mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-01-20 02:51:09 +00:00
Reformat and rewrite _get_name_params (#57)
* Reformat
* rewrite _get_name_params
* Add workflow for automatic formatting
* Revert "Add workflow for automatic formatting"
This reverts commit 9111c5dbc1.
* revert Retrieval_based_Voice_Conversion_WebUI.ipynb
---------
Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
This commit is contained in:
Ftps
@@ -1,101 +1,248 @@
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import torch,traceback,os,pdb
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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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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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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"):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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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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torch.save(opt, "weights/%s.pth"%name)
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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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return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%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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)
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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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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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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): continue
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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"):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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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["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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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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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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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):
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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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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): 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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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] = (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 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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"""
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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["info"]=info
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torch.save(opt, "weights/%s.pth"%name)
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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["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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