is really impressive; we're getting close to a dream of mine: the ability to generate proper audiobooks from EPUBs. Not just a robotic single voice for everything, but different, consistent voices for each protagonist, with the LLM analyzing the text to guess which voice to use and add an appropriate tone, much like a voice actor would do.
I've tried "EPUB to audiobook" tools, but they are really miles behind what a real narrator accomplishes and make the audiobook impossible to engage with
Realistic voice acting for audio books, realistic images for each page, realistic videos for each page, oh wait I just created a movie, maybe I can change the plot? Oh wait I just created a video game
Honestly, I’d say that’s true only for the author. Anyone else is just going to be interpreting the words to understand how to best convey the character / emotion / situation / etc., just like an AI will have to do. If an AI can do that more effectively than a human, why not?
The author could be better, because they at least have other info beyond the text to rely on, they can go off-script or add little details, etc.
As somebody who has listened to hundreds of audiobooks, I can tell you authors are generally not the best choice to voice their own work. They may know every intent, but they are writers, not actors.
The most skilled readers will make you want to read books _just because they narrated them_. They add a unique quality to the story, that you do not get from reading yourself or from watching a video adaptation.
Currently I'm in The Age of Madness, read by Steven Pacey. He's fantastic. The late Roy Dotrice is worth a mention as well, for voicing Game of Thrones and claiming the Guinness world record for most distinct voices (224) in one series.
It will be awesome if we can create readings automatically, but it will be a while before TTS can compete with the best readers out there.
I’d suggest even if the TTS sounded good, I’d still rather a human because:
1. It’s a job that seems worthwhile to support, especially as it’s “practice” that only adds to a lifetime of work and improves their central skill set
2. A voice actor will bring their own flare, just like any actor does to their job
3. They (should) prepare for the book, understanding what it’s about in its entirety, and bring that context to the reading
> Wouldn’t it be more desirable to hear an actual human on an audiobook? Ideally the author?
Of course, but it's not always available.
For example, I would love an audiobook for Stanisław Lem's "The Invincible," as I just finished its video game adaptation, yet it simply doesn't exist in my native language.
It's quite seldom that the author narrates the audiobooks I listen to, and sometimes the narrator does a horrible job, butchering the characters with exaggerated tones.
You really think people writing these papers actually have good speaking voices? LOL, theirs a reason not everyone could be an audio book maker or podcaster, a lot of peoples voices suck for audiobooks
Why a human? There are many cases where I like a book but dislike the audiobook speaker, so I essentially can't listen to that book anymore. With a machine, I can tweak the voice to my heart's content.
Sometimes, I don't care if it's bland, I just want to listen to the text. There are a lot of Asian light novels for example which never get English audiobooks, and I've listened to many of them with basic TTS, not even an AI model TTS like these more recent ones, and I thoroughly enjoyed these books even still.
It'd be nice if there were mainstream releases on GBC/GBA/PSP again too! But apparently if there's no money in something then people don't really wanna do it.
Wow. Thanks for posting the direct link to examples. Those sound incredibly good and would be impressive for a frontier lab. For two people over a few months, it's spectacular.
A little overacted, it reminds me of the voice acting in those flash cartoons you'd see in the early days of YouTube. That's not to say it isn't good work, it still sounds remarkably human. Just silly humans :)
Sounds great. One of the female examples has convincing uptalk. There must be a way to manipulate the latent space to control uptalk, vocal fry, smoker’s voice, lispiness, etc.
Is there some sort of system prompt or hint at how it should be voiced, or does it interpret it from the text?
Because it would be hilarious if it just derived it from the text and it did this sort of voice acting when you didn't want it to, like reading a matter-of-fact warning label.
Thanks, works well but slowly on a Mac Air M3 with 24gb. Will have to try it again after freeing up more ram as it was doing a bit of swapping with Chrome running too.
(later). It did nicely for the default example text but just made weird sounds for a "hello all" prompt. And took longer?!
Wow first time I have felt that this could be the end of voice acting/audio book narration etc. The speed with with the ways things are changing how soon before you can make any book any novel into a complete audio video / movie or tv show.
Insane how much low hanging fruit there is for Audio models right now. A team of two picking things up over a few months can build something that still competes with large players with tons of funding
This is amazing.
Is it possible to build in a chosen voice, a bit like Eleven Labs does?
...This may be on the git summary, being lazy and asking anyway :=)
Thanks for your work.
You can get hours of audio out of it for free with Eleven Reader, which suggests that their inference costs aren't that high. Meanwhile, those same few hours of audio, at the exact same quality, would cost something like $100 when generated through their website or API, a lot more than any other provider out there. Their pricing (and especially API pricing) makes no sense, not unless it's just price discrimination.
