Python code for neural pitch

dnn/torch/neural-pitch/utils.py

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+"""
+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