SpecAugment.py

@@ -1,5 +1,3 @@
-import os
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 import librosa
 import librosa.display
 import matplotlib.pyplot as plt
@@ -8,7 +6,7 @@ import torch
 from torchlibrosa.augmentation import SpecAugmentation
 
 # 加载音频文件
-file_path = 'test.wav'
+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)
@@ -22,7 +20,6 @@ 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')