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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, hps):
try:
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt.keys():
if "enc_q" in key:
continue
opt["weight"][key] = ckpt[key].half()
opt["config"] = [
hps.data.filter_length // 2 + 1,
32,
hps.model.inter_channels,
hps.model.hidden_channels,
hps.model.filter_channels,
hps.model.n_heads,
hps.model.n_layers,
hps.model.kernel_size,
hps.model.p_dropout,
hps.model.resblock,
hps.model.resblock_kernel_sizes,
hps.model.resblock_dilation_sizes,
hps.model.upsample_rates,
hps.model.upsample_initial_channel,
hps.model.upsample_kernel_sizes,
hps.model.spk_embed_dim,
hps.model.gin_channels,
hps.data.sampling_rate,
]
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 "Epochs: %s\nSample rate: %s\nPitch guidance: %s\nRVC Version: %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":
if version == "v1":
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,
]
else:
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[12, 10, 2, 2],
512,
[24, 20, 4, 4],
109,
256,
48000,
]
elif sr == "32k":
if version == "v1":
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,
]
else:
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, 8, 2, 2],
512,
[20, 16, 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 else 0
opt["version"] = version
opt["info"] = info
torch.save(opt, "weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()
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