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59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
"""
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Utility functions that are commonly used
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"""
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import numpy as np
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from scipy.signal import windows, lfilter
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from prettytable import PrettyTable
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# Source: https://gist.github.com/thongonary/026210fc186eb5056f2b6f1ca362d912
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def count_parameters(model):
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table = PrettyTable(["Modules", "Parameters"])
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total_params = 0
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for name, parameter in model.named_parameters():
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if not parameter.requires_grad: continue
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param = parameter.numel()
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table.add_row([name, param])
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total_params+=param
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print(table)
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print(f"Total Trainable Params: {total_params}")
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return total_params
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def stft(x, w = 'boxcar', N = 320, H = 160):
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x = np.concatenate([x,np.zeros(N)])
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# win_custom = np.concatenate([windows.hann(80)[:40],np.ones(240),windows.hann(80)[40:]])
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return np.stack([np.fft.rfft(x[i:i + N]*windows.get_window(w,N)) for i in np.arange(0,x.shape[0]-N,H)])
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def random_filter(x):
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# Randomly filter x with second order IIR filter with coefficients in between -3/8,3/8
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filter_coeff = np.random.uniform(low = -3.0/8, high = 3.0/8, size = 4)
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b = [1,filter_coeff[0],filter_coeff[1]]
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a = [1,filter_coeff[2],filter_coeff[3]]
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return lfilter(b,a,x)
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def feature_xform(feature):
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"""
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Take as input the (N * 256) xcorr features output by LPCNet and perform the following
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1. Downsample and Upsample by 2 (followed by smoothing)
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2. Append positional embeddings (of dim k) coresponding to each xcorr lag
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"""
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from scipy.signal import resample_poly, lfilter
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feature_US = lfilter([0.25,0.5,0.25],[1],resample_poly(feature,2,1,axis = 1),axis = 1)[:,:feature.shape[1]]
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feature_DS = lfilter([0.5,0.5],[1],resample_poly(feature,1,2,axis = 1),axis = 1)
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Z_append = np.zeros((feature.shape[0],feature.shape[1] - feature_DS.shape[1]))
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feature_DS = np.concatenate([feature_DS,Z_append],axis = -1)
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# pos_embedding = []
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# for i in range(k):
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# pos_embedding.append(np.cos((2**i)*np.pi*((np.repeat(np.arange(feature.shape[1]).reshape(feature.shape[1],1),feature.shape[0],axis = 1)).T/(2*feature.shape[1]))))
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# pos_embedding = np.stack(pos_embedding,axis = -1)
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feature = np.stack((feature_DS,feature,feature_US),axis = -1)
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# feature = np.concatenate((feature,pos_embedding),axis = -1)
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return feature
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