def main(conf):
model_path = os.path.join(conf["exp_dir"], "best_model.pth")
model = DPRNNTasNet.from_pretrained(model_path)
# Handle device placement
if conf["use_gpu"]:
model.cuda()
model_device = next(model.parameters()).device
if conf['file_path'] == '':
test_set = LibriMix(
csv_dir=conf["test_dir"],
task=conf["task"],
sample_rate=conf["sample_rate"],
n_src=conf["train_conf"]["masknet"]["n_src"],
segment=None,
) # Uses all segment length
def main(conf):
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})
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)
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="ddp",
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)
state_dict = torch.load(checkpoint.best_model_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"))
def main(conf):
model_path = os.path.join(conf['exp_dir'], 'best_model.pth')
model = DPRNNTasNet.from_pretrained(model_path)
# Handle device placement
if conf['use_gpu']:
model.cuda()
model_device = next(model.parameters()).device
test_set = WhamDataset(conf['test_dir'], conf['task'],
sample_rate=conf['sample_rate'],
nondefault_nsrc=model.masker.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.
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 = tensors_to_device(test_set[idx], device=model_device)
est_sources = model(mix[None, None])
loss, reordered_sources = loss_func(est_sources, sources[None],
return_est=True)
mix_np = mix[None].cpu().data.numpy()
sources_np = sources.squeeze().cpu().data.numpy()
est_sources_np = reordered_sources.squeeze().cpu().data.numpy()
utt_metrics = get_metrics(mix_np, sources_np, est_sources_np,
sample_rate=conf['sample_rate'])
utt_metrics['mix_path'] = test_set.mix[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:
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 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(conf['exp_dir'], 'final_metrics.json'), 'w') as f:
json.dump(final_results, f, indent=0)
model_dict = torch.load(model_path, map_location='cpu')
publishable = save_publishable(
os.path.join(conf['exp_dir'], 'publish_dir'), model_dict,
metrics=final_results, train_conf=train_conf
)
def main(conf):
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})
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)
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='ddp',
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)
# 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]
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'))
from asteroid import DPRNNTasNet
model = DPRNNTasNet.from_pretrained('mpariente/DPRNNTasNet_WHAM!_sepclean')
def main(args):
convert(args[0])
def convert(filename):
model.separate('./audio-data/' + filename)
if __name__ == "__main__":
import sys
main(sys.argv[1:])
def main(conf):
compute_metrics = update_compute_metrics(conf["compute_wer"],
COMPUTE_METRICS)
anno_df = pd.read_csv(
Path(conf["test_dir"]).parent.parent.parent / "test_annotations.csv")
wer_tracker = (MockWERTracker() if not conf["compute_wer"] else WERTracker(
ASR_MODEL_PATH, anno_df))
model_path = os.path.join(conf["exp_dir"], "best_model.pth")
model = DPRNNTasNet.from_pretrained(model_path)
# Handle device placement
if conf["use_gpu"]:
model.cuda()
model_device = next(model.parameters()).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,
return_id=True,
) # 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, ids = test_set[idx]
mix, sources = tensors_to_device([mix, sources], 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.cpu().data.numpy()
est_sources_np = reordered_sources.squeeze(0).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"],
metrics_list=COMPUTE_METRICS,
)
utt_metrics["mix_path"] = test_set.mixture_path
est_sources_np_normalized = normalize_estimates(est_sources_np, mix_np)
utt_metrics.update(**wer_tracker(
mix=mix_np,
clean=sources_np,
estimate=est_sources_np_normalized,
wav_id=ids,
sample_rate=conf["sample_rate"],
))
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_normalized):
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)
if conf["compute_wer"]:
print("\nWER report")
wer_card = wer_tracker.final_report_as_markdown()
print(wer_card)
# Save the report
with open(os.path.join(eval_save_dir, "final_wer.md"), "w") as f:
f.write(wer_card)
with open(os.path.join(eval_save_dir, "final_metrics.json"), "w") as f:
json.dump(final_results, f, indent=0)
model_dict = torch.load(model_path, map_location="cpu")
os.makedirs(os.path.join(conf["exp_dir"], "publish_dir"), exist_ok=True)
publishable = save_publishable(
os.path.join(conf["exp_dir"], "publish_dir"),
model_dict,
metrics=final_results,
train_conf=train_conf,
)
def main(conf):
model_path = os.path.join(conf["exp_dir"], "best_model.pth")
model = DPRNNTasNet.from_pretrained(model_path)
# Handle device placement
if conf["use_gpu"]:
model.cuda()
model_device = next(model.parameters()).device
test_set = SourceFolderDataset(
os.path.join(conf["exp_dir"], "json/"),
conf["wav_dir"],
conf["n_src"],
conf["sample_rate"],
conf["batch_size"],
train = False
) # 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.
