In case it helps anyone else, the first time I tried to run purr I got "OSError: PortAudio library not found". Installing libportaudio (apt install libportaudio2) got it running.
Also did create a cli. I had to fork the project and removed one unused import which allowed me to remove a lot of unused ml libraries: https://github.com/Mic92/puss-say
Thank you so much, that fixes an enormous pain point I was hitting. It's not just the size, that dependency chain was actually breaking on my machine and failing to install. Are we losing something by dropping the extra dependencies?
You might also like CopySpeak, a lightweight tool I've recently built for quick AI text-to-speech using the clipboard, featuring Kitten TTS and other engines.
What I love about OpenClaw is that I was able to send it a message on Discord with just this github URL and it started sending me voice messages using it within a few minutes. It also gave me a bunch of different benchmarks and sample audio.
I'm impressed with the quality given the size. I don't love the voices, but it's not bad. Running on an intel 9700 CPU, it's about 1.5x realtime using the 80M model. It wasn't any faster running on a 3080 GPU though.
yeah we'll add some more professional-sounding voices and also support for diy custom voices. we tried to add more anime/cartoon-ish voices to showcase the expressivity.
Regarding running on the 3080 gpu, can you share more details on github issues, discord or email? it should be blazing fast on that. i'll add an example to run the model on gpu too.
Oh that is a good use case. Don't connect to email and all that insecure stuff. But as a sandbox for "try this out and deploy a demo". Got me thinking!
I'm jealous. It took me far longer and much more frustration to get it to run.
Had to get the right Python version and make sure it didn't break anything with the previous Python version. A friend suggested using Docker, so I started down that path until I realized I'd probably have to set the whole thing up there myself. Eventually got it to run and I think I didn't break anything else.
Nowadays these frustrations shouldn't be a thing any more. If the author used uv, the script would be able to install its own dependencies and just work.
Even the built in venv would've solved most of his issues too. But I agree with him in that Python documentation could be better. Or have a more unified system in place. I feel like every other how to doc I read on setting something Python up uses a different environment containment product.
Conda was fantastic up to some point last year and since then I've had quite a few unresolvable version issues with it. It is really annoying, especially when you're tying multiple things together and each requires its own set of mutually exclusive specific versions of libraries. The latest like that was gnu radio and some out-of-tree stuff at the same time as a bluetooth library. High drama. I eventually gave up, rewrote the whole thing in a different language and it took less time than I had spent on trying to get the python solution duct-taped together.
Because I need a new version of python very rarely (years go by). I don't remember all the arcane incantations to set everything up.
I did eventually do that though, and I'm pretty sure I had to mess about with installing and uninstalling torch.
I dread using anything made in python because of this. It's always annoying and never just works (if the version of python is incompatible, otherwise it's fine) .
Was playing around a bit and for its size it's very impressive. Just has issues pronounciating numbers. I tried to let it generate "Startup finished in 135 ms."
I didn't expect it to pronounciate 'ms' correctly, but the number sounded just like noise. Eventually I got an acceptable result for the string "Startup finished in one hundred and thirty five seconds.
yeah we're fixing this at the model level too. but in the meantime, there is a way to add text preprocessing for you, and if you have a special use-cased, claude code should be able to one-shot custom preprocessing. its the way that most existing tts models (including sota cloud ones) deal w numbers and units, they just convert it into string.
thanks a lot for trying it and giving feedback. custom preprocessing will fix this for 95% of use-cases. and as i mentioned, this will be fixed at the model level in the next release.
The above SECDED check-bit encoding can be implemented in a similar way, but since it uses only three-bit patterns, mapping syndromes to correction masks can be done with three-input AND gates.
It sounded quite good indeed for the normal English stuff, but I guess predictably was quite bad at the domain-specific words. It misspoke "SECDED", had wrong emphasis on "syndromes", and pronounced "AND gates" like "and gates".
Could you give some example of what kind of preprocessing would help in this case? I tried some local LLMs, but they didn't do a good job (maybe my prompts sucked).
I'm not sure if you're misspelling it deliberately or not, but the word you're looking for is "pronounce" and it's verb form "pronouncing", as in "It just has issues pronouncing numbers" and "I didn't expect it to pronounce 'ms' correctly."
thanks a lot. yeah these models are way better than our previous launch. our 15M model now is better than our previous 80M model and we expect to continue seeing this rate of improvement.
great idea, we'll do that too. we just decided to launch an onnx first and get some feedback. we'll be simplifying the process of running it everywhere including a command line executable.
