diff --git a/enhancer/utils/io.py b/enhancer/utils/io.py
index afc19e8..3703285 100644
--- a/enhancer/utils/io.py
+++ b/enhancer/utils/io.py
@@ -1,41 +1,66 @@
 import os
 import librosa
-from typing import Optional
+from pathlib import Path
+from typing import Optional, Union
 import numpy as np
 import torch
 import torchaudio
 
+
 class Audio:
+    """
+    Audio utils
+    parameters:
+        sampling_rate : int, defaults to 16KHz
+            audio sampling rate
+        mono: bool, defaults to True
+        return_tensors: bool, defaults to True
+            returns torch tensor type if set to True else numpy ndarray
+    """
 
     def __init__(
-        self,
-        sampling_rate:int=16000,
-        mono:bool=True,
-        return_tensor=True
+        self, sampling_rate: int = 16000, mono: bool = True, return_tensor=True
     ) -> None:
-        
+
         self.sampling_rate = sampling_rate
         self.mono = mono
         self.return_tensor = return_tensor
 
     def __call__(
         self,
-        audio,
-        sampling_rate:Optional[int]=None,
-        offset:Optional[float] = None,
-        duration:Optional[float] = None
+        audio: Union[Path, np.ndarray, torch.Tensor],
+        sampling_rate: Optional[int] = None,
+        offset: Optional[float] = None,
+        duration: Optional[float] = None,
     ):
-        if isinstance(audio,str):
+        """
+        read and process input audio
+        parameters:
+            audio: Path to audio file or numpy array or torch tensor
+                single input audio
+            sampling_rate : int, optional
+                sampling rate of the audio input
+            offset: float, optional
+                offset from which the audio must be read, reads from beginning if unused.
+            duration: float (seconds), optional
+                read duration, reads full audio starting from offset if not used
+        """
+        if isinstance(audio, str):
             if os.path.exists(audio):
-                audio,sampling_rate = librosa.load(audio,sr=sampling_rate,mono=False,
-                offset=offset,duration=duration)
+                audio, sampling_rate = librosa.load(
+                    audio,
+                    sr=sampling_rate,
+                    mono=False,
+                    offset=offset,
+                    duration=duration,
+                )
                 if len(audio.shape) == 1:
-                    audio = audio.reshape(1,-1)
+                    audio = audio.reshape(1, -1)
             else:
                 raise FileNotFoundError(f"File {audio} deos not exist")
-        elif isinstance(audio,np.ndarray):
+        elif isinstance(audio, np.ndarray):
             if len(audio.shape) == 1:
-                audio = audio.reshape(1,-1)
+                audio = audio.reshape(1, -1)
         else:
             raise ValueError("audio should be either filepath or numpy ndarray")
 
@@ -43,40 +68,60 @@ class Audio:
             audio = self.convert_mono(audio)
 
         if sampling_rate:
-            audio =  self.__class__.resample_audio(audio,self.sampling_rate,sampling_rate)
+            audio = self.__class__.resample_audio(
+                audio, self.sampling_rate, sampling_rate
+            )
         if self.return_tensor:
             return torch.tensor(audio)
         else:
             return audio
 
     @staticmethod
-    def convert_mono(
-        audio
+    def convert_mono(audio: Union[np.ndarray, torch.Tensor]):
+        """
+        convert input audio into mono (1)
+        parameters:
+            audio: np.ndarray or torch.Tensor
+        """
+        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])
 
-    ):
-        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[1] >> audio.shape[0], f"expected input format (num_channels,num_samples) got {audio.shape}"
-        num_channels,num_samples = audio.shape
-        if num_channels>1:
-            return audio.mean(axis=0).reshape(1,num_samples)
+        assert (
+            audio.shape[1] >> audio.shape[0]
+        ), f"expected input format (num_channels,num_samples) got {audio.shape}"
+        num_channels, num_samples = audio.shape
+        if num_channels > 1:
+            return audio.mean(axis=0).reshape(1, num_samples)
         return audio
 
-
     @staticmethod
     def resample_audio(
-        audio,
-        sr:int,
-        target_sr:int
+        audio: Union[np.ndarray, torch.Tensor], sr: int, target_sr: int
     ):
-        if 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)
+        """
+        resample audio to desired sampling rate
+        parameters:
+            audio : Path to audio file or numpy array or torch tensor
+                audio waveform
+            sr : int
+                current sampling rate
+            target_sr : int
+                target sampling rate
+
+        """
+        if 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")
+                raise ValueError(
+                    "Input should be either numpy array or torch tensor"
+                )
 
         return audio