Team HelloWorld System Code Guideline

The system is based on speechtrain open-source toolkit

1.Data Preparation

All of the dev&test data need to resample to 16K.

You should modify dev&test data paths in csv files, including:

• speechbrain-develop/recipes/VoxCeleb/SpeakerRec/results/MSV_22/I_MSV/1986/csv_files/*.csv
• speechbrain-develop/recipes/VoxCeleb/SpeakerRec/results/MSV_22/I_MSV/test/csv_files/*.csv
• speechbrain-develop/recipes/VoxCeleb/SpeakerRec/results/MSV_22/I_MSV/private_test/csv_files/*.csv
• speechbrain-develop/recipes/VoxCeleb/SpeakerRec/hparams/noise.csv
• speechbrain-develop/recipes/VoxCeleb/SpeakerRec/hparams/reverb.csv

All noise and reverb wavs are from paper "A Study on Data Augmentation of Reverberant Speech for Robust Speech Recognition".

2. Training Process

You can run the following script to train the model, and you can modify the hyperparameters in the hparams/train_MSV.yaml and hparams/train_MSV_weight_regular.yaml.

python train_MSV.py hparams/train_MSV.yaml
python train_MSV_weight_regular.py hparams/train_MSV_weight_regular.yaml

3.Evaluation Process

You can run the following script to train the model, and you can modify the hyperparameters in the hparams/verification_MSV.yaml. Note that you must modify the value of pretrain_path to the path of the trained model. Finally, you can get scores at output_folder.

python speaker_verification_MSV.py hparams/verification_MSV.yaml