# Taking VoiceLK From Model to Product

Canonical HTML: https://voicelk.dev/blog/taking-voicelk-from-model-to-product

How VoiceLK integrates its language-processing pipeline, VITS speech synthesis model, frontend, and backend services into a usable application.

Author: T.H. Mihiran Thilakarathna

Published: 2026-10-21T01:19:00.717Z

Updated: 2026-10-21T01:20:49.256Z

## Taking VoiceLK From Model to Product

*Integrating language processing and neural speech synthesis into a practical application.*

Training a neural text-to-speech model was a major milestone for VoiceLK, but a trained model alone was not enough to deliver a usable solution. We needed to connect the model with the language-processing components and build an application through which users could access its capabilities.

VoiceLK brings together several components: text normalisation, Sinhala–English language routing, custom grapheme-to-phoneme conversion, and neural speech synthesis using the VITS architecture. Together, these components form a pipeline that transforms mixed-language text into generated speech.

To make this pipeline accessible through an application, we developed a software architecture that connects the user interface with the speech-processing backend. The frontend provides the interaction layer, while backend services manage requests and coordinate the processing required to generate speech.

Our technology stack includes **React** for the frontend, **Python with FastAPI and Uvicorn** for the speech-processing service, and **Java Spring Boot** for backend application services. We also use **Docker** to support deployment and a cloud virtual machine to host the application components.

This integration required more than connecting individual technologies. The components needed to communicate reliably, process input consistently, and work together as a complete system.

By moving from isolated experiments towards an integrated application, we took an important step in making VoiceLK accessible beyond model-training environments.

**The goal was not simply to build an AI voice model, but to turn that model into a system people could actually use.**