Update GAN model

TTS/vocoder/models/gan.py

@@ -19,7 +19,7 @@ from TTS.vocoder.utils.generic_utils import plot_results
 
 
 class GAN(BaseVocoder):
-    def __init__(self, config: Coqpit):
+    def __init__(self, config: Coqpit, ap: AudioProcessor=None):
         """Wrap a generator and a discriminator network. It provides a compatible interface for the trainer.
         It also helps mixing and matching different generator and disciminator networks easily.
 
@@ -28,6 +28,7 @@ class GAN(BaseVocoder):
 
         Args:
             config (Coqpit): Model configuration.
+            ap (AudioProcessor): 🐸TTS AudioProcessor instance. Defaults to None.
 
         Examples:
             Initializing the GAN model with HifiGAN generator and discriminator.
@@ -41,6 +42,7 @@ class GAN(BaseVocoder):
         self.model_d = setup_discriminator(config)
         self.train_disc = False  # if False, train only the generator.
         self.y_hat_g = None  # the last generator prediction to be passed onto the discriminator
+        self.ap = ap
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Run the generator's forward pass.
@@ -201,10 +203,9 @@ class GAN(BaseVocoder):
         self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int  # pylint: disable=unused-argument
     ) -> Tuple[Dict, np.ndarray]:
         """Call `_log()` for training."""
-        ap = assets["audio_processor"]
-        figures, audios = self._log("eval", ap, batch, outputs)
+        figures, audios = self._log("eval", self.ap, batch, outputs)
         logger.eval_figures(steps, figures)
-        logger.eval_audios(steps, audios, ap.sample_rate)
+        logger.eval_audios(steps, audios, self.ap.sample_rate)
 
     @torch.no_grad()
     def eval_step(self, batch: Dict, criterion: nn.Module, optimizer_idx: int) -> Tuple[Dict, Dict]:
@@ -215,10 +216,9 @@ class GAN(BaseVocoder):
         self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int  # pylint: disable=unused-argument
     ) -> Tuple[Dict, np.ndarray]:
         """Call `_log()` for evaluation."""
-        ap = assets["audio_processor"]
-        figures, audios = self._log("eval", ap, batch, outputs)
+        figures, audios = self._log("eval", self.ap, batch, outputs)
         logger.eval_figures(steps, figures)
-        logger.eval_audios(steps, audios, ap.sample_rate)
+        logger.eval_audios(steps, audios, self.ap.sample_rate)
 
     def load_checkpoint(
         self,
@@ -330,12 +330,11 @@ class GAN(BaseVocoder):
         Returns:
             DataLoader: Torch dataloader.
         """
-        ap = assets["audio_processor"]
         dataset = GANDataset(
-            ap=ap,
+            ap=self.ap,
             items=data_items,
             seq_len=config.seq_len,
-            hop_len=ap.hop_length,
+            hop_len=self.ap.hop_length,
             pad_short=config.pad_short,
             conv_pad=config.conv_pad,
             return_pairs=config.diff_samples_for_G_and_D if "diff_samples_for_G_and_D" in config else False,
@@ -363,5 +362,6 @@ class GAN(BaseVocoder):
         return [GeneratorLoss(self.config), DiscriminatorLoss(self.config)]
 
     @staticmethod
-    def init_from_config(config: Coqpit) -> "GAN":
-        return GAN(config)
+    def init_from_config(config: Coqpit, verbose=True) -> "GAN":
+        ap = AudioProcessor.init_from_config(config, verbose=verbose)
+        return GAN(config, ap=ap)