A collection of sample apps to demonstrate how to use Google's ML Kit APIs on Android and iOS

👄 The most accurate natural language detection library for Java and the JVM, suitable for long and short text alike

Simple embedding based text classifier inspired by fastText, implemented in tensorflow

Vietnamese NLP Toolkit for Node

Code for the paper Language Identification Using Deep Convolutional Recurrent Neural Networks

End to End Dialect Identification using Convolutional Neural Network

Rosette API Client Library for Python

Implementation of the paper "Spoken Language Recognition using X-vectors" in Pytorch

Multi-Langauge Identification

End-to-end spoken language identification out of the box. Rewrite in progress for first release (version 1).

Detect the languages from short pieces of text

Easy language identification of 380 languages

Implement a GRU/LSTM model using Keras, and train it to classify the languages using MFCC features

Language Identification Toolkit

Natural language detection, Java bindings for CLD2

Demo: Elasticsearch Language Identification

Dataset for programming language identification.

Classifier that identifies Greek text as Cypriot Greek or Standard Modern Greek

Implementation of the paper "BERTphone: Phonetically-aware Encoder Representations for Utterance-level Speaker and Language Recognition"

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

Language identification using Siamese network based on i-vector

This project is build on top of whatthelang and langid

Rosette API Client Library for PHP

Rosette API Client Library for C#

Dataiku DSS plugin to detect languages, correct misspellings and clean text data

A Multinomial Bayesian Classification for Language Identification

Chương trình dự đoán ngôn ngữ dựa vào văn bản(như Google Dịch ^^)

Spoken language identification systems (LID) allow for automatic language detection given speech data. Among the many available methods that can be applied to this classification task, modern machine learning and deep learning approaches have been reported as effective. A previous study approached the problem of spoken language identification in the image domain by transforming speech samples to spectrograms and classifying them using convolutional neural networks (CNN). We have implemented two similar types of CNNs and trained them on data for five languages from the SpeechDat database. Then, we investigated how well their performance generalised on speech samples from another source then SpeechDat. The results indicated that even though the models could achieve over 80 % in test accuracy on SpeechDat data, they did not perform well on speech samples not originating from the SpeechDat database, with the best model achieving 37.5 % accuracy.