Spaces:
Runtime error
Runtime error
File size: 4,299 Bytes
d1b91e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
import numpy as np
from pycwt import wavelet
from scipy.interpolate import interp1d
dt = 0.005
dj = 1
def convert_continuos_f0(f0):
'''CONVERT F0 TO CONTINUOUS F0
Args:
f0 (ndarray): original f0 sequence with the shape (T)
Return:
(ndarray): continuous f0 with the shape (T)
'''
# get uv information as binary
f0 = np.copy(f0)
uv = (f0 == 0).astype(float)
# get start and end of f0
if (f0 == 0).all():
print("| all of the f0 values are 0.")
return uv, f0
start_f0 = f0[f0 != 0][0]
end_f0 = f0[f0 != 0][-1]
# padding start and end of f0 sequence
start_idx = np.where(f0 == start_f0)[0][0]
end_idx = np.where(f0 == end_f0)[0][-1]
f0[:start_idx] = start_f0
f0[end_idx:] = end_f0
# get non-zero frame index
nz_frames = np.where(f0 != 0)[0]
# perform linear interpolation
f = interp1d(nz_frames, f0[nz_frames])
cont_f0 = f(np.arange(0, f0.shape[0]))
return uv, cont_f0
def get_cont_lf0(f0, frame_period=5.0):
uv, cont_f0_lpf = convert_continuos_f0(f0)
# cont_f0_lpf = low_pass_filter(cont_f0_lpf, int(1.0 / (frame_period * 0.001)), cutoff=20)
cont_lf0_lpf = np.log(cont_f0_lpf)
return uv, cont_lf0_lpf
def get_lf0_cwt(lf0):
'''
input:
signal of shape (N)
output:
Wavelet_lf0 of shape(10, N), scales of shape(10)
'''
mother = wavelet.MexicanHat()
s0 = dt * 2
J = 9
Wavelet_lf0, scales, _, _, _, _ = wavelet.cwt(np.squeeze(lf0), dt, dj, s0, J, mother)
# Wavelet.shape => (J + 1, len(lf0))
Wavelet_lf0 = np.real(Wavelet_lf0).T
return Wavelet_lf0, scales
def norm_scale(Wavelet_lf0):
mean = Wavelet_lf0.mean(0)[None, :]
std = Wavelet_lf0.std(0)[None, :]
Wavelet_lf0_norm = (Wavelet_lf0 - mean) / std
return Wavelet_lf0_norm, mean, std
def normalize_cwt_lf0(f0, mean, std):
uv, cont_lf0_lpf = get_cont_lf0(f0)
cont_lf0_norm = (cont_lf0_lpf - mean) / std
Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_norm)
Wavelet_lf0_norm, _, _ = norm_scale(Wavelet_lf0)
return Wavelet_lf0_norm
def get_lf0_cwt_norm(f0s, mean, std):
uvs = list()
cont_lf0_lpfs = list()
cont_lf0_lpf_norms = list()
Wavelet_lf0s = list()
Wavelet_lf0s_norm = list()
scaless = list()
means = list()
stds = list()
for f0 in f0s:
uv, cont_lf0_lpf = get_cont_lf0(f0)
cont_lf0_lpf_norm = (cont_lf0_lpf - mean) / std
Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm) # [560,10]
Wavelet_lf0_norm, mean_scale, std_scale = norm_scale(Wavelet_lf0) # [560,10],[1,10],[1,10]
Wavelet_lf0s_norm.append(Wavelet_lf0_norm)
uvs.append(uv)
cont_lf0_lpfs.append(cont_lf0_lpf)
cont_lf0_lpf_norms.append(cont_lf0_lpf_norm)
Wavelet_lf0s.append(Wavelet_lf0)
scaless.append(scales)
means.append(mean_scale)
stds.append(std_scale)
return Wavelet_lf0s_norm, scaless, means, stds
def inverse_cwt_torch(Wavelet_lf0, scales):
import torch
b = ((torch.arange(0, len(scales)).float().to(Wavelet_lf0.device)[None, None, :] + 1 + 2.5) ** (-2.5))
lf0_rec = Wavelet_lf0 * b
lf0_rec_sum = lf0_rec.sum(-1)
lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdim=True)) / lf0_rec_sum.std(-1, keepdim=True)
return lf0_rec_sum
def inverse_cwt(Wavelet_lf0, scales):
# mother = wavelet.MexicanHat()
# lf0_rec_sum = wavelet.icwt(Wavelet_lf0[0].T, scales, dt, dj, mother)
b = ((np.arange(0, len(scales))[None, None, :] + 1 + 2.5) ** (-2.5))
lf0_rec = Wavelet_lf0 * b
lf0_rec_sum = lf0_rec.sum(-1)
# lf0_rec_sum = lf0_rec_sum[None, ...]
lf0_rec_sum = (lf0_rec_sum - lf0_rec_sum.mean(-1, keepdims=True)) / lf0_rec_sum.std(-1, keepdims=True)
return lf0_rec_sum
def cwt2f0(cwt_spec, mean, std, cwt_scales):
assert len(mean.shape) == 1 and len(std.shape) == 1 and len(cwt_spec.shape) == 3
import torch
if isinstance(cwt_spec, torch.Tensor):
f0 = inverse_cwt_torch(cwt_spec, cwt_scales)
f0 = f0 * std[:, None] + mean[:, None]
f0 = f0.exp() # [B, T]
else:
f0 = inverse_cwt(cwt_spec, cwt_scales)
f0 = f0 * std[:, None] + mean[:, None]
f0 = np.exp(f0) # [B, T]
return f0
|