def main(conf):
# from asteroid.data.toy_data import WavSet
# train_set = WavSet(n_ex=1000, n_src=2, ex_len=32000)
# val_set = WavSet(n_ex=1000, n_src=2, ex_len=32000)
# Define data pipeline
train_set = WhamDataset(conf['data']['train_dir'],
conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
nondefault_nsrc=conf['data']['nondefault_nsrc'])
val_set = WhamDataset(conf['data']['valid_dir'],
conf['data']['task'],
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'])
val_loader = DataLoader(val_set,
shuffle=False,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'])
conf['masknet'].update({'n_src': train_set.n_src})
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(conf)
# 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)
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')
# loss_class = PITLossContainer(pairwise_neg_sisdr, n_src=train_set.n_src)
# Checkpointing callback can monitor any quantity which is returned by
# validation step, defaults to val_loss here (see System).
checkpoint_dir = os.path.join(exp_dir, 'checkpoints/')
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor='val_loss',
mode='min',
save_best_only=False)
# New PL version will come the 7th of december / will have save_top_k
system = System(model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
config=conf)
trainer = pl.Trainer(max_nb_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
default_save_path=exp_dir,
gpus=conf['main_args']['gpus'],
distributed_backend='dp')
trainer.fit(system)
def main(conf):
exp_dir = conf['main_args']['exp_dir']
# Define Dataloader
train_loader, val_loader = make_dataloaders(**conf['data'],
**conf['training'])
conf['masknet'].update({'n_src': conf['data']['n_src']})
# Define model, optimizer + scheduler
model, optimizer = make_model_and_optimizer(conf)
scheduler = None
if conf['training']['half_lr']:
scheduler = ReduceLROnPlateau(optimizer=optimizer, factor=0.5,
patience=5)
# Save config
os.makedirs(exp_dir, exist_ok=True)
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 = ChimeraLoss(alpha=conf['training']['loss_alpha'])
# Put together in System
system = ChimeraSystem(model=model, loss_func=loss_func,
optimizer=optimizer, train_loader=train_loader,
val_loader=val_loader, scheduler=scheduler,
config=conf)
# 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)
gpus=-1
# Don't ask GPU if they are not available.
if not torch.cuda.is_available():
print('No available GPU were found, set gpus to None')
gpus = None
# Train model
trainer = pl.Trainer(max_nb_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_save_path=exp_dir,
gpus=gpus,
distributed_backend='dp',
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=200,)
trainer.fit(system)
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 last model for convenience
torch.save(system.model.state_dict(),
os.path.join(exp_dir, 'checkpoints/final.pth'))
def train_model_part(conf, train_part='filterbank', pretrained_filterbank=None):
train_loader, val_loader = get_data_loaders(conf, train_part=train_part)
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(
conf, model_part=train_part, pretrained_filterbank=pretrained_filterbank
)
# Define scheduler
scheduler = None
if conf[train_part + '_training'][train_part[0] + '_half_lr']:
scheduler = ReduceLROnPlateau(optimizer=optimizer, factor=0.5,
patience=5)
# Just after instantiating, save the args. Easy loading in the future.
exp_dir, checkpoint_dir = get_encoded_paths(conf, train_part)
os.makedirs(exp_dir, exist_ok=True)
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(PairwiseNegSDR('sisdr', zero_mean=False),
pit_from='pw_mtx')
system = SystemTwoStep(model=model, loss_func=loss_func,
optimizer=optimizer, train_loader=train_loader,
val_loader=val_loader, scheduler=scheduler,
config=conf, module=train_part)
# Define callbacks
checkpoint = ModelCheckpoint(checkpoint_dir, monitor='val_loss',
mode='min', save_top_k=1, verbose=1)
early_stopping = False
if conf[train_part + '_training'][train_part[0] + '_early_stop']:
early_stopping = EarlyStopping(monitor='val_loss', patience=10,
verbose=1)
# Don't ask GPU if they are not available.
if not torch.cuda.is_available():
print('No available GPU were found, set gpus to None')
conf['main_args']['gpus'] = None
trainer = pl.Trainer(
max_nb_epochs=conf[train_part + '_training'][train_part[0] + '_epochs'],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_save_path=exp_dir,
gpus=conf['main_args']['gpus'],
distributed_backend='dp',
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=5.)
