Vox-adv-cpk.pth.tar

# Define the model architecture (e.g., based on the ResNet-voxceleb architecture) class VoxAdvModel(nn.Module): def __init__(self): super(VoxAdvModel, self).__init__() # Define the layers...

# Load the checkpoint file checkpoint = torch.load('Vox-adv-cpk.pth.tar') Vox-adv-cpk.pth.tar

# Use the loaded model for speaker verification Keep in mind that you'll need to define the model architecture and related functions (e.g., forward() method) to use the loaded model. # Define the model architecture (e

def forward(self, x): # Define the forward pass... # Define the model architecture (e.g.

import torch import torch.nn as nn