I'd love to see a monolingual Japanese model sometime in the future. Qwen3-tts works for Japanese in general, but from time to time it will mix with some Mandarin in between, making it unusable.
our next model(eta 3ish weeks) will support Japanese. would love to get your feedback then on how the quality is. can you share what usecase you want? would love to support it.
Exactly. Qwen only has one pitch accent for pure hiragana words, even though it actually work (removing mandarin mixed-in), which requires some great efforts to normalize text in order to disambiguate heteronyms, the result is (if you use voice cloning) your favorite CV speaking in some weird, unknown accent :)
That got me wondering if "you convert to hiragana" is a solved task, or a research team and five years[0], and Google showed me an article[1] that gave me a facepalm, quoting from Google Translate(square brackets are mine):
> - As a result,
> - When the string "明日["tomorrow"]" is entered into TTS, the TTS model [･皿･] outputs an ambiguous pronunciation that sounds like a mix of "asu" and "ashita" (something like "[asyeta]").
> From this, we found that by using the proposed method, it is possible to obtain data from private data in which the consistency between speech, graphemes, and phonemes is almost certainly maintained for more than 80% of the total.
> Another possible cause is a mismatch between the domain of the training data's audio (all [in read-aloud tones]) and the inference domain.
My resultant rambling follows:
1. Sounds like general state of Japanese speech dataset is a mess
1.1. they don't maintain great useful correspondence between symbols to audio
1.2. they tend to contain too much of "transatlantic" voices and less casual speeches
2. Japanese speakers generally don't denote pronunciations for text
2.1. therefore web crawls might not contain enough information as to how they're actually pronounced
2.2. (potentially) there could be some texts that don't map to pronunciations
2.3. (potentially) maybe Japanese spoken and literal languages are still a bit divergent from each others
3. The situation for Chinese/Sinitic languages are likely __nowhere__ near as absurd, and so Chinese STT/TTS might not be well equipped to deal with this mess
4. This feels like much deeper mess than what commonly observed "a cloud in a sky" Japanese TTS problems such as obvious basic alignment errors(e.g. pronouncing "potatoes" as "tato chi")
Good on device TTS is an amazing accessibility tool. Thank you for building this. Way too many of devices that use it rely on online services, this is much preferred.
yeah we tried to include those voices in this release to showcase the expressivity. but we've already started adding more professional sounding voices for prod use-cases.
damnn, lemme fix it, sorry for that. we may have forgotten to remove the redundant dependencies. i'll comment here once i push the change. thanks a lot for trying it and giving feedback.
It's mostly torch, I think. It pulls in NVIDIA libs (which … makes sense, I guess), and NVIDIA is just not at all judicious when it comes to disk space. I literally run out of disk trying to install this on Linux.
On macOS, it's a markedly different experience: it's only ~700 MiB there; I'm assuming b/c no NVIDIA libs get pulled in, b/c why would they.
For anyone who might want to play around with this: I can get down to ~3 GiB (& about 1.3 GiB if you wipe your uv cache afterwards) on Linux if I add the following to the end of `pyproject.toml`:
[tool.uv.sources]
# This tells uv to use the specific index for torch, torchvision, and torchaudio
torch = [
{index = "pytorch-cpu"}
]
torchvision = [
{index = "pytorch-cpu"}
]
torchaudio = [
{index = "pytorch-cpu"}
]
[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
& add "torch" to the direct dependencies, b/c otherwise it seems like uv is ignoring the source? (… which of course downloads a CPU-only torch.)
That's not all the libraries, either, but you can see NVIDIA here is easily over 1 GiB.
It also then crashes for me, with:
File "KittenTTS/.venv/lib/python3.14/site-packages/pydantic/v1/fields.py", line 576, in _set_default_and_type
raise errors_.ConfigError(f'unable to infer type for attribute "{self.name}"')
pydantic.v1.errors.ConfigError: unable to infer type for attribute "REGEX"
Which seems to be [this bug in spacey](https://github.com/explosion/spaCy/issues/13895), so I'm going to have to try adding `<3.14` to `requires-python` in `pyproject.toml` too I think. That is, for anyone wanting to try this out:
(This isn't really something KittenTTS should have to do, since this is a bug in spacey … and ideally, at some point, spacey will fix it.)