trainer.fit(system)
with open(os.path.join(checkpoint_dir, "best_k_models.json"), "w") as file:
json.dump(checkpoint.best_k_models, file, indent=0)
def get_model(conf):
# Create the model from recipe-local function
model, _ = make_model_and_optimizer(conf['train_conf'])
# Load state dict
last_model_path = os.path.join(conf['exp_dir'], 'final.pth')
model.load_state_dict(torch.load(last_model_path, map_location='cpu'))
# Handle device placement
if conf['use_gpu']:
model.cuda()
model.eval()
return model
def main(conf):
train_set = WSJ2mixDataset(conf['data']['tr_wav_len_list'],
conf['data']['wav_base_path'] + '/tr',
sample_rate=conf['data']['sample_rate'])
val_set = WSJ2mixDataset(conf['data']['cv_wav_len_list'],
conf['data']['wav_base_path'] + '/cv',
sample_rate=conf['data']['sample_rate'])
train_sampler = BucketingSampler(train_set,
batch_size=conf['data']['batch_size'])
valid_sampler = BucketingSampler(val_set,
batch_size=conf['data']['batch_size'])
train_loader = DataLoader(train_set,
batch_sampler=train_sampler,
collate_fn=collate_fn,
num_workers=conf['data']['num_workers'])
val_loader = DataLoader(val_set,
batch_sampler=valid_sampler,
collate_fn=collate_fn,
num_workers=conf['data']['num_workers'])
model, optimizer = make_model_and_optimizer(conf)
exp_dir = conf['main_args']['exp_dir']
os.makedirs(exp_dir, exist_ok=True)
conf_path = os.path.join(exp_dir, 'conf.yml')
with open(conf_path, 'w') as outfile:
yaml.safe_dump(conf, outfile)
loss_func = handle_multiple_loss
checkpoint_dir = os.path.join(exp_dir, 'checkpoints/')
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor='val_loss',
mode='min')
system = DcSystem(model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
config=conf)
# Don't ask GPU if they are not available.
if not torch.cuda.is_available():
print('No available GPU were found, set gpus to None')
conf['main_args']['gpus'] = None
trainer = pl.Trainer(
max_nb_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
default_save_path=exp_dir,
gpus=conf['main_args']['gpus'],
distributed_backend='dp',
train_percent_check=1.0 # Useful for fast experiment
)
trainer.fit(system)
with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
json.dump(checkpoint.best_k_models, f, indent=0)
def main(conf):
config = ParamConfig(
conf["training"]["batch_size"],
conf["training"]["epochs"],
conf["training"]["num_workers"],
cuda=True,
use_half=False,
learning_rate=conf["optim"]["lr"],
)
dataset = AVSpeechDataset(
Path("data/train.csv"), Path(EMBED_DIR), conf["main_args"]["n_src"]
)
val_dataset = AVSpeechDataset(
Path("data/val.csv"), Path(EMBED_DIR), conf["main_args"]["n_src"]
)
model, optimizer = make_model_and_optimizer(conf)
print(
f"AVFusion has {sum(np.prod(i.shape) for i in model.parameters()):,} parameters"
)
criterion = DiscriminativeLoss()
model_path = Path(conf["main_args"]["exp_dir"]) / "checkpoints" / "best_full.pth"
if model_path.is_file():
print("Loading saved model...")
resume = model_path.as_posix()
else:
resume = None
if torch.cuda.device_count() > 1:
print(f"Multiple GPUs available")
device_ids = (
list(map(int, conf["main_args"]["gpus"].split(",")))
if conf["main_args"]["gpus"] != "-1"
else None
)
model = torch.nn.DataParallel(model, device_ids=device_ids)
train(
model,
dataset,
optimizer,
criterion,
config,
val_dataset=val_dataset,
resume=resume,
logdir=conf["main_args"]["exp_dir"],
)
def main(conf):
config = ParamConfig(
conf["training"]["batch_size"],
conf["training"]["epochs"],
conf["training"]["num_workers"],
cuda=True,
use_half=False,
learning_rate=conf["optim"]["lr"],
)
dataset = AVSpeechDataset(Path("data/train.csv"), Path(EMBED_DIR),
conf["main_args"]["n_src"])
val_dataset = AVSpeechDataset(Path("data/val.csv"), Path(EMBED_DIR),
conf["main_args"]["n_src"])
model, optimizer = make_model_and_optimizer(conf)
print(
f"AVFusion has {sum(np.prod(i.shape) for i in model.parameters()):,} parameters"
)
criterion = DiscriminativeLoss()
model_path = Path(
conf["main_args"]["exp_dir"]) / "checkpoints" / "best_full.pth"
if model_path.is_file():
print("Loading saved model...")
