How One Developer Built the World's Best Open-Source Hinglish TTS From Scratch With Zero Research Background

AIDeveloper44 Team

The Goal: Beat Every Model on the Market

Harrrshall set himself what he called a "stupid goal": build the world's best Hinglish text-to-speech model from scratch, with zero prior research background. His motivation started with curiosity — using an AI companion app called Ira, he got curious about the underlying voice stack, started digging into the architecture, and decided he could do better.

What Is Hinglish TTS?

Hinglish — the natural code-switching mix of Hindi and English spoken by hundreds of millions of people across India — is notoriously difficult for TTS systems. Most commercial models are trained predominantly on English or clean Hindi, and fall apart the moment you mix the two. Producing natural-sounding Hinglish speech requires handling phoneme mixing, prosody transitions, and code-switching artifacts that most models simply weren't trained on.

The Build Process

Rather than keeping his work private, Harrrshall documented every step publicly — every dead end, every architectural decision, every mistake and course correction. The full build log is available as an open-source project at harrrshall.github.io/hinglish-tts/. This level of transparency is rare in the TTS space and makes the project valuable not just as a model but as a learning resource for anyone wanting to understand the practical process of building speech models.

Results and Impact

The final model beats every existing commercial and open-source option on Hinglish speech quality — a remarkable result for a solo builder working without institutional resources or a research team. The project has earned significant attention on X and in the Indian developer community, with over 619 reposts and 733 likes at launch.

This is a powerful reminder that the tools for building frontier-quality AI systems are now accessible to individual developers. You don't need a PhD or a GPU cluster to build something that outperforms the incumbents — you need a clear goal, systematic documentation, and the persistence to work through the dead ends.

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