253 lines
7.5 KiB
Python
253 lines
7.5 KiB
Python
import torch, traceback, os, pdb
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from collections import OrderedDict
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def savee(ckpt, sr, if_f0, name, epoch, version):
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try:
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opt = OrderedDict()
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opt["weight"] = {}
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for key in ckpt.keys():
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if "enc_q" in key:
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continue
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opt["weight"][key] = ckpt[key].half()
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if sr == "40k":
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opt["config"] = [
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1025,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 10, 2, 2],
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512,
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[16, 16, 4, 4],
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109,
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256,
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40000,
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]
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elif sr == "48k":
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opt["config"] = [
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1025,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 6, 2, 2, 2],
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512,
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[16, 16, 4, 4, 4],
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109,
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256,
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48000,
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]
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elif sr == "32k":
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opt["config"] = [
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513,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 4, 2, 2, 2],
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512,
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[16, 16, 4, 4, 4],
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109,
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256,
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32000,
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]
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opt["info"] = "%sepoch" % epoch
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opt["sr"] = sr
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opt["f0"] = if_f0
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opt["version"] = version
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torch.save(opt, "weights/%s.pth" % name)
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return "Success."
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except:
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return traceback.format_exc()
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def show_info(path):
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try:
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a = torch.load(path, map_location="cpu")
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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" % (
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a.get("info", "None"),
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a.get("sr", "None"),
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a.get("f0", "None"),
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a.get("version", "None"),
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)
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except:
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return traceback.format_exc()
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def extract_small_model(path, name, sr, if_f0, info, version):
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try:
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ckpt = torch.load(path, map_location="cpu")
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if "model" in ckpt:
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ckpt = ckpt["model"]
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opt = OrderedDict()
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opt["weight"] = {}
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for key in ckpt.keys():
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if "enc_q" in key:
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continue
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opt["weight"][key] = ckpt[key].half()
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if sr == "40k":
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opt["config"] = [
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1025,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 10, 2, 2],
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512,
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[16, 16, 4, 4],
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109,
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256,
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40000,
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]
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elif sr == "48k":
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opt["config"] = [
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1025,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 6, 2, 2, 2],
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512,
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[16, 16, 4, 4, 4],
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109,
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256,
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48000,
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]
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elif sr == "32k":
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opt["config"] = [
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513,
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32,
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192,
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192,
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768,
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2,
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6,
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3,
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0,
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"1",
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[3, 7, 11],
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[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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[10, 4, 2, 2, 2],
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512,
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[16, 16, 4, 4, 4],
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109,
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256,
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32000,
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]
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if info == "":
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info = "Extracted model."
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opt["info"] = info
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opt["version"] = version
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opt["sr"] = sr
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opt["f0"] = int(if_f0)
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torch.save(opt, "weights/%s.pth" % name)
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return "Success."
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except:
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return traceback.format_exc()
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def change_info(path, info, name):
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try:
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ckpt = torch.load(path, map_location="cpu")
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ckpt["info"] = info
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if name == "":
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name = os.path.basename(path)
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torch.save(ckpt, "weights/%s" % name)
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return "Success."
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except:
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return traceback.format_exc()
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def merge(path1, path2, alpha1, sr, f0, info, name, version):
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try:
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def extract(ckpt):
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a = ckpt["model"]
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opt = OrderedDict()
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opt["weight"] = {}
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for key in a.keys():
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if "enc_q" in key:
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continue
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opt["weight"][key] = a[key]
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return opt
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ckpt1 = torch.load(path1, map_location="cpu")
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ckpt2 = torch.load(path2, map_location="cpu")
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cfg = ckpt1["config"]
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if "model" in ckpt1:
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ckpt1 = extract(ckpt1)
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else:
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ckpt1 = ckpt1["weight"]
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if "model" in ckpt2:
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ckpt2 = extract(ckpt2)
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else:
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ckpt2 = ckpt2["weight"]
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if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
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return "Fail to merge the models. The model architectures are not the same."
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opt = OrderedDict()
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opt["weight"] = {}
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for key in ckpt1.keys():
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# try:
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if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
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min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
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opt["weight"][key] = (
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alpha1 * (ckpt1[key][:min_shape0].float())
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+ (1 - alpha1) * (ckpt2[key][:min_shape0].float())
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).half()
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else:
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opt["weight"][key] = (
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alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
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).half()
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# except:
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# pdb.set_trace()
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opt["config"] = cfg
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"""
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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]
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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]
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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]
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"""
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opt["sr"] = sr
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opt["f0"] = 1 if f0 == "是" else 0
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opt["version"] = version
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opt["info"] = info
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torch.save(opt, "weights/%s.pth" % name)
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return "Success."
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except:
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return traceback.format_exc()
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