[Fix] loudness: prevent NaN when all blocks are below absolute threshold

src/torchaudio/functional/functional.py

@@ -1578,15 +1578,35 @@ def loudness(waveform: Tensor, sample_rate: int):
     gated_blocks = loudness > gamma_abs
     gated_blocks = gated_blocks.unsqueeze(-2)
 
-    energy_filtered = torch.sum(gated_blocks * energy, dim=-1) / torch.count_nonzero(gated_blocks, dim=-1)
+    # Compute numerator and denominator
+    sum_gated_energy = torch.sum(gated_blocks * energy, dim=-1)
+    count_gated_blocks = torch.count_nonzero(gated_blocks, dim=-1)
+
+    # Use torch.where to avoid division by zero: if count is 0, set energy_filtered to 0
+    energy_filtered = torch.where(
+        count_gated_blocks > 0,
+        sum_gated_energy / count_gated_blocks,
+        torch.tensor(0.0, dtype=sum_gated_energy.dtype, device=sum_gated_energy.device)
+    )
+
     energy_weighted = torch.sum(g * energy_filtered, dim=-1)
     gamma_rel = kweight_bias + 10 * torch.log10(energy_weighted) - 10
 
     # Apply relative gating of the blocks
     gated_blocks = torch.logical_and(gated_blocks.squeeze(-2), loudness > gamma_rel.unsqueeze(-1))
     gated_blocks = gated_blocks.unsqueeze(-2)
 
-    energy_filtered = torch.sum(gated_blocks * energy, dim=-1) / torch.count_nonzero(gated_blocks, dim=-1)
+    # Compute numerator and denominator
+    sum_gated_energy = torch.sum(gated_blocks * energy, dim=-1)
+    count_gated_blocks = torch.count_nonzero(gated_blocks, dim=-1)
+
+    # Use torch.where to avoid division by zero: if count is 0, set energy_filtered to 0
+    energy_filtered = torch.where(
+        count_gated_blocks > 0,
+        sum_gated_energy / count_gated_blocks,
+        torch.tensor(0.0, dtype=sum_gated_energy.dtype, device=sum_gated_energy.device)
+    )
+
     energy_weighted = torch.sum(g * energy_filtered, dim=-1)
     LKFS = kweight_bias + 10 * torch.log10(energy_weighted)
     return LKFS