check against NaN loss in tacotron_loss

.compute

@@ -1,14 +1,14 @@
 #!/bin/bash
 yes | apt-get install sox
 yes | apt-get install ffmpeg
-yes | apt-get install espeak 
+yes | apt-get install espeak
 yes | apt-get install tmux
 yes | apt-get install zsh
 sh -c "$(curl -fsSL https://raw.githubusercontent.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"
 pip3 install https://download.pytorch.org/whl/cu100/torch-1.3.0%2Bcu100-cp36-cp36m-linux_x86_64.whl
 sudo sh install.sh
-pip install pytorch==1.3.0+cu100
-python3 setup.py develop
+# pip install pytorch==1.7.0+cu100
+# python3 setup.py develop
 # python3 distribute.py --config_path config.json  --data_path /data/ro/shared/data/keithito/LJSpeech-1.1/
 # cp -R ${USER_DIR}/Mozilla_22050 ../tmp/
 # python3 distribute.py --config_path config_tacotron_gst.json  --data_path ../tmp/Mozilla_22050/

TTS/tts/layers/losses.py

@@ -228,7 +228,7 @@ class GuidedAttentionLoss(torch.nn.Module):
 
     @staticmethod
     def _make_ga_mask(ilen, olen, sigma):
-        grid_x, grid_y = torch.meshgrid(torch.arange(olen), torch.arange(ilen))
+        grid_x, grid_y = torch.meshgrid(torch.arange(olen).to(olen), torch.arange(ilen).to(ilen))
         grid_x, grid_y = grid_x.float(), grid_y.float()
         return 1.0 - torch.exp(-(grid_y / ilen - grid_x / olen)**2 /
                                (2 * (sigma**2)))
@@ -373,6 +373,11 @@ class TacotronLoss(torch.nn.Module):
             return_dict['postnet_ssim_loss'] = postnet_ssim_loss
 
         return_dict['loss'] = loss
+
+        # check if any loss is NaN
+        for key, loss in return_dict.items():
+            if torch.isnan(loss):
+                raise RuntimeError(f" [!] NaN loss with {key}.")
         return return_dict