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