resume = model_path.as_posix()
else:
resume = None
train(
model,
dataset,
optimizer,
criterion,
config,
val_dataset=val_dataset,
resume=resume,
logdir=conf["main_args"]["exp_dir"],
)
def main(conf):
train_set = WhamRDataset(
conf["data"]["train_dir"],
conf["data"]["task"],
sample_rate=conf["data"]["sample_rate"],
nondefault_nsrc=conf["data"]["nondefault_nsrc"],
)
val_set = WhamRDataset(
conf["data"]["valid_dir"],
conf["data"]["task"],
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,
)
# Update number of source values (It depends on the task)
conf["masknet"].update({"n_src": train_set.n_src})
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(conf)
# 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)
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,
)
# Define callbacks
checkpoint_dir = os.path.join(exp_dir, "checkpoints/")
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor="val_loss",
mode="min",
save_top_k=5,
verbose=True)
early_stopping = False
if conf["training"]["early_stop"]:
early_stopping = EarlyStopping(monitor="val_loss",
patience=30,
verbose=True)
# Don't ask GPU if they are not available.
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="dp",
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=5.0,
)
trainer.fit(system)
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)
def main(conf):
total_set = DNSDataset(conf['data']['json_dir'])
train_len = int(len(total_set) * (1 - conf['data']['val_prop']))
val_len = len(total_set) - train_len
train_set, val_set = random_split(total_set, [train_len, val_len])
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)
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(conf)
# 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)
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 = partial(distance, is_complex=conf['main_args']['is_complex'])
system = SimpleSystem(model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf)
# 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=30,
verbose=1)
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
trainer = pl.Trainer(
max_nb_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_save_path=exp_dir,
gpus=gpus,
distributed_backend='dp',
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=5.,
)
trainer.fit(system)
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)
def train_model_part(conf,
train_part="filterbank",
pretrained_filterbank=None):
train_loader, val_loader = get_data_loaders(conf, train_part=train_part)
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(
conf,
model_part=train_part,
pretrained_filterbank=pretrained_filterbank)
# Define scheduler
scheduler = None
if conf[train_part + "_training"][train_part[0] + "_half_lr"]:
scheduler = ReduceLROnPlateau(optimizer=optimizer,
factor=0.5,
patience=5)
# Just after instantiating, save the args. Easy loading in the future.
exp_dir, checkpoint_dir = get_encoded_paths(conf, train_part)
os.makedirs(exp_dir, exist_ok=True)
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(PairwiseNegSDR("sisdr", zero_mean=False),
pit_from="pw_mtx")
system = SystemTwoStep(
model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf,
module=train_part,
)
# Define callbacks
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor="val_loss",
mode="min",
save_top_k=1,
verbose=True)
early_stopping = False
if conf[train_part + "_training"][train_part[0] + "_early_stop"]:
early_stopping = EarlyStopping(monitor="val_loss",
patience=30,
verbose=True)
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
trainer = pl.Trainer(
max_epochs=conf[train_part + "_training"][train_part[0] + "_epochs"],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend="dp",
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=5.0,
)
trainer.fit(system)
with open(os.path.join(checkpoint_dir, "best_k_models.json"), "w") as file:
json.dump(checkpoint.best_k_models, file, indent=0)
def main(conf):
total_set = DNSDataset(conf["data"]["json_dir"])
train_len = int(len(total_set) * (1 - conf["data"]["val_prop"]))
val_len = len(total_set) - train_len
train_set, val_set = random_split(total_set, [train_len, val_len])
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,
)
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(conf)
# 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)
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 = partial(distance, is_complex=conf["main_args"]["is_complex"])
system = SimpleSystem(
model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf,
)
# Define callbacks
callbacks = []
checkpoint_dir = os.path.join(exp_dir, "checkpoints/")
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor="val_loss",
mode="min",
save_top_k=5,
verbose=True)
callbacks.append(checkpoint)
if conf["training"]["early_stop"]:
callbacks.append(
EarlyStopping(monitor="val_loss",
mode="min",
patience=30,
verbose=True))
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
distributed_backend = "ddp" if torch.cuda.is_available() else None
trainer = pl.Trainer(
max_epochs=conf["training"]["epochs"],
callbacks=callbacks,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend=distributed_backend,
limit_train_batches=1.0, # Useful for fast experiment
gradient_clip_val=5.0,
)
trainer.fit(system)
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)
def main(conf):
