def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch
This method calls ``fit()`` again to train the discriminator
before proceeding with generator training.
"""
self.mse_metric = MetricStats(metric=self.hparams.compute_cost)
self.metrics = {"G": [], "D": []}
if stage == sb.Stage.TRAIN:
if self.hparams.target_metric == "pesq":
self.target_metric = MetricStats(metric=pesq_eval, n_jobs=40)
elif self.hparams.target_metric == "stoi":
self.target_metric = MetricStats(metric=stoi_loss)
else:
raise NotImplementedError(
"Right now we only support 'pesq' and 'stoi'"
)
# Train discriminator before we start generator training
if self.sub_stage == SubStage.GENERATOR:
self.epoch = epoch
self.train_discriminator()
self.sub_stage = SubStage.GENERATOR
print("Generator training by current data...")
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(metric=pesq_eval, n_jobs=30)
self.stoi_metric = MetricStats(metric=stoi_loss)
def on_stage_start(self, stage, epoch=None):
self.loss_metric = MetricStats(metric=self.hparams.compute_cost)
self.stoi_metric = MetricStats(metric=stoi_loss)
# Define function taking (prediction, target) for parallel eval
def pesq_eval(pred_wav, target_wav):
return pesq(
fs=16000,
ref=target_wav.cpu().numpy(),
deg=pred_wav.cpu().numpy(),
mode="wb",
)
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(metric=pesq_eval, n_jobs=4)
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch
This method calls ``fit()`` again to train the discriminator
before proceeding with generator training.
"""
self.metrics = {"G": [], "D": []}
if stage == sb.Stage.TRAIN:
if self.hparams.target_metric == "srmr":
self.target_metric = MetricStats(
metric=srmrpy_eval,
n_jobs=hparams["n_jobs"],
batch_eval=False,
)
elif self.hparams.target_metric == "dnsmos":
self.target_metric = MetricStats(
metric=dnsmos_eval,
n_jobs=hparams["n_jobs"],
batch_eval=False,
)
else:
raise NotImplementedError(
"Right now we only support 'srmr' and 'dnsmos'")
# Train discriminator before we start generator training
if self.sub_stage == SubStage.GENERATOR:
self.epoch = epoch
self.train_discriminator()
self.sub_stage = SubStage.GENERATOR
print("Generator training by current data...")
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(metric=pesq_eval,
n_jobs=hparams["n_jobs"],
batch_eval=False)
self.stoi_metric = MetricStats(metric=stoi_loss)
self.srmr_metric = MetricStats(
metric=srmrpy_eval_valid,
n_jobs=hparams["n_jobs"],
batch_eval=False,
)
self.dnsmos_metric = MetricStats(
metric=dnsmos_eval_valid,
n_jobs=hparams["n_jobs"],
batch_eval=False,
)
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch"""
self.loss_metric = MetricStats(metric=self.hparams.compute_cost)
self.stoi_metric = MetricStats(metric=stoi_loss)
# Define function taking (prediction, target) for parallel eval
def pesq_eval(pred_wav, target_wav):
"""Computes the PESQ evaluation metric"""
return pesq(
fs=16000,
ref=target_wav.numpy(),
deg=pred_wav.numpy(),
mode="wb",
)
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(metric=pesq_eval, n_jobs=4)
def test_metric_stats():
from speechbrain.utils.metric_stats import MetricStats
from speechbrain.nnet.losses import l1_loss
l1_stats = MetricStats(metric=l1_loss)
l1_stats.append(
ids=["utterance1", "utterance2"],
predictions=torch.tensor([[0.1, 0.2], [0.1, 0.2]]),
targets=torch.tensor([[0.1, 0.3], [0.2, 0.3]]),
length=torch.ones(2),
reduction="batch",
)
summary = l1_stats.summarize()
assert math.isclose(summary["average"], 0.075, rel_tol=1e-5)
assert math.isclose(summary["min_score"], 0.05, rel_tol=1e-5)
assert summary["min_id"] == "utterance1"
assert math.isclose(summary["max_score"], 0.1, rel_tol=1e-5)
assert summary["max_id"] == "utterance2"
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch"""
if stage != sb.Stage.TRAIN:
# Define function taking (prediction, target) for parallel eval
def pesq_eval(pred_wav, target_wav):
"""Computes the PESQ evaluation metric"""
psq_mode = "wb" if self.hparams.sample_rate == 16000 else "nb"
try:
return pesq(
fs=self.hparams.sample_rate,
ref=target_wav.numpy(),
deg=pred_wav.numpy(),
mode=psq_mode,
)
except Exception:
print("pesq encountered an error for this data item")
return 0
self.pesq_metric = MetricStats(
metric=pesq_eval, n_jobs=1, batch_eval=False
)
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch"""
self.loss_metric_d1 = MetricStats(
metric=self.hparams.compute_cost["d1"])
self.loss_metric_d2 = MetricStats(
metric=self.hparams.compute_cost["d2"])
self.loss_metric_g3 = MetricStats(
metric=self.hparams.compute_cost["g3"])
self.stoi_metric = MetricStats(metric=stoi_loss)
# Define function taking (prediction, target) for parallel eval
def pesq_eval(pred_wav, target_wav):
"""Computes the PESQ evaluation metric"""
return pesq(
fs=hparams["sample_rate"],
ref=target_wav.numpy().squeeze(),
deg=pred_wav.numpy().squeeze(),
mode="wb",
)
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(metric=pesq_eval,
batch_eval=False,
n_jobs=1)
class MetricGanBrain(sb.Brain):
def compute_feats(self, wavs):
"""Feature computation pipeline"""
feats = self.hparams.compute_STFT(wavs)
feats = spectral_magnitude(feats, power=0.5)
feats = torch.log1p(feats)
return feats
def compute_forward(self, batch, stage):
"Given an input batch computes the enhanced signal"
batch = batch.to(self.device)
if self.sub_stage == SubStage.HISTORICAL:
predict_wav, lens = batch.enh_sig
else:
noisy_wav, lens = batch.noisy_sig
noisy_spec = self.compute_feats(noisy_wav)
# mask with "signal approximation (SA)"
# print("check noisy spec",noisy_spec.size())
mask = self.modules.generator(noisy_spec, lengths=lens)
mask = mask.clamp(min=self.hparams.min_mask).squeeze(2)
predict_spec = torch.mul(mask, noisy_spec)
# Also return predicted wav
predict_wav = self.hparams.resynth(
torch.expm1(predict_spec), noisy_wav
)
return predict_wav
def compute_objectives(self, predictions, batch, stage, optim_name=""):
"Given the network predictions and targets compute the total loss"
predict_wav = predictions
predict_spec = self.compute_feats(predict_wav)
clean_wav, lens = batch.clean_sig
clean_spec = self.compute_feats(clean_wav)
# print(clean_wav.size(), predict_wav.size())
# print(clean_spec.size(), predict_spec.size())
mse_cost = self.hparams.compute_cost(predict_spec, clean_spec, lens)
# print("batch.id", batch.id)
ids = self.compute_ids(batch.id, optim_name)
# One is real, zero is fake
if optim_name == "generator" or optim_name == "":
if optim_name == "generator":
target_score = torch.ones(self.batch_size, 1, device=self.device)
else:
target_score = torch.ones(1, 1, device=self.device)
# if optim_name == "":
# print("predict_wav", predict_wav.size(), clean_wav.size())
# print("predict_spec: ", predict_spec.size(), clean_spec.size())
est_score = self.est_score(predict_spec, clean_spec)
self.mse_metric.append(
ids, predict_spec, clean_spec, lens, reduction="batch"
)
# D Learns to estimate the scores of clean speech
elif optim_name == "D_clean":
target_score = torch.ones(self.batch_size, 1, device=self.device)
est_score = self.est_score(clean_spec, clean_spec)
# D Learns to estimate the scores of enhanced speech
elif optim_name == "D_enh" and self.sub_stage == SubStage.CURRENT:
target_score = self.score(ids, predict_wav, clean_wav, lens)
est_score = self.est_score(predict_spec, clean_spec)
# Write enhanced wavs during discriminator training, because we
# compute the actual score here and we can save it
self.write_wavs(batch.id, ids, predict_wav, target_score, lens)
# D Relearns to estimate the scores of previous epochs
elif optim_name == "D_enh" and self.sub_stage == SubStage.HISTORICAL:
target_score = batch.score.unsqueeze(1).float()
est_score = self.est_score(predict_spec, clean_spec)
# D Learns to estimate the scores of noisy speech
elif optim_name == "D_noisy":
noisy_wav, _ = batch.noisy_sig
noisy_spec = self.compute_feats(noisy_wav)
target_score = self.score(ids, noisy_wav, clean_wav, lens)
est_score = self.est_score(noisy_spec, clean_spec)
# Save scores of noisy wavs
self.save_noisy_scores(ids, target_score)
else:
raise ValueError(f"{optim_name} is not a valid 'optim_name'")
# Compute the cost
adv_cost = self.hparams.compute_cost(est_score, target_score)
if optim_name == "generator":
adv_cost += self.hparams.mse_weight * mse_cost
self.metrics["G"].append(adv_cost.detach())
else:
self.metrics["D"].append(adv_cost.detach())
# On validation data compute scores
if stage != sb.Stage.TRAIN:
# Evaluate speech quality/intelligibility
if self.hparams.mode == 'val':
if self.hparams.target_metric == "stoi":
self.stoi_metric.append(
batch.id, predict_wav, clean_wav, lens, reduction="batch"
)
elif self.hparams.target_metric == "pesq":
self.pesq_metric.append(
batch.id, predict=predict_wav, target=clean_wav, lengths=lens
)
# Write wavs to file, for evaluation
elif self.hparams.mode == 'test':
self.stoi_metric.append(
batch.id, predict_wav, clean_wav, lens, reduction="batch"
)
self.pesq_metric.append(
batch.id, predict=predict_wav, target=clean_wav, lengths=lens
)
lens = lens * clean_wav.shape[1]
for name, pred_wav, length in zip(batch.id, predict_wav, lens):
name += ".wav"
enhance_path = os.path.join(self.hparams.enhanced_folder, name)
torchaudio.save(
enhance_path,
torch.unsqueeze(pred_wav[: int(length)].cpu(), 0),
16000,
)
# we do not use mse_cost to update model
return adv_cost
def compute_ids(self, batch_id, optim_name):
"""Returns the list of ids, edited via optimizer name."""
