update

examples/dfnet2/step_2_train_model.py

@@ -276,7 +276,7 @@ def main():
 
             loss = 1.0 * mr_stft_loss + 1.0 * neg_si_snr_loss + 1.0 * mask_loss + 0.01 * lsnr_loss
             if torch.any(torch.isnan(loss)) or torch.any(torch.isinf(loss)):
-                logger.info(f"find nan or inf in loss.")
+                logger.info(f"find nan or inf in loss. continue.")
                 continue
 
             denoise_audios_list_r = list(est_wav.detach().cpu().numpy())
@@ -352,7 +352,7 @@ def main():
 
                         loss = 1.0 * mr_stft_loss + 1.0 * neg_si_snr_loss + 1.0 * mask_loss + 0.01 * lsnr_loss
                         if torch.any(torch.isnan(loss)) or torch.any(torch.isinf(loss)):
-                            logger.info(f"find nan or inf in loss.")
+                            logger.info(f"find nan or inf in loss. continue.")
                             continue
 
                         denoise_audios_list_r = list(est_wav.detach().cpu().numpy())

toolbox/torchaudio/losses/spectral.py

@@ -218,7 +218,7 @@ class LogSTFTMagnitudeLoss(torch.nn.Module):
         loss = F.l1_loss(torch.log(denoise_magnitude + self.eps), torch.log(clean_magnitude + self.eps))
 
         if torch.any(torch.isnan(loss)) or torch.any(torch.isinf(loss)):
-            raise AssertionError("LogSTFTMagnitudeLoss, nan or inf in loss")
+            print("LogSTFTMagnitudeLoss, nan or inf in loss")
 
         return loss