You can resolve loss fluctuations in a WGAN for voice synthesis tasks by referring to the following steps:
- Use Gradient Penalty: This helps stabilize the training by penalizing large gradients, which is especially important for WGANs.
- Clip Discriminator Weights: For traditional WGAN, clip the discriminator's weights to a specific range to prevent the discriminator from becoming too strong.
- Adjust Learning Rates: Ensure that the learning rate for both the generator and discriminator is well-balanced to avoid instability.
- Increase Training of the Discriminator: Train the discriminator more frequently than the generator to ensure it provides useful feedback.
- Label Smoothing: Apply label smoothing to the discriminator to prevent overfitting and reduce fluctuations.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Gradient Penalty: Stabilizes training by penalizing large gradients between real and fake images.
- Discriminator Training: Train the discriminator more frequently than the generator to prevent overfitting and improve convergence.
- Learning Rate Adjustment: Ensure that the learning rates for the generator and discriminator are balanced.
Hence, these techniques help resolve loss fluctuations and stabilize training in WGANs, especially in complex tasks like voice synthesis.