if optim_name == "D_enh":
return [f"{uid}@{self.epoch}" for uid in batch_id]
return batch_id
def save_noisy_scores(self, batch_id, scores):
for i, score in zip(batch_id, scores):
self.noisy_scores[i] = score
def score(self, batch_id, deg_wav, ref_wav, lens):
"""Returns actual metric score, either pesq or stoi
Arguments
---------
batch_id : list of str
A list of the utterance ids for the batch
deg_wav : torch.Tensor
The degraded waveform to score
ref_wav : torch.Tensor
The reference waveform to use for scoring
length : torch.Tensor
The relative lengths of the utterances
"""
new_ids = [
i
for i, d in enumerate(batch_id)
if d not in self.historical_set and d not in self.noisy_scores
]
if len(new_ids) == 0:
pass
elif self.hparams.target_metric == "pesq":
self.target_metric.append(
ids=[batch_id[i] for i in new_ids],
predict=deg_wav[new_ids].detach(),
target=ref_wav[new_ids].detach(),
lengths=lens[new_ids],
)
score = torch.tensor(
[[s] for s in self.target_metric.scores], device=self.device,
)
elif self.hparams.target_metric == "stoi":
self.target_metric.append(
[batch_id[i] for i in new_ids],
deg_wav[new_ids],
ref_wav[new_ids],
lens[new_ids],
reduction="batch",
)
score = torch.tensor(
[[-s] for s in self.target_metric.scores], device=self.device,
)
else:
raise ValueError("Expected 'pesq' or 'stoi' for target_metric")
# Clear metric scores to prepare for next batch
self.target_metric.clear()
# Combine old scores and new
final_score = []
for i, d in enumerate(batch_id):
if d in self.historical_set:
final_score.append([self.historical_set[d]["score"]])
elif d in self.noisy_scores:
final_score.append([self.noisy_scores[d]])
else:
final_score.append([score[new_ids.index(i)]])
return torch.tensor(final_score, device=self.device)
def est_score(self, deg_spec, ref_spec):
"""Returns score as estimated by discriminator
Arguments
---------
deg_spec : torch.Tensor
The spectral features of the degraded utterance
ref_spec : torch.Tensor
The spectral features of the reference utterance
"""
combined_spec = torch.cat(
[deg_spec.unsqueeze(1), ref_spec.unsqueeze(1)], 1
)
return self.modules.discriminator(combined_spec)
def write_wavs(self, clean_id, batch_id, wavs, scores, lens):
"""Write wavs to files, for historical discriminator training
Arguments
---------
batch_id : list of str
A list of the utterance ids for the batch
wavs : torch.Tensor
The wavs to write to files
score : torch.Tensor
The actual scores for the corresponding utterances
lens : torch.Tensor
The relative lengths of each utterance
"""
lens = lens * wavs.shape[1]
record = {}
for i, (cleanid, name, pred_wav, length) in enumerate(
zip(clean_id, batch_id, wavs, lens)
):
path = os.path.join(self.hparams.MetricGAN_folder, name + ".wav")
data = torch.unsqueeze(pred_wav[: int(length)].cpu(), 0)
torchaudio.save(path, data, self.hparams.Sample_rate)
# Make record of path and score for historical training
score = float(scores[i][0])
clean_path = cleanid.split('-', 1)
clean_path = os.path.join(
self.hparams.train_clean_folder, clean_path[0], clean_path[1] + ".pkl"
)
record[name] = {
"enh_wav": path,
"score": score,
"clean_wav": clean_path,
}
# Update records for historical training
self.historical_set.update(record)
def fit_batch(self, batch):
"Compute gradients and update either D or G based on sub-stage."
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
loss_tracker = 0
if self.sub_stage == SubStage.CURRENT:
for mode in ["clean", "enh", "noisy"]:
loss = self.compute_objectives(
predictions, batch, sb.Stage.TRAIN, f"D_{mode}"
)
self.d_optimizer.zero_grad()
loss.backward()
if self.check_gradients(loss):
self.d_optimizer.step()
loss_tracker += loss.detach() / 3
elif self.sub_stage == SubStage.HISTORICAL:
loss = self.compute_objectives(
predictions, batch, sb.Stage.TRAIN, "D_enh"
)
self.d_optimizer.zero_grad()
loss.backward()
if self.check_gradients(loss):
self.d_optimizer.step()
loss_tracker += loss.detach()
elif self.sub_stage == SubStage.GENERATOR:
loss = self.compute_objectives(
predictions, batch, sb.Stage.TRAIN, "generator"
)
self.g_optimizer.zero_grad()
loss.backward()
if self.check_gradients(loss):
self.g_optimizer.step()
loss_tracker += loss.detach()
return loss_tracker
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch
This method calls ``fit()`` again to train the discriminator
before proceeding with generator training.
"""
self.mse_metric = MetricStats(metric=self.hparams.compute_cost)
self.metrics = {"G": [], "D": []}
if stage == sb.Stage.TRAIN:
if self.hparams.target_metric == "pesq":
self.target_metric = MetricStats(metric=pesq_eval, n_jobs=40)
elif self.hparams.target_metric == "stoi":
self.target_metric = MetricStats(metric=stoi_loss)
else:
raise NotImplementedError(
"Right now we only support 'pesq' and 'stoi'"
)
# Train discriminator before we start generator training
if self.sub_stage == SubStage.GENERATOR:
self.epoch = epoch
self.train_discriminator()
self.sub_stage = SubStage.GENERATOR
print("Generator training by current data...")
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(metric=pesq_eval, n_jobs=30)
self.stoi_metric = MetricStats(metric=stoi_loss)
def train_discriminator(self):
"""A total of 3 data passes to update discriminator."""
# First, iterate train subset w/ updates for clean, enh, noisy
print("Discriminator training by current data...")
self.sub_stage = SubStage.CURRENT
self.fit(
range(1),
self.train_set,
train_loader_kwargs=self.hparams.dataloader_options,
)
# Next, iterate historical subset w/ updates for enh
if self.historical_set:
print("Discriminator training by historical data...")
self.sub_stage = SubStage.HISTORICAL
self.fit(
range(1),
self.historical_set,
train_loader_kwargs=self.hparams.dataloader_options,
)
# Finally, iterate train set again. Should iterate same
# samples as before, due to ReproducibleRandomSampler
print("Discriminator training by current data again...")
self.sub_stage = SubStage.CURRENT
self.fit(
range(1),
self.train_set,
train_loader_kwargs=self.hparams.dataloader_options,
)
def on_stage_end(self, stage, stage_loss, epoch=None):
"Called at the end of each stage to summarize progress"
# epoch is awared in each stage
def ckpt_predicate(ckpt):
return ckpt.meta['epoch'] == epoch
def ckpt_predicate_lessthan(ckpt):
return ckpt.meta['epoch'] < epoch
if self.sub_stage != SubStage.GENERATOR:
return
if stage == sb.Stage.TRAIN:
self.train_loss = stage_loss
g_loss = torch.tensor(self.metrics["G"]) # batch_size
d_loss = torch.tensor(self.metrics["D"]) # batch_size
print("Avg G loss: %.3f" % torch.mean(g_loss))
print("Avg D loss: %.3f" % torch.mean(d_loss))
print("MSE distance: %.3f" % self.mse_metric.summarize("average"))
# save the checkpoint every 10 epochs
# use default timestamp or use epoch numbers as max key
# run the test as
stats = {
"epoch": epoch,
"MSE distance": stage_loss,
# "target_metric": self.target_metric.summarize("average")
}
self.checkpointer.save_checkpoint(meta=stats)
elif self.hparams.target_metric == "pesq":
stats = {
"epoch": epoch,
"MSE distance": stage_loss,
"pesq": 5 * self.pesq_metric.summarize("average") - 0.5,
}
elif self.hparams.target_metric == "stoi":
stats = {
"epoch": epoch,
"MSE distance": stage_loss,
"stoi": -self.stoi_metric.summarize("average"),
}
if stage == sb.Stage.VALID:
if self.hparams.use_tensorboard:
valid_stats = {
"mse": stage_loss,
"pesq": 5 * self.pesq_metric.summarize("average") - 0.5,
"stoi": -self.stoi_metric.summarize("average"),
}
self.hparams.tensorboard_train_logger.log_stats(valid_stats)
self.hparams.train_logger.log_stats(
{"Epoch": epoch},
train_stats={"loss": self.train_loss},
valid_stats=stats,
)
self.checkpointer.save_and_keep_only(
meta=stats, max_keys=[self.hparams.target_metric]
)
if stage == sb.Stage.TEST:
if self.hparams.mode == 'val':
ckpts = self.checkpointer.find_checkpoints(ckpt_predicate=ckpt_predicate)
assert len(ckpts) == 1
# delete old ckpt from train
self.checkpointer.delete_checkpoints(num_to_keep=0, ckpt_predicate=ckpt_predicate)
if self.hparams.use_tensorboard:
