d-vector based language identification using pytorch
This steps starts training model
python training.py --training_filepath meta/training.txt --testing_filepath meta/testing.txt --testing_filepath meta/validation_filepath
--input_dim 257 --num_classes 8 --lamda_val 0.1
--batch_size 10 --use_gpu True --num_epochs 100
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. For any queries contact : krishnadn94@gmail.com
1. GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION.[Paper]
2. RawNet: Advanced end-to-end deep neural network using raw waveformsfor text-independent speaker verification.[Paper]
3. Language Independent Gender Identification from Raw Waveform Using Multi-Scale Convolutional Neural Networks.[Paper]
Credits: G2E loss funtion code is taken from https://github.com/HarryVolek/PyTorch_Speaker_Verification