| import os |
| import torch |
| from collections import OrderedDict |
|
|
|
|
| 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, if_f0, name, model_dir, epoch, version, hps): |
| try: |
| print(f"Saved model '{model_dir}' (epoch {epoch})") |
| pth_file = f"{name}_{epoch}e.pth" |
| pth_file_old_version_path = os.path.join( |
| model_dir, f"{pth_file}_old_version.pth" |
| ) |
| 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.sampling_rate, |
| ] |
| opt["info"], opt["sr"], opt["f0"], opt["version"] = epoch, sr, if_f0, version |
| torch.save(opt, model_dir) |
|
|
| 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(error) |
|
|