valid_stats = stats
# will two tensorboard corrupt?
self.hparams.tensorboard_train_logger.log_stats(valid_stats)
self.hparams.train_logger.log_stats(
{"Epoch": epoch},
valid_stats=stats,
)
# TODO:save current checkpointer again
self.checkpointer.save_and_keep_only(
meta=stats, max_keys=[self.hparams.target_metric], ckpt_predicate=ckpt_predicate_lessthan
)
# self.checkpointer.save_checkpoint(meta=stats)
else:
print("Epoch loaded", self.hparams.epoch_counter.current)
print("stoi", -self.stoi_metric.summarize("average"))
print("pesq", 5 * self.pesq_metric.summarize("average") - 0.5)
test_stats = {
"mse": stage_loss,
"pesq": 5 * self.pesq_metric.summarize("average") - 0.5,
"stoi": -self.stoi_metric.summarize("average"),
}
# self.hparams.train_logger.log_stats(
# {"Epoch loaded": self.hparams.epoch_counter.current},
# test_stats=test_stats,
# )
def make_dataloader(
self, dataset, stage, ckpt_prefix="dataloader-", **loader_kwargs
):
"Override dataloader to insert custom sampler/dataset"
if stage == sb.Stage.TRAIN:
# Create a new dataset each time, this set grows
if self.sub_stage == SubStage.HISTORICAL:
dataset = sb.dataio.dataset.DynamicItemDataset(
data=dataset,
dynamic_items=[enh_pipeline],
output_keys=["id", "enh_sig", "clean_sig", "score"],
)
samples = round(len(dataset) * self.hparams.history_portion)
else:
samples = self.hparams.number_of_samples
# This sampler should give the same samples for D and G
epoch = self.hparams.epoch_counter.current
# Equal weights for all samples, we use "Weighted" so we can do
# both "replacement=False" and a set number of samples, reproducibly
weights = torch.ones(len(dataset))
sampler = ReproducibleWeightedRandomSampler(
weights, epoch=epoch, replacement=False, num_samples=samples
)
loader_kwargs["sampler"] = sampler
if self.sub_stage == SubStage.GENERATOR:
self.train_sampler = sampler
# Make the dataloader as normal
return super().make_dataloader(
dataset, stage, ckpt_prefix, **loader_kwargs
)
def on_fit_start(self):
"Override to prevent this from running for D training"
if self.sub_stage == SubStage.GENERATOR:
super().on_fit_start()
def init_optimizers(self):
"Initializes the generator and discriminator optimizers"
self.g_optimizer = self.hparams.g_opt_class(
self.modules.generator.parameters()
)
self.d_optimizer = self.hparams.d_opt_class(
self.modules.discriminator.parameters()
)
if self.checkpointer is not None:
self.checkpointer.add_recoverable("g_opt", self.g_optimizer)
self.checkpointer.add_recoverable("d_opt", self.d_optimizer)
class SEBrain(sb.core.Brain):
def compute_forward(self, batch, stage):
"""Forward computations from the waveform batches to the enhanced output."""
batch = batch.to(self.device)
noisy_wavs, lens = batch.noisy_sig
feats = self.hparams.compute_STFT(noisy_wavs)
feats = spectral_magnitude(feats, power=0.5)
feats = torch.log1p(feats)
predict_spec = self.hparams.model(feats)
# Also return predicted wav
if stage != sb.Stage.TRAIN:
predict_wav = self.hparams.resynth(torch.expm1(predict_spec),
noisy_wavs)
else:
predict_wav = None
return predict_spec, predict_wav
def compute_objectives(self, predictions, batch, stage):
"""Computes the loss given the predicted and targeted outputs"""
predict_spec, predict_wav = predictions
ids = batch.id
clean_wav, lens = batch.clean_sig
feats = self.hparams.compute_STFT(clean_wav)
feats = spectral_magnitude(feats, power=0.5)
feats = torch.log1p(feats)
loss = self.hparams.compute_cost(predict_spec, feats, lens)
self.loss_metric.append(ids,
predict_spec,
feats,
lens,
reduction="batch")
if stage != sb.Stage.TRAIN:
# Evaluate speech quality/intelligibility
self.stoi_metric.append(ids,
predict_wav,
clean_wav,
lens,
reduction="batch")
self.pesq_metric.append(batch.id,
predict=predict_wav,
target=clean_wav,
lengths=lens)
# Write wavs to file
if stage == sb.Stage.TEST:
lens = lens * clean_wav.shape[1]
for name, wav, length in zip(ids, predict_wav, lens):
enhance_path = os.path.join(self.hparams.enhanced_folder, name)
if not enhance_path.endswith(".wav"):
enhance_path = enhance_path + ".wav"
torchaudio.save(
enhance_path,
torch.unsqueeze(wav[:int(length)].cpu(), 0),
self.hparams.Sample_rate,
)
return loss
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch"""
self.loss_metric = MetricStats(metric=self.hparams.compute_cost)
self.stoi_metric = MetricStats(metric=stoi_loss)
# Define function taking (prediction, target) for parallel eval
def pesq_eval(pred_wav, target_wav):
"""Computes the PESQ evaluation metric"""
return pesq(
fs=16000,
ref=target_wav.cpu().numpy(),
deg=pred_wav.cpu().numpy(),
mode="wb",
)
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(metric=pesq_eval, n_jobs=4)
def on_stage_end(self, stage, stage_loss, epoch=None):
"""Gets called at the end of an epoch."""
if stage == sb.Stage.TRAIN:
self.train_loss = stage_loss
self.train_stats = {"loss": self.loss_metric.scores}
else:
stats = {
"loss": stage_loss,
"pesq": self.pesq_metric.summarize("average"),
"stoi": -self.stoi_metric.summarize("average"),
}
if stage == sb.Stage.VALID:
old_lr, new_lr = self.hparams.lr_annealing(4.5 - stats["pesq"])
sb.nnet.schedulers.update_learning_rate(self.optimizer, new_lr)
if self.hparams.use_tensorboard:
valid_stats = {
"loss": self.loss_metric.scores,
"stoi": self.stoi_metric.scores,
"pesq": self.pesq_metric.scores,
}
self.hparams.tensorboard_train_logger.log_stats(
{
"Epoch": epoch,
"lr": old_lr
},
self.train_stats,
valid_stats,
)
self.hparams.train_logger.log_stats(
{
"Epoch": epoch,
"lr": old_lr
},
train_stats={"loss": self.train_loss},
valid_stats=stats,
)
self.checkpointer.save_and_keep_only(meta=stats, max_keys=["pesq"])
if stage == sb.Stage.TEST:
self.hparams.train_logger.log_stats(
{"Epoch loaded": self.hparams.epoch_counter.current},
test_stats=stats,
)
class MetricGanBrain(sb.Brain):
def load_history(self):
if os.path.isfile(self.hparams.historical_file):
with open(self.hparams.historical_file, "rb") as fp: # Unpickling
self.historical_set = pickle.load(fp)
def compute_feats(self, wavs):
"""Feature computation pipeline"""
feats = self.hparams.compute_STFT(wavs)
spec = spectral_magnitude(feats, power=0.5)
return spec
def compute_forward(self, batch, stage):
"Given an input batch computes the enhanced signal"
batch = batch.to(self.device)
if self.sub_stage == SubStage.HISTORICAL:
predict_wav, lens = batch.enh_sig
return predict_wav
else:
noisy_wav, lens = batch.noisy_sig
noisy_spec = self.compute_feats(noisy_wav)
mask = self.modules.generator(noisy_spec, lengths=lens)
mask = mask.clamp(min=self.hparams.min_mask).squeeze(2)
predict_spec = torch.mul(mask, noisy_spec)
# Also return predicted wav
predict_wav = self.hparams.resynth(predict_spec, noisy_wav)
return predict_wav, mask
def compute_objectives(self, predictions, batch, stage, optim_name=""):
"Given the network predictions and targets compute the total loss"
if self.sub_stage == SubStage.HISTORICAL:
predict_wav = predictions
else:
predict_wav, mask = predictions
predict_spec = self.compute_feats(predict_wav)
ids = self.compute_ids(batch.id, optim_name)
if self.sub_stage != SubStage.HISTORICAL:
noisy_wav, lens = batch.noisy_sig
if optim_name == "generator":
est_score = self.est_score(predict_spec)
target_score = self.hparams.target_score * torch.ones(
self.batch_size, 1, device=self.device
)
noisy_wav, lens = batch.noisy_sig
noisy_spec = self.compute_feats(noisy_wav)
mse_cost = self.hparams.compute_cost(predict_spec, noisy_spec, lens)
# D Learns to estimate the scores of enhanced speech
elif optim_name == "D_enh" and self.sub_stage == SubStage.CURRENT:
target_score = self.score(
ids, predict_wav, predict_wav, lens
) # no clean_wav is needed
est_score = self.est_score(predict_spec)
# Write enhanced wavs during discriminator training, because we
# compute the actual score here and we can save it
self.write_wavs(ids, predict_wav, target_score, lens)
# D Relearns to estimate the scores of previous epochs
elif optim_name == "D_enh" and self.sub_stage == SubStage.HISTORICAL:
target_score = batch.score.unsqueeze(1).float()
est_score = self.est_score(predict_spec)
# D Learns to estimate the scores of noisy speech
elif optim_name == "D_noisy":
noisy_spec = self.compute_feats(noisy_wav)
target_score = self.score(
ids, noisy_wav, noisy_wav, lens
) # no clean_wav is needed
est_score = self.est_score(noisy_spec)
# Save scores of noisy wavs
self.save_noisy_scores(ids, target_score)
if stage == sb.Stage.TRAIN:
# Compute the cost
cost = self.hparams.compute_cost(est_score, target_score)
if optim_name == "generator":
cost += self.hparams.mse_weight * mse_cost
self.metrics["G"].append(cost.detach())
else:
self.metrics["D"].append(cost.detach())
# Compute scores on validation data
if stage != sb.Stage.TRAIN:
clean_wav, lens = batch.clean_sig
cost = self.hparams.compute_si_snr(predict_wav, clean_wav, lens)
# Evaluate speech quality/intelligibility
self.stoi_metric.append(
batch.id, predict_wav, clean_wav, lens, reduction="batch"
)
self.pesq_metric.append(
batch.id, predict=predict_wav, target=clean_wav, lengths=lens
)
if (
self.hparams.calculate_dnsmos_on_validation_set
): # Note: very time consuming........
