diff --git a/scripts/demo.py b/scripts/demo.py
new file mode 100644
index 0000000..0344329
--- /dev/null
+++ b/scripts/demo.py
@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+# -*-coding:utf-8 -*-
+"""
+@File    :   test.py
+@Time    :   2024/05/13 00:59:00
+@Author  :   Oriol Nieto
+@Contact :   onieto@adobe.com
+@License :   (C)Copyright 2024, Adobe Research
+@Desc    :   Demo script comparing torchaudio's spectrogram with DFT version
+    of the ConvertibleSpectrogram.
+"""
+
+import librosa
+import numpy as np
+import torch
+import torchaudio
+from scipy import signal as sig
+
+from convmelspec.stft import ConvertibleSpectrogram as Spectrogram
+
+FFT_SIZE = 1024
+SR = 16_000
+HOP_SIZE = 512
+MEL_BANDS = None
+
+
+def get_hann_torch(win_size, sym=True):
+    wn = sig.windows.hann(win_size, sym=sym).astype(np.float32)
+    return torch.from_numpy(wn)
+
+
+def get_audio():
+    example_audio_path = librosa.example("nutcracker")
+    y, sr = librosa.load(example_audio_path, sr=SR)
+    total_sec = 1
+    y = y[int(sr) : (total_sec * sr + sr)].astype(np.float32)
+    return torch.from_numpy(y).unsqueeze(0)
+
+
+def main():
+
+    x = get_audio()
+    wn = sig.windows.hann(FFT_SIZE, sym=True)
+
+    stft = Spectrogram(
+        sr=SR,
+        n_fft=FFT_SIZE,
+        hop_size=HOP_SIZE,
+        padding=0,
+        window=wn,
+        spec_mode="DFT",
+        dtype=torch.float32,
+    )
+
+    stft_ta = torchaudio.transforms.Spectrogram(
+        n_fft=FFT_SIZE,
+        hop_length=HOP_SIZE,
+        window_fn=get_hann_torch,
+        power=2.0,
+        center=False,
+    )
+
+    # AssertionError
+    assert torch.allclose(stft(x), stft_ta(x), atol=1e-5)
+    print("DFT and Torchaudio's Spectrogram all close!")
+
+
+if __name__ == "__main__":
+    main()