This github project includes PyTorch implementation for reproducing experiments and DNN models used in the paper Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms which is submitted to Interspeech2020 as a conference paper. Trained model is available at 'Pre-trained_model/rawnet2_best_weights.pt' and extracted speaker embeddings are available at spk_embd/.

For reproduction of the original RawNet paper, please refer to 'RawNet1' folder.

We used Nvidia GPU Cloud for conducting our experiments. We used the 'nvcr.io/nvidia/pytorch:19.10-py3' image. Refer to launch_ngc.sh. We used two Titan V GPUs for training.

1. Download VoxCeleb1&2 datasets and move to DB/.
   (or just give directories to your DB as arguments using --DB DIR_TO_VOX1 and --DB_vox2 DIR_TO_VOX2)
   Filetree will be added as reference in meantime.

2. (selectively) Enter virtual environment using NGC.

3. Run train_RawNet2.py -name NAME

1. Go into Pre-trained_model folder.
2. Download extracted RawNet2 speaker embeddings for the VoxCeleb1 devset Here (Too big to upload in Github)
3. Move downloaded speaker embedding to spk_embd/
4. Run evaluate_pretrained_RawNet2.py

We encourage to use the extracted speaker embeddings for further speaker embedding enhancement studies or back-end studies since RawNet2 paper adopts simple cosine similarity for back-end classification.

Speaker embeddings are located under spk_embd/ and are saved using pickle, where it contains a dictionay.
Key : Utterance ID (Spk/videoID/segID) Value : Speaker embedding

This reposity provides the code for reproducing below papers.

@article{jung2020improved,
  title={Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms},
  author={Jung, Jee-weon and Kim, Seung-bin and Shim, Hye-jin and Kim, Ju-ho and Yu, Ha-Jin},
  journal={arXiv preprint arXiv:2004.00526},
  year={2020}
}

@article{jung2019RawNet,
  title={RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification},
  author={Jung, Jee-weon and Heo, Hee-soo and Kim, ju-ho and Shim, Hye-jin and Yu, Ha-jin},
  journal={Proc. Interspeech 2019},
  pages={1268--1272},
  year={2019}
}

1. Add comments to codes.
2. Add filetree of Datasets

• 2020.04.01. : Initial commit
• 2020.04.02. : Evaluate Pre-trained Model validated
• 2020.04.02. : Evaluated training