diff --git a/parakeet/models/tacotron2.py b/parakeet/models/tacotron2.py
index 599e6a2..9949e1d 100644
--- a/parakeet/models/tacotron2.py
+++ b/parakeet/models/tacotron2.py
@@ -238,10 +238,7 @@ class Tacotron2Decoder(nn.Layer):
         querys = paddle.concat(
             [
                 paddle.zeros(
-                    shape=[
-                        querys.shape[0], 1,
-                        querys.shape[-1] * self.reduction_factor
-                    ],
+                    shape=[querys.shape[0], 1, querys.shape[-1]],
                     dtype=querys.dtype), querys
             ],
             axis=1)
@@ -266,7 +263,7 @@ class Tacotron2Decoder(nn.Layer):
         return mel_outputs, stop_logits, alignments
 
     def infer(self, key, stop_threshold=0.5, max_decoder_steps=1000):
-        decoder_input = paddle.zeros(
+        query = paddle.zeros(
             shape=[key.shape[0], self.d_mels * self.reduction_factor],
             dtype=key.dtype)  #[B, C]
 
@@ -275,8 +272,8 @@ class Tacotron2Decoder(nn.Layer):
 
         mel_outputs, stop_logits, alignments = [], [], []
         while True:
-            decoder_input = self.prenet(decoder_input)
-            mel_output, stop_logit, alignment = self._decode(decoder_input)
+            query = self.prenet(query)
+            mel_output, stop_logit, alignment = self._decode(query)
 
             mel_outputs += [mel_output]
             stop_logits += [stop_logit]
@@ -288,7 +285,7 @@ class Tacotron2Decoder(nn.Layer):
                 print("Warning! Reached max decoder steps!!!")
                 break
 
-            decoder_input = mel_output
+            query = mel_output
 
         alignments = paddle.stack(alignments, axis=1)
         stop_logits = paddle.concat(stop_logits, axis=1)
@@ -350,7 +347,7 @@ class Tacotron2(nn.Layer):
             attention_kernel_size, p_prenet_dropout, p_attention_dropout,
             p_decoder_dropout)
         self.postnet = DecoderPostNet(
-            d_mels=d_mels,
+            d_mels=d_mels * reduction_factor,
             d_hidden=d_postnet,
             kernel_size=postnet_kernel_size,
             padding=int((postnet_kernel_size - 1) / 2),