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import os
import argparse
import torch
from logger import utils
from data_loaders import get_data_loaders
from solver import train
from ddsp.vocoder import Sins, CombSub, CombSubFast
from ddsp.loss import RSSLoss
def parse_args(args=None, namespace=None):
"""Parse command-line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config",
type=str,
required=True,
help="path to the config file")
return parser.parse_args(args=args, namespace=namespace)
if __name__ == '__main__':
# parse commands
cmd = parse_args()
# load config
args = utils.load_config(cmd.config)
print(' > config:', cmd.config)
print(' > exp:', args.env.expdir)
# load model
model = None
if args.model.type == 'Sins':
model = Sins(
sampling_rate=args.data.sampling_rate,
block_size=args.data.block_size,
n_harmonics=args.model.n_harmonics,
n_mag_allpass=args.model.n_mag_allpass,
n_mag_noise=args.model.n_mag_noise,
n_unit=args.data.encoder_out_channels,
n_spk=args.model.n_spk)
elif args.model.type == 'CombSub':
model = CombSub(
sampling_rate=args.data.sampling_rate,
block_size=args.data.block_size,
n_mag_allpass=args.model.n_mag_allpass,
n_mag_harmonic=args.model.n_mag_harmonic,
n_mag_noise=args.model.n_mag_noise,
n_unit=args.data.encoder_out_channels,
n_spk=args.model.n_spk)
elif args.model.type == 'CombSubFast':
model = CombSubFast(
sampling_rate=args.data.sampling_rate,
block_size=args.data.block_size,
n_unit=args.data.encoder_out_channels,
n_spk=args.model.n_spk)
else:
raise ValueError(f" [x] Unknown Model: {args.model.type}")
# load parameters
optimizer = torch.optim.AdamW(model.parameters())
initial_global_step, model, optimizer = utils.load_model(args.env.expdir, model, optimizer, device=args.device)
for param_group in optimizer.param_groups:
param_group['lr'] = args.train.lr
param_group['weight_decay'] = args.train.weight_decay
# loss
loss_func = RSSLoss(args.loss.fft_min, args.loss.fft_max, args.loss.n_scale, device = args.device)
# device
if args.device == 'cuda':
torch.cuda.set_device(args.env.gpu_id)
model.to(args.device)
for state in optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.to(args.device)
loss_func.to(args.device)
# datas
loader_train, loader_valid = get_data_loaders(args, whole_audio=False)
# run
train(args, initial_global_step, model, optimizer, loss_func, loader_train, loader_valid)
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