Also:
+ curated-tokenizers==0.0.9
This version is so utterly ancient that there aren't wheels for it anymore, so that means a loooong wait while this builds. It's pulled in via misaki, and my editor says your one import of misaki is unused.
Hilariously, removing it breaks but only on macOS machine. I think you're using it solely for the side-effect that it tweaks phonemizer to use espeakng, but you can just do that tweak yourself, & then I think that dependency can be dropped. That drops a good number of dependencies & really speeds up the installation since we're not compiling a bunch of stuff.
You need to add `phonemizer-fork` to your dependencies. (If you remove misaki, you'll find this missing.)
The size/quality tradeoff here is interesting. 25MB for a TTS model that's usable is a real achievement, but the practical bottleneck for most edge deployments isn't model size -- it's the inference latency on low-power hardware and the audio streaming architecture around it. Curious how this performs on something like a Raspberry Pi 4 for real-time synthesis. The voice quality tradeoff at that size usually shows up most in prosody and sentence-final intonation rather than phoneme accuracy.
Emotion based tagging control would be the most helpful narrowing it down. Tags like [sarcastically] [happily] [joyfully] [fearfully]: so a subsection of adverbs.
A stretch goal is 'arbitrary tags' from [singing] [sung to the tune of {x}] [pausing for emphasis] [slowly decreasing speed for emphasis] [emphasizing the object of this sentence] [clapping] [car crash in the distance] [laser's pew pew].
But yeah: instruction/control via [tags] is the deciding feature for me, provided prompt adherence is strong enough.
Also: a thought...
Everyone is using [] for different kinds of tags in this space: which is very simple. Maybe it makes sense to differentiate kinds of tags? I.E. [tags for modifying how text is spoken] vs {tags for creating sounds not specifically speech: not modifying anything... but instead it's own 'sound/word'}
not OP but something like [<intention>] where intention might be something like anger, curiousness, etc. [long pause], [gasp], [laughter] stuff like that.
To the folks and Kitten team: I'm working on TTS as a problem statement (for an application), and what is the best model at the latency/cost inference. I'm currently settling for gemini TTS, which allows for a lot of expressiveness, but a word at 150ms starts to hurt when the content is a few sentences.
my current best approach is wrapping around gemini-flash native, and the model speaking the text i send it, which allows me end to end latency under a second.
are there other models at this or better pricing i can be looking at.
There's a number of recent, good quality, small TTS models.
If the author doesn't describe some detail about the data, training, or a novel architecture, etc, I only assume they just took another one, do a little finetuning, and repackage as a new product.
the dependency chain issue is a real barrier for edge deployment. i've been running tts models on a raspberry pi for a home automation project and anything that pulls torch + cuda makes the whole thing a non-starter. 25MB is genuinely exciting for that use case.
curious about the latency characteristics though. 1.5x realtime on a 9700 is fine for batch processing but for interactive use you need first-chunk latency under 200ms or the conversation feels broken. does anyone know if it supports streaming output or is it full-utterance only?
the phoneme-based approach should help with pronunciation consistency too. the models i've tried that work on raw text tend to mispronounce technical terms unpredictably — same word pronounced differently across runs.
The Github readme doesn't list this: what data trained this? Was it done by the voices of the creators, or was this trained on data scraped from the internet or other archives?
I'm interested in pushing the envelope a bit on the raspberry Pi to do personal assistant projects with it. The pi zero 2 is a surprisingly powerful little device, it is comparable to a pi 3B, except it has less RAM.
From my point of view, Parakeet is not very good at formatting the output, so it would be nice if a small model focused on having nicely formatted (and correct) text, not just the lowest WER score. Rewarding the model for inserting logical line breaks, quotation marks, etc.
Fingers crossed for a normal-sounding voice this time around. The cute Kitten voices are nice, but I want something I can take seriously when I'm listening to an audiobook.
How is the Bruno voice for this one? there will also be another release in ~15-20 days where we have more professional voices. if you'd like to get early access and give feedback lmk, or dm me.
Huge fan of Ava multilingual and hopefully there are many other others with similar taste, so my feedback might shape things towards a halfway decent direction at least for some.
btw, use case is most often to listen to news/articles.
