Launch HN: Uplift (YC S25) – Voice models for under-served languages

A billion people worldwide can't read. In countries like Pakistan – the 5th most populous country – 42% of adults are illiterate. This holds back the entire economy: patients can't read medical reports, parents can't help with homework, banks can't go fully digital, farmers can't research best practices, and people memorize smartphone app button sequences. Voice AI interfaces can fix all of this, and we think this will perhaps be one of the great benefits of modern AI.

Right now, existing voice models barely work for these languages, and big tech is moving slowly.

Uplift AI was originally a side project to make datasets for translation and voice models. For us it was a "cool side-thing" to work on, not an "important full-time thing" to work on. With some initial data we hacked together a Urdu Voice Bot on Whatsapp and gave it to one domestic worker. In two days 800 people were using it. When we dived deeper into understanding the users, we learned that text interfaces don't work for sooo many. So we started Uplift AI to solve this problem fulltime.

The most challenging part is that all the building blocks needed for great voice models are broken for these languages. For example, if you are creating a speech synthesis model, you will scrape a lot of data from youtube and auto-label it using a transcription model… all very easy to do in English. But it doesn't work in under-served languages because the transcription modes are not accurate.

There are many other challenges. Like when you hire human transcribers to label the data, often they don't have any spell correctors for their languages, and this creates lots of noise in the data… making it hard to train models with low data. There are many more challenges in phonemes, silence detection, diacritization etc.

We solve these problems by making great internal tooling to help with data labeling. Also, we source our own data and don't buy it. This is counterintuitive, but a big advantage over companies buying data and then training. By sourcing our own data we create the right data distributions and get much better models with much less data. By doing the entire thing inhouse, (data, labeling, training, deploying) we are able to make a lot faster progress.

Today we publicly offer a text to speech APIs for Urdu, Sindhi, and Balochi. Here's a video which shows this: https://www.loom.com/share/dcd5020967444c228e9c127151e7a9f5.

Khan Academy is using our tech to dub videos to Urdu (https://ur.khanacademy.org).

Our models excel at informational use cases (like AI bots) but need more work in emotive use-cases like poetry.

We have been giving a lot of people private access in beta mode, and today are launching our models publicly. We believe this will be the fastest way for us to learn about areas that are not performing well so we can fix them quickly.

We'd love to hear from all of you, especially around your experiences with under-served languages (not just the Pakistani ones we're starting with) and your comments in general.