from collections import OrderedDict import torch import utils from models import SynthesizerTrn def copyStateDict(state_dict): if list(state_dict.keys())[0].startswith('module'): start_idx = 1 else: start_idx = 0 new_state_dict = OrderedDict() for k, v in state_dict.items(): name = ','.join(k.split('.')[start_idx:]) new_state_dict[name] = v return new_state_dict def removeOptimizer(config: str, input_model: str, ishalf: bool, output_model: str): hps = utils.get_hparams_from_file(config) net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model) optim_g = torch.optim.AdamW(net_g.parameters(), hps.train.learning_rate, betas=hps.train.betas, eps=hps.train.eps) state_dict_g = torch.load(input_model, map_location="cpu") new_dict_g = copyStateDict(state_dict_g) keys = [] for k, v in new_dict_g['model'].items(): keys.append(k) new_dict_g = {k: new_dict_g['model'][k].half() for k in keys} if ishalf else {k: new_dict_g['model'][k] for k in keys} torch.save( { 'model': new_dict_g, 'iteration': 0, 'optimizer': optim_g.state_dict(), 'learning_rate': 0.0001 }, output_model) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser() parser.add_argument("-c", "--config", type=str, default='configs/config.json') parser.add_argument("-i", "--input", type=str) parser.add_argument("-o", "--output", type=str, default=None) parser.add_argument('-hf', '--half', action='store_true', default=False, help='Save as FP16') args = parser.parse_args() output = args.output if output is None: import os.path filename, ext = os.path.splitext(args.input) half = "_half" if args.half else "" output = filename + "_release" + half + ext removeOptimizer(args.config, args.input, args.half, output)