diff --git a/enhancer/inference.py b/enhancer/inference.py
new file mode 100644
index 0000000..fc60624
--- /dev/null
+++ b/enhancer/inference.py
@@ -0,0 +1,84 @@
+from json import load
+import wave
+import numpy as np
+from scipy.signal import get_window
+from typing import List, Optional, Union
+import torch
+import torch.nn.functional as F
+from pathlib import Path
+from librosa import load as load_audio
+
+from enhancer.utils import Audio
+from enhancer.utils.config import DEFAULT_DEVICE
+
+class Inference:
+
+    @staticmethod
+    def read_input(audio, sr, model_sr):
+
+        if isinstance(audio,(np.ndarray,torch.Tensor)):
+            assert sr is not None, "Invalid sampling rate!"
+
+        if isinstance(audio,str):
+            audio = Path(audio)
+            if not audio.is_file():
+                raise ValueError(f"Input file {audio} does not exist")
+            else:
+                audio,sr = load_audio(audio,sr=sr,)
+        else:
+            assert audio.shape[0] == 1, "Enhance inference only supports single waveform"
+
+        waveform = Audio.resample_audio(audio,sr=sr,target_sr=model_sr)
+        waveform = Audio.convert_mono(waveform)
+        if isinstance(waveform,np.ndarray):
+            waveform = torch.from_numpy(waveform)
+
+        return waveform
+
+    @staticmethod
+    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)
+        step_size = window_size//2 if step_size is None else step_size
+
+        if num_samples >= window_size:
+            waveform_batch = F.unfold(waveform[None,...], kernel_size=(window_size,1),
+            stride=(step_size,1), padding=(window_size,0))
+            waveform_batch  = waveform_batch.permute(2,0,1)
+        
+        
+        return waveform_batch
+
+
+    def aggreagate(self,data:torch.Tensor,window_size:int, step_size:Optional[int]=None):
+        """
+        takes input as tensor outputs aggregated waveform
+        """
+        batch_size,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),
+            kernel_size=(window_size,1),
+            stride=(step_size,1),
+            padding=(window_size,0))
+
+        return data
+
+
+
+
+
+
+        
+
+
+    
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