# FIXME : Make a function to return loaders, which take conf['data'] as inp.
# Where is the mode min of max?
train_set = WhamDataset(conf['data']['train_dir'],
conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
nondefault_nsrc=conf['data']['nondefault_nsrc'])
val_set = WhamDataset(conf['data']['valid_dir'],
conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
nondefault_nsrc=conf['data']['nondefault_nsrc'])
train_loader = DataLoader(train_set,
shuffle=True,
batch_size=conf['data']['batch_size'],
num_workers=conf['data']['num_workers'],
drop_last=True)
val_loader = DataLoader(val_set,
shuffle=True,
batch_size=conf['data']['batch_size'],
num_workers=conf['data']['num_workers'],
drop_last=True)
# Update number of source values (It depends on the task)
conf['masknet'].update({'n_src': train_set.n_src})
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(conf)
# 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)
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, mode='pairwise')
# Checkpointing callback can monitor any quantity which is returned by
# validation step, defaults to val_loss here (see System).
checkpoint_dir = os.path.join(exp_dir, 'checkpoints/')
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor='val_loss',
mode='min',
save_best_only=False)
# New PL version will come the 7th of december / will have save_top_k
system = System(model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
config=conf)
# Don't ask GPU if they are not available.
if not torch.cuda.is_available():
print('No available GPU were found, set gpus to None')
conf['main_args']['gpus'] = None
trainer = pl.Trainer(
max_nb_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
default_save_path=exp_dir,
gpus=conf['main_args']['gpus'],
distributed_backend='dp',
train_percent_check=1.0 # Useful for fast experiment
)
trainer.fit(system)
def main(conf):
best_model_path = os.path.join(conf["exp_dir"], "best_model.pth")
if not os.path.exists(best_model_path):
# make pth from checkpoint
model = load_best_model(conf["train_conf"],
conf["exp_dir"],
sample_rate=conf["sample_rate"])
torch.save(model.state_dict(), best_model_path)
else:
model, _ = make_model_and_optimizer(conf["train_conf"],
sample_rate=conf["sample_rate"])
model.eval()
model.load_state_dict(torch.load(best_model_path))
# Handle device placement
if conf["use_gpu"]:
model.cuda()
model_device = next(model.parameters()).device
test_dirs = [
conf["test_dir"].format(n_src)
for n_src in conf["train_conf"]["masknet"]["n_srcs"]
]
test_set = Wsj0mixVariable(
json_dirs=test_dirs,
n_srcs=conf["train_conf"]["masknet"]["n_srcs"],
sample_rate=conf["train_conf"]["data"]["sample_rate"],
seglen=None,
minlen=None,
)
# Randomly choose the indexes of sentences to save.
ex_save_dir = os.path.join(conf["exp_dir"], "examples/")
if conf["n_save_ex"] == -1:
conf["n_save_ex"] = len(test_set)
save_idx = random.sample(range(len(test_set)), conf["n_save_ex"])
series_list = []
torch.no_grad().__enter__()
for idx in tqdm(range(len(test_set))):
# Forward the network on the mixture.
mix, sources = [
torch.Tensor(x)
for x in tensors_to_device(test_set[idx], device=model_device)
]
est_sources = model.separate(mix[None])
p_si_snr = Penalized_PIT_Wrapper(pairwise_neg_sisdr_loss)(est_sources,
sources)
utt_metrics = {
"P-Si-SNR": p_si_snr.item(),
"counting_accuracy": float(sources.size(0) == est_sources.size(0)),
}
utt_metrics["mix_path"] = test_set.data[idx][0]
series_list.append(pd.Series(utt_metrics))
# Save some examples in a folder. Wav files and metrics as text.
if idx in save_idx:
mix_np = mix[None].cpu().data.numpy()
sources_np = sources.cpu().data.numpy()
est_sources_np = est_sources.cpu().data.numpy()
local_save_dir = os.path.join(ex_save_dir, "ex_{}/".format(idx))
os.makedirs(local_save_dir, exist_ok=True)
sf.write(local_save_dir + "mixture.wav", mix_np[0],
conf["sample_rate"])
# Loop over the sources and estimates
for src_idx, src in enumerate(sources_np):
sf.write(local_save_dir + "s{}.wav".format(src_idx + 1), src,
conf["sample_rate"])
for src_idx, est_src in enumerate(est_sources_np):
sf.write(
local_save_dir + "s{}_estimate.wav".format(src_idx + 1),
est_src,
conf["sample_rate"],
)
