File size: 4,340 Bytes
79f7f06
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
145
146
147
import librosa
import numpy as np
from pycwt import wavelet
from scipy.interpolate import interp1d


def load_wav(wav_file, sr):
    wav, _ = librosa.load(wav_file, sr=sr, mono=True)
    return wav


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 = np.float32(f0 != 0)

    # 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()
    dt = 0.005
    dj = 1
    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):
    Wavelet_lf0_norm = np.zeros((Wavelet_lf0.shape[0], Wavelet_lf0.shape[1]))
    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):
    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 - 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