refactor IO functions

enhancer/utils/io.py

@@ -50,13 +50,18 @@ class Audio:
         else:
             return audio
 
+    @staticmethod
     def convert_mono(
-        self,
         audio
 
     ):
+        if len(audio.shape)>2:
+            assert audio.shape[0] == 1, "convert mono only accepts single waveform"
+            audio = audio.reshape(audio.shape[1],audio.shape[2])
+         
+        assert audio.shape[0] > audio.shape[1], "expected input format (num_channels,num_samples)"
         num_channels,num_samples = audio.shape
-        if num_channels>1 and self.mono:
+        if num_channels>1:
             return audio.mean(axis=0).reshape(1,num_samples)
         return audio
 
@@ -68,17 +73,11 @@ class Audio:
         target_sr:int
     ):
         if sr!=target_sr:
-            audio = librosa.resample(audio,orig_sr=sr,target_sr=target_sr)
+            if isinstance(audio,np.ndarray):
+                audio = librosa.resample(audio,orig_sr=sr,target_sr=target_sr)
+            elif isinstance(audio,torch.Tensor):
+                audio = torchaudio.functional.resample(audio,orig_freq=sr,new_freq=target_sr)
+            else:
+                raise ValueError("Input should be either numpy array or torch tensor")
 
         return audio
-
-    @staticmethod
-    def pt_resample_audio(
-        audio,
-        sr:int,
-        target_sr:int
-    ):
-        if sr!=target_sr:
-            audio = torchaudio.functional.resample(audio,orig_freq=sr,new_freq=target_sr)
-
-        return audio