This is awesome, well done. Been doing lot of work with voice assistants, if you can replicate voice cloning Qwen3-TTS into this small factor, you will be absolute legends!
thanks a lot, our voice cloning model will be out by May. we're experimenting w some very cool ways of doing voice cloning at 15M but will have a range of models going upto 500M
glad you liked it, thank you so much for the kind words. our team is really good at squeezing performance out of small models. we are working on a new launch and hope to release a technical report along with that which includes details. fyi, our current 14M model is better than our previous 80M model. and we expect this trend to continue.
A lot of good small TTS models in recent times. Most seem to struggle hard on prosody though.
Kokoro TTS for example has a very good Norwegian voice but the rhythm and emphasizing is often so out of whack the generated speech is almost incomprehensible.
Haven't had time to check this model out yet, how does it fare here? What's needed to improve the models in this area now that the voice part is more or less solved?
small models struggle with prosody due to limited capacity. this version does much better than the precious one and is the best among other <25MB models. Kokoro is a really good model for its size, its competitive on artificial analysis too.
i think by the next release we should have something kokoro quality but a fifth of the size. Adding control for rhythm seems to be quite important too, and we should start looking at that for other languages.
Listened to the video examples, sounded very good though wasn't terribly challenging text.
If only I could have that in Norwegian my SO would be pleased.
Also I totally misremembered regarding Kokoro TTS. It's good, but not what was butchering Norwegian. Forgot which one I was thinking of, maybe it was the old VITS stuff Rhaspy uses. Points stand, the voice was good but could barely understand what was said.
yes. the current release of our model is english-only. so other languages are not expected to perform well. we'll try to look out for this in our multilingual release.
What's the source of that voice then in training data? It sounds insanely close to her voice. Strange parallel to openAI denying they trained on Scarlet J's voice.
I think I tried on my Android everything I could try and 1. outside webpage reading, not many options; 2. as browser extensions, also not many (I don't like to copy URLs in your app) 3. they all insist reading every little shit, not only buttons but also "wave arrow pointing directly right" which some people use in their texts. So basically reading text aloud is a bunch of shitty options. Anyone jumping in this market opening?
shouldn't be hard. what backend/hardware are you interested in running this with? i'll add an example for using C++ onnx model. btw check out roadmap, our inference engine will be out 1-2 weeks and it is expected to be faster than onnx.
yes, our mobile sdk is coming soon(eta 2 weeks) so we should be able to replace the built-in version of it. can you share what tts use-case you're thinking of?
I use an epub reader like Moon+ with the built in TTS to turn epubs into audiobooks, and I tried Kokoro TTS but the issue was too much lag between sentences plus it doesn't preprocess the next sentence while it reads out the current one.
okay this seems pretty doable, i think i know someone who is working on an epub reader using kittentts. if they don't post about it, i'll do it once its done.
Working on a reader and server that use pockettts to turn epubs into audio books https://github.com/gabrielcsapo/compendus shows a virtual scroller for the text and audio
Nice, but it's weird that no "language" or "English" is mentioned on the github page, and only from the "Release multilingual TTS" Roadmap item could I guess it's probably English only for now.
Is there any way to do a custom voice as a DIY? Or we need to go through you? If so, would you consider making a pricing page for purchasing a license/alternative voice? All but one of the voices are unusable in a business context.
thanks a lot for the feedback. yes, we're working on a diy way to add custom voices and will also be releasing a model with more professional voices in the next 2-3 weeks. as of now, we're providing commercial support for custom voices, languages and deployment through the support form on our github. can you share more about your business use-case? if possible, i'd like to ensure the next release can serve that.
Right now it's outgoing calls for a small business client that checks information. Although if they call back they don't mind an automated system, on outgoing calls the person answering will often hang up if they detect AI right away, so we use a realistic custom voice with an accent.
This is a mind numbing task that requires workers to make hundreds of calls each day with only minor variations, sometimes navigating phone trees, half the time leaving almost the exact same message.
Anyway, I believe almost all such businesses will be automated within months. Human labour just cannot compete on cost.
Why do you assume it's the worst use case? It's checking important info that has been entered into forms. People lie. Someone has to verify info. It's very tedious and something that obviously should be automated. And it's about 70% automated already.
