minor bug fixes

enhancer/inference.py

@@ -26,6 +26,8 @@ class Inference:
                 raise ValueError(f"Input file {audio} does not exist")
             else:
                 audio,sr = load_audio(audio,sr=sr,)
+                if len(audio.shape) == 1:
+                    audio = audio.reshape(1,-1)
         else:
             assert audio.shape[0] == 1, "Enhance inference only supports single waveform"
 
@@ -40,11 +42,10 @@ class Inference:
     def batchify(waveform: torch.Tensor, window_size:int, step_size:Optional[int]=None):
         """
         break input waveform into samples with duration specified. 
-        Wrap into tensors of specified batch size
         """
         assert waveform.ndim == 2, f"Expcted input waveform with 2 dimensions (channels,samples), got {waveform.ndim}"
         _,num_samples = waveform.shape
-        waveform = waveform.unsqueeze(0)
+        waveform = waveform.unsqueeze(-1)
         step_size = window_size//2 if step_size is None else step_size
 
         if num_samples >= window_size:
@@ -55,24 +56,25 @@ class Inference:
         
         return waveform_batch
 
-
-    def aggreagate(self,data:torch.Tensor,window_size:int, step_size:Optional[int]=None):
+    @staticmethod
+    def aggreagate(data:torch.Tensor,window_size:int,total_frames:int,step_size:Optional[int]=None,
+         window="hanning",):
         """
         takes input as tensor outputs aggregated waveform
         """
-        batch_size,n_channels,num_frames = data.shape
+        num_chunks,n_channels,num_frames = data.shape
         window = get_window(window=window,Nx=data.shape[-1])
         window = torch.from_numpy(window).to(data.device)
         data *= window
 
         data = data.permute(1,2,0)
         data = F.fold(data,
-            (num_frames,1),
+            (total_frames,1),
             kernel_size=(window_size,1),
             stride=(step_size,1),
-            padding=(window_size,0))
+            padding=(window_size,0)).squeeze(-1)
 
-        return data
+        return data.reshape(1,n_channels,-1)
 
     @staticmethod
     def write_output(waveform:torch.Tensor,filename:Union[str,Path],sr:int):