Update test_run in wavernn and wavegrad

TTS/vocoder/models/wavegrad.py

@@ -270,12 +270,13 @@ class Wavegrad(BaseVocoder):
     ) -> None:
         pass
 
-    def test_run(self, assets: Dict, samples: List[Dict], outputs: Dict):  # pylint: disable=unused-argument
+    def test(self, assets: Dict, test_loader:"DataLoader", outputs=None):  # pylint: disable=unused-argument
         # setup noise schedule and inference
         ap = assets["audio_processor"]
         noise_schedule = self.config["test_noise_schedule"]
         betas = np.linspace(noise_schedule["min_val"], noise_schedule["max_val"], noise_schedule["num_steps"])
         self.compute_noise_level(betas)
+        samples = test_loader.dataset.load_test_samples(1)
         for sample in samples:
             x = sample[0]
             x = x[None, :, :].to(next(self.parameters()).device)
@@ -307,12 +308,12 @@ class Wavegrad(BaseVocoder):
         return {"input": m, "waveform": y}
 
     def get_data_loader(
-        self, config: Coqpit, assets: Dict, is_eval: True, data_items: List, verbose: bool, num_gpus: int
+        self, config: Coqpit, assets: Dict, is_eval: True, samples: List, verbose: bool, num_gpus: int
     ):
         ap = assets["audio_processor"]
         dataset = WaveGradDataset(
             ap=ap,
-            items=data_items,
+            items=samples,
             seq_len=self.config.seq_len,
             hop_len=ap.hop_length,
             pad_short=self.config.pad_short,

TTS/vocoder/models/wavernn.py

@@ -568,12 +568,13 @@ class Wavernn(BaseVocoder):
         return self.train_step(batch, criterion)
 
     @torch.no_grad()
-    def test_run(
-        self, assets: Dict, samples: List[Dict], output: Dict  # pylint: disable=unused-argument
+    def test(
+        self, assets: Dict, test_loader: "DataLoader", output: Dict  # pylint: disable=unused-argument
     ) -> Tuple[Dict, Dict]:
         ap = assets["audio_processor"]
         figures = {}
         audios = {}
+        samples = test_loader.dataset.load_test_samples(1)
         for idx, sample in enumerate(samples):
             x = torch.FloatTensor(sample[0])
             x = x.to(next(self.parameters()).device)
@@ -600,14 +601,14 @@ class Wavernn(BaseVocoder):
         config: Coqpit,
         assets: Dict,
         is_eval: True,
-        data_items: List,
+        samples: List,
         verbose: bool,
         num_gpus: int,
     ):
         ap = assets["audio_processor"]
         dataset = WaveRNNDataset(
             ap=ap,
-            items=data_items,
+            items=samples,
             seq_len=config.seq_len,
             hop_len=ap.hop_length,
             pad=config.model_args.pad,