291–300 of 383 posts
I hope this is the future. Offline, small ML models, running inference on ubiquitous, inexpensive hardware. Models that are easy to integrate into other things, into devices and apps, and even to drive from other models maybe.
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This would only apply if they were distributing the GPL licensed code alongside their own code. If my MIT-licensed one-line Python library has this line of code… run([“bash”, “-c”, “echo hello”]) …I’m not suddenly subject to bash’s licensing. For anyone wanting to run my stuff though, they’re going to need to make sure they themselves have bash installed. (But, to argue against my own point, if an OS vendor ships my…
This is a false analogy. It's quite straightforward. Running bash (via exec()/fork()/spawn()/etc) isn't the same as (statically or dynamically) linking with its codebase. If your MIT-licensed one-liner links to code that's GPL licensed, then it gets infected by the GPL license.
I don't know if this has ever been tested in court.
I ask because their models are pretty small. Some sound awesome and there is no depdendency hell like I'm seeing here.
Example: https://rhasspy.github.io/piper-samples/#en_US-ryan-high
Hmm the quality is not so impressive. I'm looking for a really naturally sounding model. Not very happy with piper/kokoro, XTTS was a bit complex to set up. For STT whisper is really amazing. But I miss a good TTS. And I don't mind throwing GPU power at it. But anyway. this isn't it either, this sounds worse than kokoro.
> Hmm the quality is not so impressive. [...] And I don't mind throwing GPU power at it. This isn't for you, then. You should evaluate quality here based on the fact you don't need a GPU. Back in the pre-Tacotron2 days, I was running slim TTS and vocoder models like GlowTTS and MelGAN on Digital Ocean droplets. No GPU to speak of. It cost next to nothing to run. Since then, the trend has been to scale up. We need mor…
I know but it was more of a general comment. A really good TTS just isn't around yes in the OSS sphere. I looked at some of the other suggestions here but they have too many quirks. Dia sounds great but messages must have certain lengths etc and it picks a random voice every time. I'd love to have something self hosted that's as good as openai.
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We are working to fix that. Thanks
"Fixing python packaging" is somewhat harder than AGI.
Why is Python so bad at that? It's less kludgy than Bash scripts, but even those are easier to get working.
I hope this is the future. Offline, small ML models, running inference on ubiquitous, inexpensive hardware. Models that are easy to integrate into other things, into devices and apps, and even to drive from other models maybe.
This is what Apple is envisioning with their SLMs, like having a model specifically for managing calendar events. It doesn't need to have the full knowledge of all humanity in it - just what it needs to manage the calendar.
System Requirements Works literally everywhere Haha, on one of my machines my python version is too old, and the package/dependencies don't want to install. On another machie the python version is too new, and the package/dependencies don't want to install.
Such an ignorant thing to say for something that requires 25MB RAM.
Now, RISC architectures are much more common, so instead of the rare 68K Apple/Amiga/etc computer that existed at the time, it's super common to want to run software on an ARM or occasionally RISC-V processor, so writing in x86 assembly language would require emulation, making for worse performance than a compiled language.
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Not sure what the size has to do with anything. I send you a 500kb Windows .exe file and claim it runs literally everywhere. Would it be ignorant to say anything against it because of its size?
we all know runs anywhere in this context means compute wise. It's dumb to blame author for your dev setup issues.
I don't mind so much the size in MB, the fact that it's pure CPU and the quality, what I do mind however is the latency. I hope it's fast. Aside: Are there any models for understanding voice to text, fully offline, without training? I will be very impressed when we will be able to have a conversation with an AI at a natural rate and not "probe, space, response"
>Aside: Are there any models for understanding voice to text, fully offline, without training? OpenAI's whisper is a few years old and pretty solid. https://github.com/openai/whisper
[0]: https://github.com/openai/whisper/discussions/679 [1]: https://github.com/openai/whisper/discussions/928 [2]: https://github.com/openai/whisper/discussions/2608