# MiniLibriMix is a tiny version of LibriMix (https://github.com/JorisCos/LibriMix),
# which is a free speech separation dataset.
from asteroid.data import LibriMix
# Asteroid's System is a convenience wrapper for PyTorch-Lightning.
from asteroid.engine import System
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--seed', type=int, default=1234)
parser.add_argument('--download', type=bool, default=True)
parser.add_argument('--max_epochs', type=int, default=1)
parser.add_argument('--learning_rate', type=float, default=1e-3)
parser.add_argument('--gpus', type=int, default=None)
args = parser.parse_args()
# This will automatically download MiniLibriMix from Zenodo on the first run.
train_loader, val_loader = LibriMix.loaders_from_mini(task="sep_clean",
batch_size=16)
# Tell DPRNN that we want to separate to 2 sources.
model = DPRNNTasNet(n_src=2)
# PITLossWrapper works with any loss function.
loss = PITLossWrapper(pairwise_neg_sisdr, pit_from="pw_mtx")
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
system = System(model, optimizer, loss, train_loader, val_loader)
# Train for 1 epoch using a single GPU. If you're running this on Google Colab,
# be sure to select a GPU runtime (Runtime → Change runtime type → Hardware accelarator).
trainer = Trainer(max_epochs=args.max_epochs, gpus=args.gpus)
trainer.fit(system)
def _train(args):
train_dir = args.train
val_dir = args.test
with open('conf.yml') as f:
def_conf = yaml.safe_load(f)
pp = argparse.ArgumentParser()
parser = prepare_parser_from_dict(def_conf, parser=pp)
arg_dic, plain_args = parse_args_as_dict(parser, return_plain_args=True)
print(arg_dic)
conf = arg_dic
train_set = WhamDataset_no_sf(
train_dir,
conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
segment=conf['data']['segment'],
nondefault_nsrc=conf['data']['nondefault_nsrc'])
val_set = WhamDataset_no_sf(
val_dir,
conf['data']['task'],
segment=conf['data']['segment'],
sample_rate=conf['data']['sample_rate'],
nondefault_nsrc=conf['data']['nondefault_nsrc'])
train_loader = DataLoader(train_set,
shuffle=True,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'],
drop_last=True)
val_loader = DataLoader(val_set,
shuffle=False,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'],
drop_last=True)
# train_loader = DataLoader(train_set, shuffle=True,
# batch_size=args.batch_size,
# num_workers=conf['training']['num_workers'],
# drop_last=True)
# val_loader = DataLoader(val_set, shuffle=False,
# batch_size=args.batch_size,
# num_workers=conf['training']['num_workers'],
# drop_last=True)
# Update number of source values (It depends on the task)
print("!!!!!!!!!")
print(train_set.__getitem__(0))
print(val_set.__getitem__(0))
print("!!!!!!!!!")
conf['masknet'].update({'n_src': train_set.n_src})
model = DPRNNTasNet(**conf['filterbank'], **conf['masknet'])
optimizer = make_optimizer(model.parameters(), **conf['optim'])
# Define scheduler
scheduler = None
if conf['training']['half_lr']:
scheduler = ReduceLROnPlateau(optimizer=optimizer,
factor=0.5,
patience=5)
# Just after instantiating, save the args. Easy loading in the future.
# exp_dir = conf['main_args']['exp_dir']
# os.makedirs(exp_dir, exist_ok=True)
exp_dir = args.model_dir
conf_path = os.path.join(exp_dir, 'conf.yml')
with open(conf_path, 'w') as outfile:
yaml.safe_dump(conf, outfile)
# Define Loss function.
loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')
system = System(model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf)
system.batch_size = 1
# Define callbacks
# checkpoint_dir = os.path.join(exp_dir, 'checkpoints/')
# checkpoint = ModelCheckpoint(checkpoint_dir, monitor='val_loss',
# mode='min', save_top_k=5, verbose=1)
# early_stopping = False
# if conf['training']['early_stop']:
# early_stopping = EarlyStopping(monitor='val_loss', patience=10,
# verbose=1)
# Don't ask GPU if they are not available.
# print("!!!!!!!{}".format(torch.cuda.is_available()))
# print(torch.__version__)
gpus = -1 if torch.cuda.is_available() else None
# trainer = pl.Trainer(max_epochs=conf['training']['epochs'],
# checkpoint_callback=checkpoint,
# early_stop_callback=early_stopping,
# default_root_dir=exp_dir,
# gpus=gpus,
# distributed_backend='ddp',
# gradient_clip_val=conf['training']["gradient_clipping"])
trainer = pl.Trainer(
max_epochs=args.epochs,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend='ddp',
gradient_clip_val=conf['training']["gradient_clipping"])
trainer.fit(system)
# print("!!!!!!!!!!!!!!")
# print(checkpoint)
# print(checkpoint.best_k_models)
# print(checkpoint.best_k_models.items())
# onlyfiles = [f for f in listdir(checkpoint_dir) if isfile(os.path.join(checkpoint_dir, f))]
# print(onlyfiles)
# best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()}
# with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
# json.dump(best_k, f, indent=0)
# # Save best model (next PL version will make this easier)
# best_path = [b for b, v in best_k.items() if v == min(best_k.values())][0]
best_path = os.path.join(exp_dir, "__temp_weight_ddp_end.ckpt")
state_dict = torch.load(best_path)
system.load_state_dict(state_dict=state_dict['state_dict'])
system.cpu()
to_save = system.model.serialize()
# to_save.update(train_set.get_infos())
torch.save(to_save, os.path.join(exp_dir, 'best_model.pth'))