File size: 5,594 Bytes
ed1cdd1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
148
149
from modules.commons.common_layers import *
from utils.hparams import hparams
from modules.fastspeech.tts_modules import PitchPredictor
from utils.pitch_utils import denorm_f0


class Prenet(nn.Module):
    def __init__(self, in_dim=80, out_dim=256, kernel=5, n_layers=3, strides=None):
        super(Prenet, self).__init__()
        padding = kernel // 2
        self.layers = []
        self.strides = strides if strides is not None else [1] * n_layers
        for l in range(n_layers):
            self.layers.append(nn.Sequential(
                nn.Conv1d(in_dim, out_dim, kernel_size=kernel, padding=padding, stride=self.strides[l]),
                nn.ReLU(),
                nn.BatchNorm1d(out_dim)
            ))
            in_dim = out_dim
        self.layers = nn.ModuleList(self.layers)
        self.out_proj = nn.Linear(out_dim, out_dim)

    def forward(self, x):
        """

        :param x: [B, T, 80]
        :return: [L, B, T, H], [B, T, H]
        """
        # padding_mask = x.abs().sum(-1).eq(0).data  # [B, T]
        padding_mask = x.abs().sum(-1).eq(0).detach()
        nonpadding_mask_TB = 1 - padding_mask.float()[:, None, :]  # [B, 1, T]
        x = x.transpose(1, 2)
        hiddens = []
        for i, l in enumerate(self.layers):
            nonpadding_mask_TB = nonpadding_mask_TB[:, :, ::self.strides[i]]
            x = l(x) * nonpadding_mask_TB
        hiddens.append(x)
        hiddens = torch.stack(hiddens, 0)  # [L, B, H, T]
        hiddens = hiddens.transpose(2, 3)  # [L, B, T, H]
        x = self.out_proj(x.transpose(1, 2))  # [B, T, H]
        x = x * nonpadding_mask_TB.transpose(1, 2)
        return hiddens, x


class ConvBlock(nn.Module):
    def __init__(self, idim=80, n_chans=256, kernel_size=3, stride=1, norm='gn', dropout=0):
        super().__init__()
        self.conv = ConvNorm(idim, n_chans, kernel_size, stride=stride)
        self.norm = norm
        if self.norm == 'bn':
            self.norm = nn.BatchNorm1d(n_chans)
        elif self.norm == 'in':
            self.norm = nn.InstanceNorm1d(n_chans, affine=True)
        elif self.norm == 'gn':
            self.norm = nn.GroupNorm(n_chans // 16, n_chans)
        elif self.norm == 'ln':
            self.norm = LayerNorm(n_chans // 16, n_chans)
        elif self.norm == 'wn':
            self.conv = torch.nn.utils.weight_norm(self.conv.conv)
        self.dropout = nn.Dropout(dropout)
        self.relu = nn.ReLU()

    def forward(self, x):
        """

        :param x: [B, C, T]
        :return: [B, C, T]
        """
        x = self.conv(x)
        if not isinstance(self.norm, str):
            if self.norm == 'none':
                pass
            elif self.norm == 'ln':
                x = self.norm(x.transpose(1, 2)).transpose(1, 2)
            else:
                x = self.norm(x)
        x = self.relu(x)
        x = self.dropout(x)
        return x


class ConvStacks(nn.Module):
    def __init__(self, idim=80, n_layers=5, n_chans=256, odim=32, kernel_size=5, norm='gn',
                 dropout=0, strides=None, res=True):
        super().__init__()
        self.conv = torch.nn.ModuleList()
        self.kernel_size = kernel_size
        self.res = res
        self.in_proj = Linear(idim, n_chans)
        if strides is None:
            strides = [1] * n_layers
        else:
            assert len(strides) == n_layers
        for idx in range(n_layers):
            self.conv.append(ConvBlock(
                n_chans, n_chans, kernel_size, stride=strides[idx], norm=norm, dropout=dropout))
        self.out_proj = Linear(n_chans, odim)

    def forward(self, x, return_hiddens=False):
        """

        :param x: [B, T, H]
        :return: [B, T, H]
        """
        x = self.in_proj(x)
        x = x.transpose(1, -1)  # (B, idim, Tmax)
        hiddens = []
        for f in self.conv:
            x_ = f(x)
            x = x + x_ if self.res else x_  # (B, C, Tmax)
            hiddens.append(x)
        x = x.transpose(1, -1)
        x = self.out_proj(x)  # (B, Tmax, H)
        if return_hiddens:
            hiddens = torch.stack(hiddens, 1)  # [B, L, C, T]
            return x, hiddens
        return x


class PitchExtractor(nn.Module):
    def __init__(self, n_mel_bins=80, conv_layers=2):
        super().__init__()
        self.hidden_size = hparams['hidden_size']
        self.predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
        self.conv_layers = conv_layers

        self.mel_prenet = Prenet(n_mel_bins, self.hidden_size, strides=[1, 1, 1])
        if self.conv_layers > 0:
            self.mel_encoder = ConvStacks(
                    idim=self.hidden_size, n_chans=self.hidden_size, odim=self.hidden_size, n_layers=self.conv_layers)
        self.pitch_predictor = PitchPredictor(
            self.hidden_size, n_chans=self.predictor_hidden,
            n_layers=5, dropout_rate=0.1, odim=2,
            padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel'])

    def forward(self, mel_input=None):
        ret = {}
        mel_hidden = self.mel_prenet(mel_input)[1]
        if self.conv_layers > 0:
            mel_hidden = self.mel_encoder(mel_hidden)

        ret['pitch_pred'] = pitch_pred = self.pitch_predictor(mel_hidden)

        pitch_padding = mel_input.abs().sum(-1) == 0
        use_uv = hparams['pitch_type'] == 'frame' #and hparams['use_uv']
        ret['f0_denorm_pred'] = denorm_f0(
            pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None,
            hparams, pitch_padding=pitch_padding)
        return ret