Fix usage of abstract class in vocoders

TTS/vocoder/models/wavegrad.py

@@ -9,10 +9,10 @@ from torch.nn.utils import weight_norm
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
-from TTS.model import BaseModel
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.io import load_fsspec
 from TTS.utils.trainer_utils import get_optimizer, get_scheduler
+from TTS.vocoder.base_vocoder import BaseVocoder
 from TTS.vocoder.datasets import WaveGradDataset
 from TTS.vocoder.layers.wavegrad import Conv1d, DBlock, FiLM, UBlock
 from TTS.vocoder.utils.generic_utils import plot_results
@@ -33,7 +33,7 @@ class WavegradArgs(Coqpit):
     )
 
 
-class Wavegrad(BaseModel):
+class Wavegrad(BaseVocoder):
     """🐸 🌊 WaveGrad 🌊 model.
     Paper - https://arxiv.org/abs/2009.00713
 
@@ -257,14 +257,18 @@ class Wavegrad(BaseModel):
         loss = criterion(noise, noise_hat)
         return {"model_output": noise_hat}, {"loss": loss}
 
-    def train_log(self, ap: AudioProcessor, batch: Dict, outputs: Dict) -> Tuple[Dict, np.ndarray]:
+    def train_log(  # pylint: disable=no-self-use
+        self, ap: AudioProcessor, batch: Dict, outputs: Dict  # pylint: disable=unused-argument
+    ) -> Tuple[Dict, np.ndarray]:
         return None, None
 
     @torch.no_grad()
     def eval_step(self, batch: Dict, criterion: nn.Module) -> Tuple[Dict, Dict]:
         return self.train_step(batch, criterion)
 
-    def eval_log(self, ap: AudioProcessor, batch: Dict, outputs: Dict) -> Tuple[Dict, np.ndarray]:
+    def eval_log(  # pylint: disable=no-self-use
+        self, ap: AudioProcessor, batch: Dict, outputs: Dict  # pylint: disable=unused-argument
+    ) -> Tuple[Dict, np.ndarray]:
         return None, None
 
     def test_run(self, ap: AudioProcessor, samples: List[Dict], ouputs: Dict):  # pylint: disable=unused-argument
@@ -291,7 +295,8 @@ class Wavegrad(BaseModel):
     def get_scheduler(self, optimizer):
         return get_scheduler(self.config.lr_scheduler, self.config.lr_scheduler_params, optimizer)
 
-    def get_criterion(self):
+    @staticmethod
+    def get_criterion():
         return torch.nn.L1Loss()
 
     @staticmethod