diff --git a/audio_toolset/__init__.py b/audio_toolset/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/audio_toolset/audio_io.py b/audio_toolset/audio_io.py
new file mode 100644
index 0000000..839fafd
--- /dev/null
+++ b/audio_toolset/audio_io.py
@@ -0,0 +1,92 @@
+import librosa
+import torch
+from scipy.signal import butter, lfilter
+
+from ml_lib.modules.utils import AutoPad
+
+
+def butter_lowpass(cutoff, sr, order=5):
+    nyq = 0.5 * sr
+    normal_cutoff = cutoff / nyq
+    b, a = butter(order, normal_cutoff, btype='low', analog=False)
+    return b, a
+
+
+def butter_lowpass_filter(data, cutoff, sr, order=5):
+    b, a = butter_lowpass(cutoff, sr, order=order)
+    y = lfilter(b, a, data)
+    return y
+
+
+class MFCC(object):
+    def __init__(self, **kwargs):
+        self.__dict__.update(kwargs)
+
+    def __call__(self, y):
+        mfcc = librosa.feature.mfcc(y, **self.__dict__)
+        return mfcc
+
+
+class NormalizeLocal(object):
+    def __init__(self):
+        self.cache: torch.Tensor
+        pass
+
+    def __call__(self, x: torch.Tensor):
+        mean = x.mean()
+        std = x.std()
+
+        x = x.__sub__(mean).__div__(std)
+        x[torch.isnan(x)] = 0
+        x[torch.isinf(x)] = 0
+        return x
+
+
+class NormalizeMelband(object):
+    def __init__(self):
+        self.cache: torch.Tensor
+        pass
+
+    def __call__(self, x: torch.Tensor):
+        mean = x.mean(-1).unsqueeze(-1)
+        std = x.std(-1).unsqueeze(-1)
+
+        x = x.__sub__(mean).__div__(std)
+        x[torch.isnan(x)] = 0
+        x[torch.isinf(x)] = 0
+        return x
+
+
+class AutoPadTransform(object):
+    def __init__(self, **kwargs):
+        self.__dict__.update(kwargs)
+        self.padder = AutoPad()
+
+    def __call__(self, y):
+        if not torch.is_tensor(y):
+            y = torch.as_tensor(y)
+        return self.padder(y)
+
+    def __repr__(self):
+        return 'AutoPadTransform()'
+
+
+class Melspectogram(object):
+    def __init__(self, **kwargs):
+        self.__dict__.update(kwargs)
+
+    def __call__(self, y):
+        mel = librosa.feature.melspectrogram(y, **self.__dict__)
+        mel = librosa.amplitude_to_db(mel, ref=np.max)
+        return mel
+
+    def __repr__(self):
+        return f'MelSpectogram({self.__dict__})'
+
+
+class LowPass(object):
+    def __init__(self, sr=16000):
+        self.sr = sr
+
+    def __call__(self, x):
+        return butter_lowpass_filter(x, 1000, 1)
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