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MMS is designed to handle speech recognition across multiple languages simultaneously, providing a more scalable solution for languages with limited resources. By training on a variety of languages in a single model, MMS can generalize across languages and reduce the need for language-specific models. This is particularly useful for low-resource languages like Nepali and other regional languages, where dedicated datasets might not be available.

M2M100 is a multilingual translation model developed by Facebook that supports direct translation between any pair of 100 languages, including low-resource languages. Unlike traditional translation models that require pivot languages (e.g., English), M2M100 allows direct translation from one language to another, enhancing the translation efficiency and improving quality for language pairs with limited resources. This model leverages a shared multilingual vocabulary and can perform translation with fewer language-specific constraints.