# Write local metrics to the example folder.
with open(local_save_dir + "metrics.json", "w") as f:
json.dump(utt_metrics, f, indent=0)
# Save all metrics to the experiment folder.
all_metrics_df = pd.DataFrame(series_list)
all_metrics_df.to_csv(os.path.join(conf["exp_dir"], "all_metrics.csv"))
# Print and save summary metrics
final_results = {}
for metric_name in ["P-Si-SNR", "counting_accuracy"]:
final_results[metric_name] = all_metrics_df[metric_name].mean()
print("Overall metrics :")
pprint(final_results)
with open(os.path.join(conf["exp_dir"], "final_metrics.json"), "w") as f:
json.dump(final_results, f, indent=0)
def main(conf):
# from asteroid.data.toy_data import WavSet
# train_set = WavSet(n_ex=1000, n_src=2, ex_len=32000)
# val_set = WavSet(n_ex=1000, n_src=2, ex_len=32000)
# Define data pipeline
train_set = WhamDataset(
conf["data"]["train_dir"],
conf["data"]["task"],
sample_rate=conf["data"]["sample_rate"],
nondefault_nsrc=conf["data"]["nondefault_nsrc"],
)
val_set = WhamDataset(
conf["data"]["valid_dir"],
conf["data"]["task"],
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"],
)
val_loader = DataLoader(
val_set,
shuffle=False,
batch_size=conf["training"]["batch_size"],
num_workers=conf["training"]["num_workers"],
)
conf["masknet"].update({"n_src": train_set.n_src})
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(conf)
# 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)
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")
# loss_class = PITLossContainer(pairwise_neg_sisdr, n_src=train_set.n_src)
# Checkpointing callback can monitor any quantity which is returned by
# validation step, defaults to val_loss here (see System).
checkpoint_dir = os.path.join(exp_dir, "checkpoints/")
checkpoint = ModelCheckpoint(
checkpoint_dir, monitor="val_loss", mode="min", save_best_only=False
)
# New PL version will come the 7th of december / will have save_top_k
system = System(
model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
config=conf,
)
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
trainer = pl.Trainer(
max_nb_epochs=conf["training"]["epochs"],
checkpoint_callback=checkpoint,
default_save_path=exp_dir,
gpus=gpus,
distributed_backend="dp",
)
trainer.fit(system)
def main(conf):
exp_dir = conf["main_args"]["exp_dir"]
# Define Dataloader
train_loader, val_loader = make_dataloaders(**conf["data"], **conf["training"])
conf["masknet"].update({"n_src": conf["data"]["n_src"]})
# Define model, optimizer + scheduler
model, optimizer = make_model_and_optimizer(conf)
scheduler = None
if conf["training"]["half_lr"]:
scheduler = ReduceLROnPlateau(optimizer=optimizer, factor=0.5, patience=5)
# Save config
os.makedirs(exp_dir, exist_ok=True)
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 = ChimeraLoss(alpha=conf["training"]["loss_alpha"])
# Put together in System
system = ChimeraSystem(
model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf,
)
# Callbacks
checkpoint_dir = os.path.join(exp_dir, "checkpoints/")
checkpoint = ModelCheckpoint(
checkpoint_dir, monitor="val_loss", mode="min", save_top_k=5, verbose=True
)
early_stopping = False
if conf["training"]["early_stop"]:
early_stopping = EarlyStopping(monitor="val_loss", patience=30, verbose=True)
