diff --git a/SpecAugment.py b/SpecAugment.py
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+++ b/SpecAugment.py
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+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from torchlibrosa.augmentation import SpecAugmentation
+
+# 加载音频文件
+file_path = '00_BRUSH.wav'
+y, sr = librosa.load(file_path, sr=None)
+
+# 计算音频信号的 Short-Time Fourier Transform (STFT)
+D = librosa.stft(y)
+
+# 计算 spectrogram
+spectrogram = librosa.amplitude_to_db(abs(D), ref=np.max)
+
+# 绘制原始音频信号的频谱图
+plt.figure(figsize=(10, 6))
+librosa.display.specshow(spectrogram, sr=sr, x_axis='time', y_axis='log')
+plt.colorbar(format='%+2.0f dB')
+plt.title('origin spectrogram')
+plt.savefig('origin_spectrogram.png')
+
+
+# 转换为 PyTorch 张量
+spectrogram_tensor = torch.from_numpy(spectrogram).unsqueeze(0).unsqueeze(0)
+
+# 应用 SpecAugmentation
+spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, freq_drop_width=8, freq_stripes_num=2)
+augmented_spectrogram_tensor = spec_augmenter(spectrogram_tensor)
+
+# 将增强的频谱图转换回 numpy 数组
+augmented_spectrogram = augmented_spectrogram_tensor.squeeze(0).squeeze(0).numpy()
+
+
+
+# 绘制使用 SpecAugmentation 的频谱图
+plt.figure(figsize=(10, 6))
+librosa.display.specshow(augmented_spectrogram, sr=sr, x_axis='time', y_axis='log')
+plt.colorbar(format='%+2.0f dB')
+plt.title('after spec augment spectrogram')
+plt.savefig('spec_augment.png')
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diff --git a/test.wav b/test.wav
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