OPUS-MT-train VS Opus-MT

https://translatelocally.com/ is a nice gui around marian/bergamot. So far not very many bundled pairs, though I would guess any of the models from https://github.com/Helsinki-NLP/Opus-MT-train/tree/master/mo... and https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/maste... should be usable.

There is also Apertium, a rule-based system which is very good for some closely-related pairs that have had a lot of work put into them (especially translation between Romance languages, e.g. Spanish→Catalan, and Norwegian Bokmål→Nynorsk), and the only OK translator for some lesser-resourced languages (e.g. Northern Saami→Norwegian Bokmål), but very underdeveloped for anything to/from English (it feels a bit pointless writing rules for English where there is so much available data; RBMT shines where there's not enough available data, ie. most of the languages of the world)

- mention on GitHub repo of a translation model https://github.com/Helsinki-NLP/Opus-MT/issues/62

I'm curious to see if anyone else has interesting encounters with this

Yes, most models these days, except the exceptionally large ones, are possible to train on a laptop. Of course it helps if your laptop has Nvidia CUDA GPU, but even if it doesn't you can rent an AWS 4 core/16GB GPU instance for 0.5 cents an hour. 24 hours of training time would be quite a lot for most models, unless you're trying to train a FB any to any language type model, but typically the big huge models are not the most interesting ones, and you can get very good results, and interesting models with substantially smaller sets of data. Opus MT models are only one language to one language, but they're about 300MB a model, and the quality rivals FB's models, and the speed is substantially faster. I don't have as many examples from the music space, as it's still a fairly under explored area, but Google has released Magenta which is a pretrained Tensorflow music model(actually a group of 3-4 models).