self.dnsmos_metric.append(
batch.id,
predict=predict_wav,
target=predict_wav,
lengths=lens, # no clean_wav is needed
)
# Write wavs to file, for evaluation
lens = lens * clean_wav.shape[1]
for name, pred_wav, length in zip(batch.id, predict_wav, lens):
name += ".wav"
enhance_path = os.path.join(self.hparams.enhanced_folder, name)
torchaudio.save(
enhance_path,
torch.unsqueeze(pred_wav[: int(length)].cpu(), 0),
16000,
)
return cost
def compute_ids(self, batch_id, optim_name):
"""Returns the list of ids, edited via optimizer name."""
if optim_name == "D_enh":
return [f"{uid}@{self.epoch}" for uid in batch_id]
return batch_id
def save_noisy_scores(self, batch_id, scores):
for i, score in zip(batch_id, scores):
self.noisy_scores[i] = score
def score(self, batch_id, deg_wav, ref_wav, lens):
"""Returns actual metric score, either pesq or stoi
Arguments
---------
batch_id : list of str
A list of the utterance ids for the batch
deg_wav : torch.Tensor
The degraded waveform to score
ref_wav : torch.Tensor
The reference waveform to use for scoring
length : torch.Tensor
The relative lengths of the utterances
"""
new_ids = [
i
for i, d in enumerate(batch_id)
if d not in self.historical_set and d not in self.noisy_scores
]
if len(new_ids) == 0:
pass
elif self.hparams.target_metric == "srmr" or "dnsmos":
self.target_metric.append(
ids=[batch_id[i] for i in new_ids],
predict=deg_wav[new_ids].detach(),
target=ref_wav[
new_ids
].detach(), # target is not used in the function !!!
lengths=lens[new_ids],
)
score = torch.tensor(
[[s] for s in self.target_metric.scores], device=self.device,
)
else:
raise ValueError("Expected 'srmr' or 'dnsmos' for target_metric")
# Clear metric scores to prepare for next batch
self.target_metric.clear()
# Combine old scores and new
final_score = []
for i, d in enumerate(batch_id):
if d in self.historical_set:
final_score.append([self.historical_set[d]["score"]])
elif d in self.noisy_scores:
final_score.append([self.noisy_scores[d]])
else:
final_score.append([score[new_ids.index(i)]])
return torch.tensor(final_score, device=self.device)
def est_score(self, deg_spec):
"""Returns score as estimated by discriminator
Arguments
---------
deg_spec : torch.Tensor
The spectral features of the degraded utterance
ref_spec : torch.Tensor
The spectral features of the reference utterance
"""
"""
combined_spec = torch.cat(
[deg_spec.unsqueeze(1), ref_spec.unsqueeze(1)], 1
)
"""
return self.modules.discriminator(deg_spec.unsqueeze(1))
def write_wavs(self, batch_id, wavs, score, lens):
"""Write wavs to files, for historical discriminator training
Arguments
---------
batch_id : list of str
A list of the utterance ids for the batch
wavs : torch.Tensor
The wavs to write to files
score : torch.Tensor
The actual scores for the corresponding utterances
lens : torch.Tensor
The relative lengths of each utterance
"""
lens = lens * wavs.shape[1]
record = {}
for i, (name, pred_wav, length) in enumerate(zip(batch_id, wavs, lens)):
path = os.path.join(self.hparams.MetricGAN_folder, name + ".wav")
data = torch.unsqueeze(pred_wav[: int(length)].cpu(), 0)
torchaudio.save(path, data, self.hparams.Sample_rate)
# Make record of path and score for historical training
score = float(score[i][0])
record[name] = {
"enh_wav": path,
"score": score,
}
# Update records for historical training
self.historical_set.update(record)
with open(self.hparams.historical_file, "wb") as fp: # Pickling
pickle.dump(self.historical_set, fp)
def fit_batch(self, batch):
"Compute gradients and update either D or G based on sub-stage."
predictions = self.compute_forward(batch, sb.Stage.TRAIN)
loss_tracker = 0
if self.sub_stage == SubStage.CURRENT:
for mode in ["enh", "noisy"]:
loss = self.compute_objectives(
predictions, batch, sb.Stage.TRAIN, f"D_{mode}"
)
self.d_optimizer.zero_grad()
loss.backward()
if self.check_gradients(loss):
self.d_optimizer.step()
loss_tracker += loss.detach() / 3
elif self.sub_stage == SubStage.HISTORICAL:
loss = self.compute_objectives(
predictions, batch, sb.Stage.TRAIN, "D_enh"
)
self.d_optimizer.zero_grad()
loss.backward()
if self.check_gradients(loss):
self.d_optimizer.step()
loss_tracker += loss.detach()
elif self.sub_stage == SubStage.GENERATOR:
for name, param in self.modules.generator.named_parameters():
if "Learnable_sigmoid" in name:
param.data = torch.clamp(
param, max=3.5
) # to prevent gradient goes to infinity
loss = self.compute_objectives(
predictions, batch, sb.Stage.TRAIN, "generator"
)
self.g_optimizer.zero_grad()
loss.backward()
if self.check_gradients(loss):
self.g_optimizer.step()
loss_tracker += loss.detach()
return loss_tracker
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch
This method calls ``fit()`` again to train the discriminator
before proceeding with generator training.
"""
self.metrics = {"G": [], "D": []}
if stage == sb.Stage.TRAIN:
if self.hparams.target_metric == "srmr":
self.target_metric = MetricStats(
metric=srmrpy_eval,
n_jobs=hparams["n_jobs"],
batch_eval=False,
)
elif self.hparams.target_metric == "dnsmos":
self.target_metric = MetricStats(
metric=dnsmos_eval,
n_jobs=hparams["n_jobs"],
batch_eval=False,
)
else:
raise NotImplementedError(
"Right now we only support 'srmr' and 'dnsmos'"
)
# Train discriminator before we start generator training
if self.sub_stage == SubStage.GENERATOR:
self.epoch = epoch
self.train_discriminator()
self.sub_stage = SubStage.GENERATOR
print("Generator training by current data...")
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(
metric=pesq_eval, n_jobs=hparams["n_jobs"], batch_eval=False
)
self.stoi_metric = MetricStats(metric=stoi_loss)
self.srmr_metric = MetricStats(
metric=srmrpy_eval_valid,
n_jobs=hparams["n_jobs"],
batch_eval=False,
)
self.dnsmos_metric = MetricStats(
metric=dnsmos_eval_valid,
n_jobs=hparams["n_jobs"],
batch_eval=False,
)
def train_discriminator(self):
"""A total of 3 data passes to update discriminator."""
