| import torch, traceback, os, pdb
|
| from collections import OrderedDict
|
|
|
|
|
| def savee(ckpt, sr, if_f0, name, epoch):
|
| 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
|
| 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" % (
|
| a.get("info", "None"),
|
| a.get("sr", "None"),
|
| a.get("f0", "None"),
|
| )
|
| except:
|
| return traceback.format_exc()
|
|
|
|
|
| def extract_small_model(path, name, sr, if_f0, info):
|
| 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["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):
|
| 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():
|
|
|
| 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()
|
|
|
|
|
| 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["info"] = info
|
| torch.save(opt, "weights/%s.pth" % name)
|
| return "Success."
|
| except:
|
| return traceback.format_exc()
|
|
|
