update

examples/nx_clean_unet/step_2_train_model.py

@@ -242,6 +242,8 @@ def main():
             clean_audios = clean_audios.to(device)
             noisy_audios = noisy_audios.to(device)
             one_labels = torch.ones(clean_audios.shape[0]).to(device)
+            print(f"clean_audios: {clean_audios.shape}")
+            print(f"noisy_audios: {noisy_audios.shape}")
 
             audio_g = generator.forward(noisy_audios)
 

toolbox/torchaudio/models/nx_clean_unet/discriminator.py

@@ -116,8 +116,10 @@ def main():
     discriminator = MetricDiscriminator(config=config)
 
     # shape: [batch_size, num_samples]
-    x = torch.ones([4, int(4.5 * 16000)])
-    y = torch.ones([4, int(4.5 * 16000)])
+    # x = torch.ones([4, int(4.5 * 16000)])
+    # y = torch.ones([4, int(4.5 * 16000)])
+    x = torch.ones([4, 16000])
+    y = torch.ones([4, 16000])
 
     output = discriminator.forward(x, y)
     print(output.shape)