# First, iterate train subset w/ updates for enh, noisy
print("Discriminator training by current data...")
self.sub_stage = SubStage.CURRENT
self.fit(
range(1),
self.train_set,
train_loader_kwargs=self.hparams.dataloader_options,
)
# Next, iterate historical subset w/ updates for enh
if self.historical_set:
print("Discriminator training by historical data...")
self.sub_stage = SubStage.HISTORICAL
self.fit(
range(1),
self.historical_set,
train_loader_kwargs=self.hparams.dataloader_options,
)
# Finally, iterate train set again. Should iterate same
# samples as before, due to ReproducibleRandomSampler
print("Discriminator training by current data again...")
self.sub_stage = SubStage.CURRENT
self.fit(
range(1),
self.train_set,
train_loader_kwargs=self.hparams.dataloader_options,
)
def on_stage_end(self, stage, stage_loss, epoch=None):
"Called at the end of each stage to summarize progress"
if self.sub_stage != SubStage.GENERATOR:
return
if stage == sb.Stage.TRAIN:
self.train_loss = stage_loss
g_loss = torch.tensor(self.metrics["G"]) # batch_size
d_loss = torch.tensor(self.metrics["D"]) # batch_size
print("Avg G loss: %.3f" % torch.mean(g_loss))
print("Avg D loss: %.3f" % torch.mean(d_loss))
else:
if self.hparams.calculate_dnsmos_on_validation_set:
stats = {
"SI-SNR": -stage_loss,
"pesq": 5 * self.pesq_metric.summarize("average") - 0.5,
"stoi": -self.stoi_metric.summarize("average"),
"dnsmos": self.dnsmos_metric.summarize("average"),
}
else:
stats = {
"SI-SNR": -stage_loss,
"pesq": 5 * self.pesq_metric.summarize("average") - 0.5,
"stoi": -self.stoi_metric.summarize("average"),
}
if stage == sb.Stage.VALID:
old_lr, new_lr = self.hparams.lr_annealing(5.0 - stats["pesq"])
sb.nnet.schedulers.update_learning_rate(self.g_optimizer, new_lr)
if self.hparams.use_tensorboard:
if (
self.hparams.calculate_dnsmos_on_validation_set
): # Note: very time consuming........
valid_stats = {
"SI-SNR": -stage_loss,
"pesq": 5 * self.pesq_metric.summarize("average") - 0.5,
"stoi": -self.stoi_metric.summarize("average"),
"dnsmos": self.dnsmos_metric.summarize("average"),
}
else:
valid_stats = {
"SI-SNR": -stage_loss,
"pesq": 5 * self.pesq_metric.summarize("average") - 0.5,
"stoi": -self.stoi_metric.summarize("average"),
}
self.hparams.tensorboard_train_logger.log_stats(
{"lr": old_lr}, valid_stats
)
self.hparams.train_logger.log_stats(
{"Epoch": epoch, "lr": old_lr},
train_stats={"loss": self.train_loss},
valid_stats=stats,
)
self.checkpointer.save_and_keep_only(meta=stats, max_keys=["pesq"])
if stage == sb.Stage.TEST:
self.hparams.train_logger.log_stats(
{"Epoch loaded": self.hparams.epoch_counter.current},
test_stats=stats,
)
def make_dataloader(
self, dataset, stage, ckpt_prefix="dataloader-", **loader_kwargs
):
"Override dataloader to insert custom sampler/dataset"
if stage == sb.Stage.TRAIN:
# Create a new dataset each time, this set grows
if self.sub_stage == SubStage.HISTORICAL:
dataset = sb.dataio.dataset.DynamicItemDataset(
data=dataset,
dynamic_items=[enh_pipeline],
output_keys=["id", "enh_sig", "score"],
)
samples = round(len(dataset) * self.hparams.history_portion)
else:
samples = self.hparams.number_of_samples
# This sampler should give the same samples for D and G
epoch = self.hparams.epoch_counter.current
# Equal weights for all samples, we use "Weighted" so we can do
# both "replacement=False" and a set number of samples, reproducibly
weights = torch.ones(len(dataset))
sampler = ReproducibleWeightedRandomSampler(
weights, epoch=epoch, replacement=False, num_samples=samples
)
loader_kwargs["sampler"] = sampler
if self.sub_stage == SubStage.GENERATOR:
self.train_sampler = sampler
# Make the dataloader as normal
return super().make_dataloader(
dataset, stage, ckpt_prefix, **loader_kwargs
)
def on_fit_start(self):
"Override to prevent this from running for D training"
if self.sub_stage == SubStage.GENERATOR:
super().on_fit_start()
def init_optimizers(self):
"Initializes the generator and discriminator optimizers"
self.g_optimizer = self.hparams.g_opt_class(
self.modules.generator.parameters()
)
self.d_optimizer = self.hparams.d_opt_class(
self.modules.discriminator.parameters()
)
if self.checkpointer is not None:
self.checkpointer.add_recoverable("g_opt", self.g_optimizer)
self.checkpointer.add_recoverable("d_opt", self.d_optimizer)
class SEBrain(sb.Brain):
def compute_forward(self, batch, stage):
"""Forward computations from the waveform batches to the enhanced output"""
batch = batch.to(self.device)
noisy_wavs, lens = batch.noisy_sig
noisy_wavs = torch.unsqueeze(noisy_wavs, -1)
predict_wavs = self.modules.model(noisy_wavs)[:, :, 0]
return predict_wavs
def compute_objectives(self, predict_wavs, batch, stage):
"""Computes the loss given the predicted and targeted outputs"""
clean_wavs, lens = batch.clean_sig
loss = self.hparams.compute_cost(predict_wavs, clean_wavs, lens)
self.loss_metric.append(
batch.id, predict_wavs, clean_wavs, lens, reduction="batch"
)
if stage != sb.Stage.TRAIN:
# Evaluate speech quality/intelligibility
self.stoi_metric.append(
batch.id, predict_wavs, clean_wavs, lens, reduction="batch"
)
self.pesq_metric.append(
batch.id, predict=predict_wavs, target=clean_wavs, lengths=lens
)
# Write wavs to file
if stage == sb.Stage.TEST:
lens = lens * clean_wavs.shape[1]
for name, pred_wav, length in zip(batch.id, predict_wavs, lens):
name += ".wav"
enhance_path = os.path.join(
self.hparams.enhanced_folder, name
)
pred_wav = pred_wav / torch.max(torch.abs(pred_wav)) * 0.99
torchaudio.save(
enhance_path,
torch.unsqueeze(pred_wav[: int(length)].cpu(), 0),
16000,
)
return loss
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch"""
self.loss_metric = MetricStats(metric=self.hparams.compute_cost)
self.stoi_metric = MetricStats(metric=stoi_loss)
# Define function taking (prediction, target) for parallel eval
def pesq_eval(pred_wav, target_wav):
"""Computes the PESQ evaluation metric"""
return pesq(
fs=16000,
ref=target_wav.numpy(),
deg=pred_wav.numpy(),
mode="wb",
)
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(
metric=pesq_eval, n_jobs=1, batch_eval=False
)
def on_stage_end(self, stage, stage_loss, epoch=None):
"""Gets called at the end of an epoch."""
if stage == sb.Stage.TRAIN:
self.train_loss = stage_loss
self.train_stats = {"loss": self.loss_metric.scores}
else:
stats = {
"loss": stage_loss,
"pesq": self.pesq_metric.summarize("average"),
"stoi": -self.stoi_metric.summarize("average"),
}
if stage == sb.Stage.VALID:
if self.hparams.use_tensorboard:
valid_stats = {
"loss": self.loss_metric.scores,
"stoi": self.stoi_metric.scores,
"pesq": self.pesq_metric.scores,
}
self.hparams.tensorboard_train_logger.log_stats(
{"Epoch": epoch}, self.train_stats, valid_stats
)
self.hparams.train_logger.log_stats(
{"Epoch": epoch},
train_stats={"loss": self.train_loss},
valid_stats=stats,
)
self.checkpointer.save_and_keep_only(meta=stats, max_keys=["pesq"])
if stage == sb.Stage.TEST:
self.hparams.train_logger.log_stats(
{"Epoch loaded": self.hparams.epoch_counter.current},
test_stats=stats,
)
class SEBrain(sb.Brain):
def compute_forward(self, batch, stage):
"""Forward computations from the waveform batches to the enhanced output."""
batch = batch.to(self.device)
noisy_wavs, lens = batch.noisy_sig
noisy_feats = self.compute_feats(noisy_wavs)
# mask with "signal approximation (SA)"
mask = self.modules.model(noisy_feats)
mask = torch.squeeze(mask, 2)
predict_spec = torch.mul(mask, noisy_feats)
# Also return predicted wav
predict_wav = self.hparams.resynth(
torch.expm1(predict_spec), noisy_wavs
)
return predict_spec, predict_wav
def compute_feats(self, wavs):
"""Feature computation pipeline"""
feats = self.hparams.compute_STFT(wavs)
feats = spectral_magnitude(feats, power=0.5)
feats = torch.log1p(feats)
return feats
def compute_objectives(self, predictions, batch, stage):
"""Computes the loss given the predicted and targeted outputs"""
predict_spec, predict_wav = predictions
clean_wavs, lens = batch.clean_sig
if getattr(self.hparams, "waveform_target", False):
loss = self.hparams.compute_cost(predict_wav, clean_wavs, lens)
self.loss_metric.append(
batch.id, predict_wav, clean_wavs, lens, reduction="batch"
)
else:
clean_spec = self.compute_feats(clean_wavs)
loss = self.hparams.compute_cost(predict_spec, clean_spec, lens)
self.loss_metric.append(
batch.id, predict_spec, clean_spec, lens, reduction="batch"
)
if stage != sb.Stage.TRAIN:
# Evaluate speech quality/intelligibility
self.stoi_metric.append(
batch.id, predict_wav, clean_wavs, lens, reduction="batch"
)
self.pesq_metric.append(
batch.id, predict=predict_wav, target=clean_wavs, lengths=lens
)
# Write wavs to file
if stage == sb.Stage.TEST:
lens = lens * clean_wavs.shape[1]
for name, pred_wav, length in zip(batch.id, predict_wav, lens):
name += ".wav"
enhance_path = os.path.join(
self.hparams.enhanced_folder, name
)
torchaudio.save(
enhance_path,
torch.unsqueeze(pred_wav[: int(length)].cpu(), 0),
16000,
)
return loss
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch"""
self.loss_metric = MetricStats(metric=self.hparams.compute_cost)
self.stoi_metric = MetricStats(metric=stoi_loss)
# Define function taking (prediction, target) for parallel eval
def pesq_eval(pred_wav, target_wav):
"""Computes the PESQ evaluation metric"""
return pesq(
fs=16000,
ref=target_wav.numpy(),
deg=pred_wav.numpy(),
mode="wb",
)
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(metric=pesq_eval, n_jobs=4)
def on_stage_end(self, stage, stage_loss, epoch=None):
"""Gets called at the end of an epoch."""
