98 lines
4.8 KiB
Python
98 lines
4.8 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):
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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): continue
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opt["weight"][key] = ckpt[key].half()
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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], 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, 4,4], 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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opt["info"] = "%sepoch"%epoch
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opt["sr"] = sr
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opt["f0"] =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 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"%(a.get("info","None"),a.get("sr","None"),a.get("f0","None"),)
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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):
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try:
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ckpt = torch.load(path, map_location="cpu")
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if("model"in ckpt):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): continue
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opt["weight"][key] = ckpt[key].half()
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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], 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, 4,4], 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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if(info==""):info="Extracted model."
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opt["info"] = info
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opt["sr"] = sr
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opt["f0"] =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==""):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):
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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): 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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if("model"in ckpt1):ckpt1=extract(ckpt1)
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else:ckpt1=ckpt1["weight"]
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if("model"in ckpt2):ckpt2=extract(ckpt2)
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else:ckpt2=ckpt2["weight"]
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if(sorted(list(ckpt1.keys()))!=sorted(list(ckpt2.keys()))):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] = (alpha1 * (ckpt1[key][:min_shape0].float()) + (1 - alpha1) * (ckpt2[key][:min_shape0].float())).half()
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else:
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opt["weight"][key] = (alpha1*(ckpt1[key].float())+(1-alpha1)*(ckpt2[key].float())).half()
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# except:
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# pdb.set_trace()
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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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opt["sr"]=sr
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opt["f0"]=1 if f0=="是"else 0
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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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