Changes for review

TTS/trainer.py

@@ -769,9 +769,9 @@ class Trainer:
                 return None  # TODO: Fix inference on WaveGrad
             if hasattr(self.eval_loader.dataset, "load_test_samples"):
                 samples = self.eval_loader.dataset.load_test_samples(1)
-                figures, audios = self.model.test_run(self.ap, samples, None, self.use_cuda)
+                figures, audios = self.model.test_run(self.ap, samples, None)
             else:
-                figures, audios = self.model.test_run(self.ap, self.use_cuda)
+                figures, audios = self.model.test_run(self.ap)
             self.tb_logger.tb_test_audios(self.total_steps_done, audios, self.config.audio["sample_rate"])
             self.tb_logger.tb_test_figures(self.total_steps_done, figures)
         return None

TTS/tts/models/base_tts.py

@@ -200,7 +200,7 @@ class BaseTTS(BaseModel):
             )
         return loader
 
-    def test_run(self, ap, use_cuda) -> Tuple[Dict, Dict]:
+    def test_run(self, ap) -> Tuple[Dict, Dict]:
         """Generic test run for `tts` models used by `Trainer`.
 
         You can override this for a different behaviour.
@@ -218,7 +218,7 @@ class BaseTTS(BaseModel):
                 self,
                 sen,
                 self.config,
-                use_cuda,
+                "cuda" in str(next(self.parameters()).device),
                 ap,
                 speaker_id=aux_inputs["speaker_id"],
                 d_vector=aux_inputs["d_vector"],

TTS/vocoder/models/wavegrad.py

@@ -261,9 +261,7 @@ class Wavegrad(BaseModel):
     def eval_log(self, ap: AudioProcessor, batch: Dict, outputs: Dict) -> Tuple[Dict, np.ndarray]:
         return None, None
 
-    def test_run(
-        self, ap: AudioProcessor, samples: List[Dict], ouputs: Dict, use_cuda
-    ):  # pylint: disable=unused-argument
+    def test_run(self, ap: AudioProcessor, samples: List[Dict], ouputs: Dict):  # pylint: disable=unused-argument
         # setup noise schedule and inference
         noise_schedule = self.config["test_noise_schedule"]
         betas = np.linspace(noise_schedule["min_val"], noise_schedule["max_val"], noise_schedule["num_steps"])
@@ -271,8 +269,7 @@ class Wavegrad(BaseModel):
         for sample in samples:
             sample = self.format_batch(sample)
             x = sample["input"]
-            if use_cuda:
-                x = x.cuda()
+            x = x.to(next(self.parameters()).device)
             y = sample["waveform"]
             # compute voice
             y_pred = self.inference(x)

TTS/vocoder/models/wavernn.py

@@ -322,7 +322,7 @@ class Wavernn(BaseVocoder):
 
         with torch.no_grad():
             if isinstance(mels, np.ndarray):
-                mels = torch.FloatTensor(mels)
+                mels = torch.FloatTensor(mels).to(str(next(self.parameters()).device))
 
             if mels.ndim == 2:
                 mels = mels.unsqueeze(0)
@@ -571,14 +571,13 @@ class Wavernn(BaseVocoder):
 
     @torch.no_grad()
     def test_run(
-        self, ap: AudioProcessor, samples: List[Dict], output: Dict, use_cuda  # pylint: disable=unused-argument
+        self, ap: AudioProcessor, samples: List[Dict], output: Dict  # pylint: disable=unused-argument
     ) -> Tuple[Dict, Dict]:
         figures = {}
         audios = {}
         for idx, sample in enumerate(samples):
             x = torch.FloatTensor(sample[0])
-            if use_cuda:
-                x = x.cuda()
+            x = x.to(next(self.parameters()).device)
             y_hat = self.inference(x, self.config.batched, self.config.target_samples, self.config.overlap_samples)
             x_hat = ap.melspectrogram(y_hat)
             figures.update(