63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
import sys
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import torch
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import argparse
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import numpy as np
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from utils.load_yaml import HpsYaml
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from models.ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
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# For reproducibility, comment these may speed up training
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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def main():
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# Arguments
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parser = argparse.ArgumentParser(description=
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'Training PPG2Mel VC model.')
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parser.add_argument('--config', type=str,
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help='Path to experiment config, e.g., config/vc.yaml')
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parser.add_argument('--name', default=None, type=str, help='Name for logging.')
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parser.add_argument('--logdir', default='log/', type=str,
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help='Logging path.', required=False)
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parser.add_argument('--ckpdir', default='ckpt/', type=str,
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help='Checkpoint path.', required=False)
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parser.add_argument('--outdir', default='result/', type=str,
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help='Decode output path.', required=False)
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parser.add_argument('--load', default=None, type=str,
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help='Load pre-trained model (for training only)', required=False)
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parser.add_argument('--warm_start', action='store_true',
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help='Load model weights only, ignore specified layers.')
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parser.add_argument('--seed', default=0, type=int,
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help='Random seed for reproducable results.', required=False)
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parser.add_argument('--njobs', default=8, type=int,
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help='Number of threads for dataloader/decoding.', required=False)
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parser.add_argument('--cpu', action='store_true', help='Disable GPU training.')
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# parser.add_argument('--no-pin', action='store_true',
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# help='Disable pin-memory for dataloader')
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parser.add_argument('--no-msg', action='store_true', help='Hide all messages.')
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###
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paras = parser.parse_args()
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setattr(paras, 'gpu', not paras.cpu)
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setattr(paras, 'pin_memory', not paras.no_pin)
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setattr(paras, 'verbose', not paras.no_msg)
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# Make the config dict dot visitable
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config = HpsYaml(paras.config)
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np.random.seed(paras.seed)
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torch.manual_seed(paras.seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(paras.seed)
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print(">>> OneShot VC training ...")
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mode = "train"
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solver = Solver(config, paras, mode)
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solver.load_data()
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solver.set_model()
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solver.exec()
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print(">>> Oneshot VC train finished!")
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sys.exit(0)
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if __name__ == "__main__":
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main()
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