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Runtime error
| import os, sys | |
| import torch | |
| import hashlib | |
| import datetime | |
| from collections import OrderedDict | |
| import json | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| def replace_keys_in_dict(d, old_key_part, new_key_part): | |
| if isinstance(d, OrderedDict): | |
| updated_dict = OrderedDict() | |
| else: | |
| updated_dict = {} | |
| for key, value in d.items(): | |
| new_key = key.replace(old_key_part, new_key_part) | |
| if isinstance(value, dict): | |
| value = replace_keys_in_dict(value, old_key_part, new_key_part) | |
| updated_dict[new_key] = value | |
| return updated_dict | |
| def extract_model( | |
| ckpt, | |
| sr, | |
| pitch_guidance, | |
| name, | |
| model_dir, | |
| epoch, | |
| step, | |
| version, | |
| hps, | |
| overtrain_info, | |
| ): | |
| try: | |
| print(f"Saved model '{model_dir}' (epoch {epoch} and step {step})") | |
| model_dir_path = os.path.dirname(model_dir) | |
| os.makedirs(model_dir_path, exist_ok=True) | |
| if "best_epoch" in model_dir: | |
| pth_file = f"{name}_{epoch}e_{step}s_best_epoch.pth" | |
| else: | |
| pth_file = f"{name}_{epoch}e_{step}s.pth" | |
| pth_file_old_version_path = os.path.join( | |
| model_dir_path, f"{pth_file}_old_version.pth" | |
| ) | |
| model_dir_path = os.path.dirname(model_dir) | |
| if os.path.exists(os.path.join(model_dir_path, "model_info.json")): | |
| with open(os.path.join(model_dir_path, "model_info.json"), "r") as f: | |
| data = json.load(f) | |
| dataset_lenght = data.get("total_dataset_duration", None) | |
| embedder_model = data.get("embedder_model", None) | |
| speakers_id = data.get("speakers_id", 1) | |
| else: | |
| dataset_lenght = None | |
| with open(os.path.join(now_dir, "assets", "config.json"), "r") as f: | |
| data = json.load(f) | |
| model_author = data.get("model_author", None) | |
| opt = OrderedDict( | |
| weight={ | |
| key: value.half() for key, value in ckpt.items() if "enc_q" not in key | |
| } | |
| ) | |
| 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.sample_rate, | |
| ] | |
| opt["epoch"] = epoch | |
| opt["step"] = step | |
| opt["sr"] = sr | |
| opt["f0"] = pitch_guidance | |
| opt["version"] = version | |
| opt["creation_date"] = datetime.datetime.now().isoformat() | |
| hash_input = f"{str(ckpt)} {epoch} {step} {datetime.datetime.now().isoformat()}" | |
| model_hash = hashlib.sha256(hash_input.encode()).hexdigest() | |
| opt["model_hash"] = model_hash | |
| opt["overtrain_info"] = overtrain_info | |
| opt["dataset_lenght"] = dataset_lenght | |
| opt["model_name"] = name | |
| opt["author"] = model_author | |
| opt["embedder_model"] = embedder_model | |
| opt["speakers_id"] = speakers_id | |
| torch.save(opt, os.path.join(model_dir_path, pth_file)) | |
| model = torch.load(model_dir, map_location=torch.device("cpu")) | |
| torch.save( | |
| replace_keys_in_dict( | |
| replace_keys_in_dict( | |
| model, ".parametrizations.weight.original1", ".weight_v" | |
| ), | |
| ".parametrizations.weight.original0", | |
| ".weight_g", | |
| ), | |
| pth_file_old_version_path, | |
| ) | |
| os.remove(model_dir) | |
| os.rename(pth_file_old_version_path, model_dir) | |
| except Exception as error: | |
| print(f"An error occurred extracting the model: {error}") | |