diff --git a/mozilla_voice_tts/speaker_encoder/losses.py b/mozilla_voice_tts/speaker_encoder/losses.py
index 9065ccfd..f4687949 100644
--- a/mozilla_voice_tts/speaker_encoder/losses.py
+++ b/mozilla_voice_tts/speaker_encoder/losses.py
@@ -124,14 +124,14 @@ class GE2ELoss(nn.Module):
 
 # adapted from https://github.com/clovaai/voxceleb_trainer/blob/master/loss/angleproto.py
 class AngleProtoLoss(nn.Module):
-    """ 
+    """
     Implementation of the Angular Prototypical loss defined in https://arxiv.org/abs/2003.11982
         Accepts an input of size (N, M, D)
             where N is the number of speakers in the batch,
             M is the number of utterances per speaker,
             and D is the dimensionality of the embedding vector
         Args:
-            - init_w (float): defines the initial value of w 
+            - init_w (float): defines the initial value of w
             - init_b (float): definies the initial value of b
     """
     def __init__(self, init_w=10.0, init_b=-5.0):
@@ -148,13 +148,13 @@ class AngleProtoLoss(nn.Module):
         """
         Calculates the AngleProto loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
         """
-        out_anchor = torch.mean(x[:,1:,:],1)
-        out_positive = x[:,0,:]
+        out_anchor = torch.mean(x[:, 1:, :], 1)
+        out_positive = x[:, 0, :]
         num_speakers = out_anchor.size()[0]
 
-        cos_sim_matrix  = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1,-1,num_speakers),out_anchor.unsqueeze(-1).expand(-1,-1,num_speakers).transpose(0,2))
+        cos_sim_matrix  = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1, -1, num_speakers),out_anchor.unsqueeze(-1).expand(-1, -1,num_speakers).transpose(0, 2))
         torch.clamp(self.w, 1e-6)
         cos_sim_matrix = cos_sim_matrix * self.w + self.b
-        label = torch.from_numpy(np.asarray(range(0,num_speakers))).to(cos_sim_matrix.device)
+        label = torch.from_numpy(np.asarray(range(0, num_speakers))).to(cos_sim_matrix.device)
         L = self.criterion(cos_sim_matrix, label)
         return L
\ No newline at end of file