This github project includes codes for reproducing experiments and DNN models used in the paper ''RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification'' * Currently submitted to Interspeech2019 as a conference paper. * arXiv pre-print is at https://arxiv.org/abs/1904.08104 Below are few notes for reproduction 1. Script 'lunch_ngc.sh' is used to create a virtual environment for DNN training using NGC(nvidia gpu cloud). 2. Script '00-pre_process_waveforms.py' was conducted in another workstation when we reproduced experiemnts regarding RawNet. 3. For back-end research or front-end verification, we provide speaker embeddings extracted with RawNet at 'data/speaker_embeddings_RawNet'. Cosine similarity metric with this embeddings demonstrate EER of 4.8 % on the VoxCeleb1 evaluation set. This file can also obtained by running script '01-trn_RawNet.py' (minor differences can occur due to random seed). Other guidelines are currently being updated. Email jeewon.leo.jung@gmail.com for other details :-). Log 2019.04.18 : executing 01 script