Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit

Kaldi-based Korean ASR (한국어 음성인식) open-source project

Time delay neural network (TDNN) implementation in Pytorch using unfold method

PyTorch implementation of the Factorized TDNN (TDNN-F) from "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks" and Kaldi

基于PaddlePaddle实现的音频分类，支持EcapaTdnn、PANNS、TDNN、Res2Net、ResNetSE等各种模型，还有多种预处理方法

Time Delayed NN implemented in pytorch

PyTorch implementation of a Time Delay Neural Network (TDNN)

Deep Learning using Neural Network Toolbox + Finance Portfolio Selection with MorningStar

tdnn (time delay neural network) tensorflow implementation

This project partially embodies the state-of-the-art practices in speaker verification technology up until 2020, while attaining the state-of-the-art performance on the VoxCeleb1 test sets.

Steps to prepare the data and train a simple HMM-GMM and TDNN model in Kaldi

Developed a speech recognition system using TDNN, preprocessing audio, extracting MFCC features, and training the model. Fine-tuning with augmented data (19,000 rows) improved accuracy from 9% to 80% training and 40% validation. Data augmentation proved crucial for enhancing model performance and generalization. Still working to increase the acc.