Auto fixes from PEP8, fixes from flake8.

scraibe/audio.py

@@ -28,6 +28,7 @@ import torch
 SAMPLE_RATE = 16000
 NORMALIZATION_FACTOR = 32768.0
 
+
 class AudioProcessor:
     """
     Audio Processor class that leverages PyTorchaudio to provide functionalities
@@ -39,10 +40,9 @@ class AudioProcessor:
         sr: int
             The sample rate of the audio.
     """
-    
-    def __init__(self, waveform: torch.Tensor, sr : int = SAMPLE_RATE,
+
+    def __init__(self, waveform: torch.Tensor, sr: int = SAMPLE_RATE,
                  *args, **kwargs) -> None:
-        
         """
         Initialize the AudioProcessor object.
 
@@ -56,16 +56,17 @@ class AudioProcessor:
         Raises:
             ValueError: If the provided sample rate is not of type int.
         """
-        
-        device = kwargs.get("device", "cuda" if torch.cuda.is_available() else "cpu")
-                
+
+        device = kwargs.get(
+            "device", "cuda" if torch.cuda.is_available() else "cpu")
+
         self.waveform = waveform.to(device)
         self.sr = sr
-        
+
         if not isinstance(self.sr, int):
-            raise ValueError("Sample rate should be a single value of type int," \
+            raise ValueError("Sample rate should be a single value of type int,"
                              f"not {len(self.sr)} and type {type(self.sr)}")
-        
+
     @classmethod
     def from_file(cls, file: str, *args, **kwargs) -> 'AudioProcessor':
         """
@@ -77,14 +78,13 @@ class AudioProcessor:
         Returns:
             AudioProcessor: An instance of the AudioProcessor class containing the loaded audio.
         """
-        
-        audio, sr = cls.load_audio(file , *args, **kwargs)
+
+        audio, sr = cls.load_audio(file, *args, **kwargs)
 
         audio = torch.from_numpy(audio)
-        
+
         return cls(audio, sr)
-    
-    
+
     def cut(self, start: float, end: float) -> torch.Tensor:
         """
         Cut a segment from the audio waveform between the specified start and end times.
@@ -96,7 +96,7 @@ class AudioProcessor:
         Returns:
             torch.Tensor: The cut waveform segment.
         """
-        
+
         start = int(start * self.sr)
         if (isinstance(end, float) or isinstance(end, int)) and isinstance(self.sr, int):
             end = int(np.ceil(end * self.sr))
@@ -140,11 +140,13 @@ class AudioProcessor:
         try:
             out = run(cmd, capture_output=True, check=True).stdout
         except CalledProcessError as e:
-            raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+            raise RuntimeError(
+                f"Failed to load audio: {e.stderr.decode()}") from e
+
+        out = np.frombuffer(out, np.int16).flatten().astype(
+            np.float32) / NORMALIZATION_FACTOR
+
+        return out, sr
 
-        out = np.frombuffer(out, np.int16).flatten().astype(np.float32) / NORMALIZATION_FACTOR
-        
-        return out , sr
-    
     def __repr__(self) -> str:
-        return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'
+        return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'