253 lines
7.4 KiB
Python
253 lines
7.4 KiB
Python
import torch, traceback, os, pdb
|
|
from collections import OrderedDict
|
|
|
|
|
|
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 == "是" else 0
|
|
opt["version"] = version
|
|
opt["info"] = info
|
|
torch.save(opt, "weights/%s.pth" % name)
|
|
return "Success."
|
|
except:
|
|
return traceback.format_exc()
|