Hugging Face's logo

Spaces:
manoskary
/
MuseControlLite
Sleeping

App Files Community
MuseControlLite
File size: 7,219 Bytes 
7a421a5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
import torch.nn as nn
import torch.nn.functional as F

class dynamics_extractor_full_stereo(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1d_1 = nn.Conv1d(2, 16, kernel_size=3, padding=1, stride=2)  
        self.conv1d_2 = nn.Conv1d(16, 16, kernel_size=3, padding=1)  
        self.conv1d_3 = nn.Conv1d(16, 128, kernel_size=3, padding=1, stride=2)
        self.conv1d_4 = nn.Conv1d(128, 128, kernel_size=3, padding=1)
        self.conv1d_5 = nn.Conv1d(128, 256, kernel_size=3, padding=1, stride=2)
    def forward(self, x):
        # original shape: (batchsize, 1, 8280)
        # x = x.unsqueeze(1) # shape: (batchsize, 1, 8280)
        x = self.conv1d_1(x)  # shape: (batchsize, 16, 4140)
        x = F.silu(x)
        x = self.conv1d_2(x)  # shape: (batchsize, 16, 4140)
        x = F.silu(x)
        x = self.conv1d_3(x)  # shape: (batchsize, 128, 2070)
        x = F.silu(x)
        x = self.conv1d_4(x)  # shape: (batchsize, 128, 2070)
        x = F.silu(x)
        x = self.conv1d_5(x)  # shape: (batchsize, 192, 1035)
        return x
class melody_extractor_full_mono(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1d_1 = nn.Conv1d(128, 256, kernel_size=3, padding=0, stride=2)  
        self.conv1d_2 = nn.Conv1d(256, 256, kernel_size=3, padding=1)  
        self.conv1d_3 = nn.Conv1d(256, 512, kernel_size=3, padding=1, stride=2)  
        self.conv1d_4 = nn.Conv1d(512, 512, kernel_size=3, padding=1)
        self.conv1d_5 = nn.Conv1d(512, 768, kernel_size=3, padding=1)
    def forward(self, x):
        # original shape: (batchsize, 12, 1296)
        x = self.conv1d_1(x)# shape: (batchsize, 64, 2048)
        x = F.silu(x)
        x = self.conv1d_2(x) # shape: (batchsize, 64, 2048)
        x = F.silu(x)
        x = self.conv1d_3(x) # shape: (batchsize, 128, 1024)
        x = F.silu(x)
        x = self.conv1d_4(x) # shape: (batchsize, 128, 1024)
        x = F.silu(x)
        x = self.conv1d_5(x) # shape: (batchsize, 768, 1024)
        return x
class melody_extractor_mono(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1d_1 = nn.Conv1d(128, 128, kernel_size=3, padding=0, stride=2)  
        self.conv1d_2 = nn.Conv1d(128, 192, kernel_size=3, padding=1, stride=2)  
        self.conv1d_3 = nn.Conv1d(192, 192, kernel_size=3, padding=1)
    def forward(self, x):
        # original shape: (batchsize, 12, 1296)
        x = self.conv1d_1(x)# shape: (batchsize, 64, 2048)
        x = F.silu(x)
        x = self.conv1d_2(x) # shape: (batchsize, 64, 2048)
        x = F.silu(x)
        x = self.conv1d_3(x) # shape: (batchsize, 128, 1024)
        return x
    
class melody_extractor_full_stereo(nn.Module):
    def __init__(self):
        super().__init__()
        self.embed = nn.Embedding(num_embeddings=129, embedding_dim=48)

        # Four Conv1d layers, each with kernel_size=3, padding=1:
        self.conv1 = nn.Conv1d(384, 384, kernel_size=3, padding=1)
        self.conv2 = nn.Conv1d(384, 768, kernel_size=3, padding=1)
        self.conv3 = nn.Conv1d(768, 768, kernel_size=3, padding=1)

    def forward(self, melody_idxs):
        # melody_idxs: LongTensor of shape (B, 8, 4096)
        B, eight, L = melody_idxs.shape  # L == 4096

        # 1) Embed:
        #    (B, 8, 4096) → (B, 8, 4096, 48)
        embedded = self.embed(melody_idxs)

        # 2) Permute & reshape → (B, 8*48, 4096) = (B, 384, 4096)
        x = embedded.permute(0, 1, 3, 2)      # (B, 8, 48, 4096)
        x = x.reshape(B, eight * 48, L)       # (B, 384, 4096)

