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:albemic: | Real-time testing...

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NikkeDoy
2025-05-04 22:48:57 +03:00
parent d70c86c257
commit 660b41aef8
4 changed files with 107 additions and 75 deletions
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14
training_utils.py
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@ -20,12 +20,10 @@ def mel_spectrogram_l1_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tenso
mel_spec_true = mel_transform(y_true)
mel_spec_pred = mel_transform(y_pred)
# Ensure same time dimension length (due to potential framing differences)
min_len = min(mel_spec_true.shape[-1], mel_spec_pred.shape[-1])
mel_spec_true = mel_spec_true[..., :min_len]
mel_spec_pred = mel_spec_pred[..., :min_len]
# L1 Loss (Mean Absolute Error)
loss = torch.mean(torch.abs(mel_spec_true - mel_spec_pred))
return loss
@ -69,11 +67,11 @@ def discriminator_train(high_quality, low_quality, real_labels, fake_labels, dis
optimizer.zero_grad()
# Forward pass for real samples
discriminator_decision_from_real = discriminator(high_quality[0])
discriminator_decision_from_real = discriminator(high_quality)
d_loss_real = criterion(discriminator_decision_from_real, real_labels)
with torch.no_grad():
generator_output = generator(low_quality[0])
generator_output = generator(low_quality)
discriminator_decision_from_fake = discriminator(generator_output)
d_loss_fake = criterion(discriminator_decision_from_fake, fake_labels.expand_as(discriminator_decision_from_fake))
@ -105,7 +103,7 @@ def generator_train(
):
g_optimizer.zero_grad()
generator_output = generator(low_quality[0])
generator_output = generator(low_quality)
discriminator_decision = discriminator(generator_output)
adversarial_loss = adv_criterion(discriminator_decision, real_labels.expand_as(discriminator_decision))
@ -116,15 +114,15 @@ def generator_train(
# Calculate Mel L1 Loss if weight is positive
if lambda_mel_l1 > 0:
mel_l1 = mel_spectrogram_l1_loss(mel_transform, high_quality[0], generator_output)
mel_l1 = mel_spectrogram_l1_loss(mel_transform, high_quality, generator_output)
# Calculate Log STFT L1 Loss if weight is positive
if lambda_log_stft > 0:
log_stft_l1 = log_stft_magnitude_loss(stft_transform, high_quality[0], generator_output)
log_stft_l1 = log_stft_magnitude_loss(stft_transform, high_quality, generator_output)
# Calculate MFCC Loss if weight is positive
if lambda_mfcc > 0:
mfcc_l = gpu_mfcc_loss(mfcc_transform, high_quality[0], generator_output)
mfcc_l = gpu_mfcc_loss(mfcc_transform, high_quality, generator_output)
mel_l1_tensor = torch.tensor(mel_l1, device=device) if isinstance(mel_l1, float) else mel_l1
log_stft_l1_tensor = torch.tensor(log_stft_l1, device=device) if isinstance(log_stft_l1, float) else log_stft_l1