diff --git a/TTS/tts/models/glow_tts.py b/TTS/tts/models/glow_tts.py
index 2c944008..af52ba1c 100755
--- a/TTS/tts/models/glow_tts.py
+++ b/TTS/tts/models/glow_tts.py
@@ -185,13 +185,13 @@ class GlowTTS(nn.Module):
         y_mean, y_log_scale, o_attn_dur = self.compute_outputs(attn, o_mean, o_log_scale, x_mask)
         attn = attn.squeeze(1).permute(0, 2, 1)
         outputs = {
-            "model_outputs": z,
+            "model_outputs": z.transpose(1, 2),
             "logdet": logdet,
-            "y_mean": y_mean,
-            "y_log_scale": y_log_scale,
+            "y_mean": y_mean.transpose(1, 2),
+            "y_log_scale": y_log_scale.transpose(1, 2),
             "alignments": attn,
-            "durations_log": o_dur_log,
-            "total_durations_log": o_attn_dur,
+            "durations_log": o_dur_log.transpose(1, 2),
+            "total_durations_log": o_attn_dur.transpose(1, 2),
         }
         return outputs
 
@@ -246,13 +246,13 @@ class GlowTTS(nn.Module):
         # reverse the decoder and predict using the aligned distribution
         y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
         outputs = {
-            "model_outputs": y,
+            "model_outputs": z.transpose(1, 2),
             "logdet": logdet,
-            "y_mean": y_mean,
-            "y_log_scale": y_log_scale,
+            "y_mean": y_mean.transpose(1, 2),
+            "y_log_scale": y_log_scale.transpose(1, 2),
             "alignments": attn,
-            "durations_log": o_dur_log,
-            "total_durations_log": o_attn_dur,
+            "durations_log": o_dur_log.transpose(1, 2),
+            "total_durations_log": o_attn_dur.transpose(1, 2),
         }
         return outputs
 
@@ -285,7 +285,7 @@ class GlowTTS(nn.Module):
         y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
 
         outputs = {}
-        outputs["model_outputs"] = y
+        outputs["model_outputs"] = y.transpose(1, 2)
         outputs["logdet"] = logdet
         return outputs
 
@@ -317,13 +317,13 @@ class GlowTTS(nn.Module):
         y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
         attn = attn.squeeze(1).permute(0, 2, 1)
         outputs = {
-            "model_outputs": y,
+            "model_outputs": y.transpose(1, 2),
             "logdet": logdet,
-            "y_mean": y_mean,
-            "y_log_scale": y_log_scale,
+            "y_mean": y_mean.transpose(1, 2),
+            "y_log_scale": y_log_scale.transpose(1, 2),
             "alignments": attn,
-            "durations_log": o_dur_log,
-            "total_durations_log": o_attn_dur,
+            "durations_log": o_dur_log.transpose(1, 2),
+            "total_durations_log": o_attn_dur.transpose(1, 2),
         }
         return outputs