The legitimate objection people have to AI in this use case is that it can be slow or stupid in a way that wastes time. By acting more humanlike, we signal that we are going to be closer to human level performance.
minor nit to pick: Welsh accents are British accents as Wales is in Britain. In fact by some definitions it's the most British part.
People from outside the UK often use British as synonymous with English, and in the context of accents, often a South East English accent or some sort of Received Pronunciation (RP) accent. Technically a "British" accent could be from anywhere in England, Scotland, or Wales, and therefore by extension might not even be the English language.
While I'm here, since it's generally confusing, the UK is Great Britain and Northern Ireland. Great Britain is England, Scotland, and Wales.
I am actually English but I'm so used to speaking with international people I instinctively say British instead of English - because that's what people expect.
So being factually correct doesn't really matter. Nobody cares and nobody wants to learn so I adapt for them.
In the same way I almost exclusively write with American spelling now. Life is just easier when you stop fighting.
not v long. until then you start running tts on phones, wearables and r pis. at the model level, we'll have a model for this kind of mcu's later this year.
yes, we're releasing an official mobile sdk and inference engine very soon. if you want to use something until then, some folks from the oss community have built ways to run kitten on ios. if you search kittentts ios on github you should find a few.
if you cant find it, feel free to ping me and i can help you set it up. thanks a lot for your support and feedback!
I'm still looking for the "perfect" setup in order to clone my voice and use it locally to send voice replies in telegram via openclaw. Does anyone have auch a setup?
You need to provide info on your hardware. Pocket-TTS does cloning on CPU, but for me randomly outputs something pretty weird sounding mixed in with like 90% good outputs. So it hasn't been quite stable enough to run without checking output. But maybe it depends on your voice sample.
Qwen 3 TTS is good for voice cloning but requires GPU of some sort.
that would be awesome, I was using pockettts then I had to run it through whisper to get the accurate alignment. Not super productive for realtime work.
The <25MB figure is what stands out. Been wanting to add TTS to a few Next.js projects for offline/edge scenarios but model sizes have always made it impractical to ship.
At 25MB you can actually bundle it with the app. Going to test whether this works in a Vercel Edge Function context -- if latency is acceptable there it opens up a lot of use cases that currently require a round-trip to a hosted API.
Running the example is 3 MiB for the repo, +667 MiB of Python dependencies, +86 MiB of models that will get downloaded from HuggingFace. =756 MiB.
(That's using the example as-is. If you switch it to the smaller model, modify the above with +57 MiB of models from HuggingFace, or =727 MiB.)
So I toyed with this a bit + the Rust library "ort", and ort is only 224M in release (non-debug) mode, and it was pretty simple to run this model with it. (I did not know ort before just now.) I didn't replicate the preprocessing the Python does before running the model, though. (You have to turn the text into an array of floats, essentially; the library is doing text -> phonemes -> tokens; the latter step is straight-forward.)
So, that was on macOS. It's actually huge on Linux, and I've run out of disk space trying to pull dependencies. It's nvidia, who always shows great judgement in their use of disk.
I'm thinking of giving "voice" to my virtual pets (think Pokemon but less than a dozen). The pets are made up animals but based on real animal, like Mouseier from Mouse (something like that). Is this possible?
Tldr: generate human-like voice based on animal sound. Anyway maybe it doesn't make sense.
it'd be an interesting experiment to try what kind of information is extracted from the samples of the pet sounds. it'd be so cool if it can just get the features of the audio and then still be able to reproduce the audio in english lol. we would need a really good "speaker" encoder i think.
as of now its english only. the training for multilingual model is underway and should be out in April! what languages are you most interested in? Right now, we are providing deployments for custom languages + voices through support form on the github.
french, spanish and german models will also be out v soon. these are languages we are working on already. some lower resource languages will take longer.
So, one thing I noticed, and this could easily be user error, is that if I set the text & voice in the example to:
text ="""
Hello world. This is Kitten TTS.
Look, it's working!
"""
voice = 'Luna'
On macOS, I get "Kitten TTS", but on Linux, I get "Kit… TTS". Both OSes generate the same phonemes of,
Phonemes: ðɪs ɪz kˈɪʔn ̩ tˌiːtˌiːˈɛs ,
which makes me really confused as to where it's going off the rails on Linux, since from there it should just be invoking the model.
edit: it really helps to use the same model facepalm. It's the 80M model, and it happens on both OS. Wildly the nano gets it better? I'm going to join the Discord lol.
hey sorry for this issue, i think its a bug in our preprocessing. let me look into it and help fix it. i think you posted this in our discord so lets carry the conversation there.
