File size: 7,481 Bytes
4de32eb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
import torch, traceback, os, pdb, sys

now_dir = os.getcwd()
sys.path.append(now_dir)
from collections import OrderedDict
from i18n import I18nAuto

i18n = I18nAuto()


def savee(ckpt, sr, if_f0, name, epoch, version):
    try:
        opt = OrderedDict()
        opt["weight"] = {}
        for key in ckpt.keys():
            if "enc_q" in key:
                continue
            opt["weight"][key] = ckpt[key].half()
        if sr == "40k":
            opt["config"] = [
                1025,
                32,
                192,
                192,
                768,
                2,
                6,
                3,
                0,
                "1",
                [3, 7, 11],
                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
                [10, 10, 2, 2],
                512,
                [16, 16, 4, 4],
                109,
                256,
                40000,
            ]
        elif sr == "48k":
            opt["config"] = [
                1025,
                32,
                192,
                192,
                768,
                2,
                6,
                3,
                0,
                "1",
                [3, 7, 11],
                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
                [10, 6, 2, 2, 2],
                512,
                [16, 16, 4, 4, 4],
                109,
                256,
                48000,
            ]
        elif sr == "32k":
            opt["config"] = [
                513,
                32,
                192,
                192,
                768,
                2,
                6,
                3,
                0,
                "1",
                [3, 7, 11],
                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
                [10, 4, 2, 2, 2],
                512,
                [16, 16, 4, 4, 4],
                109,
                256,
                32000,
            ]
        opt["info"] = "%sepoch" % epoch
        opt["sr"] = sr
        opt["f0"] = if_f0
        opt["version"] = version
        torch.save(opt, "weights/%s.pth" % name)
        return "Success."
    except:
        return traceback.format_exc()


def show_info(path):
    try:
        a = torch.load(path, map_location="cpu")
        return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % (
            a.get("info", "None"),
            a.get("sr", "None"),
            a.get("f0", "None"),
            a.get("version", "None"),
        )
    except:
        return traceback.format_exc()


def extract_small_model(path, name, sr, if_f0, info, version):
    try:
        ckpt = torch.load(path, map_location="cpu")
        if "model" in ckpt:
            ckpt = ckpt["model"]
        opt = OrderedDict()
        opt["weight"] = {}
        for key in ckpt.keys():
            if "enc_q" in key:
                continue
            opt["weight"][key] = ckpt[key].half()
        if sr == "40k":
            opt["config"] = [
                1025,
                32,
                192,
                192,
                768,
                2,
                6,
                3,
                0,
                "1",
                [3, 7, 11],
                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
                [10, 10, 2, 2],
                512,
                [16, 16, 4, 4],
                109,
                256,
                40000,
            ]
        elif sr == "48k":
            opt["config"] = [
                1025,
                32,
                192,
                192,
                768,
                2,
                6,
                3,
                0,
                "1",
                [3, 7, 11],
                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
                [10, 6, 2, 2, 2],
                512,
                [16, 16, 4, 4, 4],
                109,
                256,
                48000,
            ]
        elif sr == "32k":
            opt["config"] = [
                513,
                32,
                192,
                192,
                768,
                2,
                6,
                3,
                0,
                "1",
                [3, 7, 11],
                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
                [10, 4, 2, 2, 2],
                512,
                [16, 16, 4, 4, 4],
                109,
                256,
                32000,
            ]
        if info == "":
            info = "Extracted model."
        opt["info"] = info
        opt["version"] = version
        opt["sr"] = sr
        opt["f0"] = int(if_f0)
        torch.save(opt, "weights/%s.pth" % name)
        return "Success."
    except:
        return traceback.format_exc()


def change_info(path, info, name):
    try:
        ckpt = torch.load(path, map_location="cpu")
        ckpt["info"] = info
        if name == "":
            name = os.path.basename(path)
        torch.save(ckpt, "weights/%s" % name)
        return "Success."
    except:
        return traceback.format_exc()


def merge(path1, path2, alpha1, sr, f0, info, name, version):
    try:

        def extract(ckpt):
            a = ckpt["model"]
            opt = OrderedDict()
            opt["weight"] = {}
            for key in a.keys():
                if "enc_q" in key:
                    continue
                opt["weight"][key] = a[key]
            return opt

        ckpt1 = torch.load(path1, map_location="cpu")
        ckpt2 = torch.load(path2, map_location="cpu")
        cfg = ckpt1["config"]
        if "model" in ckpt1:
            ckpt1 = extract(ckpt1)
        else:
            ckpt1 = ckpt1["weight"]
        if "model" in ckpt2:
            ckpt2 = extract(ckpt2)
        else:
            ckpt2 = ckpt2["weight"]
        if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
            return "Fail to merge the models. The model architectures are not the same."
        opt = OrderedDict()
        opt["weight"] = {}
        for key in ckpt1.keys():
            # try:
            if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
                min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
                opt["weight"][key] = (
                    alpha1 * (ckpt1[key][:min_shape0].float())
                    + (1 - alpha1) * (ckpt2[key][:min_shape0].float())
                ).half()
            else:
                opt["weight"][key] = (
                    alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
                ).half()
        # except:
        #     pdb.set_trace()
        opt["config"] = cfg
        """
        if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
        elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
        elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
        """
        opt["sr"] = sr
        opt["f0"] = 1 if f0 == i18n("是") else 0
        opt["version"] = version
        opt["info"] = info
        torch.save(opt, "weights/%s.pth" % name)
        return "Success."
    except:
        return traceback.format_exc()