diff --git a/TTS/speaker_encoder/models/lstm.py b/TTS/speaker_encoder/models/lstm.py
index 3c2eafee..7ac08514 100644
--- a/TTS/speaker_encoder/models/lstm.py
+++ b/TTS/speaker_encoder/models/lstm.py
@@ -127,7 +127,7 @@ class LSTMSpeakerEncoder(nn.Module):
 
     @torch.no_grad()
     def inference(self, x, l2_norm=True):
-        d = self.layers.forward(x, l2_norm=l2_norm)
+        d = self.forward(x, l2_norm=l2_norm)
         return d
 
     def compute_embedding(self, x, num_frames=250, num_eval=10, return_mean=True):
diff --git a/TTS/tts/models/vits.py b/TTS/tts/models/vits.py
index 2a2cedb5..e4e64240 100644
--- a/TTS/tts/models/vits.py
+++ b/TTS/tts/models/vits.py
@@ -398,10 +398,10 @@ class Vits(BaseTTS):
             self.num_speakers = self.speaker_manager.num_speakers
 
         if self.args.use_speaker_embedding:
-            self._init_speaker_embedding(config)
+            self._init_speaker_embedding()
 
         if self.args.use_d_vector_file:
-            self._init_d_vector(config)
+            self._init_d_vector()
 
         # TODO: make this a function
         if self.args.use_speaker_encoder_as_loss:
@@ -436,14 +436,14 @@ class Vits(BaseTTS):
                     self.audio_transform = None
                 """
 
-    def _init_speaker_embedding(self, config):
+    def _init_speaker_embedding(self):
         # pylint: disable=attribute-defined-outside-init
         if self.num_speakers > 0:
             print(" > initialization of speaker-embedding layers.")
             self.embedded_speaker_dim = self.args.speaker_embedding_channels
             self.emb_g = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
 
-    def _init_d_vector(self, config):
+    def _init_d_vector(self):
         # pylint: disable=attribute-defined-outside-init
         if hasattr(self, "emb_g"):
             raise ValueError("[!] Speaker embedding layer already initialized before d_vector settings.")
diff --git a/TTS/tts/utils/speakers.py b/TTS/tts/utils/speakers.py
index a6e15cdd..07076d90 100644
--- a/TTS/tts/utils/speakers.py
+++ b/TTS/tts/utils/speakers.py
@@ -270,7 +270,7 @@ class SpeakerManager:
             waveform = self.speaker_encoder_ap.load_wav(wav_file, sr=self.speaker_encoder_ap.sample_rate)
             if not self.speaker_encoder_config.model_params.get("use_torch_spec", False):
                 m_input = self.speaker_encoder_ap.melspectrogram(waveform)
-                m_input = torch.from_numpy(m_input.T)
+                m_input = torch.from_numpy(m_input)
             else:
                 m_input = torch.from_numpy(waveform)
 
diff --git a/tests/aux_tests/test_speaker_encoder.py b/tests/aux_tests/test_speaker_encoder.py
index 3c897aa9..97b3b92f 100644
--- a/tests/aux_tests/test_speaker_encoder.py
+++ b/tests/aux_tests/test_speaker_encoder.py
@@ -13,7 +13,7 @@ file_path = get_tests_input_path()
 class LSTMSpeakerEncoderTests(unittest.TestCase):
     # pylint: disable=R0201
     def test_in_out(self):
-        dummy_input = T.rand(4, 20, 80)  # B x T x D
+        dummy_input = T.rand(4, 80, 20)  # B x D x T
         dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
         model = LSTMSpeakerEncoder(input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3)
         # computing d vectors
@@ -34,7 +34,7 @@ class LSTMSpeakerEncoderTests(unittest.TestCase):
         assert output.type() == "torch.FloatTensor"
         assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
         # compute d for a given batch
-        dummy_input = T.rand(1, 240, 80)  # B x T x D
+        dummy_input = T.rand(1, 80, 240)  # B x T x D
         output = model.compute_embedding(dummy_input, num_frames=160, num_eval=5)
         assert output.shape[0] == 1
         assert output.shape[1] == 256
@@ -44,7 +44,7 @@ class LSTMSpeakerEncoderTests(unittest.TestCase):
 class ResNetSpeakerEncoderTests(unittest.TestCase):
     # pylint: disable=R0201
     def test_in_out(self):
-        dummy_input = T.rand(4, 20, 80)  # B x T x D
+        dummy_input = T.rand(4, 80, 20)  # B x D x T
         dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
         model = ResNetSpeakerEncoder(input_dim=80, proj_dim=256)
         # computing d vectors
@@ -61,7 +61,7 @@ class ResNetSpeakerEncoderTests(unittest.TestCase):
         assert output.type() == "torch.FloatTensor"
         assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
         # compute d for a given batch
-        dummy_input = T.rand(1, 240, 80)  # B x T x D
+        dummy_input = T.rand(1, 80, 240)  # B x D x T
         output = model.compute_embedding(dummy_input, num_frames=160, num_eval=10)
         assert output.shape[0] == 1
         assert output.shape[1] == 256
diff --git a/tests/aux_tests/test_speaker_manager.py b/tests/aux_tests/test_speaker_manager.py
index b56c5258..fff49b13 100644
--- a/tests/aux_tests/test_speaker_manager.py
+++ b/tests/aux_tests/test_speaker_manager.py
@@ -38,7 +38,7 @@ class SpeakerManagerTest(unittest.TestCase):
         # load a sample audio and compute embedding
         waveform = ap.load_wav(sample_wav_path)
         mel = ap.melspectrogram(waveform)
-        d_vector = manager.compute_d_vector(mel.T)
+        d_vector = manager.compute_d_vector(mel)
         assert d_vector.shape[1] == 256
 
         # compute d_vector directly from an input file