diff --git a/tests/__init__.py b/tests/__init__.py
index 45aee23a..0a0c3379 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -38,3 +38,14 @@ def run_cli(command):
 
 def get_test_data_config():
     return BaseDatasetConfig(name="ljspeech", path="tests/data/ljspeech/", meta_file_train="metadata.csv")
+
+
+def assertHasAttr(test_obj, obj, intendedAttr):
+    # from https://stackoverflow.com/questions/48078636/pythons-unittest-lacks-an-asserthasattr-method-what-should-i-use-instead
+    testBool = hasattr(obj, intendedAttr)
+    test_obj.assertTrue(testBool, msg=f"obj lacking an attribute. obj: {obj}, intendedAttr: {intendedAttr}")
+
+
+def assertHasNotAttr(test_obj, obj, intendedAttr):
+    testBool = hasattr(obj, intendedAttr)
+    test_obj.assertFalse(testBool, msg=f"obj should not have an attribute. obj: {obj}, intendedAttr: {intendedAttr}")
diff --git a/tests/tts_tests/test_vits.py b/tests/tts_tests/test_vits.py
index 335472a5..de075a5c 100644
--- a/tests/tts_tests/test_vits.py
+++ b/tests/tts_tests/test_vits.py
@@ -1,13 +1,14 @@
 import os
-import torch
 import unittest
-from TTS.config import load_config
-from TTS.tts.models.vits import Vits, VitsArgs
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.utils.speakers import SpeakerManager
-from tests import assertHasAttr, assertHasNotAttr, get_tests_input_path
-from TTS.speaker_encoder.utils.generic_utils import setup_speaker_encoder_model
 
+import torch
+
+from tests import assertHasAttr, assertHasNotAttr, get_tests_input_path
+from TTS.config import load_config
+from TTS.speaker_encoder.utils.generic_utils import setup_speaker_encoder_model
+from TTS.tts.configs.vits_config import VitsConfig
+from TTS.tts.models.vits import Vits, VitsArgs
+from TTS.tts.utils.speakers import SpeakerManager
 
 LANG_FILE = os.path.join(get_tests_input_path(), "language_ids.json")
 SPEAKER_ENCODER_CONFIG = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
@@ -18,21 +19,21 @@ use_cuda = torch.cuda.is_available()
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 
+#pylint: disable=no-self-use
 class TestVits(unittest.TestCase):
-
     def test_init_multispeaker(self):
         num_speakers = 10
         args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
         model = Vits(args)
-        assertHasAttr(self, model, 'emb_g')
+        assertHasAttr(self, model, "emb_g")
 
         args = VitsArgs(num_speakers=0, use_speaker_embedding=True)
         model = Vits(args)
-        assertHasNotAttr(self, model, 'emb_g')
+        assertHasNotAttr(self, model, "emb_g")
 
         args = VitsArgs(num_speakers=10, use_speaker_embedding=False)
         model = Vits(args)
-        assertHasNotAttr(self, model, 'emb_g')
+        assertHasNotAttr(self, model, "emb_g")
 
         args = VitsArgs(d_vector_dim=101, use_d_vector_file=True)
         model = Vits(args)
@@ -67,12 +68,12 @@ class TestVits(unittest.TestCase):
         aux_input = {"speaker_ids": None, "style_wav": None, "d_vectors": None, "language_ids": None}
         args = VitsArgs()
         model = Vits(args)
-        aux_out= model.get_aux_input(aux_input)
+        aux_out = model.get_aux_input(aux_input)
 
         speaker_id = torch.randint(10, (1,))
         language_id = torch.randint(10, (1,))
         d_vector = torch.rand(1, 128)
-        aux_input = {"speaker_ids": speaker_id, "style_wav": None, "d_vectors":  d_vector, "language_ids": language_id}
+        aux_input = {"speaker_ids": speaker_id, "style_wav": None, "d_vectors": d_vector, "language_ids": language_id}
         aux_out = model.get_aux_input(aux_input)
         self.assertEqual(aux_out["speaker_ids"].shape, speaker_id.shape)
         self.assertEqual(aux_out["language_ids"].shape, language_id.shape)
@@ -88,8 +89,8 @@ class TestVits(unittest.TestCase):
 
         ref_inp = torch.randn(1, spec_len, 513)
         ref_inp_len = torch.randint(1, spec_effective_len, (1,))
-        ref_spk_id = torch.randint(0, num_speakers, (1,))
-        tgt_spk_id = torch.randint(0, num_speakers, (1,))
+        ref_spk_id = torch.randint(1, num_speakers, (1,))
+        tgt_spk_id = torch.randint(1, num_speakers, (1,))
         o_hat, y_mask, (z, z_p, z_hat) = model.voice_conversion(ref_inp, ref_inp_len, ref_spk_id, tgt_spk_id)
 
