diff --git a/dnn/torch/osce/engine/bwe_engine.py b/dnn/torch/osce/engine/bwe_engine.py
index 838cf368c..9ea4c0b73 100644
--- a/dnn/torch/osce/engine/bwe_engine.py
+++ b/dnn/torch/osce/engine/bwe_engine.py
@@ -85,6 +85,7 @@ def evaluate(model, criterion, dataloader, device, preemph_gamma=0, log_interval
                     batch[key] = batch[key].to(device)
 
                 target = batch['x_48']
+                x_up = model.upsampler(batch['x_16'].unsqueeze(1))
 
                 # calculate model output
                 output = model(batch['x_16'].unsqueeze(1), batch['features'])
@@ -95,7 +96,7 @@ def evaluate(model, criterion, dataloader, device, preemph_gamma=0, log_interval
                 output = preemph(output, preemph_gamma)
 
                 # calculate loss
-                loss = criterion(target, output.squeeze(1), model.upsampler(batch['x_16'].unsqueeze(1)))
+                loss = criterion(target, output.squeeze(1), x_up)
 
                 # update running loss
                 running_loss += float(loss.cpu())
diff --git a/dnn/torch/osce/train_bwe_model.py b/dnn/torch/osce/train_bwe_model.py
index 1ec4a2c1e..b2c49ba4f 100644
--- a/dnn/torch/osce/train_bwe_model.py
+++ b/dnn/torch/osce/train_bwe_model.py
@@ -263,7 +263,7 @@ def criterion(x, y, x_up):
 
 print(f"{count_parameters(model.cpu()) / 1e6:5.3f} M parameters")
 if hasattr(model, 'flop_count'):
-    print(f"{model.flop_count(16000) / 1e6:5.3f} MFLOPS")
+    print(f"{model.flop_count(48000) / 1e6:5.3f} MFLOPS")
 
 
 best_loss = 1e9