def smoothing(wav_data):
r = 3
wav = zeros(len(wav_data))
for i in range(r, len(wav) - r):
wav[i] = sum(wav_data[i - r:i + r + 1]) / (2 * r + 1)
return wav
def test_rand(self):
import random
def zeros_sol(n):
x = n // 5
return x + zeros_sol(x) if x else 0
for _ in range(100):
n = random.randint(0, 1000000000)
self.assertEqual(zeros(n), zeros_sol(n), "Testing with n = %d" % n)
def test(self):
self.assertEqual(zeros(0), 0, "Testing with n = 0")
self.assertEqual(zeros(6), 1, "Testing with n = 6")
self.assertEqual(zeros(30), 7, "Testing with n = 30")
self.assertEqual(zeros(100), 24, "Testing with n = 100")
self.assertEqual(zeros(1000), 249, "Testing with n = 1000")
self.assertEqual(zeros(100000), 24999, "Testing with n = 100000")
self.assertEqual(zeros(1000000000), 249999998,
"Testing with n = 1000000000")
def shiftallpeaks(peaks1, peaks2, wav_data3):
""" Shift all peaks paramaters(peaks1,peaks2,wav_data3)
"""
n = len(peaks1)
x3 = transform(wav_data3,False)
new = zeros(len(x3) // 2)
new = new.astype(dtype=np.complex)
Peaks1 = zeros(n + 2)
Peaks1[1:n + 1] = peaks1
Peaks1[n + 1] = len(x3) // 2
Peaks1 = Peaks1.astype(np.int16)
Peaks2 = zeros(n + 2)
Peaks2[1:n + 1] = peaks2
Peaks2[n + 1] = len(x3) // 2
Peaks2 = Peaks2.astype(np.int16)
for j in range(0, n + 1):
k = 0
for i in range(Peaks2[j], Peaks2[j + 1]):
index = Peaks1[j] + np.floor(scale[j + 1] * k)
k += 1
index = int(index)
new[i] = x3[index]
# run this if you wish to replace duplicates with zeros
for j in range(len(new) - 1):
if new[j] == new[j + 1]:
new[j] = 0.0
return new
# input in waves here FILENAME1 = "source.wav" FILENAME2 = "map.wav" FILENAME3 = "target.wav" rate1, wav_data1 = wavfile.read(FILENAME1) rate2, wav_data2 = wavfile.read(FILENAME2) rate3, wav_data3 = wavfile.read(FILENAME3) # coeficients of regression coef1 = regressionCoefficients(wav_data1, 0.01) coef2 = regressionCoefficients(wav_data2, 0.3) # include end points of regression n = len(coef1) C1 = zeros(n + 2) C1[1:n + 1] = coef1 C1[n + 1] = 1.2 * C1[n] C2 = zeros(n + 2) C2[1:n + 1] = coef2 C2[n + 1] = 1.2 * C2[n] # plot coefficients plt.plot(C1, C2, 'o') plt.show() # Construct polynomial of order n-1 X = np.asmatrix(C1) Y = np.asmatrix(C2) b = np.transpose(Y) A = zeros([n, n])
f2 = linspace(0, 8000, L2)
# x2 = np.fft.fft(wav2)
x2 = transform(wav2,False)
X2 = np.abs(x2) / L2
# magnitude of coefficient of FFT of target voice to be replicated
ratio = abs(x2) / abs(x1) # ratio of coefficients r=d/c
Freq = linspace(0, 8000, L1)
plt.plot(Freq, ratio) # plot ratio over double sided FFT spectrum
# This will help us cut down on the ratio .
# You need to pick values of h>1.
# The closer to 1 , the more it shrinks
# The farther h is from 1 , a the smaller the reduction
h = 1.25
Ratio = zeros(len(ratio))
for i in range(len(ratio)):
Ratio[i] = ((h - 1) * ratio[i] + 1) / h
plt.plot(Freq , Ratio)
# plt.show()
# Apply ratio multipilcation on third audio sample
L3 = len(wav3)
f3 = linspace(0, 8000, L3)
x3 = transform(wav3,False)
x3 = x3 * Ratio
ifftplot=transform(x3,True)
# ifftplot=smoothing(ifftplot)
filteredwrite=np.round(ifftplot).astype('int16')