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Runtime error
| import torch, traceback, os, pdb, sys | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| from collections import OrderedDict | |
| from 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, "weights/%s.pth" % name) | |
| return "Success." | |
| except: | |
| return traceback.format_exc() | |
| def show_info(path): | |
| try: | |
| a = torch.load(path, map_location="cpu") | |
| return "Epochs: %s\nSample rate: %s\nPitch guidance: %s\nRVC Version: %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") | |
| 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, "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, 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") | |
| ckpt2 = torch.load(path2, map_location="cpu") | |
| 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(): | |
| # 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["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 else 0 | |
| opt["version"] = version | |
| opt["info"] = info | |
| torch.save(opt, "weights/%s.pth" % name) | |
| return "Success." | |
| except: | |
| return traceback.format_exc() | |