# coding: utf-8
from utils.trainer import MyArgs, STFT_Separator_enhance_Trainer
from models.dpcl import DPCL
if __name__ == '__main__':
p = MyArgs()
# DPCL model to load + params
p.parser.add_argument('--model_folder',
help='Path to the Model folder to load',
required=True)
p.add_stft_args()
p.add_separator_args()
p.add_enhance_layer_args()
args = p.get_args()
trainer = STFT_Separator_enhance_Trainer(DPCL, 'STFT_DPCL_enhance',
**vars(args))
trainer.train()
# coding: utf-8
from utils.trainer import MyArgs, STFT_Separator_FineTune_Trainer
from models.L41 import L41Model
if __name__ == '__main__':
p = MyArgs()
p.parser.add_argument('--model_folder',
help='Path to the Model folder to load',
required=True)
p.add_stft_args()
p.add_separator_args()
args = p.get_args()
trainer = STFT_Separator_FineTune_Trainer(L41Model, 'STFT_L41_finetuning',
**vars(args))
trainer.train()
# coding: utf-8
from utils.trainer import MyArgs, Front_Separator_Finetuning_Trainer
from models.L41 import L41Model
if __name__ == '__main__':
p = MyArgs()
# Adapt model to load + params
p.parser.add_argument('--model_folder',
help='Path to model folder to load',
required=True)
p.add_adapt_args()
p.add_separator_args()
args = p.get_args()
trainer = Front_Separator_Finetuning_Trainer(L41Model,
'front_L41_finetuning',
pretraining=False,
**vars(args))
trainer.build_model()
trainer.train()
# coding: utf-8
from utils.trainer import MyArgs, Front_Separator_Enhance_Finetuning_Trainer
from models.dpcl import DPCL
if __name__ == '__main__':
p = MyArgs()
# Adapt model to load + params
p.parser.add_argument('--model_folder',
help='Path to model folder to load',
required=True)
p.add_adapt_args()
p.add_separator_args()
p.add_finetuning_args()
p.add_enhance_layer_args()
args = p.get_args()
trainer = Front_Separator_Enhance_Finetuning_Trainer(
DPCL, 'front_DPCL_finetuning', pretraining=False, **vars(args))
trainer.train()
# coding: utf-8
from utils.trainer import MyArgs, STFT_Separator_FineTune_Trainer
from models.L41 import L41Model
if __name__ == '__main__':
p = MyArgs()
p.parser.add_argument('--model_folder',
help='Path to the Model folder to load',
required=True)
p.add_stft_args()
p.add_finetuning_args()
p.add_separator_args()
args = p.get_args()
trainer = STFT_Separator_FineTune_Trainer(L41Model, 'STFT_L41_finetuning',
**vars(args))
trainer.train()
# coding: utf-8
from utils.trainer import MyArgs, Front_Separator_Inference, STFT_inference, STFT_finetuned_inference
from models.L41 import L41Model
from utils.bss_eval import bss_eval_sources
import numpy as np
if __name__ == '__main__':
p = MyArgs()
p.parser.add_argument('--model_folder',
help='Path to the Model folder to load',
required=True)
p.select_inferencer()
p.add_adapt_args()
p.add_separator_args()
args = p.get_args()
# Switch on different model Inferencer:
if args.model == 'front_L41':
inferencer = Front_Separator_Inference
elif args.model == 'STFT_L41':
inferencer = STFT_inference
inferencer = inferencer(L41Model, 'inference', **vars(args))
sdr = 0.0
sir = 0.0
sar = 0.0
i = 0
for mix, non_mix, separated in inferencer.inference():
for m, n_m, s in zip(list(mix), list(non_mix), list(separated)):
# coding: utf-8
from utils.trainer import MyArgs, Adapt_Pretrainer
if __name__ == '__main__':
p = MyArgs()
#Preprocess arguments
p.add_adapt_args()
args = p.get_args()
trainer = Adapt_Pretrainer(pretraining=True, **vars(args))
trainer.build_model()
trainer.train()
#Network arguments