File size: 5,906 Bytes
45916af
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import librosa
import pytorch_lightning as pl
import torch
from auraloss.freq import STFTLoss, MultiResolutionSTFTLoss, apply_reduction, SpectralConvergenceLoss, STFTMagnitudeLoss

from config import CONFIG


class STFTLossDDP(STFTLoss):
    def __init__(self,
                 fft_size=1024,
                 hop_size=256,
                 win_length=1024,
                 window="hann_window",
                 w_sc=1.0,
                 w_log_mag=1.0,
                 w_lin_mag=0.0,
                 w_phs=0.0,
                 sample_rate=None,
                 scale=None,
                 n_bins=None,
                 scale_invariance=False,
                 eps=1e-8,
                 output="loss",
                 reduction="mean",
                 device=None):
        super(STFTLoss, self).__init__()
        self.fft_size = fft_size
        self.hop_size = hop_size
        self.win_length = win_length
        self.window = getattr(torch, window)(win_length)
        self.w_sc = w_sc
        self.w_log_mag = w_log_mag
        self.w_lin_mag = w_lin_mag
        self.w_phs = w_phs
        self.sample_rate = sample_rate
        self.scale = scale
        self.n_bins = n_bins
        self.scale_invariance = scale_invariance
        self.eps = eps
        self.output = output
        self.reduction = reduction
        self.device = device

        self.spectralconv = SpectralConvergenceLoss()
        self.logstft = STFTMagnitudeLoss(log=True, reduction=reduction)
        self.linstft = STFTMagnitudeLoss(log=False, reduction=reduction)

        # setup mel filterbank
        if self.scale == "mel":
            assert (sample_rate is not None)  # Must set sample rate to use mel scale
            assert (n_bins <= fft_size)  # Must be more FFT bins than Mel bins
            fb = librosa.filters.mel(sample_rate, fft_size, n_mels=n_bins)
            self.fb = torch.tensor(fb).unsqueeze(0)
        elif self.scale == "chroma":
            assert (sample_rate is not None)  # Must set sample rate to use chroma scale
            assert (n_bins <= fft_size)  # Must be more FFT bins than chroma bins
            fb = librosa.filters.chroma(sample_rate, fft_size, n_chroma=n_bins)
            self.fb = torch.tensor(fb).unsqueeze(0)

        if scale is not None and device is not None:
            self.fb = self.fb.to(self.device)  # move filterbank to device

    def compressed_loss(self, x, y, alpha=None):
        self.window = self.window.to(x.device)
        x_mag, x_phs = self.stft(x.view(-1, x.size(-1)))
        y_mag, y_phs = self.stft(y.view(-1, y.size(-1)))

        if alpha is not None:
            x_mag = x_mag ** alpha
            y_mag = y_mag ** alpha

        # apply relevant transforms
        if self.scale is not None:
            x_mag = torch.matmul(self.fb.to(x_mag.device), x_mag)
            y_mag = torch.matmul(self.fb.to(y_mag.device), y_mag)

        # normalize scales
        if self.scale_invariance:
            alpha = (x_mag * y_mag).sum([-2, -1]) / ((y_mag ** 2).sum([-2, -1]))
            y_mag = y_mag * alpha.unsqueeze(-1)

        # compute loss terms
        sc_loss = self.spectralconv(x_mag, y_mag) if self.w_sc else 0.0
        mag_loss = self.logstft(x_mag, y_mag) if self.w_log_mag else 0.0
        lin_loss = self.linstft(x_mag, y_mag) if self.w_lin_mag else 0.0

        # combine loss terms
        loss = (self.w_sc * sc_loss) + (self.w_log_mag * mag_loss) + (self.w_lin_mag * lin_loss)
        loss = apply_reduction(loss, reduction=self.reduction)
        return loss

    def forward(self, x, y):
        return self.compressed_loss(x, y, 0.3)


class MRSTFTLossDDP(MultiResolutionSTFTLoss):
    def __init__(self,
                 fft_sizes=(1024, 2048, 512),
                 hop_sizes=(120, 240, 50),
                 win_lengths=(600, 1200, 240),
                 window="hann_window",
                 w_sc=1.0,
                 w_log_mag=1.0,
                 w_lin_mag=0.0,
                 w_phs=0.0,
                 sample_rate=None,
                 scale=None,
                 n_bins=None,
                 scale_invariance=False,
                 **kwargs):
        super(MultiResolutionSTFTLoss, self).__init__()
        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)  # must define all
        self.stft_losses = torch.nn.ModuleList()
        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
            self.stft_losses += [STFTLossDDP(fs,
                                             ss,
                                             wl,
                                             window,
                                             w_sc,
                                             w_log_mag,
                                             w_lin_mag,
                                             w_phs,
                                             sample_rate,
                                             scale,
                                             n_bins,
                                             scale_invariance,
                                             **kwargs)]


class Loss(pl.LightningModule):
    def __init__(self):
        super(Loss, self).__init__()
        self.stft_loss = MRSTFTLossDDP(sample_rate=CONFIG.DATA.sr, device="cpu", w_log_mag=0.0, w_lin_mag=1.0)
        self.window = torch.sqrt(torch.hann_window(CONFIG.DATA.window_size))

    def forward(self, x, y):
        x = x.permute(0, 2, 3, 1)
        y = y.permute(0, 2, 3, 1)
        wave_x = torch.istft(torch.view_as_complex(x.contiguous()), CONFIG.DATA.window_size, CONFIG.DATA.stride,
                             window=self.window.to(x.device))
        wave_y = torch.istft(torch.view_as_complex(y.contiguous()), CONFIG.DATA.window_size, CONFIG.DATA.stride,
                             window=self.window.to(y.device))
        loss = self.stft_loss(wave_x, wave_y)
        return loss