diff --git a/tests/tts_tests/test_vits_multilingual_train.py b/tests/tts_tests/test_vits_multilingual_train.py
new file mode 100644
index 00000000..5fc4787d
--- /dev/null
+++ b/tests/tts_tests/test_vits_multilingual_train.py
@@ -0,0 +1,66 @@
+import glob
+import os
+import shutil
+
+from tests import get_device_id, get_tests_output_path, run_cli
+from TTS.tts.configs import BaseDatasetConfig, VitsConfig
+
+config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
+output_path = os.path.join(get_tests_output_path(), "train_outputs")
+
+
+dataset_config1 = BaseDatasetConfig(
+    name="ljspeech", meta_file_train="metadata.csv", meta_file_val="metadata.csv", path="tests/data/ljspeech", language="en"
+)
+
+dataset_config2 = BaseDatasetConfig(
+    name="ljspeech", meta_file_train="metadata.csv", meta_file_val="metadata.csv", path="tests/data/ljspeech", language="en2"
+)
+
+config = VitsConfig(
+    batch_size=2,
+    eval_batch_size=2,
+    num_loader_workers=0,
+    num_eval_loader_workers=0,
+    text_cleaner="english_cleaners",
+    use_phonemes=True,
+    use_espeak_phonemes=True,
+    phoneme_language="en-us",
+    phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1,
+    print_step=1,
+    print_eval=True,
+    test_sentences=[
+        ["Be a voice, not an echo.", "ljspeech", None, "en"],
+        ["Be a voice, not an echo.", "ljspeech", None, "en2"],
+    ],
+    datasets=[dataset_config1, dataset_config2],
+)
+# set audio config
+config.audio.do_trim_silence = True
+config.audio.trim_db = 60
+
+# active multilingual mode
+config.model_args.use_language_embedding = True
+# active language sampler
+config.use_language_weighted_sampler = True
+
+config.save_json(config_path)
+
+# train the model for one epoch
+command_train = (
+    f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
+    f"--coqpit.output_path {output_path} "
+    "--coqpit.test_delay_epochs 0"
+)
+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_tts.py --continue_path {continue_path} "
+run_cli(command_train)
+shutil.rmtree(continue_path)