diff --git a/tests/test_glow_tts.py b/tests/test_glow_tts.py
index 8e699faf..e71c167a 100644
--- a/tests/test_glow_tts.py
+++ b/tests/test_glow_tts.py
@@ -129,3 +129,58 @@ class GlowTTSTrainTest(unittest.TestCase):
                 count, param.shape, param, param_ref
             )
             count += 1
+
+class GlowTTSInferenceTest(unittest.TestCase):
+    @staticmethod
+    def test_inference():
+        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
+        input_lengths = torch.randint(100, 129, (8,)).long().to(device)
+        input_lengths[-1] = 128
+        mel_spec = torch.rand(8, c.audio["num_mels"], 30).to(device)
+        mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
+        speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
+
+        # create model
+        model = GlowTTS(
+            num_chars=32,
+            hidden_channels_enc=48,
+            hidden_channels_dec=48,
+            hidden_channels_dp=32,
+            out_channels=80,
+            encoder_type="rel_pos_transformer",
+            encoder_params={
+                "kernel_size": 3,
+                "dropout_p": 0.1,
+                "num_layers": 6,
+                "num_heads": 2,
+                "hidden_channels_ffn": 16,  # 4 times the hidden_channels
+                "input_length": None,
+            },
+            use_encoder_prenet=True,
+            num_flow_blocks_dec=12,
+            kernel_size_dec=5,
+            dilation_rate=1,
+            num_block_layers=4,
+            dropout_p_dec=0.0,
+            num_speakers=0,
+            c_in_channels=0,
+            num_splits=4,
+            num_squeeze=1,
+            sigmoid_scale=False,
+            mean_only=False,
+        ).to(device)
+
+        model.eval()
+        print(" > Num parameters for GlowTTS model:%s" % (count_parameters(model)))
+
+        # inference encoder and decoder with MAS
+        y, _, _, _, _, _, _ = model.inference_with_MAS(
+                input_dummy, input_lengths, mel_spec, mel_lengths, None
+            )
+
+        y_dec, _ = model.decoder_inference(mel_spec, mel_lengths
+            )
+
+        assert (y_dec.shape == y.shape), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
+                y.shape, y_dec.shape
+            )