if stage == sb.Stage.TRAIN:
self.train_loss = stage_loss
self.train_stats = {"loss": self.loss_metric.scores}
else:
stats = {
"loss": stage_loss,
"pesq": self.pesq_metric.summarize("average"),
"stoi": -self.stoi_metric.summarize("average"),
}
if stage == sb.Stage.VALID:
if self.hparams.use_tensorboard:
valid_stats = {
"loss": self.loss_metric.scores,
"stoi": self.stoi_metric.scores,
"pesq": self.pesq_metric.scores,
}
self.hparams.tensorboard_train_logger.log_stats(
{"Epoch": epoch}, self.train_stats, valid_stats
)
self.hparams.train_logger.log_stats(
{"Epoch": epoch},
train_stats={"loss": self.train_loss},
valid_stats=stats,
)
self.checkpointer.save_and_keep_only(meta=stats, max_keys=["pesq"])
if stage == sb.Stage.TEST:
self.hparams.train_logger.log_stats(
{"Epoch loaded": self.hparams.epoch_counter.current},
test_stats=stats,
)
class Separation(sb.Brain):
def compute_forward(self, mix, targets, stage, noise=None):
"""Forward computations from the mixture to the separated signals."""
# Unpack lists and put tensors in the right device
mix, mix_lens = mix
mix, mix_lens = mix.to(self.device), mix_lens.to(self.device)
# Convert targets to tensor
targets = torch.cat(
[targets[i][0].unsqueeze(-1) for i in range(self.hparams.num_spks)],
dim=-1,
).to(self.device)
# Add speech distortions
if stage == sb.Stage.TRAIN:
with torch.no_grad():
if self.hparams.use_speedperturb or self.hparams.use_rand_shift:
mix, targets = self.add_speed_perturb(targets, mix_lens)
if "whamr" in self.hparams.data_folder:
try:
targets_rev = [
self.hparams.reverb(targets[:, :, i], None)
for i in range(self.hparams.num_spks)
]
except Exception:
print("reverb error, not adding reverb")
targets_rev = [
targets[:, :, i]
for i in range(self.hparams.num_spks)
]
targets_rev = torch.stack(targets_rev, dim=-1)
mix = targets_rev.sum(-1)
# if we do not dereverberate, we set the targets to be reverberant
if not self.hparams.dereverberate:
targets = targets_rev
else:
mix = targets.sum(-1)
noise = noise.to(self.device)
len_noise = noise.shape[1]
len_mix = mix.shape[1]
min_len = min(len_noise, len_mix)
# add the noise
mix = mix[:, :min_len] + noise[:, :min_len]
# fix the length of targets also
targets = targets[:, :min_len, :]
if self.hparams.use_wavedrop:
mix = self.hparams.wavedrop(mix, mix_lens)
if self.hparams.limit_training_signal_len:
mix, targets = self.cut_signals(mix, targets)
# torchaudio.save(
# 'mix.wav', mix.data.cpu(), self.hparams.sample_rate
# )
# torchaudio.save(
# 'targets.wav', targets.squeeze(-1).data.cpu(), self.hparams.sample_rate
# )
# Separation
if self.use_freq_domain:
mix_w = self.compute_feats(mix)
est_mask = self.modules.masknet(mix_w)
sep_h = mix_w * est_mask
est_source = self.hparams.resynth(torch.expm1(sep_h), mix)
else:
mix_w = self.hparams.Encoder(mix)
est_mask = self.modules.masknet(mix_w)
mix_w = torch.stack([mix_w] * self.hparams.num_spks)
sep_h = mix_w * est_mask
est_source = torch.cat(
[
self.hparams.Decoder(sep_h[i]).unsqueeze(-1)
for i in range(self.hparams.num_spks)
],
dim=-1,
)
# T changed after conv1d in encoder, fix it here
T_origin = mix.size(1)
T_est = est_source.size(1)
est_source = est_source.squeeze(-1)
if T_origin > T_est:
est_source = F.pad(est_source, (0, T_origin - T_est))
else:
est_source = est_source[:, :T_origin]
return [est_source, sep_h], targets.squeeze(-1)
def compute_feats(self, wavs):
"""Feature computation pipeline"""
feats = self.hparams.Encoder(wavs)
feats = spectral_magnitude(feats, power=0.5)
feats = torch.log1p(feats)
return feats
def compute_objectives(self, predictions, targets):
"""Computes the si-snr loss"""
predicted_wavs, predicted_specs = predictions
if self.use_freq_domain:
target_specs = self.compute_feats(targets)
return self.hparams.loss(target_specs, predicted_specs)
else:
return self.hparams.loss(
targets.unsqueeze(-1), predicted_wavs.unsqueeze(-1)
)
def fit_batch(self, batch):
"""Trains one batch"""
# Unpacking batch list
mixture = batch.mix_sig
targets = [batch.s1_sig, batch.s2_sig]
noise = batch.noise_sig[0]
if self.auto_mix_prec:
with autocast():
predictions, targets = self.compute_forward(
mixture, targets, sb.Stage.TRAIN, noise
)
loss = self.compute_objectives(predictions, targets)
# hard threshold the easy dataitems
if self.hparams.threshold_byloss:
th = self.hparams.threshold
loss_to_keep = loss[loss > th]
if loss_to_keep.nelement() > 0:
loss = loss_to_keep.mean()
else:
loss = loss.mean()
if (
loss < self.hparams.loss_upper_lim and loss.nelement() > 0
): # the fix for computational problems
self.scaler.scale(loss).backward()
if self.hparams.clip_grad_norm >= 0:
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(
self.modules.parameters(), self.hparams.clip_grad_norm,
)
self.scaler.step(self.optimizer)
self.scaler.update()
else:
self.nonfinite_count += 1
logger.info(
"infinite loss or empty loss! it happened {} times so far - skipping this batch".format(
self.nonfinite_count
)
)
loss.data = torch.tensor(0).to(self.device)
else:
predictions, targets = self.compute_forward(
mixture, targets, sb.Stage.TRAIN, noise
)
loss = self.compute_objectives(predictions, targets)
if self.hparams.threshold_byloss:
th = self.hparams.threshold
loss_to_keep = loss[loss > th]
if loss_to_keep.nelement() > 0:
loss = loss_to_keep.mean()
else:
loss = loss.mean()
if (
loss < self.hparams.loss_upper_lim and loss.nelement() > 0
): # the fix for computational problems
loss.backward()
if self.hparams.clip_grad_norm >= 0:
torch.nn.utils.clip_grad_norm_(
self.modules.parameters(), self.hparams.clip_grad_norm
)
self.optimizer.step()
else:
self.nonfinite_count += 1
logger.info(
"infinite loss or empty loss! it happened {} times so far - skipping this batch".format(
self.nonfinite_count
)
)
loss.data = torch.tensor(0).to(self.device)
self.optimizer.zero_grad()
return loss.detach().cpu()
def evaluate_batch(self, batch, stage):
"""Computations needed for validation/test batches"""
snt_id = batch.id
mixture = batch.mix_sig
targets = [batch.s1_sig, batch.s2_sig]
with torch.no_grad():
predictions, targets = self.compute_forward(mixture, targets, stage)
loss = self.compute_objectives(predictions, targets)
if stage != sb.Stage.TRAIN:
self.pesq_metric.append(
ids=batch.id, predict=predictions[0].cpu(), target=targets.cpu()
)
# Manage audio file saving
if stage == sb.Stage.TEST and self.hparams.save_audio:
if hasattr(self.hparams, "n_audio_to_save"):
if self.hparams.n_audio_to_save > 0:
self.save_audio(snt_id[0], mixture, targets, predictions[0])
self.hparams.n_audio_to_save += -1
else:
self.save_audio(snt_id[0], mixture, targets, predictions[0])
return loss.detach()
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch"""
if stage != sb.Stage.TRAIN:
# Define function taking (prediction, target) for parallel eval
def pesq_eval(pred_wav, target_wav):
"""Computes the PESQ evaluation metric"""
psq_mode = "wb" if self.hparams.sample_rate == 16000 else "nb"
try:
return pesq(
fs=self.hparams.sample_rate,
ref=target_wav.numpy(),
deg=pred_wav.numpy(),
mode=psq_mode,
)
except Exception:
print("pesq encountered an error for this data item")
return 0
self.pesq_metric = MetricStats(
metric=pesq_eval, n_jobs=1, batch_eval=False
)
def on_stage_end(self, stage, stage_loss, epoch):
"""Gets called at the end of a epoch."""
