voice-conversion/convert.py at master · nihal111/voice-conversion · GitHub

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
import librosa
import numpy as np
import hyperparams as hp
from scipy import signal
import sys


def db_to_amplitude(x):
    return 10.0**(x / 10.0)


def preemphasis(x, coeff=0.97):
    '''
    Applies a pre-emphasis filter on x
    '''
    return signal.lfilter([1, -coeff], [1], x)


def deemphasis(x, coeff=0.97):
    return signal.lfilter([1], [1, -coeff], x)


def _get_mfcc_log_spec_and_log_mel_spec(wav, preemphasis_coeff,
                                        n_fft, win_length, hop_length):
    '''
    Args:
    wav - Wave object loaded using librosa

    Returns:
    mfcc - coefficients
    mag - magnitude spectrum
    mel
    '''
    # Pre-emphasis
    y_preem = preemphasis(wav, coeff=preemphasis_coeff)

    # Get spectrogram
    D = librosa.stft(y=y_preem, n_fft=n_fft,
                     hop_length=hop_length, win_length=win_length)
    mag = np.abs(D)

    # Get mel-spectrogram
    mel_basis = librosa.filters.mel(
        hp.Default.sr,
        hp.Default.n_fft,
        hp.Default.n_mels)  # (n_mels, 1+n_fft//2)
    mel = np.dot(mel_basis, mag)  # (n_mels, t) # mel spectrogram
    product = np.dot(mel_basis.T, mel_basis)
    print(product)
    print(mel_basis.shape)
    # p_inv = np.dot(np.linalg.inv(np.dot(mel_basis.T, mel_basis)),
    #                mel_basis.T)
    # mag_pred = np.dot(p_inv, mel)
    # print(mag)
    # print(mag_pred)

    # Get mfccs
    db = librosa.amplitude_to_db(mel)
    dctm = librosa.filters.dct(hp.Default.n_mfcc, db.shape[0])
    mfccs = np.dot(dctm, db)
    # Log
    mag = np.log(mag + sys.float_info.epsilon)
    mel = np.log(mel + sys.float_info.epsilon)

    # Normalization
    # self.y_log_spec = (y_log_spec - hp.mean_log_spec) / hp.std_log_spec
    # self.y_log_spec = (y_log_spec - hp.min_log_spec) / \
    #     (hp.max_log_spec - hp.min_log_spec)

    return mfccs.T, mag.T, mel.T  # (t, n_mfccs), (t, 1+n_fft/2), (t, n_mels)


def spectrogram2wav(mag, n_fft, win_length, hop_length,
                    num_iters, phase_angle=None, length=None):
    assert(num_iters > 0)
    if phase_angle is None:
        phase_angle = np.pi * np.random.rand(*mag.shape)
    spec = mag * np.exp(1.j * phase_angle)
    for i in range(num_iters):
        wav = librosa.istft(spec, win_length=win_length,
                            hop_length=hop_length, length=length)
        if i != num_iters - 1:
            spec = librosa.stft(
                wav, n_fft=n_fft, win_length=win_length, hop_length=hop_length)
            _, phase = librosa.magphase(spec)
            phase_angle = np.angle(phase)
            spec = mag * np.exp(1.j * phase_angle)
    return deemphasis(wav)


def _get_wav_from_mfccs(mfccs, preemphasis_coeff,
                        n_fft, win_length, hop_length, n_wav):
    dctm = librosa.filters.dct(hp.Default.n_mfcc, hp.Default.n_mels)
    mel_basis = librosa.filters.mel(
        hp.Default.sr, hp.Default.n_fft, hp.Default.n_mels)
    # bin_scaling = 1.0 / \
    #     np.maximum(0.0005, np.sum(np.dot(mel_basis.T, mel_basis), axis=0))
    mel_db = np.dot(dctm.T, mfccs.T)
    mel = db_to_amplitude(mel_db)
    recon_magsq = np.dot(mel_basis.T, mel)
    # bin_scaling[:, np.newaxis] *
    mag = np.sqrt(recon_magsq)
    # excitation = np.random.randn(n_wav)
    # E = librosa.stft(excitation,
    #                  n_fft=n_fft,
    #                  hop_length=hop_length,
    #                  win_length=win_length)
    # recon = librosa.core.istft(
    #     np.sqrt(recon_stft),
    #     hop_length=hop_length,
    #     win_length=win_length)
    recon = spectrogram2wav(mag, n_fft, win_length,
                            hop_length, hp.Default.n_iter)
    recon = deemphasis(recon, coeff=preemphasis_coeff)
    return recon


wav_file = "datasets/arctic/bdl/arctic_a0001.wav"
wav, sr = librosa.load(wav_file, sr=hp.Default.sr)
mfccs, mag, _ = _get_mfcc_log_spec_and_log_mel_spec(wav,
                                                    hp.Default.preemphasis,
                                                    hp.Default.n_fft,
                                                    hp.Default.win_length,
                                                    hp.Default.hop_length)
print(mag)
print (mag.shape)
audio = _get_wav_from_mfccs(mfccs,
                            hp.Default.preemphasis,
                            hp.Default.n_fft,
                            hp.Default.win_length,
                            hp.Default.hop_length,
                            len(wav))
audio2 = spectrogram2wav((np.e**mag).T,
                         hp.Default.n_fft,
                         hp.Default.win_length,
                         hp.Default.hop_length,
                         hp.Default.n_iter)
librosa.output.write_wav("recon2.wav", audio2, hp.Default.sr, norm=True)