| | import os
|
| | import sys
|
| | import traceback
|
| | from collections import OrderedDict
|
| |
|
| | import torch
|
| |
|
| | from i18n.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, "assets/weights/%s.pth" % name)
|
| | return "Success."
|
| | except:
|
| | return traceback.format_exc()
|
| |
|
| |
|
| | def show_info(path):
|
| | try:
|
| | a = torch.load(path, map_location="cpu", weights_only=False)
|
| | 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", weights_only=False)
|
| | 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, "assets/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", weights_only=False)
|
| | ckpt["info"] = info
|
| | if name == "":
|
| | name = os.path.basename(path)
|
| | torch.save(ckpt, "assets/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", weights_only=False)
|
| | ckpt2 = torch.load(path2, map_location="cpu", weights_only=False)
|
| | 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 == i18n("是") else 0
|
| | opt["version"] = version
|
| | opt["info"] = info
|
| | torch.save(opt, "assets/weights/%s.pth" % name)
|
| | return "Success."
|
| | except:
|
| | return traceback.format_exc()
|
| |
|