Somebody with slightly deeper pockets than academics or one guy in a garage needs to start competing with them and drive costs down.
Open TTS models don't even seem to utilize audiobooks or data scraped off the internet, most are still Librivox / LJ Speech. That's like training an LLM on just Wikipedia and expecting great results. That may have worked in 2018, but even in 2020 we knew better, not to mention 2025.
TTS models never had their "Stable Diffusion moment", it's time we get one. I think all it would take is somebody doing open-weight models applying the lessons we learned from LLMs and image generation to TTS models, namely more data, more scraping, more GPUs, less qualms and less safety. Eleven Labs already did, and they're profiting from it handsomely.
Is this Apache licensed or a custom one? The README contains this:
> This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
> This project offers a high-fidelity speech generation model *intended solely for research and educational use*. The following uses are strictly forbidden:
> Identity Misuse: Do not produce audio resembling real individuals without permission.
> ...
Specifically the phrase "intended solely for research and educational use".
Sorry for the confusion. the license is plain Apache 2.0, and we changed the wording to "intended for research and educational use." The point was, users are free to use it for their use cases, just don't do shady stuff with it.
201 comments
[ 3.1 ms ] story [ 220 ms ] threadUnlike TTS models that generate each speaker turn and stitch them together, Dia generates the entire conversation in a single pass. This makes it faster, more natural, and easier to use for dialogue generation.
It also supports audio prompts — you can condition the output on a specific voice/emotion and it will continue in that style.
Demo page comparing it to ElevenLabs and Sesame-1B https://yummy-fir-7a4.notion.site/dia
We started this project after falling in love with NotebookLM’s podcast feature. But over time, the voices and content started to feel repetitive. We tried to replicate the podcast-feel with APIs but it did not sound like human conversations.
So we decided to train a model ourselves. We had no prior experience with speech models and had to learn everything from scratch — from large-scale training, to audio tokenization. It took us a bit over 3 months.
Our work is heavily inspired by SoundStorm and Parakeet. We plan to release a lightweight technical report to share what we learned and accelerate research.
We’d love to hear what you think! We are a tiny team, so open source contributions are extra-welcomed. Please feel free to check out the code, and share any thoughts or suggestions with us.
Example voices seems like over loud, over excitement like Andrew Tate, Speed or advertisement. It's lacking calm, normal conversation or normal podcast like interaction.
This is a tangent point but it would have been nicer if it wasn't a notion site. You could put the same page on github pages and it will be much lighter to open, navigate and link (like people trying to link some audio)
It's quite concerning that the community around here is usually livid about FOSS license violations, which typically use copyright law as leverage, but somehow is perfectly OK with training models on copyrighted work and just labels that as "fair use".
I am allowed to criticize things without having to live like a hermit. I make moderate use of ChatGPT, yet at the same time I think that its training does not fall under fair use, and that creators should get compensated. If OpenAI's business model does not allow for this, then it should fail, and that's fine by me. I lived without ChatGPT, and I can live without it again.
If I cut up a song or TV show & put it on Youtube (and screech about fair use/parody law) then that's fine, but people will balk at something like this.
AI is here, people.
1. What GPU did you use to train the model? I'd love to train a model like this, but currently, I only have a 16GB MacBook. Thinking about buying a 5090 if it's worth.
2. Is it possible to use this for real time audio generation, similar to the demo on the Sesame website?
> When we apply CFG to Parakeet sampling, quality is significantly improved. However, on inspecting generations, there tends to be a dramatic speed-up over the duration of the sample (i.e. the rate of speaking increases significantly over time). Our intuition for this problem is as follows: Say that is our model is (at some level) predicting phonemes and the ground truth distribution for the next phoneme occuring is 25% at a given timestep. Our conditional model may predict 20%, but because our uncondtional model cannot see the text transcription, its prediction for the correct next phoneme will be much lower, say 5%. With a reasonable level of CFG, because [the logit delta] will be large for the correct next phoneme, we’ll obtain a much higher final probability, say 50%, which biases our generation towards faster speech. [emphasis mine]
Parakeet details a solution to this, though this was not adopted (yet?) by Dia:
> To address this, we introduce CFG-filter, a modification to CFG that mitigates the speed drift. The idea is to first apply the CFG calculation to obtain a new set of logits as before, but rather than use these logits to sample, we use these logits to obtain a top-k mask to apply to our original conditional logits. Intuitively, this serves to constrict the space of possible “phonemes” to text-aligned phonemes without heavily biasing the relative probabilities of these phonemes (or for example, start next word vs pause more). [emphasis mine]
The paper contains audio samples with ablations you can listen to.
[1]: https://jordandarefsky.com/blog/2024/parakeet/#classifier-fr...
