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