simple use for benchmarking and profiling module

Python project which mixes and tests sentences from the TIMIT dataset using LAS

My magister (Bac+5+2) topic is about the Timit phonems multi_classification using GA and SVM. My works are presented here to help the research community, thanks !

TIMIT forced alignment with the Montreal Forced Aligner

Python project that mixes phonemes from the TIMIT dataset

A simple CRDNN based ASR model for my own understanding of how ASR works and are trained. (Work in progress) If anyone finds any error or have any suggestion please do let me know.

pretrained SpeechBrain wav2vec seq2seq+CTC model trained on TIMIT dataset. Created by Kip McCharen, Siddharth Surapaneni, and Pavan Bondalapati

다양한 feature를 이용한 음성인식 LAS model입니다. (한국어는 개발예정)

[🏆 Silver Medal at CWSF] Tensorflow Implementation of TIMIT Deep BLSTM-CTC with Tensorboard Support

A collection of benchmarking tools.

Implementation of the paper "Listen, Attend and Spell" Paper in Pytorch

A toolkit providing deep learning based audio recognition algorithm powered by Mxnet Gluon. Now only Text-Independent Speaker Recognition is implemented.

Implementation of WaveNet network based on Tensorflow.

Sum-Product Networks (SPNs) for Robust Automatic Speaker Identification.

A database of clean and noisy speech for audio research

Python/numpy/pandas convenience wrapper for the TIMIT database.

Extract mfcc vectors and phones from TIMIT dataset

Attention-based end-to-end ASR on TIMIT in PyTorch

A Simple Automatic Speech Recognition (ASR) Model in Tensorflow, which only needs to focus on Deep Neural Network. It's easy to test popular cells (most are LSTM and its variants) and models (unidirectioanl RNN, bidirectional RNN, ResNet and so on). Moreover, you are welcome to play with self-defined cells or models.