# Compute/store important stats
stage_stats = {"loss": stage_loss}
if stage == sb.Stage.TRAIN:
self.train_stats = stage_stats
else:
stats = {
"loss": stage_loss,
"pesq": self.pesq_metric.summarize("average"),
}
# Perform end-of-iteration things, like annealing, logging, etc.
if stage == sb.Stage.VALID:
# Learning rate annealing
if isinstance(
self.hparams.lr_scheduler, schedulers.ReduceLROnPlateau
):
current_lr, next_lr = self.hparams.lr_scheduler(
[self.optimizer], epoch, stage_loss
)
schedulers.update_learning_rate(self.optimizer, next_lr)
else:
# if we do not use the reducelronplateau, we do not change the lr
current_lr = self.hparams.optimizer.optim.param_groups[0]["lr"]
self.hparams.train_logger.log_stats(
stats_meta={"epoch": epoch, "lr": current_lr},
train_stats=self.train_stats,
valid_stats=stats,
)
if (
hasattr(self.hparams, "save_all_checkpoints")
and self.hparams.save_all_checkpoints
):
self.checkpointer.save_checkpoint(meta={"pesq": stats["pesq"]})
else:
self.checkpointer.save_and_keep_only(
meta={"pesq": stats["pesq"]}, max_keys=["pesq"],
)
elif stage == sb.Stage.TEST:
self.hparams.train_logger.log_stats(
stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
test_stats=stats,
)
def add_speed_perturb(self, targets, targ_lens):
"""Adds speed perturbation and random_shift to the input signals"""
min_len = -1
recombine = False
if self.hparams.use_speedperturb:
# Performing speed change (independently on each source)
new_targets = []
recombine = True
for i in range(targets.shape[-1]):
new_target = self.hparams.speedperturb(
targets[:, :, i], targ_lens
)
new_targets.append(new_target)
if i == 0:
min_len = new_target.shape[-1]
else:
if new_target.shape[-1] < min_len:
min_len = new_target.shape[-1]
if self.hparams.use_rand_shift:
# Performing random_shift (independently on each source)
recombine = True
for i in range(targets.shape[-1]):
rand_shift = torch.randint(
self.hparams.min_shift, self.hparams.max_shift, (1,)
)
new_targets[i] = new_targets[i].to(self.device)
new_targets[i] = torch.roll(
new_targets[i], shifts=(rand_shift[0],), dims=1
)
# Re-combination
if recombine:
if self.hparams.use_speedperturb:
targets = torch.zeros(
targets.shape[0],
min_len,
targets.shape[-1],
device=targets.device,
dtype=torch.float,
)
for i, new_target in enumerate(new_targets):
targets[:, :, i] = new_targets[i][:, 0:min_len]
mix = targets.sum(-1)
return mix, targets
def cut_signals(self, mixture, targets):
"""This function selects a random segment of a given length withing the mixture.
The corresponding targets are selected accordingly"""
randstart = torch.randint(
0,
1 + max(0, mixture.shape[1] - self.hparams.training_signal_len),
(1,),
).item()
targets = targets[
:, randstart : randstart + self.hparams.training_signal_len, :
]
mixture = mixture[
:, randstart : randstart + self.hparams.training_signal_len
]
return mixture, targets
def reset_layer_recursively(self, layer):
"""Reinitializes the parameters of the neural networks"""
if hasattr(layer, "reset_parameters"):
layer.reset_parameters()
for child_layer in layer.modules():
if layer != child_layer:
self.reset_layer_recursively(child_layer)
def save_results(self, test_data):
"""This script computes the SDR and SI-SNR metrics and saves
them into a csv file"""
# This package is required for SDR computation
from mir_eval.separation import bss_eval_sources
# Create folders where to store audio
save_file = os.path.join(self.hparams.output_folder, "test_results.csv")
# Variable init
all_sdrs = []
all_sdrs_i = []
all_sisnrs = []
all_sisnrs_i = []
all_pesqs = []
csv_columns = ["snt_id", "sdr", "sdr_i", "si-snr", "si-snr_i", "pesq"]
test_loader = sb.dataio.dataloader.make_dataloader(
test_data, **self.hparams.dataloader_opts
)
with open(save_file, "w") as results_csv:
writer = csv.DictWriter(results_csv, fieldnames=csv_columns)
writer.writeheader()
# Loop over all test sentence
with tqdm(test_loader, dynamic_ncols=True) as t:
for i, batch in enumerate(t):
# Apply Separation
mixture, mix_len = batch.mix_sig
snt_id = batch.id
targets = [batch.s1_sig, batch.s2_sig]
if self.hparams.num_spks == 3:
targets.append(batch.s3_sig)
with torch.no_grad():
predictions, targets = self.compute_forward(
batch.mix_sig, targets, sb.Stage.TEST
)
# Compute SI-SNR
sisnr = self.compute_objectives(predictions, targets)
# Compute SI-SNR improvement
mixture_signal = torch.stack(
[mixture] * self.hparams.num_spks, dim=-1
)
mixture_signal = mixture_signal.to(targets.device)
sisnr_baseline = self.compute_objectives(
[mixture_signal.squeeze(-1), None], targets
)
sisnr_i = sisnr - sisnr_baseline
# Compute SDR
sdr, _, _, _ = bss_eval_sources(
targets[0].t().cpu().numpy(),
predictions[0][0].t().detach().cpu().numpy(),
)
sdr_baseline, _, _, _ = bss_eval_sources(
targets[0].t().cpu().numpy(),
mixture_signal[0].t().detach().cpu().numpy(),
)
sdr_i = sdr.mean() - sdr_baseline.mean()
# Compute PESQ
psq_mode = (
"wb" if self.hparams.sample_rate == 16000 else "nb"
)
psq = pesq(
self.hparams.sample_rate,
targets.squeeze().cpu().numpy(),
predictions[0].squeeze().cpu().numpy(),
mode=psq_mode,
)
# Saving on a csv file
row = {
"snt_id": snt_id[0],
"sdr": sdr.mean(),
"sdr_i": sdr_i,
"si-snr": -sisnr.item(),
"si-snr_i": -sisnr_i.item(),
"pesq": psq,
}
writer.writerow(row)
# Metric Accumulation
all_sdrs.append(sdr.mean())
all_sdrs_i.append(sdr_i.mean())
all_sisnrs.append(-sisnr.item())
all_sisnrs_i.append(-sisnr_i.item())
all_pesqs.append(psq)
row = {
"snt_id": "avg",
"sdr": np.array(all_sdrs).mean(),
"sdr_i": np.array(all_sdrs_i).mean(),
"si-snr": np.array(all_sisnrs).mean(),
"si-snr_i": np.array(all_sisnrs_i).mean(),
"pesq": np.array(all_pesqs).mean(),
}
writer.writerow(row)
logger.info("Mean SISNR is {}".format(np.array(all_sisnrs).mean()))
logger.info("Mean SISNRi is {}".format(np.array(all_sisnrs_i).mean()))
logger.info("Mean SDR is {}".format(np.array(all_sdrs).mean()))
logger.info("Mean SDRi is {}".format(np.array(all_sdrs_i).mean()))
logger.info("Mean PESQ {}".format(np.array(all_pesqs).mean()))
def save_audio(self, snt_id, mixture, targets, predictions):
"saves the test audio (mixture, targets, and estimated sources) on disk"
# Create outout folder
save_path = os.path.join(self.hparams.save_folder, "audio_results")
if not os.path.exists(save_path):
os.mkdir(save_path)
# Estimated source
signal = predictions[0, :]
signal = signal / signal.abs().max()
save_file = os.path.join(
save_path, "item{}_sourcehat.wav".format(snt_id)
)
torchaudio.save(
save_file, signal.unsqueeze(0).cpu(), self.hparams.sample_rate
)
# Original source
signal = targets[0, :]
signal = signal / signal.abs().max()
save_file = os.path.join(save_path, "item{}_source.wav".format(snt_id))
torchaudio.save(
save_file, signal.unsqueeze(0).cpu(), self.hparams.sample_rate
)
# Mixture
signal = mixture[0][0, :]
signal = signal / signal.abs().max()
save_file = os.path.join(save_path, "item{}_mix.wav".format(snt_id))
torchaudio.save(
save_file, signal.unsqueeze(0).cpu(), self.hparams.sample_rate
)
class SEBrain(sb.Brain):
def compute_forward_g(self, noisy_wavs):
"""Forward computations of the generator. Input noisy signal,
output clean signal"""
noisy_wavs = noisy_wavs.to(self.device)
predict_wavs = self.modules["model_g"](noisy_wavs)
return predict_wavs
def compute_forward_d(self, noisy_wavs, clean_wavs):
"""Forward computations from discriminator. Input denoised-noisy pair,
output whether denoising was properly acheived"""
noisy_wavs = noisy_wavs.to(self.device)
clean_wavs = clean_wavs.to(self.device)
inpt = torch.cat((noisy_wavs, clean_wavs), -1)
out = self.modules["model_d"](inpt)
return out
def compute_objectives_d1(self, d_outs, batch):
"""Computes the loss of a discriminator given predicted and
targeted outputs, with target being clean"""
loss = self.hparams.compute_cost["d1"](d_outs)
self.loss_metric_d1.append(batch.id, d_outs, reduction="batch")
return loss
def compute_objectives_d2(self, d_outs, batch):
"""Computes the loss of a discriminator given predicted and targeted outputs,
with target being noisy"""
loss = self.hparams.compute_cost["d2"](d_outs)
self.loss_metric_d2.append(batch.id, d_outs, reduction="batch")
return loss
def compute_objectives_g3(
self,
d_outs,
predict_wavs,
clean_wavs,
batch,
stage,
z_mean=None,
z_logvar=None,
):
"""Computes the loss of the generator based on discriminator and generator losses"""
clean_wavs_orig, lens = batch.clean_sig
clean_wavs_orig = clean_wavs_orig.to(self.device)
clean_wavs = clean_wavs.to(self.device)
loss = self.hparams.compute_cost["g3"](
d_outs,
predict_wavs,
clean_wavs,
lens,
l1LossCoeff=self.hparams.l1LossCoeff,
klLossCoeff=self.hparams.klLossCoeff,
z_mean=z_mean,
z_logvar=z_logvar,
)
self.loss_metric_g3.append(
batch.id,
d_outs,
predict_wavs,
clean_wavs,
lens,
l1LossCoeff=self.hparams.l1LossCoeff,
klLossCoeff=self.hparams.klLossCoeff,
z_mean=z_mean,
z_logvar=z_logvar,
reduction="batch",
)
if stage != sb.Stage.TRAIN:
# Evaluate speech quality/intelligibility
predict_wavs = predict_wavs.reshape(self.batch_current, -1)
clean_wavs = clean_wavs.reshape(self.batch_current, -1)
predict_wavs = predict_wavs[:, 0:self.original_len]
clean_wavs = clean_wavs[:, 0:self.original_len]
self.stoi_metric.append(batch.id,
predict_wavs,
clean_wavs,
lens,
reduction="batch")
self.pesq_metric.append(batch.id,
predict=predict_wavs.cpu(),
target=clean_wavs.cpu())
# Write enhanced test wavs to file
if stage == sb.Stage.TEST:
lens = lens * clean_wavs.shape[1]
for name, pred_wav, length in zip(batch.id, predict_wavs,
lens):
name += ".wav"
enhance_path = os.path.join(self.hparams.enhanced_folder,
name)
print(enhance_path)
pred_wav = pred_wav / torch.max(torch.abs(pred_wav)) * 0.99
torchaudio.save(
enhance_path,
pred_wav[:int(length)].cpu().unsqueeze(0),
hparams["sample_rate"],
)
return loss
def fit_batch(self, batch):
"""Fit one batch, override to do multiple updates.
