Of the open source speech-to-text libraries I mentioned, the one with the most amount of different voices would likely be CMU Sphinx.

CMU Sphinx includes several pre-built acoustic models that are trained on different languages and dialects, which allows it to recognize a wide range of speech patterns. Additionally, it supports the use of custom acoustic and language models, which can be trained on specific speakers or domains to further improve accuracy.

That being said, the number of voices available for a speech-to-text library can depend on several factors, such as the language and dialect of the voices and the availability of training data for those voices. Some libraries may have more voices available for certain languages or dialects than others. It's always a good idea to check the documentation and community resources for the library you're interested in to see what voices are available and how to use them.
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