gpus = -1
# Don't ask GPU if they are not available.
if not torch.cuda.is_available():
print("No available GPU were found, set gpus to None")
gpus = None
# Train model
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="dp",
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=200,
)
trainer.fit(system)
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 last model for convenience
torch.save(system.model.state_dict(), os.path.join(exp_dir, "checkpoints/final.pth"))
def main(conf):
# Make the model
model, _ = make_model_and_optimizer(conf['train_conf'])
# Load best model
with open(os.path.join(conf['exp_dir'], 'best_k_models.json'), "r") as f:
best_k = json.load(f)
best_model_path = min(best_k, key=best_k.get)
# Load checkpoint
checkpoint = torch.load(best_model_path, map_location='cpu')
state = checkpoint['state_dict']
state_copy = state.copy()
# Remove unwanted keys
for keys, values in state.items():
if keys.startswith('loss'):
del state_copy[keys]
print(keys)
model = torch_utils.load_state_dict_in(state_copy, model)
# Handle device placement
if conf['use_gpu']:
model.cuda()
model_device = next(model.parameterss()).device
test_set = LibriMix(csv_dir=conf['test_dir'],
task=conf['task'],
sample_rate=conf['sample_rate'],
n_src=conf['train_conf']['data']['n_src'],
segment=None) # Uses all segment length
# Used to reorder sources only
loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')
# Randomly choose the indexes of sentences to save.
eval_save_dir = os.path.join(conf['exp_dir'], conf['out_dir'])
ex_save_dir = os.path.join(eval_save_dir, 'examples/')
if conf['n_save_ex'] == -1:
conf['n_save_ex'] = len(test_set)
save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
series_list = []
torch.no_grad().__enter__()
for idx in tqdm(range(len(test_set))):
# Forward the network on the mixture.
mix, sources = tensors_to_device(test_set[idx], device=model_device)
est_sources = model(mix.unsqueeze(0))
loss, reordered_sources = loss_func(est_sources,
sources[None],
return_est=True)
mix_np = mix.cpu().data.numpy()
sources_np = sources.squeeze().cpu().data.numpy()
est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
# For each utterance, we get a dictionary with the mixture path,
# the input and output metrics
utt_metrics = get_metrics(mix_np,
sources_np,
est_sources_np,
sample_rate=conf['sample_rate'])
utt_metrics['mix_path'] = test_set.mixture_path
series_list.append(pd.Series(utt_metrics))
# Save some examples in a folder. Wav files and metrics as text.
if idx in save_idx:
local_save_dir = os.path.join(ex_save_dir, 'ex_{}/'.format(idx))
os.makedirs(local_save_dir, exist_ok=True)
sf.write(local_save_dir + "mixture.wav", mix_np,
conf['sample_rate'])
# Loop over the sources and estimates
for src_idx, src in enumerate(sources_np):
sf.write(local_save_dir + "s{}.wav".format(src_idx), src,
conf['sample_rate'])
for src_idx, est_src in enumerate(est_sources_np):
sf.write(local_save_dir + "s{}_estimate.wav".format(src_idx),
est_src, conf['sample_rate'])
# Write local metrics to the example folder.
with open(local_save_dir + 'metrics.json', 'w') as f:
json.dump(utt_metrics, f, indent=0)
# Save all metrics to the experiment folder.
all_metrics_df = pd.DataFrame(series_list)
all_metrics_df.to_csv(os.path.join(eval_save_dir, 'all_metrics.csv'))
# Print and save summary metrics
final_results = {}
for metric_name in compute_metrics:
input_metric_name = 'input_' + metric_name
ldf = all_metrics_df[metric_name] - all_metrics_df[input_metric_name]
final_results[metric_name] = all_metrics_df[metric_name].mean()
final_results[metric_name + '_imp'] = ldf.mean()
print('Overall metrics :')
pprint(final_results)
with open(os.path.join(eval_save_dir, 'final_metrics.json'), 'w') as f:
json.dump(final_results, f, indent=0)
def main(conf):
if conf["data"]["data_augmentation"]:
from local.augmented_wham import AugmentedWhamDataset
train_set = AugmentedWhamDataset(
task=conf['data']['task'],
segment=conf['data']['segment'],
json_dir=conf["data"]["train_dir"],
sample_rate=conf['data']['sample_rate'],
nondefault_nsrc=conf['data']['nondefault_nsrc'],
**conf["augmentation"])
else:
train_set = WhamDataset(
conf['data']['train_dir'],
conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
segment=conf['data']['segment'],
nondefault_nsrc=conf['data']['nondefault_nsrc'])
val_set = WhamDataset(conf['data']['valid_dir'],
conf['data']['task'],
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)
# Update number of source values (It depends on the task)
conf['masknet'].update({'n_src': train_set.n_src})
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(conf)
# 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)
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)
# 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=30,
verbose=1)
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
trainer = pl.Trainer(
max_nb_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_save_path=exp_dir,
gpus=gpus,
distributed_backend='dp',
gradient_clip_val=conf['training']["gradient_clipping"])
trainer.fit(system)
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)
def main(conf):
train_set = LibriMix(csv_dir=conf['data']['train_dir'],
task=conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
n_src=conf['data']['n_src'],
segment=conf['data']['segment'])
val_set = LibriMix(csv_dir=conf['data']['valid_dir'],
task=conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
n_src=conf['data']['n_src'],
segment=conf['data']['segment'])
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=True,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'],
drop_last=True)
conf['masknet'].update({'n_src': conf['data']['n_src']})
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(conf)
# 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)
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, mode='pairwise')
system = System(model=model, loss_func=loss_func, optimizer=optimizer,
train_loader=train_loader, val_loader=val_loader,
scheduler=scheduler, config=conf)
