Interesting approach! I've thought about a similar method after reading about the PLATO platform.
When playing astro‑maze, the delay is noticeable, and in a 2D action game such delays are especially apparent. Games that don’t rely on tight real‑time input might perform better. (I'm connecting from Europe, though.)
If you add support for drawing from images (such as spritesheets or tilesheets) in the future, and the client stores those images and sounds locally, the entire screen could be drawn from these assets, so no pixel data would need to be transferred, only commands like "draw tile 56 at position (x, y)."
Thanks! But I get the impression that with Kokoro, a strong CPU still requires about two seconds to generate one sentence, which is too much of a delay for a TTS voice in an AAC app.
I'd rather accept a little compromise regarding the voice and intonation quality, as long as the TTS system doesn't frequently garble words. The AAC app is used on tablet PCs running from battery, so the lower the CPU usage and energy draw, the better.
I had already suspected that I hadn't found all the possibilities regarding Tortoise TTS, Coqui, Piper, etc. It is sometimes difficult to determine how good a TTS framework really is.
Do you possibly have links to the voices you found?
The sample sounds impressive, but based on their claim -- 'Streaming inference is faster than playback even on an A100 40GB for the 3 billion parameter model' -- I don't think this could run on a standard laptop.
Large text-to-speech and speech-to-text models have been greatly improving recently.
But I wish there were an offline, on-device, multilingual text-to-speech solution with good voices for a standard PC — one that doesn't require a GPU, tons of RAM, or max out the CPU.
In my research, I didn't find anything that fits the bill. People often mention Tortoise TTS, but I think it garbles words too often. The only plug-in solution for desktop apps I know of is the commercial and rather pricey Acapela SDK.
I hope someone can shrink those new neural network–based models to run efficiently on a typical computer. Ideally, it should run at under 50% CPU load on an average Windows laptop that’s several years old, and start speaking almost immediately (less than 400ms delay).
The same goes for speech-to-text. Whisper.cpp is fine, but last time I looked, it wasn't able to transcribe audio at real-time speed on a standard laptop.
I'd pay for something like this as long as it's less expensive than Acapela.
In my experience, this bug - lags and overheating when drawing with the Apple Pencil - exists since iPadOS 16. When searching for it on the web, I found lots of reports and no indication that it is solved, including by hardware replacements.
In any case, HN's guidelines ask to use the original title of an article, unless it is misleading or linkbait. I'd agree that Apple's software quality has been going down.
I've used Apple's Automator app to add a new custom Quick Action which does exactly this. After right-clicking a folder, the right-click menu shows my custom Quick Action to create an empty text file.
This requires about 5 to 10 minutes to set up. You'll find instructions for this on the web or via some LLM. I've looked right now for a suitable article, but the ones I've found are subtly different from my Quick Action. I've asked ChatGPT and its instructions seem to be correct.
In the example I mentioned, ChatGPT 4 did keep all essential statements of my texts when reproducing shorter versions of them. For example, it often wrote one high-level sentence which skillfully summarized a paragraph of the original text. As far as I understand, this is what the author meant by 'summarizing' vs. 'shortening (while missing essential statements)'.
I was impressed at those high-level summaries. If I had assigned this task to several humans, I'm not sure how many would have been able to achieve similar results.
Am I blind or is there no mention at all of the GPT model he used?
The author states his conclusions but doesn't give the reader the information required to examine the problem.
- Whether the article to be summarized fits into the tested GPT model's context size
- The prompt
- The number of attempts
- He doesn't always state which information in the summary, specifically, is missing or wrong
For example: "I first tried to let ChatGPT one of my key posts (...). ChatGPT made a total mess of it. What it said had little to do with the original post, and where it did, it said the opposite of what the post said." He doesn't say which statements of the original article were reproduced falsely by ChatGPT.
My experience is that ChatGPT 4 is good when summarizing articles, and extremely helpful when I need to shorten my own writing. Recently I had to write a grant application with a strict size limit of 10 pages, and ChatGPT 4 helped me a lot by skillfully condensing my chapters into shorter texts. The model's understanding of the (rather niche) topic was very good. I never fed it more than about two pages of text at once. It also adopted my style of writing to a sufficient degree. A hypothetical human who'd have to help on short notice probably would have needed a whole stressful day to do comparable work.
When playing astro‑maze, the delay is noticeable, and in a 2D action game such delays are especially apparent. Games that don’t rely on tight real‑time input might perform better. (I'm connecting from Europe, though.)
If you add support for drawing from images (such as spritesheets or tilesheets) in the future, and the client stores those images and sounds locally, the entire screen could be drawn from these assets, so no pixel data would need to be transferred, only commands like "draw tile 56 at position (x, y)."
(By the way, opening abstra.io in a German-language browser leads to https://www.abstra.io/deundefined which shows a 404 error.)