I would be very surprised if there is no improvement if the codec is biased towards particular dialects or other distinctive subsets of the data. And we could c

I would be very surprised if there is no improvement if the codec is biased towards particular dialects or other distinctive subsets of the data. And we could certainly be fine with some kinds of bias. Speech codecs are intended to transmit human speech after all. Not that of dogs, bats, or hypothetical extraterrestrials. On the other hand, a wider dataset might reduce overfitting and force the model to learn better. If the codec has the intention of working best for human voice in general, then it is simply not possible to define sensible subsets of the user base to optimize for. Curating an appropriate training set has therefore technical impact on the performance of the codec. Realistically, I admit that the percentages of speech samples of languages in such a dataset would be according to the relative amount of speakers. This is of course a very fuzzy number with many sources of systematic error (like what counts as one language, do non-native speakers count, which level of proficiency is considered relevant, etc.), and ultimately English is a bit more important since it is de-facto the international lingua franca of this era. In short, a good training set is important unless one opines that certain subsets of humanity will never ever use the codec, which is equivalent to being blind to the reality that more and more parts of the world are getting access to the internet.