diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index bdfdf88a..0b6b16db 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -56,8 +56,8 @@
 
     "stft_loss_weight": 0.5,
     "subband_stft_loss_weight": 0.5,
-    "mse_gan_loss_weight": 2.5,
-    "hinge_gan_loss_weight": 2.5,
+    "mse_G_loss_weight": 2.5,
+    "hinge_G_loss_weight": 2.5,
     "feat_match_loss_weight": 25,
 
     "stft_loss_params": {
diff --git a/vocoder/train.py b/vocoder/train.py
index 05805c68..54b36e6b 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -181,7 +181,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         ##############################
         # DISCRIMINATOR
         ##############################
-        if global_step > c.steps_to_start_discriminator:
+        if global_step >= c.steps_to_start_discriminator:
             # discriminator pass
             with torch.no_grad():
                 y_hat = model_G(c_D)
@@ -295,7 +295,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
 
 
 @torch.no_grad()
-def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
+def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch):
     data_loader = setup_loader(ap, is_val=True, verbose=(epoch == 0))
     model_G.eval()
     model_D.eval()
@@ -355,13 +355,52 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
         for key, value in loss_G_dict.items():
             loss_dict[key] = value.item()
 
+        ##############################
+        # DISCRIMINATOR
+        ##############################
+
+        if global_step >= c.steps_to_start_discriminator:
+            # discriminator pass
+            with torch.no_grad():
+                y_hat = model_G(c_G)
+
+            # PQMF formatting
+            if y_hat.shape[1] > 1:
+                y_hat = model_G.pqmf_synthesis(y_hat)
+
+            # run D with or without cond. features
+            if len(signature(model_D.forward).parameters) == 2:
+                D_out_fake = model_D(y_hat.detach(), c_G)
+                D_out_real = model_D(y_G, c_G)
+            else:
+                D_out_fake = model_D(y_hat.detach())
+                D_out_real = model_D(y_G)
+
+            # format D outputs
+            if isinstance(D_out_fake, tuple):
+                scores_fake, feats_fake = D_out_fake
+                if D_out_real is None:
+                    scores_real, feats_real = None, None
+                else:
+                    scores_real, feats_real = D_out_real
+            else:
+                scores_fake = D_out_fake
+                scores_real = D_out_real
+
+            # compute losses
+            loss_D_dict = criterion_D(scores_fake, scores_real)
+
+            for key, value in loss_D_dict.items():
+                loss_dict[key] = value.item()
+
+
         step_time = time.time() - start_time
         epoch_time += step_time
 
         # update avg stats
         update_eval_values = dict()
-        for key, value in loss_G_dict.items():
-            update_eval_values['avg_' + key] = value.item()
+        for key, value in loss_dict.items():
+            update_eval_values['avg_' + key] = value
         update_eval_values['avg_loader_time'] = loader_time
         update_eval_values['avg_step_time'] = step_time
         keep_avg.update_values(update_eval_values)