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import sys,os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import torch
import argparse
from omegaconf import OmegaConf
from vits.models import SynthesizerInfer
def load_model(checkpoint_path, model):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
saved_state_dict = checkpoint_dict["model_g"]
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items():
try:
new_state_dict[k] = saved_state_dict[k]
except:
new_state_dict[k] = v
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict)
else:
model.load_state_dict(new_state_dict)
return model
def save_pretrain(checkpoint_path, save_path):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
torch.save({
'model_g': checkpoint_dict['model_g'],
'model_d': checkpoint_dict['model_d'],
}, save_path)
def save_model(model, checkpoint_path):
if hasattr(model, 'module'):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save({'model_g': state_dict}, checkpoint_path)
def main(args):
hp = OmegaConf.load(args.config)
model = SynthesizerInfer(
hp.data.filter_length // 2 + 1,
hp.data.segment_size // hp.data.hop_length,
hp)
# save_pretrain(args.checkpoint_path, "sovits5.0.pretrain.pth")
load_model(args.checkpoint_path, model)
save_model(model, "sovits5.0.pth")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, required=True,
help="yaml file for config. will use hp_str from checkpoint if not given.")
parser.add_argument('-p', '--checkpoint_path', type=str, required=True,
help="path of checkpoint pt file for evaluation")
args = parser.parse_args()
main(args)
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