[2]: https://news.ycombinator.com/item?id=43758686
https://gitlab.gnome.org/GNOME/dia
I'm sure they can... talk it over.
I'll show myself out.
A high quality, affordable TTS model that can consistently nail medical terminology while maintaining an American accent has been frustratingly elusive.
But AFAIK, even if you have just a few hours of audio containing specific terminology (and correct pronunciation), fine-tuning on that data will significantly improve performance.
Also, you don't need to explicitly create and activate a venv if you're using uv - it deals with that nonsense itself. Just `uv sync`.
We are aware of the fact that you do not need to create a venv when using pre-existing uv. Just added it for people spinning up new GPUs on cloud. But I'll update the README to make that a bit clearer. Thanks for the feedback :)
The full version of Dia requires around 10GB of VRAM to run.
If you have a 16gb of VRAM, I guess you could pair this with a 3B param model along side it, or really probably only 1B param with reasonable context window.
We've seen Bark from Suno go from 16GB requirement -> 4GB requirement + running on CPUs. Won't be too hard, just need some time to work on it.
time to first audio is something that is crucial for us to reduce the latency - wondering if dia works with output streaming?
the python code snippet seems to imply that the entire audio bytes are generated directly?
It's way past bedtime where I live, so will be able to get back to you after a few hours. Thanks for the interest :)
I've tried "EPUB to audiobook" tools, but they are really miles behind what a real narrator accomplishes and make the audiobook impossible to engage with
The author could be better, because they at least have other info beyond the text to rely on, they can go off-script or add little details, etc.
The most skilled readers will make you want to read books _just because they narrated them_. They add a unique quality to the story, that you do not get from reading yourself or from watching a video adaptation.
Currently I'm in The Age of Madness, read by Steven Pacey. He's fantastic. The late Roy Dotrice is worth a mention as well, for voicing Game of Thrones and claiming the Guinness world record for most distinct voices (224) in one series.
It will be awesome if we can create readings automatically, but it will be a while before TTS can compete with the best readers out there.
1. It’s a job that seems worthwhile to support, especially as it’s “practice” that only adds to a lifetime of work and improves their central skill set
2. A voice actor will bring their own flare, just like any actor does to their job
3. They (should) prepare for the book, understanding what it’s about in its entirety, and bring that context to the reading
Of course, but it's not always available.
For example, I would love an audiobook for Stanisław Lem's "The Invincible," as I just finished its video game adaptation, yet it simply doesn't exist in my native language.
It's quite seldom that the author narrates the audiobooks I listen to, and sometimes the narrator does a horrible job, butchering the characters with exaggerated tones.
> [S1] Oh fire! Oh my goodness! What's the procedure? What to we do people? The smoke could be coming through an air duct!
Seriously impressive. Wish I could direct link the audio.
Kudos to the Dia team.
Is there some sort of system prompt or hint at how it should be voiced, or does it interpret it from the text?
Because it would be hilarious if it just derived it from the text and it did this sort of voice acting when you didn't want it to, like reading a matter-of-fact warning label.
https://github.com/nari-labs/dia/pull/4
(later). It did nicely for the default example text but just made weird sounds for a "hello all" prompt. And took longer?!
I would absolutely love something like this for practicing Chinese, or even just adding Chinese dialogue to a project.
https://github.com/SparkAudio/Spark-TTS
Insane how much low hanging fruit there is for Audio models right now. A team of two picking things up over a few months can build something that still competes with large players with tons of funding
You can get hours of audio out of it for free with Eleven Reader, which suggests that their inference costs aren't that high. Meanwhile, those same few hours of audio, at the exact same quality, would cost something like $100 when generated through their website or API, a lot more than any other provider out there. Their pricing (and especially API pricing) makes no sense, not unless it's just price discrimination.
Somebody with slightly deeper pockets than academics or one guy in a garage needs to start competing with them and drive costs down.
Open TTS models don't even seem to utilize audiobooks or data scraped off the internet, most are still Librivox / LJ Speech. That's like training an LLM on just Wikipedia and expecting great results. That may have worked in 2018, but even in 2020 we knew better, not to mention 2025.
TTS models never had their "Stable Diffusion moment", it's time we get one. I think all it would take is somebody doing open-weight models applying the lessons we learned from LLMs and image generation to TTS models, namely more data, more scraping, more GPUs, less qualms and less safety. Eleven Labs already did, and they're profiting from it handsomely.
With the budget one tenth that of Stable Diffusion and less ethical qualms, you could easily 10x or 100x this.
> This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
> This project offers a high-fidelity speech generation model *intended solely for research and educational use*. The following uses are strictly forbidden:
> Identity Misuse: Do not produce audio resembling real individuals without permission.
> ...
Specifically the phrase "intended solely for research and educational use".
Thanks for the feedback :)