diff --git a/TTS/utils/synthesizer.py b/TTS/utils/synthesizer.py
index e0ffbd71ed..d0392fc943 100644
--- a/TTS/utils/synthesizer.py
+++ b/TTS/utils/synthesizer.py
@@ -358,7 +358,10 @@ def tts(
         if speaker_wav is not None and self.tts_model.speaker_manager is not None:
             speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip(speaker_wav)
 
+        vocoder_device = "cpu"
         use_gl = self.vocoder_model is None
+        if not use_gl:
+            vocoder_device = next(self.vocoder_model.parameters()).device
 
         if not reference_wav:  # not voice conversion
             for sen in sens:
@@ -390,7 +393,6 @@ def tts(
                     mel_postnet_spec = outputs["outputs"]["model_outputs"][0].detach().cpu().numpy()
                     # denormalize tts output based on tts audio config
                     mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T
-                    device_type = "cuda" if self.use_cuda else "cpu"
                     # renormalize spectrogram based on vocoder config
                     vocoder_input = self.vocoder_ap.normalize(mel_postnet_spec.T)
                     # compute scale factor for possible sample rate mismatch
@@ -405,7 +407,7 @@ def tts(
                         vocoder_input = torch.tensor(vocoder_input).unsqueeze(0)  # pylint: disable=not-callable
                     # run vocoder model
                     # [1, T, C]
-                    waveform = self.vocoder_model.inference(vocoder_input.to(device_type))
+                    waveform = self.vocoder_model.inference(vocoder_input.to(vocoder_device))
                 if self.use_cuda and not use_gl:
                     waveform = waveform.cpu()
                 if not use_gl:
@@ -455,7 +457,6 @@ def tts(
                 mel_postnet_spec = outputs[0].detach().cpu().numpy()
                 # denormalize tts output based on tts audio config
                 mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T
-                device_type = "cuda" if self.use_cuda else "cpu"
                 # renormalize spectrogram based on vocoder config
                 vocoder_input = self.vocoder_ap.normalize(mel_postnet_spec.T)
                 # compute scale factor for possible sample rate mismatch
@@ -470,9 +471,8 @@ def tts(
                     vocoder_input = torch.tensor(vocoder_input).unsqueeze(0)  # pylint: disable=not-callable
                 # run vocoder model
                 # [1, T, C]
-                waveform = self.vocoder_model.inference(vocoder_input.to(device_type))
-            if self.use_cuda:
-                waveform = waveform.cpu()
+                waveform = self.vocoder_model.inference(vocoder_input.to(vocoder_device))
+            waveform = waveform.cpu()
             if not use_gl:
                 waveform = waveform.numpy()
             wavs = waveform.squeeze()