This is something I've been looking for (the <50MB models in particular). Unfortunately my feedback is as follows:
Downloading https://github.com/KittenML/KittenTTS/releases/download/0.8.1/kittentts-0.8.1-py3-none-any.whl (22 kB)
Collecting num2words (from kittentts==0.8.1)
Using cached num2words-0.5.14-py3-none-any.whl.metadata (13 kB)
Collecting spacy (from kittentts==0.8.1)
Using cached spacy-3.8.11-cp314-cp314-win_amd64.whl.metadata (28 kB)
Collecting espeakng_loader (from kittentts==0.8.1)
Using cached espeakng_loader-0.2.4-py3-none-win_amd64.whl.metadata (1.3 kB)
INFO: pip is looking at multiple versions of kittentts to determine which version is compatible with other requirements. This could take a while.
ERROR: Ignored the following versions that require a different python version: 0.7.10 Requires-Python >=3.8,<3.13; 0.7.11 Requires-Python >=3.8,<3.13; 0.7.12 Requires-Python >=3.8,<3.13; 0.7.13 Requires-Python >=3.8,<3.13; 0.7.14 Requires-Python >=3.8,<3.13; 0.7.15 Requires-Python >=3.8,<3.13; 0.7.16 Requires-Python >=3.8,<3.13; 0.7.17 Requires-Python >=3.8,<3.13; 0.7.5 Requires-Python >=3.8,<3.13; 0.7.6 Requires-Python >=3.8,<3.13; 0.7.7 Requires-Python >=3.8,<3.13; 0.7.8 Requires-Python >=3.8,<3.13; 0.7.9 Requires-Python >=3.8,<3.13; 0.8.0 Requires-Python >=3.8,<3.13; 0.8.1 Requires-Python >=3.8,<3.13; 0.8.2 Requires-Python >=3.8,<3.13; 0.8.3 Requires-Python >=3.8,<3.13; 0.8.4 Requires-Python >=3.8,<3.13; 0.9.0 Requires-Python >=3.8,<3.13; 0.9.2 Requires-Python >=3.8,<3.13; 0.9.3 Requires-Python >=3.8,<3.13; 0.9.4 Requires-Python >=3.8,<3.13; 3.8.3 Requires-Python >=3.9,<3.13; 3.8.5 Requires-Python >=3.9,<3.13; 3.8.6 Requires-Python >=3.9,<3.13; 3.8.7 Requires-Python >=3.9,<3.14; 3.8.8 Requires-Python >=3.9,<3.14; 3.8.9 Requires-Python >=3.9,<3.14
ERROR: Could not find a version that satisfies the requirement misaki>=0.9.4 (from kittentts) (from versions: 0.1.0, 0.3.0, 0.3.5, 0.3.9, 0.4.0, 0.4.4, 0.4.5, 0.4.6, 0.4.7, 0.4.8, 0.4.9, 0.5.0, 0.5.1, 0.5.2, 0.5.3, 0.5.4, 0.5.5, 0.5.6, 0.5.7, 0.5.8, 0.5.9, 0.6.0, 0.6.1, 0.6.2, 0.6.3, 0.6.4, 0.6.5, 0.6.6, 0.6.7, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.7.4)
ERROR: No matching distribution found for misaki>=0.9.4
I realize that I can run a multiple versions of python on my system, and use venv to managed them (or whatever equivalent is now trendy), but as I near retirement age all those deep dependencies nets required by modern software is really depressing me. Have you ever tried to build a node app that hasn't been updated in 18 months? It can't be done. Old man yelling at cloud I guess shrugs.
25MB is impressive. What's the tradeoff vs the 80M model — is it mainly voice quality or does it also affect pronunciation accuracy on less common words?
80M model is the highest quality while also being quite efficient. it is superior in terms of pronunciation accuracy for less common words, and also is more stable in terms of speed. its my fav model. i think the 40M is quite similar to 80M for most usecases. 15M is for resource cpus, loading onto a browser etc.
The new 15M is way better than the previous 80M model(v0.1). So we're able to predictably improve the quality which is very encouraging.