diff --git a/tests/test_speaker_encoder.py b/tests/test_speaker_encoder.py
index 8939ccf6..3b45d2e2 100644
--- a/tests/test_speaker_encoder.py
+++ b/tests/test_speaker_encoder.py
@@ -4,8 +4,8 @@ import torch as T
 
 from tests import get_tests_input_path
 from TTS.speaker_encoder.losses import AngleProtoLoss, GE2ELoss
-from TTS.speaker_encoder.model import SpeakerEncoder
-
+from TTS.speaker_encoder.models.lstm import LSTMSpeakerEncoder
+# from TTS.speaker_encoder.models.resnet import ResNetSpeakerEncoder
 file_path = get_tests_input_path()
 
 
@@ -14,7 +14,7 @@ class SpeakerEncoderTests(unittest.TestCase):
     def test_in_out(self):
         dummy_input = T.rand(4, 20, 80)  # B x T x D
         dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
-        model = SpeakerEncoder(input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3)
+        model = LSTMSpeakerEncoder(input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3)
         # computing d vectors
         output = model.forward(dummy_input)
         assert output.shape[0] == 4
@@ -96,17 +96,3 @@ class AngleProtoLossTests(unittest.TestCase):
         loss = AngleProtoLoss()
         output = loss.forward(dummy_input)
         assert output.item() < 0.005
-
-
-# class LoaderTest(unittest.TestCase):
-#     def test_output(self):
-#         items = libri_tts("/home/erogol/Data/Libri-TTS/train-clean-360/")
-#         ap = AudioProcessor(**c['audio'])
-#         dataset = MyDataset(ap, items, 1.6, 64, 10)
-#         loader = DataLoader(dataset, batch_size=32, shuffle=False, num_workers=0, collate_fn=dataset.collate_fn)
-#         count = 0
-#         for mel, spk in loader:
-#             print(mel.shape)
-#             if count == 4:
-#                 break
-#             count += 1