The default implementation depends on a few methods being defined
with a particular behavior:
* ``compute_forward()``
* ``compute_objectives()``
Also depends on having optimizers passed at initialization.
Arguments
---------
batch : list of torch.Tensors
Batch of data to use for training. Default implementation assumes
this batch has two elements: inputs and targets.
Returns
-------
detached loss
"""
noisy_wavs, lens = batch.noisy_sig
clean_wavs, lens = batch.clean_sig
# split sentences in smaller chunks
noisy_wavs = create_chunks(
noisy_wavs,
chunk_size=hparams["chunk_size"],
chunk_stride=hparams["chunk_stride"],
)
clean_wavs = create_chunks(
clean_wavs,
chunk_size=hparams["chunk_size"],
chunk_stride=hparams["chunk_stride"],
)
# first of three step training process detailed in SEGAN paper
out_d1 = self.compute_forward_d(noisy_wavs, clean_wavs)
loss_d1 = self.compute_objectives_d1(out_d1, batch)
loss_d1.backward()
if self.check_gradients(loss_d1):
self.optimizer_d.step()
self.optimizer_d.zero_grad()
# second training step
z_mean = None
z_logvar = None
if self.modules["model_g"].latent_vae:
out_g2, z_mean, z_logvar = self.compute_forward_g(noisy_wavs)
else:
out_g2 = self.compute_forward_g(noisy_wavs)
out_d2 = self.compute_forward_d(out_g2, clean_wavs)
loss_d2 = self.compute_objectives_d2(out_d2, batch)
loss_d2.backward(retain_graph=True)
if self.check_gradients(loss_d2):
self.optimizer_d.step()
self.optimizer_d.zero_grad()
# third (last) training step
self.optimizer_g.zero_grad()
out_d3 = self.compute_forward_d(out_g2, clean_wavs)
loss_g3 = self.compute_objectives_g3(
out_d3,
out_g2,
clean_wavs,
batch,
sb.Stage.TRAIN,
z_mean=z_mean,
z_logvar=z_logvar,
)
loss_g3.backward()
if self.check_gradients(loss_g3):
self.optimizer_g.step()
self.optimizer_g.zero_grad()
self.optimizer_d.zero_grad()
loss_d1.detach().cpu()
loss_d2.detach().cpu()
loss_g3.detach().cpu()
return loss_d1 + loss_d2 + loss_g3
def evaluate_batch(self, batch, stage):
"""Evaluate one batch, override for different procedure than train.
The default implementation depends on two methods being defined
with a particular behavior:
* ``compute_forward()``
* ``compute_objectives()``
Arguments
---------
batch : list of torch.Tensors
Batch of data to use for evaluation. Default implementation assumes
this batch has two elements: inputs and targets.
stage : Stage
The stage of the experiment: Stage.VALID, Stage.TEST
Returns
-------
detached loss
"""
noisy_wavs, lens = batch.noisy_sig
clean_wavs, lens = batch.clean_sig
self.batch_current = clean_wavs.shape[0]
self.original_len = clean_wavs.shape[1]
# Add padding to make sure all the signal will be processed.
padding_elements = torch.zeros(clean_wavs.shape[0],
hparams["chunk_size"],
device=clean_wavs.device)
clean_wavs = torch.cat([clean_wavs, padding_elements], dim=1)
noisy_wavs = torch.cat([noisy_wavs, padding_elements], dim=1)
# Split sentences in smaller chunks
noisy_wavs = create_chunks(
noisy_wavs,
chunk_size=hparams["chunk_size"],
chunk_stride=hparams["chunk_size"],
)
clean_wavs = create_chunks(
clean_wavs,
chunk_size=hparams["chunk_size"],
chunk_stride=hparams["chunk_size"],
)
# Perform speech enhancement with the current model
out_d1 = self.compute_forward_d(noisy_wavs, clean_wavs)
loss_d1 = self.compute_objectives_d1(out_d1, batch)
z_mean = None
z_logvar = None
if self.modules["model_g"].latent_vae:
out_g2, z_mean, z_logvar = self.compute_forward_g(noisy_wavs)
else:
out_g2 = self.compute_forward_g(noisy_wavs)
out_d2 = self.compute_forward_d(out_g2, clean_wavs)
loss_d2 = self.compute_objectives_d2(out_d2, batch)
loss_g3 = self.compute_objectives_g3(
out_d2,
out_g2,
clean_wavs,
batch,
stage=stage,
z_mean=z_mean,
z_logvar=z_logvar,
)
loss_d1.detach().cpu()
loss_d2.detach().cpu()
loss_g3.detach().cpu()
return loss_d1 + loss_d2 + loss_g3
def init_optimizers(self):
"""Called during ``on_fit_start()``, initialize optimizers
after parameters are fully configured (e.g. DDP, jit).
The default implementation of this method depends on an optimizer
class being passed at initialization that takes only a list
of parameters (e.g., a lambda or a partial function definition).
This creates a single optimizer that optimizes all trainable params.
Override this class if there are multiple optimizers.
"""
if self.opt_class is not None:
self.optimizer_d = self.opt_class(
self.modules["model_d"].parameters())
self.optimizer_g = self.opt_class(
self.modules["model_g"].parameters())
if self.checkpointer is not None:
self.checkpointer.add_recoverable("optimizer_g",
self.optimizer_g)
self.checkpointer.add_recoverable("optimizer_d",
self.optimizer_d)
def on_stage_start(self, stage, epoch=None):
"""Gets called at the beginning of each epoch"""
self.loss_metric_d1 = MetricStats(
metric=self.hparams.compute_cost["d1"])
self.loss_metric_d2 = MetricStats(
metric=self.hparams.compute_cost["d2"])
self.loss_metric_g3 = MetricStats(
metric=self.hparams.compute_cost["g3"])
self.stoi_metric = MetricStats(metric=stoi_loss)
# Define function taking (prediction, target) for parallel eval
def pesq_eval(pred_wav, target_wav):
"""Computes the PESQ evaluation metric"""
return pesq(
fs=hparams["sample_rate"],
ref=target_wav.numpy().squeeze(),
deg=pred_wav.numpy().squeeze(),
mode="wb",
)
if stage != sb.Stage.TRAIN:
self.pesq_metric = MetricStats(metric=pesq_eval,
batch_eval=False,
n_jobs=1)
def on_stage_end(self, stage, stage_loss, epoch=None):
"""Gets called at the end of an epoch."""
if stage == sb.Stage.TRAIN:
self.train_loss = stage_loss
self.train_stats = { # "loss": self.loss_metric.scores,
"loss_d1": self.loss_metric_d1.scores,
"loss_d2": self.loss_metric_d2.scores,
"loss_g3": self.loss_metric_g3.scores,
}
else:
stats = {
"loss": stage_loss,
"pesq": self.pesq_metric.summarize("average"),
"stoi": -self.stoi_metric.summarize("average"),
}
if stage == sb.Stage.VALID:
if self.hparams.use_tensorboard:
valid_stats = {
# "loss": self.loss_metric.scores,
"loss_d1": self.loss_metric_d1.scores,
"loss_d2": self.loss_metric_d2.scores,
"loss_g3": self.loss_metric_g3.scores,
"stoi": self.stoi_metric.scores,
"pesq": self.pesq_metric.scores,
}
self.hparams.tensorboard_train_logger.log_stats(
{"Epoch": epoch}, self.train_stats, valid_stats)
self.hparams.train_logger.log_stats(
{"Epoch": epoch},
train_stats={"loss": self.train_loss},
valid_stats=stats,
)
self.checkpointer.save_and_keep_only(meta=stats, max_keys=["pesq"])
if stage == sb.Stage.TEST:
self.hparams.train_logger.log_stats(
{"Epoch loaded": self.hparams.epoch_counter.current},
test_stats=stats,
)