         self.assertEqual(o_hat.shape, (1, 1, spec_len * 256))
@@ -110,7 +111,9 @@ class TestVits(unittest.TestCase):
         return input_dummy, input_lengths, spec, spec_lengths, waveform
 
     def _check_forward_outputs(self, config, output_dict, encoder_config=None):
-        self.assertEqual(output_dict['model_outputs'].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"])
+        self.assertEqual(
+            output_dict["model_outputs"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
+        )
         self.assertEqual(output_dict["alignments"].shape, (8, 128, 30))
         self.assertEqual(output_dict["alignments"].max(), 1)
         self.assertEqual(output_dict["alignments"].min(), 0)
@@ -120,13 +123,15 @@ class TestVits(unittest.TestCase):
         self.assertEqual(output_dict["logs_p"].shape, (8, config.model_args.hidden_channels, 30))
         self.assertEqual(output_dict["m_q"].shape, (8, config.model_args.hidden_channels, 30))
         self.assertEqual(output_dict["logs_q"].shape, (8, config.model_args.hidden_channels, 30))
-        self.assertEqual(output_dict['waveform_seg'].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"])
+        self.assertEqual(
+            output_dict["waveform_seg"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
+        )
         if encoder_config:
-            self.assertEqual(output_dict['gt_spk_emb'].shape, (8, encoder_config.model_params["proj_dim"]))
-            self.assertEqual(output_dict['syn_spk_emb'].shape, (8, encoder_config.model_params["proj_dim"]))
+            self.assertEqual(output_dict["gt_spk_emb"].shape, (8, encoder_config.model_params["proj_dim"]))
+            self.assertEqual(output_dict["syn_spk_emb"].shape, (8, encoder_config.model_params["proj_dim"]))
         else:
-            self.assertEqual(output_dict['gt_spk_emb'], None)
-            self.assertEqual(output_dict['syn_spk_emb'], None)
+            self.assertEqual(output_dict["gt_spk_emb"], None)
+            self.assertEqual(output_dict["syn_spk_emb"], None)
 
     def test_forward(self):
         num_speakers = 0
@@ -147,7 +152,9 @@ class TestVits(unittest.TestCase):
         speaker_ids = torch.randint(0, num_speakers, (8,)).long().to(device)
 
         model = Vits(config).to(device)
-        output_dict = model.forward(input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids})
+        output_dict = model.forward(
+            input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids}
+        )
         self._check_forward_outputs(config, output_dict)
 
     def test_multilingual_forward(self):
@@ -162,7 +169,14 @@ class TestVits(unittest.TestCase):
         lang_ids = torch.randint(0, num_langs, (8,)).long().to(device)
 
         model = Vits(config).to(device)
-        output_dict = model.forward(input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids})
+        output_dict = model.forward(
+            input_dummy,
+            input_lengths,
+            spec,
+            spec_lengths,
+            waveform,
+            aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
+        )
         self._check_forward_outputs(config, output_dict)
 
     def test_secl_forward(self):
@@ -175,7 +189,12 @@ class TestVits(unittest.TestCase):
         speaker_manager = SpeakerManager()
         speaker_manager.speaker_encoder = speaker_encoder
 
-        args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, spec_segment_size=10, use_speaker_encoder_as_loss=True)
+        args = VitsArgs(
+            language_ids_file=LANG_FILE,
+            use_language_embedding=True,
+            spec_segment_size=10,
+            use_speaker_encoder_as_loss=True,
+        )
         config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
         config.audio.sample_rate = 16000
 
@@ -184,7 +203,14 @@ class TestVits(unittest.TestCase):
         lang_ids = torch.randint(0, num_langs, (8,)).long().to(device)
 
         model = Vits(config, speaker_manager=speaker_manager).to(device)
-        output_dict = model.forward(input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids})
+        output_dict = model.forward(
+            input_dummy,
+            input_lengths,
+            spec,
+            spec_lengths,
+            waveform,
+            aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
+        )
         self._check_forward_outputs(config, output_dict, speaker_encoder_config)
 
     def test_inference(self):
@@ -211,4 +237,4 @@ class TestVits(unittest.TestCase):
         speaker_ids = torch.randint(0, num_speakers, (1,)).long().to(device)
         lang_ids = torch.randint(0, num_langs, (1,)).long().to(device)
         model = Vits(config).to(device)
-        _ = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})
\ No newline at end of file
+        _ = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})