So let’s play, if you can guess the align method I’ll open source it :)
But that seems a lot more complicated... so, unlikely.
A way to cheat that would probably work good enough most of the time would be to spectrographic analysis on the audio stream to identify syllables, and then similarly just count syllables in the known text and line those up. That works better the more consistent your spelling system is, though, and still requires language-specific modelling. If you actually want to do a decent job cross-linguistically, you'd need in the general case a dictionary for every supported language listing syllable counts for each word (because not everybody's orthography is transparent enough to make simple models like counting character sequences work).
If you actually have a fully language-agnostic algorithm for aligning text to audio that's actually decently accurate, though, that's gotta be worth at least a Master's degree in computational linguistics, 'cause on the face of it it doesn't seem to me (who has such a Masters degree) that it should even theoretically be possible.
Even with only line-level accuracy, that would've been nice to have 7 years ago... but I see the first commit to the project is only in 2015. Might still be useful to some of my old colleagues, though; I'll have to see if they've heard of it.
My intuition, however, is that a meet-in-the-middle approach using automatic speech recognition and then aligning the resulting text streams would be the optimal approach, and indeed every other major forced-alignment tool besides aeneas (https://github.com/pettarin/forced-alignment-tools) does seem to use that approach. The catch, of course, is that you actually need decent ASR language models for every target language to make that work, and gas you can see from tat list, it is rare for any given engine to support more than a few languages; CMU Sphinx probably has the widest support, although it's not the highest end toolkit for popular languages like English. So, if you really want to maintain the broadest possible language support, and you can afford the API fees, building a new alignment engine that piggy-backs on MicroSoft or IBM's speech recognition APIs is probably the best option--or, to keep it cheap I'd go ahead and use Sphinx's aligner as a preferred option for all the languages that it has models for, and either fall back on aeneas for remaining languages, or (if you can afford occasional API calls to commercial services for the occasional less-popular language) upgrade to MicroSoft/IBM services for the remaining languages.
Espeak-ng supporting 108 languages is maybe a bit misleading. They have pronunciation definitions for many languages, but the actual level of support varies widely.
For Mandarin, espeak-ng 1.49.2 has a bug where it reads the tone numbers out loud instead of modifying the pitch contour, so e.g. the number 四 (four) is pronounced si si instead of sì, because it has the fourth tone. That's the version packaged for Ubuntu, so you may be using it for your API.
For Japanese, kanji aren't supported at all, so 四 is pronounced as "Chinese letter" (in English). For proper Japanese support, you'd need to switch to a different TTS engine like Open JTalk or preprocess the text to transform it into kana.
Also note that Aeneas is licensed under AGPL, which requires you to offer the source code if you let others interact with the program over a network (which is what your API does). So your attempt to keep the secret sauce private and only reveal it once someone guessed the algorithm was likely illegal. You should add proper copyright notices to your program and audioai.online