diff --git a/tests/vocoder_tests/test_wavegrad_train.py b/tests/vocoder_tests/test_wavegrad_train.py
index fe56ee783f..9b10759505 100644
--- a/tests/vocoder_tests/test_wavegrad_train.py
+++ b/tests/vocoder_tests/test_wavegrad_train.py
@@ -1,43 +1,54 @@
 import glob
 import os
 import shutil
+import unittest
 
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.vocoder.configs import WavegradConfig
 
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = WavegradConfig(
-    batch_size=8,
-    eval_batch_size=8,
-    num_loader_workers=0,
-    num_eval_loader_workers=0,
-    run_eval=True,
-    test_delay_epochs=-1,
-    epochs=1,
-    seq_len=8192,
-    eval_split_size=1,
-    print_step=1,
-    print_eval=True,
-    data_path="tests/data/ljspeech",
-    output_path=output_path,
-    test_noise_schedule={"min_val": 1e-6, "max_val": 1e-2, "num_steps": 2},
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
-    f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
+
+class WavegradTrainingTest(unittest.TestCase):
+    # TODO: Reactivate after improving CI run times
+    # This test currently takes ~2h on CI (15min/step vs 8sec/step locally)
+    if os.getenv("GITHUB_ACTIONS") == "true":
+        __test__ = False
+
+    def test_train(self):  # pylint: disable=no-self-use
+        config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
+        output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+        config = WavegradConfig(
+            batch_size=8,
+            eval_batch_size=8,
+            num_loader_workers=0,
+            num_eval_loader_workers=0,
+            run_eval=True,
+            test_delay_epochs=-1,
+            epochs=1,
+            seq_len=8192,
+            eval_split_size=1,
+            print_step=1,
+            print_eval=True,
+            data_path="tests/data/ljspeech",
+            output_path=output_path,
+            test_noise_schedule={"min_val": 1e-6, "max_val": 1e-2, "num_steps": 2},
+        )
+        config.audio.do_trim_silence = True
+        config.audio.trim_db = 60
+        config.save_json(config_path)
+
+        # train the model for one epoch
+        command_train = (
+            f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
+        )
+        run_cli(command_train)
+
+        # Find latest folder
+        continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
+
+        # restore the model and continue training for one more epoch
+        command_train = (
+            f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
+        )
+        run_cli(command_train)
+        shutil.rmtree(continue_path)