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import os |
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import cv2 as cv |
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import numpy as np |
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from python_speech_features import sigproc |
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from python_speech_features import mfcc |
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from sklearn import preprocessing |
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def wave2spectrum(sample_rate, wave, winlen=0.025, winstep=0.01, nfft=512): |
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"""计算功率谱图像""" |
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frames = sigproc.framesig( |
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sig=wave, |
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frame_len=winlen * sample_rate, |
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frame_step=winstep * sample_rate, |
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winfunc=np.hamming |
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) |
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spectrum = sigproc.powspec( |
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frames=frames, |
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NFFT=nfft |
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) |
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spectrum = spectrum.T |
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return spectrum |
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def wave2spectrum_image( |
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wave, sample_rate, |
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xmax=10, xmin=-50, |
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winlen=0.025, winstep=0.01, nfft=512, |
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n_low_freq=None |
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): |
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""" |
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:return: numpy.ndarray, shape=(time_step, n_dim) |
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""" |
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spectrum = wave2spectrum( |
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sample_rate, wave, |
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winlen=winlen, |
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winstep=winstep, |
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nfft=nfft, |
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) |
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spectrum = np.log(spectrum, out=np.zeros_like(spectrum), where=(spectrum != 0)) |
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spectrum = spectrum.T |
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gray = 255 * (spectrum - xmin) / (xmax - xmin) |
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gray = np.array(gray, dtype=np.uint8) |
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if n_low_freq is not None: |
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gray = gray[:, :n_low_freq] |
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return gray |
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def compute_delta(specgram: np.ndarray, win_length: int = 5): |
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""" |
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:param specgram: shape=[time_steps, n_mels] |
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:param win_length: |
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:return: |
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""" |
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n = (win_length - 1) // 2 |
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specgram = np.array(specgram, dtype=np.float32) |
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kernel = np.arange(-n, n + 1, 1) |
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kernel = np.reshape(kernel, newshape=(2 * n + 1, 1)) |
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kernel = np.array(kernel, dtype=np.float32) / 10 |
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delta = cv.filter2D( |
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src=specgram, |
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ddepth=cv.CV_32F, |
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kernel=kernel, |
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) |
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return delta |
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def delta_mfcc_feature(signal, sample_rate): |
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""" |
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为 GMM UBM 声纹识别模型, 编写此代码. |
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https://github.com/pventrella20/Speaker_identification_-GMM-UBM- |
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https://github.com/MChamith/Speaker_verification_gmm_ubm |
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:param signal: np.ndarray |
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:param sample_rate: frequenza del file audio |
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:return: |
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""" |
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mfcc_feat = mfcc( |
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signal=signal, |
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samplerate=sample_rate, |
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winlen=0.025, |
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winstep=0.01, |
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numcep=20, |
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appendEnergy=True |
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) |
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mfcc_feat = preprocessing.scale(mfcc_feat) |
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delta = compute_delta(mfcc_feat) |
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combined = np.hstack(tup=(mfcc_feat, delta)) |
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return combined |
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if __name__ == '__main__': |
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pass |
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