Joel-Speech-TTS

A Text-to-Speech (TTS) model developed for the Mizo language that converts written Mizo text into natural-sounding speech, supporting the development of Mizo-language voice applications and accessibility solutions.

A Text-to-Speech (TTS) model developed for the Mizo language that converts written Mizo text into natural-sounding speech, supporting the development of Mizo-language voice applications and accessibility solutions.

This Machine Translation (MT) model translates text from one language to another using a trained neural network. It can support multilingual communication and language translation applications. Translation quality depends on language coverage, training data, and contextual accuracy. For Mizo, evaluate the model using representative test sentences before deployment.

The TTS was built using 5.18 hours of Mizo speech data. The Mizo text-to-speech (TTS) synthesis system built using the Variational Inference with Adversarial Learning for Text-to-Speech (VITS) framework.

A mizo ASR model.

A Machine Translation model for English-Mizo

COILD Multilingual Translation Model (COILD-Mul-MT)

Fine-tuned pre trained model for Mizo-English machine translation, trained on Mizo-English parallel datasets to improve translation performance for the Mizo language.

VITS text-to-speech models for Sourashtra, a low-resource Indo-Aryan language. There are male and female voices, and each voice accepts text in either Tamil script or Sourashtra script.

This dataset is designed for training and evaluating Optical Character Recognition (OCR) models capable of extracting text from document images. It contains 14,000 labeled samples consisting of image filenames and their corresponding ground-truth text annotations, along with metadata such as font type, font size, and document category. The dataset includes diverse document styles, including printed books and scanned documents, enabling models to learn robust text recognition under varying visual