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| import traceback | |
| from collections import OrderedDict | |
| from time import time as ttime | |
| import shutil | |
| import os | |
| import torch | |
| from tools.i18n.i18n import I18nAuto | |
| i18n = I18nAuto() | |
| def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path | |
| dir = os.path.dirname(path) | |
| name = os.path.basename(path) | |
| tmp_path = "%s.pth" % (ttime()) | |
| torch.save(fea, tmp_path) | |
| shutil.move(tmp_path, "%s/%s" % (dir, name)) | |
| from io import BytesIO | |
| model_version2byte={ | |
| "v3":b"03", | |
| "v4":b"04", | |
| "v2Pro":b"05", | |
| "v2ProPlus":b"06", | |
| } | |
| def my_save2(fea, path, model_version): | |
| bio = BytesIO() | |
| torch.save(fea, bio) | |
| bio.seek(0) | |
| data = bio.getvalue() | |
| byte = model_version2byte[model_version] | |
| data = byte + data[2:] | |
| with open(path, "wb") as f: | |
| f.write(data) | |
| def savee(ckpt, name, epoch, steps, hps, model_version=None, lora_rank=None): | |
| 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 | |
| opt["info"] = "%sepoch_%siteration" % (epoch, steps) | |
| if lora_rank: | |
| opt["lora_rank"] = lora_rank | |
| my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version) | |
| elif (model_version!=None and "Pro"in model_version): | |
| my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version) | |
| else: | |
| my_save(opt, "%s/%s.pth" % (hps.save_weight_dir, name)) | |
| return "Success." | |
| except: | |
| return traceback.format_exc() | |
| """ | |
| 00:v1 | |
| 01:v2 | |
| 02:v3 | |
| 03:v3lora | |
| 04:v4lora | |
| 05:v2Pro | |
| 06:v2ProPlus | |
| """ | |
| head2version = { | |
| b"00": ["v1", "v1", False], | |
| b"01": ["v2", "v2", False], | |
| b"02": ["v2", "v3", False], | |
| b"03": ["v2", "v3", True], | |
| b"04": ["v2", "v4", True], | |
| b"05": ["v2", "v2Pro", False], | |
| b"06": ["v2", "v2ProPlus", False], | |
| } | |
| hash_pretrained_dict = { | |
| "dc3c97e17592963677a4a1681f30c653": ["v2", "v2", False], # s2G488k.pth#sovits_v1_pretrained | |
| "43797be674a37c1c83ee81081941ed0f": ["v2", "v3", False], # s2Gv3.pth#sovits_v3_pretrained | |
| "6642b37f3dbb1f76882b69937c95a5f3": ["v2", "v2", False], # s2G2333K.pth#sovits_v2_pretrained | |
| "4f26b9476d0c5033e04162c486074374": ["v2", "v4", False], # s2Gv4.pth#sovits_v4_pretrained | |
| "c7e9fce2223f3db685cdfa1e6368728a": ["v2", "v2Pro", False], # s2Gv2Pro.pth#sovits_v2Pro_pretrained | |
| "66b313e39455b57ab1b0bc0b239c9d0a": ["v2", "v2ProPlus", False], # s2Gv2ProPlus.pth#sovits_v2ProPlus_pretrained | |
| } | |
| import hashlib | |
| def get_hash_from_file(sovits_path): | |
| with open(sovits_path, "rb") as f: | |
| data = f.read(8192) | |
| hash_md5 = hashlib.md5() | |
| hash_md5.update(data) | |
| return hash_md5.hexdigest() | |
| def get_sovits_version_from_path_fast(sovits_path): | |
| ###1-if it is pretrained sovits models, by hash | |
| hash = get_hash_from_file(sovits_path) | |
| if hash in hash_pretrained_dict: | |
| return hash_pretrained_dict[hash] | |
| ###2-new weights, by head | |
| with open(sovits_path, "rb") as f: | |
| version = f.read(2) | |
| if version != b"PK": | |
| return head2version[version] | |
| ###3-old weights, by file size | |
| if_lora_v3 = False | |
| size = os.path.getsize(sovits_path) | |
| """ | |
| v1weights:about 82942KB | |
| half thr:82978KB | |
| v2weights:about 83014KB | |
| v3weights:about 750MB | |
| """ | |
| if size < 82978 * 1024: | |
| model_version = version = "v1" | |
| elif size < 700 * 1024 * 1024: | |
| model_version = version = "v2" | |
| else: | |
| version = "v2" | |
| model_version = "v3" | |
| return version, model_version, if_lora_v3 | |
| def load_sovits_new(sovits_path): | |
| f = open(sovits_path, "rb") | |
| meta = f.read(2) | |
| if meta != "PK": | |
| data = b"PK" + f.read() | |
| bio = BytesIO() | |
| bio.write(data) | |
| bio.seek(0) | |
| return torch.load(bio, map_location="cpu", weights_only=False) | |
| return torch.load(sovits_path, map_location="cpu", weights_only=False) | |