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 = tensors_to_device(test_set[idx], device=model_device)
mix = mix.unsqueeze(0)
sources = sources.unsqueeze(0)
est_sources = model(mix)
#print(test_set[idx])
#print(est_sources.shape, sources.shape, mix.shape, len(test_set))
loss, reordered_sources = loss_func(est_sources, sources, return_est=True)
#mix_np = mix.squeeze(0).cpu().data.numpy()
mix_np = mix.cpu().data.numpy()
sources_np = sources.squeeze(0).cpu().data.numpy()
est_sources_np = reordered_sources.squeeze(0).cpu().data.numpy()
utt_metrics = get_metrics(
mix_np,
sources_np,
est_sources_np,
sample_rate=conf["sample_rate"],
metrics_list=compute_metrics,
)
utt_metrics["mix_path"] = test_set.mix[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:
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", np.swapaxes(mix_np,0,1), 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):
est_src *= np.max(np.abs(mix_np)) / np.max(np.abs(est_src))
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 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(conf["exp_dir"], "final_metrics.json"), "w") as f:
json.dump(final_results, f, indent=0)
model_dict = torch.load(model_path, map_location="cpu")
os.makedirs(os.path.join(conf["exp_dir"], "publish_dir"), exist_ok=True)
publishable = save_publishable(
os.path.join(conf["exp_dir"], "publish_dir"),
model_dict,
metrics=final_results,
train_conf=train_conf,
)
#import pdb; pdb.set_trace()
total_df.sort_values(['SI-SDR', 'PESQ', 'STOI'], inplace=True)
total_df = total_df.round({'SI-SDR': 3, 'PESQ': 3, 'STOI': 3})
print(total_df)
return total_df
models = {
'input': None,
'baseline': RegressionFCNN.from_pretrained('models/baseline_model_v1.pt'),
'vae': VAE.from_pretrained('/jmain01/home/JAD007/txk02/aaa18-txk02/workspace/models/VAE.pt'),
'auto_encoder': VAE.from_pretrained('/jmain01/home/JAD007/txk02/aaa18-txk02/workspace/models/AutoEncoder.pt'),
'waveunet_v1': WaveUNet.from_pretrained('models/waveunet_model_adapt.pt'),
'dcunet_20': DCUNet.from_pretrained('models/dcunet_20_random_v2.pt'),
'dccrn': DCCRNet.from_pretrained('models/dccrn_random_v1.pt'),
'smolnet': SMoLnet.from_pretrained('models/SMoLnet.pt'),
'dprnn': DPRNNTasNet.from_pretrained('models/dprnn_model.pt'),
'conv_tasnet': ConvTasNet.from_pretrained('models/convtasnet_model.pt'),
'dptnet': DPTNet.from_pretrained('models/dptnet_model.pt'),
'demucs': Demucs.from_pretrained('models/Demucs.pt'),
}
def eval_all_and_plot(models, test_set, directory, plot_name):
results_dfs = {}
for model_name, model in models.items():
print(f'Evaluating {model_labels[model_name]}')
csv_path = f'/jmain01/home/JAD007/txk02/aaa18-txk02/DRONE_project/asteroid/notebooks/{directory}/{model_name}.csv'
if os.path.isfile(csv_path):
print('Results already available')
df = pd.read_csv(csv_path)
def main(conf):
# 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_set = LibriMix(
csv_dir=conf["data"]["train_dir"],
task=conf["data"]["task"],
sample_rate=conf["data"]["sample_rate"],
n_src=conf["masknet"]["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["masknet"]["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=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)
# TODO: redundant
conf["masknet"].update({"n_src": train_set.n_src})
model = DPRNNTasNet(**conf["filterbank"],
**conf["masknet"],
sample_rate=conf['data']['sample_rate'])
# from torchsummary import summary
# model.cuda()
# summary(model, (24000,))
# import pdb
# pdb.set_trace()
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)
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
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
if conf["training"]["cont"]:
from glob import glob
ckpts = glob('%s/*.ckpt' % checkpoint_dir)
ckpts.sort()
latest_ckpt = ckpts[-1]
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=conf["training"]["gradient_clipping"],
resume_from_checkpoint=latest_ckpt)
else:
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=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)
# 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]
# state_dict = torch.load(best_path)
state_dict = torch.load(checkpoint.best_model_path)
# state_dict = torch.load('exp/train_dprnn_130d5f9a/checkpoints/epoch=154.ckpt')
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"))
def main(conf):
model_path = os.path.join(conf["exp_dir"], "best_model.pth")
model = DPRNNTasNet.from_pretrained(model_path)
drop_last=True)
val_loader = DataLoader(timit_val,
batch_size=BATCH_SIZE,
num_workers=NUM_WORKERS,
drop_last=True)
# some random parameters, does it look sensible?
LR = 1e-3
REDUCE_LR_PATIENCE = 5
EARLY_STOP_PATIENCE = 20
MAX_EPOCHS = 300
# the model here should be constructed in the script accordingly to the passed config (including the model type)
# most of the models accept `sample_rate` parameter for encoders, which is important (default is 16000, override)
#model = DCUNet("DCUNet-20", fix_length_mode="trim", sample_rate=SAMPLE_RATE)
model = DPRNNTasNet(n_src=1)
from pytorch_lightning.callbacks import ModelCheckpoint
checkpoint = ModelCheckpoint(filename='{epoch:02d}-{val_loss:.2f}',
monitor="val_loss",
mode="min",
save_top_k=5,
verbose=True)
optimizer = optim.Adam(model.parameters(), lr=LR)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=REDUCE_LR_PATIENCE)
early_stopping = EarlyStopping(monitor='val_loss',
patience=EARLY_STOP_PATIENCE)
# Probably we also need to subclass `System`, in order to log the target metrics on the validation set (PESQ/STOI)