Hey HN, I’m open sourcing my Hurston-1 narrowband TTS model (Apache-2.0). It is very good with American English and does especially well with Southern American and Regional African English accents. Destined AI is based in Atlanta, and I built this because I’m amazed by what AI can do, and I wanted to help introduce and welcome more people to feel comfortable using and interacting with AI.

Since 2021, I’ve been collecting fully consented voice data from over 20,000 people, 1M audio recordings, and over 1,800 hrs of audio captured to date. For this model, I used a small part of the data, 136 hrs, to finetune Maya1 and expand it’s capabilities.

Hurston-1 is a narrowband TTS model that focuses the output on telephone use because that is still where most of these voices will be heard. I’ve used it to support customers such as Amazon and Salesforce and generated over half-mill revenue with it. Now I’m open sourcing it to help unlock opportunities for others as well.

Limitations: this model doesn’t maintain the exact same voice across prompts and doesn’t support voice cloning. For many use cases, this is fine but maybe not so - if you’re creating podcasts.

This is the first release - I’m already working on the next version with improvements.

I would appreciate, if anyone is interested in this space, download the model from huggingface, check it out and let me know any feedback. Also, open to connecting with others in this space as well.

congrats, this is a very exciting release.

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.

Sounds like your project is for southern American English presumably including African-American Vernacular English (AAVE), and for regional accents in the African continent specifically for the English language.

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.

Seeing Atlanna inspire an open weight model was not on my 2026 bingo card.

Regional African English accents, so Nigerian, Kenyan, South African?

Wow this is really cool. AAVE and Southern lexes have been underserved linguistically for a long time.

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.

Southern American? It's Argentine or Chilean?