But you have to make those assumptions if you want to make a model for rhyming because large quantities of raw prose does not contain any information whatsoeve

But you have to make those assumptions if you want to make a model for rhyming because large quantities of raw prose does not contain any information whatsoever about which words rhyme, how they are pronounced or where the accents lie. And of course that would be worse for other tasks, that's kind of the whole point - discarding irrelevant information is the key part that distinguishes learning from memorizing; for most use cases you do want to discard the pronunciation part and other nuances of representation to focus on the semantics, but for some (like this) you may want to discard some other parts in order to focus on how the language sounds like. The 'no free lunch theorem' is conceptually relevant even if it doesn't literally apply here. Your example of their/they're/there has some data because the whole words can be mistaken for each other, but even if you take a billion pages of prose, you won't get data to deduce that 'there' rhymes with 'pear' but not 'here', that 'great' rhymes with 'straight' but 'cough', 'dough' and 'through' do not rhyme. A model can't learn something that's simply not represented in the training data. So you either have to bring in external information (i.e. pronunciation models, or dictionary data with pronunciation guides, or audio recordings) or you have to have sufficient rhyming text inside your training data - i.e. train it on corpora of poetry instead of prose. Also, I'm not sure if the variation of pronunciation is critical - any accent variations that affect every instance of some sound similarly would preserve rhyming. There are certain differences e.g. I recall a discussion about some parts of Shakespeare which rhyme perfectly in pronunciation of that period but do not in modern English, but I think that the variations of modern English accents should be mostly OK from that perspective.