The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others.

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.

Extract mfcc vectors and phones from TIMIT dataset

pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.

The DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus.

A collection of benchmarking tools.

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

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

Python project that mixes phonemes from the TIMIT dataset

MXNet implementation of RNN Transducer (Graves 2012): Sequence Transduction with Recurrent Neural Networks

Keras (tensorflow) implementation of SincNet (Mirco Ravanelli, Yoshua Bengio - https://github.com/mravanelli/SincNet)

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

SincNet is a neural architecture for efficiently processing raw audio samples.

다양한 feature를 이용한 음성인식 LAS model입니다. (한국어는 개발예정)

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 !

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

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

simple use for benchmarking and profiling module

CTC end -to-end ASR for timit and 863 corpus.