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import os |
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import sys |
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import traceback |
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from collections import OrderedDict |
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import torch |
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from i18n import I18nAuto |
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i18n = I18nAuto() |
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def savee(ckpt, sr, if_f0, name, epoch, version, hps): |
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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: |
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continue |
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opt["weight"][key] = ckpt[key].half() |
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opt["config"] = [ |
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hps.data.filter_length // 2 + 1, |
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32, |
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hps.model.inter_channels, |
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hps.model.hidden_channels, |
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hps.model.filter_channels, |
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hps.model.n_heads, |
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hps.model.n_layers, |
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hps.model.kernel_size, |
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hps.model.p_dropout, |
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hps.model.resblock, |
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hps.model.resblock_kernel_sizes, |
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hps.model.resblock_dilation_sizes, |
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hps.model.upsample_rates, |
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hps.model.upsample_initial_channel, |
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hps.model.upsample_kernel_sizes, |
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hps.model.spk_embed_dim, |
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hps.model.gin_channels, |
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hps.data.sampling_rate, |
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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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opt["version"] = version |
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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\n版本:%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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a.get("version", "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, version): |
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try: |
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ckpt = torch.load(path, map_location="cpu") |
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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: |
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continue |
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opt["weight"][key] = ckpt[key].half() |
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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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if version == "v1": |
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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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else: |
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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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[12, 10, 2, 2], |
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512, |
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[24, 20, 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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if version == "v1": |
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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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else: |
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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, 8, 2, 2], |
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512, |
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[20, 16, 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["version"] = version |
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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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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 == "": |
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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, version): |
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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: |
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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: |
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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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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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opt["config"] = cfg |
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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 == i18n("是") else 0 |
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opt["version"] = version |
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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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