From 7244a42a4de988ddcb1aa862a22b435b8e109109 Mon Sep 17 00:00:00 2001 From: YS <2904972168@qq.com> Date: Sun, 14 Jul 2024 10:08:08 +0800 Subject: [PATCH] =?UTF-8?q?SpecAugment=E6=95=B0=E6=8D=AE=E5=A2=9E=E5=BC=BA?= =?UTF-8?q?=E4=BB=A3=E7=A0=81=E6=9B=B4=E6=AD=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- SpecAugment.py | 43 +++++++++++++++++++++++++++++++++++++++++++ test.wav | Bin 0 -> 375720 bytes 2 files changed, 43 insertions(+) create mode 100644 SpecAugment.py create mode 100644 test.wav diff --git a/SpecAugment.py b/SpecAugment.py new file mode 100644 index 0000000..ddefeee --- /dev/null +++ b/SpecAugment.py @@ -0,0 +1,43 @@ +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 = 'test.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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