diff --git a/SV2TTS/encoder/model.py b/SV2TTS/encoder/model.py
index ee64d5d99872eb8b0f6ee9af92647615f3ed71d4..dad29115f9b8fc271d304bc2199f4c5c3ca65f3e 100644
--- a/SV2TTS/encoder/model.py
+++ b/SV2TTS/encoder/model.py
@@ -80,18 +80,17 @@ class SpeakerEncoder(nn.Module):
         centroids_excl = centroids_excl.clone() / torch.norm(centroids_excl, dim=2, keepdim=True)
                          
         # Similarity matrix
-        sim_matrix = torch.zeros(speakers_per_batch * utterances_per_speaker, 
+        sim_matrix = torch.zeros(speakers_per_batch, utterances_per_speaker,  
                                  speakers_per_batch).to(self.loss_device)
         for j in range(speakers_per_batch):
-            for i in range(utterances_per_speaker):
-                ji = j * utterances_per_speaker + i
-                for k in range(speakers_per_batch):
-                    centroid = centroids_excl[j, i] if j == k else centroids_incl[k]
-                    # The cosine similarity is the dot product when vectors are normalized
-                    sim_matrix[ji, k] = torch.dot(embeds[j, i], centroid)
+            for k in range(speakers_per_batch):
+                centroid = centroids_excl[j] if j == k else centroids_incl[k]
+                sim_matrix[j, :, k] = (embeds[j] * centroid).sum(dim=1)
         sim_matrix = sim_matrix * self.similarity_weight + self.similarity_bias
 
         # Loss
+        sim_matrix = sim_matrix.view((speakers_per_batch * utterances_per_speaker, 
+                                      speakers_per_batch))
         ground_truth = np.repeat(np.arange(speakers_per_batch), utterances_per_speaker)
         target = torch.from_numpy(ground_truth).long().to(self.loss_device)
         loss = self.loss_fn(sim_matrix, target)
diff --git a/SV2TTS/encoder/train.py b/SV2TTS/encoder/train.py
index 09ca269e6f1eb0d5b3fec15eeea811ad6377de7c..7f5efcc7c416d8111d58e93b3a6db52d7b178b6b 100644
--- a/SV2TTS/encoder/train.py
+++ b/SV2TTS/encoder/train.py
@@ -61,10 +61,13 @@ def train(run_id: str, clean_data_root: Path, models_dir: Path, vis_every: int,
         
         # Forward pass
         inputs = torch.from_numpy(speaker_batch.data).to(device)
+        torch.cuda.synchronize(device)
         profiler.tick("Data to %s" % device)
-        embeds = model(inputs).to(loss_device)
+        embeds = model(inputs)
+        torch.cuda.synchronize(device)
         profiler.tick("Forward pass")
-        loss, eer = model.loss(embeds.view((speakers_per_batch, utterances_per_speaker, -1)))
+        embeds_loss = embeds.view((speakers_per_batch, utterances_per_speaker, -1)).to(loss_device)
+        loss, eer = model.loss(embeds_loss)
         profiler.tick("Loss")
 
         # Backward pass
@@ -84,8 +87,8 @@ def train(run_id: str, clean_data_root: Path, models_dir: Path, vis_every: int,
             print("Drawing and saving projections (step %d)" % step)
             backup_dir.mkdir(exist_ok=True)
             projection_fpath = backup_dir.joinpath("%s_umap_%06d.png" % (run_id, step))
-            embeds_numpy = embeds.detach().numpy()
-            vis.draw_projections(embeds_numpy, utterances_per_speaker, step, projection_fpath)
+            embeds = embeds.detach().cpu().numpy()
+            vis.draw_projections(embeds, utterances_per_speaker, step, projection_fpath)
             vis.save()
 
         # Overwrite the latest version of the model
diff --git a/SV2TTS/encoder/ui/visualizations.py b/SV2TTS/encoder/ui/visualizations.py
index 7a1a906573dbe04aece3b2742f6befeeb3d4934b..317ab0be1d217ccd448201003f47391c5d06cdf5 100644
--- a/SV2TTS/encoder/ui/visualizations.py
+++ b/SV2TTS/encoder/ui/visualizations.py
@@ -45,7 +45,7 @@ class Visualizations:
         except ConnectionError:
             raise Exception("No visdom server detected. Run the command \"visdom\" in your CLI to "
                             "start it.")
-        webbrowser.open("http://localhost:8097/env/" + self.env_name)
+        # webbrowser.open("http://localhost:8097/env/" + self.env_name)
         
         self.loss_win = None
         self.eer_win = None