Show HN: Hurston-1 open source TTS that speak Southern American and Afro-English

But tangentially related, for non-English languages of the African continent, the Gates Foundation are pledging some grants to improve AI access outside of English-speaking regions: https://news.bpdata.com/article/gates-foundation-pledges-1-b...

Despite being focused on English, your project could be of interest for their funding. It's probably worth reaching out to let them know about your work.

That said, my hunch is your Africa speicifc datasets might lossy, as entire regions of Africa are very linguistically diverse and someone who speaks Bambara and French will affect a different English accent than someone who speaks Maninka and English.

Is it likely that they are using a model like this? Or are they actually trying to voice clone the original and then translate to English? I'm not even sure if the few Spanish-accented English auto-dubs I've heard are the same voice or not.

(Actually, that reminds me of growing up in Italy, where for a long time all English movies were shown dubbed, not subtitled, and they had the same lead male voice for all male leads, and the same lead female voice for all female leads, across every movie. I can still hear those voices in my head now thirty years later...)

Are you talking about the English of Africans in different parts of Africa? If so, are you including the voices of white and Mediterranean Africans?

I'm confused. As a black American with roots (going back a couple centuries) in the Ark-La-Tex, I would think our dialects would be covered under "Southern American," which makes me suspect that you were referring to me with all of those references to another continent.

I cannot speak to the other accents, but my experience has been that American south accents are really poorly represented in mainstream Speach-To-Text models. If your STT model is pointed at end users, this can cause quite a quality drift when looking at eval vs prod performance depending on who your user base is.

I am not sure what terms you have collected the speach data on, but I am sure there are lots of people working with voice models that would be very interested in this as an eval set.