Retrieval-based-Voice-Conversion-WebUI/train/process_ckpt.py

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"] =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")
        opt["config"] = ckpt1["config"]
        '''
        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]
        '''
        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["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()