# 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.
if not torch.cuda.is_available():
print('No available GPU were found, set gpus to None')
conf['main_args']['gpus'] = None
trainer = pl.Trainer(max_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_save_path=exp_dir,
gpus=conf['main_args']['gpus'],
distributed_backend='dp',
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=5.)
trainer.fit(system)
with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
json.dump(checkpoint.best_k_models, f, indent=0)
def main(conf):
train_dirs = [
conf["data"]["train_dir"].format(n_src)
for n_src in conf["masknet"]["n_srcs"]
]
valid_dirs = [
conf["data"]["valid_dir"].format(n_src)
for n_src in conf["masknet"]["n_srcs"]
]
train_set = Wsj0mixVariable(
json_dirs=train_dirs,
n_srcs=conf["masknet"]["n_srcs"],
sample_rate=conf["data"]["sample_rate"],
seglen=conf["data"]["seglen"],
minlen=conf["data"]["minlen"],
)
val_set = Wsj0mixVariable(
json_dirs=valid_dirs,
n_srcs=conf["masknet"]["n_srcs"],
sample_rate=conf["data"]["sample_rate"],
seglen=conf["data"]["seglen"],
minlen=conf["data"]["minlen"],
)
train_loader = DataLoader(
train_set,
shuffle=True,
batch_size=conf["training"]["batch_size"],
num_workers=conf["training"]["num_workers"],
drop_last=True,
collate_fn=_collate_fn,
)
val_loader = DataLoader(
val_set,
shuffle=False,
batch_size=conf["training"]["batch_size"],
num_workers=conf["training"]["num_workers"],
drop_last=True,
collate_fn=_collate_fn,
)
model, optimizer = make_model_and_optimizer(
conf, sample_rate=conf["data"]["sample_rate"])
scheduler = []
if conf["training"]["half_lr"]:
scheduler.append(
ReduceLROnPlateau(optimizer=optimizer, factor=0.5, patience=5))
if conf["training"]["lr_decay"]:
scheduler.append(ExponentialLR(optimizer=optimizer, gamma=0.99))
exp_dir = conf["main_args"]["exp_dir"]
os.makedirs(exp_dir, exist_ok=True)
conf_path = os.path.join(exp_dir, "conf.yml")
with open(conf_path, "w") as outfile:
yaml.safe_dump(conf, outfile)
loss_func = WeightedPITLoss(n_srcs=conf["masknet"]["n_srcs"],
lamb=conf["loss"]["lambda"])
# Put together in System
system = VarSpkrSystem(
model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf,
)
# Define callbacks
callbacks = []
checkpoint_dir = os.path.join(exp_dir, "checkpoints/")
checkpoint = ModelCheckpoint(
dirpath=checkpoint_dir,
filename="{epoch}-{step}",
monitor="avg_sdr",
mode="max",
save_top_k=5,
verbose=True,
)
callbacks.append(checkpoint)
if conf["training"]["early_stop"]:
callbacks.append(
EarlyStopping(monitor="avg_sdr",
mode="max",
patience=30,
verbose=True))
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
distributed_backend = "dp" if torch.cuda.is_available() else None
# Train model
trainer = pl.Trainer(
max_epochs=conf["training"]["epochs"],
callbacks=callbacks,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend=distributed_backend,
limit_train_batches=1.0, # Useful for fast experiment
gradient_clip_val=200,
resume_from_checkpoint=conf["main_args"]["resume_from"],
)
trainer.fit(system)
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 last model for convenience
torch.save(system.model.state_dict(),
os.path.join(exp_dir, "final_model.pth"))