        # 3) Conv1 → (B, 384, 4096)
        x = F.silu(self.conv1(x))

        # 4) Conv2 → (B, 768, 4096)
        x = F.silu(self.conv2(x))

        # 5) Conv3 → (B, 768, 4096)
        x = F.silu(self.conv3(x))

        # Now x is (B, 1536, 4096) and can be sent on to whatever comes next
        return x
class melody_extractor_stereo(nn.Module):
    def __init__(self):
        super().__init__()
        self.embed = nn.Embedding(num_embeddings=129, embedding_dim=4)

        # Four Conv1d layers, each with kernel_size=3, padding=1:
        self.conv1 = nn.Conv1d(32, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv1d(64, 64, kernel_size=3, padding=0, stride=2)
        self.conv3 = nn.Conv1d(64, 128, kernel_size=3, padding=1)
        self.conv4 = nn.Conv1d(128, 128, kernel_size=3, padding=1, stride=2)
        self.conv5 = nn.Conv1d(128, 256, kernel_size=3, padding=1)

    def forward(self, melody_idxs):
        # melody_idxs: LongTensor of shape (B, 8, 4096)
        B, eight, L = melody_idxs.shape  # L == 4096

        # 1) Embed:
        #    (B, 8, 4096) → (B, 8, 4096, 4)
        embedded = self.embed(melody_idxs)

        # 2) Permute & reshape → (B, 8*4, 4096) = (B, 32, 4096)
        x = embedded.permute(0, 1, 3, 2)      # (B, 8, 4, 4096)
        x = x.reshape(B, eight * 4, L)       # (B, 32, 4096)

        # 3) Conv1 → (B, 384, 4096)
        x = F.silu(self.conv1(x))

        # 4) Conv2 → (B, 768, 4096)
        x = F.silu(self.conv2(x))

        # 5) Conv3 → (B, 768, 4096)
        x = F.silu(self.conv3(x))

        x = F.silu(self.conv4(x))

        x = F.silu(self.conv5(x))

        # Now x is (B, 1536, 4096) and can be sent on to whatever comes next
        return x

class dynamics_extractor(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1d_1 = nn.Conv1d(1, 16, kernel_size=3, padding=1, stride=2)  
        self.conv1d_2 = nn.Conv1d(16, 16, kernel_size=3, padding=1)  
        self.conv1d_3 = nn.Conv1d(16, 128, kernel_size=3, padding=1, stride=2)
        self.conv1d_4 = nn.Conv1d(128, 128, kernel_size=3, padding=1)
        self.conv1d_5 = nn.Conv1d(128, 192, kernel_size=3, padding=1, stride=2)
    def forward(self, x):
        # original shape: (batchsize, 1, 8280)
        # x = x.unsqueeze(1) # shape: (batchsize, 1, 8280)
        x = self.conv1d_1(x)  # shape: (batchsize, 16, 4140)
        x = F.silu(x)
        x = self.conv1d_2(x)  # shape: (batchsize, 16, 4140)
        x = F.silu(x)
        x = self.conv1d_3(x)  # shape: (batchsize, 128, 2070)
        x = F.silu(x)
        x = self.conv1d_4(x)  # shape: (batchsize, 128, 2070)
        x = F.silu(x)
        x = self.conv1d_5(x)  # shape: (batchsize, 192, 1035)
        return x
class rhythm_extractor(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1d_1 = nn.Conv1d(2, 16, kernel_size=3, padding=1)  
        self.conv1d_2 = nn.Conv1d(16, 64, kernel_size=3, padding=1)  
        self.conv1d_3 = nn.Conv1d(64, 128, kernel_size=3, padding=1, stride=2)  
        self.conv1d_4 = nn.Conv1d(128, 128, kernel_size=3, padding=1)
        self.conv1d_5 = nn.Conv1d(128, 192, kernel_size=3, padding=1, stride=2)
    def forward(self, x):
        # original shape: (batchsize, 2, 3000)
        x = self.conv1d_1(x)# shape: (batchsize, 64, 3000)
        x = F.silu(x)
        x = self.conv1d_2(x) # shape: (batchsize, 64, 3000)
        x = F.silu(x)
        x = self.conv1d_3(x) # shape: (batchsize, 128, 1500)
        x = F.silu(x)
        x = self.conv1d_4(x) # shape: (batchsize, 128, 1500)
        x = F.silu(x)
        x = self.conv1d_5(x) # shape:
        return x