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import os
import torch
import hashlib
import datetime
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, step, version, hps):
try:
print(f"Saved model '{model_dir}' (epoch {epoch} and step {step})")
pth_file = f"{name}_{epoch}e_{step}s.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["epoch"] = epoch
opt["step"] = step
opt["sr"] = sr
opt["f0"] = if_f0
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
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)
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