mirror of https://github.com/drowe67/phasenn.git
83 lines
2.3 KiB
Python
Executable File
83 lines
2.3 KiB
Python
Executable File
#!/usr/bin/python3
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# gen_test_model.py
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#
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# David Rowe Dec 2019
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#
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# Generate some contrived pusedo-speech test data for driving phasenn_train.py
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import numpy as np
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import sys
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import matplotlib.pyplot as plt
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from scipy import signal
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import codec2_model
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# constants
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N = 80 # number of time domain samples in frame
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width = 256
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nb_samples = 100000
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fo_min = 50
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fo_max = 400
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Fs = 8000
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# Generate training data.
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print("Generate training data")
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# amplitude and phase at rate L
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A = np.zeros((nb_samples, width))
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phase = np.zeros((nb_samples, width))
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# side information
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Wo = np.zeros(nb_samples)
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L = np.zeros(nb_samples, dtype=int)
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voiced = np.zeros(nb_samples, dtype=int)
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for i in range(nb_samples):
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# distribute fo randomly on a log scale, gives us more training
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# data with low freq frames which have more harmonics and are
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# harder to match
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r = np.random.rand(1)
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log_fo = np.log10(fo_min) + (np.log10(fo_max)-np.log10(fo_min))*r[0]
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fo = fo_min
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fo = 10 ** log_fo
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Wo[i] = fo*2*np.pi/Fs
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L[i] = int(np.floor(np.pi/Wo[i]))
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# pitch period in samples
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P = 2*L[i]
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r = np.random.rand(1)
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voiced[i] = r[0] > 0.5
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# sample 2nd order IIR filter with random peak freq
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r1 = np.random.rand(2)
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r2 = np.random.rand(2)
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if voiced[i]:
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# choose alpha and gamma to get something like voiced speech
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alpha1 = 0.05*np.pi + 0.25*np.pi*r1[0]
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gamma1 = 0.9 + 0.09*r1[1]
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alpha2 = alpha1 + 0.4*np.pi*r2[0]
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gamma2 = 0.9 + 0.05*r2[1]
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else:
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alpha1 = 0.5*np.pi + 0.4*np.pi*r1[0]
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gamma1 = 0.8 + 0.1*r1[1]
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alpha2 = 0.5*np.pi + 0.4*np.pi*r2[0]
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gamma2 = 0.8 + 0.1*r2[1]
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w1,h1 = signal.freqz(1, [1, -2*gamma1*np.cos(alpha1), gamma1*gamma1], range(1,L[i]+1)*Wo[i])
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w2,h2 = signal.freqz(1, [1, -2*gamma2*np.cos(alpha2), gamma2*gamma2], range(1,L[i]+1)*Wo[i])
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for m in range(1,L[i]+1):
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A[i,m] = np.abs(h1[m-1]*h2[m-1])
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if voiced[i]:
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phase[i,m] = np.angle(h1[m-1]*h2[m-1])
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else:
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r = np.random.rand(1)
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phase[i,m] = -np.pi + r[0]*2*np.pi
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# write to a Codec 2 model file
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codec2_model.write(Wo, L, A, phase, voiced, "gen_test.model")
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