espnet2.gan_tts.vits.loss.KLDivergenceLossWithoutFlow

source

class espnet2.gan_tts.vits.loss.KLDivergenceLossWithoutFlow(*args, **kwargs)

Bases: Module

KL divergence loss without flow.

This class implements the calculation of the Kullback-Leibler (KL) divergence loss in a variational inference framework, specifically designed for cases where flow-based representations are not used.

None

• Parameters:
  • m_q (Tensor) – Posterior encoder projected mean (B, H, T_feats).
  • logs_q (Tensor) – Posterior encoder projected scale (B, H, T_feats).
  • m_p (Tensor) – Expanded text encoder projected mean (B, H, T_feats).
  • logs_p (Tensor) – Expanded text encoder projected scale (B, H, T_feats).
• Returns: KL divergence loss, averaged over the batch.
• Return type: Tensor

Examples

>>> kl_loss = KLDivergenceLossWithoutFlow()
>>> m_q = torch.randn(32, 64, 100)  # Example tensor for m_q
>>> logs_q = torch.randn(32, 64, 100)  # Example tensor for logs_q
>>> m_p = torch.randn(32, 64, 100)  # Example tensor for m_p
>>> logs_p = torch.randn(32, 64, 100)  # Example tensor for logs_p
>>> loss = kl_loss(m_q, logs_q, m_p, logs_p)
>>> print(loss)  # Output will be the calculated KL divergence loss

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(m_q: Tensor, logs_q: Tensor, m_p: Tensor, logs_p: Tensor) → Tensor

Calculate KL divergence loss without flow.

• Parameters:
  • m_q (Tensor) – Posterior encoder projected mean (B, H, T_feats).
  • logs_q (Tensor) – Posterior encoder projected scale (B, H, T_feats).
  • m_p (Tensor) – Expanded text encoder projected mean (B, H, T_feats).
  • logs_p (Tensor) – Expanded text encoder projected scale (B, H, T_feats).