Popular GitHub Repositories for Hindi Transliteration

Rhasspy voice assistant for offline home automation

CogComp's Natural Language Processing Libraries and Demos: Modules include lemmatizer, ner, pos, prep-srl, quantifier, question type, relation-extraction, similarity, temporal normalizer, tokenizer, transliteration, verb-sense, and more.

Multilingual Generative Pretrained Model

Can neural networks transliterate Romaji into Japanese correctly?

Gita GPT A personal productivity assistant (RAG), a platform of AI chatbots, Ask Krishna GPT that uses Bhagavad Gita references to answer your questions.

Quran, Hadith, Translations, Tafaseer, Corpus Linguistics. Everything for NLP

Automatic transliteration with LSTM

A multilingual text-to-speech synthesis system for ten lower-resourced Turkic languages: Azerbaijani, Bashkir, Kazakh, Kyrgyz, Sakha, Tatar, Turkish, Turkmen, Uyghur, and Uzbek.

The Greek NLP toolkit for Python. Supports NER/DP/POS Tagging/Greeklish-to-Greek Transliteration. Visit the playground here: https://huggingface.co/spaces/AUEB-NLP/greek-nlp-toolkit-demo (paper presented at COLING 2025)

Towards Building Text-To-Speech Systems for the Next Billion Users - Microsoft Research Intern Work - Accepted at ICASSP 2023

CodeSwitch is a NLP tool, can use for language identification, pos tagging, name entity recognition, sentiment analysis of code mixed data.

Machine translation is the task of converting one language to other. Unlike the traditional phrase-based translation system which consists of many small sub-components that are tuned separately, neural machine translation attempts to build and train a single, large neural network that reads a sentence and outputs a correct translation.

Python Hindi Concatenative Based TTS using Phoneme Database

multilingual RAG

Tutorial on English to Hindi Transliteration using Seq2Seq Architecture in Tensorflow

Automated Headline generation and Aspect Based Sentiment Analysis

This repository contains the HiNER dataset released with our paper at LREC 2022

Name Entity Recognition Tool for Hindi Language

The main aim of the project is to develop a sentiment analyzer that can be used on twitter data to classify it as positive or negative. Our project takes care of the challenge of bilingual comments, where people tweet in two languages, in this case Hindi and English, in the Latin Alphabet.

The project,being part of Kagglex BIPOC Mentorship Program final project, aims to train two separate Hindi ASR models using the Facebook Wav2Vec2 (300M parameters) and OpenAI Whisper-Small models, respectively. The goal is to compare their performance, with a target WER of less than 13%, across various Hindi accents and dialects.