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import auraloss
import numpy as np
import pytorch_lightning as pl
from deepafx_st.callbacks.plotting import plot_multi_spectrum
from deepafx_st.metrics import (
LoudnessError,
SpectralCentroidError,
CrestFactorError,
PESQ,
MelSpectralDistance,
)
class LogAudioCallback(pl.callbacks.Callback):
def __init__(self, num_examples=4, peak_normalize=True, sample_rate=22050):
super().__init__()
self.num_examples = 4
self.peak_normalize = peak_normalize
self.metrics = {
"PESQ": PESQ(sample_rate),
"MRSTFT": auraloss.freq.MultiResolutionSTFTLoss(
fft_sizes=[32, 128, 512, 2048, 8192, 32768],
hop_sizes=[16, 64, 256, 1024, 4096, 16384],
win_lengths=[32, 128, 512, 2048, 8192, 32768],
w_sc=0.0,
w_phs=0.0,
w_lin_mag=1.0,
w_log_mag=1.0,
),
"MSD": MelSpectralDistance(sample_rate),
"SCE": SpectralCentroidError(sample_rate),
"CFE": CrestFactorError(),
"LUFS": LoudnessError(sample_rate),
}
self.outputs = []
def on_validation_batch_end(
self,
trainer,
pl_module,
outputs,
batch,
batch_idx,
dataloader_idx,
):
"""Called when the validation batch ends."""
if outputs is not None:
examples = np.min([self.num_examples, outputs["x"].shape[0]])
self.outputs.append(outputs)
if batch_idx == 0:
for n in range(examples):
if batch_idx == 0:
self.log_audio(
outputs,
n,
pl_module.hparams.sample_rate,
pl_module.hparams.val_length,
trainer.global_step,
trainer.logger,
)
def on_validation_end(self, trainer, pl_module):
metrics = {
"PESQ": [],
"MRSTFT": [],
"MSD": [],
"SCE": [],
"CFE": [],
"LUFS": [],
}
for output in self.outputs:
for metric_name, metric in self.metrics.items():
try:
val = metric(output["y_hat"], output["y"])
metrics[metric_name].append(val)
except:
pass
# log final mean metrics
for metric_name, metric in metrics.items():
val = np.mean(metric)
trainer.logger.experiment.add_scalar(
f"metrics/{metric_name}", val, trainer.global_step
)
# clear outputs
self.outputs = []
def compute_metrics(self, metrics_dict, outputs, batch_idx, global_step):
# extract audio
y = outputs["y"][batch_idx, ...].float()
y_hat = outputs["y_hat"][batch_idx, ...].float()
# compute all metrics
for metric_name, metric in self.metrics.items():
try:
val = metric(y_hat.view(1, 1, -1), y.view(1, 1, -1))
metrics_dict[metric_name].append(val)
except:
pass
def log_audio(self, outputs, batch_idx, sample_rate, n_fft, global_step, logger):
x = outputs["x"][batch_idx, ...].float()
y = outputs["y"][batch_idx, ...].float()
y_hat = outputs["y_hat"][batch_idx, ...].float()
if self.peak_normalize:
x /= x.abs().max()
y /= y.abs().max()
y_hat /= y_hat.abs().max()
logger.experiment.add_audio(
f"x/{batch_idx+1}",
x[0:1, :],
global_step,
sample_rate=sample_rate,
)
logger.experiment.add_audio(
f"y/{batch_idx+1}",
y[0:1, :],
global_step,
sample_rate=sample_rate,
)
logger.experiment.add_audio(
f"y_hat/{batch_idx+1}",
y_hat[0:1, :],
global_step,
sample_rate=sample_rate,
)
if "y_ref" in outputs:
y_ref = outputs["y_ref"][batch_idx, ...].float()
if self.peak_normalize:
y_ref /= y_ref.abs().max()
logger.experiment.add_audio(
f"y_ref/{batch_idx+1}",
y_ref[0:1, :],
global_step,
sample_rate=sample_rate,
)
logger.experiment.add_image(
f"spec/{batch_idx+1}",
compare_spectra(
y_hat[0:1, :],
y[0:1, :],
x[0:1, :],
sample_rate=sample_rate,
n_fft=n_fft,
),
global_step,
)
def compare_spectra(
deepafx_y_hat, y, x, baseline_y_hat=None, sample_rate=44100, n_fft=16384
):
legend = ["Corrupted"]
signals = [x]
if baseline_y_hat is not None:
legend.append("Baseline")
signals.append(baseline_y_hat)
legend.append("DeepAFx")
signals.append(deepafx_y_hat)
legend.append("Target")
signals.append(y)
image = plot_multi_spectrum(
ys=signals,
legend=legend,
sample_rate=sample_rate,
n_fft=n_fft,
)
return image
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