File size: 6,181 Bytes
3c7a160
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
# This is Multi-reference timbre encoder

import torch
from torch import nn
from torch.nn.utils import remove_weight_norm, weight_norm
from module.attentions import MultiHeadAttention


class MRTE(nn.Module):
    def __init__(
        self,
        content_enc_channels=192,
        hidden_size=512,
        out_channels=192,
        kernel_size=5,
        n_heads=4,
        ge_layer=2,
    ):
        super(MRTE, self).__init__()
        self.cross_attention = MultiHeadAttention(hidden_size, hidden_size, n_heads)
        self.c_pre = nn.Conv1d(content_enc_channels, hidden_size, 1)
        self.text_pre = nn.Conv1d(content_enc_channels, hidden_size, 1)
        self.c_post = nn.Conv1d(hidden_size, out_channels, 1)

    def forward(self, ssl_enc, ssl_mask, text, text_mask, ge, test=None):
        if ge == None:
            ge = 0
        attn_mask = text_mask.unsqueeze(2) * ssl_mask.unsqueeze(-1)

        ssl_enc = self.c_pre(ssl_enc * ssl_mask)
        text_enc = self.text_pre(text * text_mask)
        if test != None:
            if test == 0:
                x = (
                    self.cross_attention(
                        ssl_enc * ssl_mask, text_enc * text_mask, attn_mask
                    )
                    + ssl_enc
                    + ge
                )
            elif test == 1:
                x = ssl_enc + ge
            elif test == 2:
                x = (
                    self.cross_attention(
                        ssl_enc * 0 * ssl_mask, text_enc * text_mask, attn_mask
                    )
                    + ge
                )
            else:
                raise ValueError("test should be 0,1,2")
        else:
            x = (
                self.cross_attention(
                    ssl_enc * ssl_mask, text_enc * text_mask, attn_mask
                )
                + ssl_enc
                + ge
            )
        x = self.c_post(x * ssl_mask)
        return x


class SpeakerEncoder(torch.nn.Module):
    def __init__(
        self,
        mel_n_channels=80,
        model_num_layers=2,
        model_hidden_size=256,
        model_embedding_size=256,
    ):
        super(SpeakerEncoder, self).__init__()
        self.lstm = nn.LSTM(
            mel_n_channels, model_hidden_size, model_num_layers, batch_first=True
        )
        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
        self.relu = nn.ReLU()

    def forward(self, mels):
        self.lstm.flatten_parameters()
        _, (hidden, _) = self.lstm(mels.transpose(-1, -2))
        embeds_raw = self.relu(self.linear(hidden[-1]))
        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)


class MELEncoder(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers

        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
        self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers)
        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)

    def forward(self, x):
        # print(x.shape,x_lengths.shape)
        x = self.pre(x)
        x = self.enc(x)
        x = self.proj(x)
        return x


class WN(torch.nn.Module):
    def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers):
        super(WN, self).__init__()
        assert kernel_size % 2 == 1
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers

        self.in_layers = torch.nn.ModuleList()
        self.res_skip_layers = torch.nn.ModuleList()

        for i in range(n_layers):
            dilation = dilation_rate**i
            padding = int((kernel_size * dilation - dilation) / 2)
            in_layer = nn.Conv1d(
                hidden_channels,
                2 * hidden_channels,
                kernel_size,
                dilation=dilation,
                padding=padding,
            )
            in_layer = weight_norm(in_layer)
            self.in_layers.append(in_layer)

            # last one is not necessary
            if i < n_layers - 1:
                res_skip_channels = 2 * hidden_channels
            else:
                res_skip_channels = hidden_channels

            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
            res_skip_layer = weight_norm(res_skip_layer, name="weight")
            self.res_skip_layers.append(res_skip_layer)

    def forward(self, x):
        output = torch.zeros_like(x)
        n_channels_tensor = torch.IntTensor([self.hidden_channels])

        for i in range(self.n_layers):
            x_in = self.in_layers[i](x)

            acts = fused_add_tanh_sigmoid_multiply(x_in, n_channels_tensor)

            res_skip_acts = self.res_skip_layers[i](acts)
            if i < self.n_layers - 1:
                res_acts = res_skip_acts[:, : self.hidden_channels, :]
                x = x + res_acts
                output = output + res_skip_acts[:, self.hidden_channels :, :]
            else:
                output = output + res_skip_acts
        return output

    def remove_weight_norm(self):
        for l in self.in_layers:
            remove_weight_norm(l)
        for l in self.res_skip_layers:
            remove_weight_norm(l)


@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input, n_channels):
    n_channels_int = n_channels[0]
    t_act = torch.tanh(input[:, :n_channels_int, :])
    s_act = torch.sigmoid(input[:, n_channels_int:, :])
    acts = t_act * s_act
    return acts


if __name__ == "__main__":
    content_enc = torch.randn(3, 192, 100)
    content_mask = torch.ones(3, 1, 100)
    ref_mel = torch.randn(3, 128, 30)
    ref_mask = torch.ones(3, 1, 30)
    model = MRTE()
    out = model(content_enc, content_mask, ref_mel, ref_mask)
    print(out.shape)