Merge branch 'dev'

TTS/VERSION

@@ -1 +1 @@
-0.11.1
+0.12.0

TTS/tts/models/neuralhmm_tts.py

@@ -179,6 +179,7 @@ class NeuralhmmTTS(BaseTTS):
         Args:
             aux_inputs (Dict): Dictionary containing the auxiliary inputs.
         """
+        default_input_dict = default_input_dict.copy()
         default_input_dict.update(
             {
                 "sampling_temp": self.sampling_temp,
@@ -187,8 +188,8 @@ class NeuralhmmTTS(BaseTTS):
             }
         )
         if aux_input:
-            return format_aux_input(aux_input, default_input_dict)
-        return None
+            return format_aux_input(default_input_dict, aux_input)
+        return default_input_dict
 
     @torch.no_grad()
     def inference(
@@ -319,7 +320,7 @@ class NeuralhmmTTS(BaseTTS):
         # sample one item from the batch -1 will give the smalles item
         print(" | > Synthesising audio from the model...")
         inference_output = self.inference(
-            batch["text_input"][-1].unsqueeze(0), aux_input={"x_lenghts": batch["text_lengths"][-1].unsqueeze(0)}
+            batch["text_input"][-1].unsqueeze(0), aux_input={"x_lengths": batch["text_lengths"][-1].unsqueeze(0)}
         )
         figures["synthesised"] = plot_spectrogram(inference_output["model_outputs"][0], fig_size=(12, 3))
 

TTS/tts/models/overflow.py

@@ -192,6 +192,7 @@ class Overflow(BaseTTS):
         Args:
             aux_inputs (Dict): Dictionary containing the auxiliary inputs.
         """
+        default_input_dict = default_input_dict.copy()
         default_input_dict.update(
             {
                 "sampling_temp": self.sampling_temp,
@@ -200,8 +201,8 @@ class Overflow(BaseTTS):
             }
         )
         if aux_input:
-            return format_aux_input(aux_input, default_input_dict)
-        return None
+            return format_aux_input(default_input_dict, aux_input)
+        return default_input_dict
 
     @torch.no_grad()
     def inference(
@@ -335,7 +336,7 @@ class Overflow(BaseTTS):
         # sample one item from the batch -1 will give the smalles item
         print(" | > Synthesising audio from the model...")
         inference_output = self.inference(
-            batch["text_input"][-1].unsqueeze(0), aux_input={"x_lenghts": batch["text_lengths"][-1].unsqueeze(0)}
+            batch["text_input"][-1].unsqueeze(0), aux_input={"x_lengths": batch["text_lengths"][-1].unsqueeze(0)}
         )
         figures["synthesised"] = plot_spectrogram(inference_output["model_outputs"][0], fig_size=(12, 3))
 

TTS/tts/models/vits.py

@@ -1628,13 +1628,23 @@ class Vits(BaseTTS):
                     pin_memory=False,
                 )
             else:
-                loader = DataLoader(
-                    dataset,
-                    batch_sampler=sampler,
-                    collate_fn=dataset.collate_fn,
-                    num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
-                    pin_memory=False,
-                )
+                if num_gpus > 1:
+                    loader = DataLoader(
+                        dataset,
+                        sampler=sampler,
+                        batch_size=config.eval_batch_size if is_eval else config.batch_size,
+                        collate_fn=dataset.collate_fn,
+                        num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
+                        pin_memory=False,
+                    )
+                else:
+                    loader = DataLoader(
+                        dataset,
+                        batch_sampler=sampler,
+                        collate_fn=dataset.collate_fn,
+                        num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
+                        pin_memory=False,
+                    )
         return loader
 
     def get_optimizer(self) -> List:

TTS/utils/generic_utils.py

@@ -167,9 +167,10 @@ def format_aux_input(def_args: Dict, kwargs: Dict) -> Dict:
     Returns:
         Dict: arguments with formatted auxilary inputs.
     """
+    kwargs = kwargs.copy()
     for name in def_args:
-        if name not in kwargs:
-            kwargs[def_args[name]] = None
+        if name not in kwargs or kwargs[name] is None:
+            kwargs[name] = def_args[name]
     return kwargs