diff --git a/tests/test_speaker_manager.py b/tests/test_speaker_manager.py
index 40914224..3e272f42 100644
--- a/tests/test_speaker_manager.py
+++ b/tests/test_speaker_manager.py
@@ -1,4 +1,5 @@
 import os
+import torch
 import unittest
 
 import numpy as np
@@ -11,9 +12,9 @@ from TTS.utils.io import load_config
 encoder_config_path = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
 encoder_model_path = os.path.join(get_tests_input_path(), "dummy_speaker_encoder.pth.tar")
 sample_wav_path = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0001.wav")
+sample_wav_path2 = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0002.wav")
 x_vectors_file_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.json")
 
-
 class SpeakerManagerTest(unittest.TestCase):
     """Test SpeakerManager for loading embedding files and computing x_vectors from waveforms"""
     @staticmethod
@@ -32,6 +33,21 @@ class SpeakerManagerTest(unittest.TestCase):
         x_vector = manager.compute_x_vector(mel.T)
         assert x_vector.shape[1] == 256
 
+        # compute x_vector directly from an input file
+        x_vector = manager.compute_x_vector_from_clip(sample_wav_path)
+        x_vector2 = manager.compute_x_vector_from_clip(sample_wav_path)
+        x_vector = torch.FloatTensor(x_vector)
+        x_vector2 = torch.FloatTensor(x_vector2)
+        assert x_vector.shape[0] == 256
+        assert (x_vector - x_vector2).sum() == 0.0
+
+        # compute x_vector from a list of wav files.
+        x_vector3 = manager.compute_x_vector_from_clip([sample_wav_path, sample_wav_path2])
+        x_vector3 = torch.FloatTensor(x_vector3)
+        assert x_vector3.shape[0] == 256
+        assert (x_vector - x_vector3).sum() != 0.0
+
+
     @staticmethod
     def test_speakers_file_processing():
         manager = SpeakerManager(x_vectors_file_path=x_vectors_file_path)