widdiot Goto Github PK

aplus_track

arabic_pronounce

Pronounce Arabic words

asr_labs

ASR labs

bag-of-visual-words

This has he BoVW model to classify the images of same object together among: airplanes, bikes, cars, faces.

best-readme-template

An awesome README template to jumpstart your projects!

camel_tools

A suite of Arabic natural language processing tools developed by the CAMeL Lab at New York University Abu Dhabi.

crnn-lid

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

ctcdecode

PyTorch CTC Decoder bindings

da-lang-id

Domain Adaptation for Spoken Language ID

demo

example code for remind myself, especial the api

digit-recognition

A CNN LeNet model to classify images of digits as 0 - 9.

e2e-asr

PyTorch Implementations for End-to-End Automatic Speech Recognition

eend

End-to-End Neural Diarization

irisa-text-normalizer

Text normalization scripts from IRISA lab

kaldi

This is the official location of the Kaldi project.

kaldi-postproc

kashmiri-parsing-pipeline

A dependency parsing pipeline for Kashmiri which includes a POS-tagger, a Chunker and an Intra-chunk Dependency Parser.

marytts-lexicon-de

German lexicon for MaryTTS

neural_sp

End-to-end ASR/LM implementation with PyTorch

pika

a lightweight speech processing toolkit based on Pytorch and (Py)Kaldi

pychain_example

pytorch-streamloader

speech-training-recorder

Simple GUI application to help record audio dictated from given text prompts, for use with training speech recognition or speech synthesis.

spoteno

Spoken text normalization for asr

text-binarization

tipr-assignment-1

tipr_assignment_2

triplet-entropy-loss

Project repository for the work done in Triplet Entropy Loss: Improving The Generalization of Short Speech Language Identification Systems

tsne

Visualising High Dimensional Data using tSNE

tuplemax-loss

Unofficial implementation of pairwise tuplemax loss. TUPLEMAX LOSS FOR LANGUAGE IDENTIFICATION https://arxiv.org/pdf/1811.12290.pdf Eq. (2). works only for batch_size = 1