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training.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchaudio
+import tqdm
+
+from torch.utils.data import random_split
+from torch.utils.data import DataLoader
+
+from data import AudioDataset
+from generator import SISUGenerator
+from discriminator import SISUDiscriminator
+
+# Check for CUDA availability
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# Initialize dataset and dataloader
+dataset_dir = './dataset/good'
+dataset = AudioDataset(dataset_dir, target_duration=2.0)  # 5 seconds target duration
+
+dataset_size = len(dataset)
+train_size = int(dataset_size * .9)
+val_size = int(dataset_size-train_size)
+
+train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
+
+train_data_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
+val_data_loader = DataLoader(val_dataset, batch_size=4, shuffle=True)
+
+# Initialize models and move them to device
+generator = SISUGenerator()
+discriminator = SISUDiscriminator()
+
+generator = generator.to(device)
+discriminator = discriminator.to(device)
+
+# Loss and optimizers
+criterion = nn.MSELoss()  # Use Mean Squared Error loss
+optimizer_g = optim.Adam(generator.parameters(), lr=0.0005, betas=(0.5, 0.999))
+optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
+
+# Learning rate scheduler
+scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_d, mode='min', factor=0.1, patience=5)
+
+# Training loop
+num_epochs = 500
+for epoch in range(num_epochs):
+    latest_crap_audio = torch.empty((2,3), dtype=torch.int64)
+    for high_quality, low_quality in tqdm.tqdm(train_data_loader):
+        # Check for NaN values in input tensors
+        if torch.isnan(low_quality).any() or torch.isnan(high_quality).any():
+            continue
+
+        high_quality = high_quality.to(device)
+        low_quality = low_quality.to(device)
+
+        batch_size = low_quality.size(0)
+
+        # Labels
+        real_labels = torch.ones(batch_size, 1).to(device)
+        fake_labels = torch.zeros(batch_size, 1).to(device)
+
+        # Train Discriminator
+        optimizer_d.zero_grad()
+        outputs = discriminator(high_quality)
+        d_loss_real = criterion(outputs, real_labels)
+        d_loss_real.backward()
+
+        resampled_audio = generator(low_quality)
+
+        outputs = discriminator(resampled_audio.detach())
+        d_loss_fake = criterion(outputs, fake_labels)
+        d_loss_fake.backward()
+
+
+        # Gradient clipping for discriminator
+        clip_value = 2.0
+        for param in discriminator.parameters():
+            if param.grad is not None:
+                param.grad.clamp_(-clip_value, clip_value)
+
+        optimizer_d.step()
+
+        d_loss = d_loss_real + d_loss_fake
+
+        # Train Generator
+        optimizer_g.zero_grad()
+        outputs = discriminator(resampled_audio)
+        g_loss = criterion(outputs, real_labels)
+        g_loss.backward()
+
+        # Gradient clipping for generator
+        clip_value = 1.0
+        for param in generator.parameters():
+            if param.grad is not None:
+                param.grad.clamp_(-clip_value, clip_value)
+
+        optimizer_g.step()
+
+        scheduler.step(d_loss + g_loss)
+        latest_crap_audio = resampled_audio
+
+    if epoch % 10 == 0:
+        print(latest_crap_audio.size())
+        torchaudio.save(f"./epoch-{epoch}-audio.wav", latest_crap_audio[0].cpu(), 44100)
+    print(f'Epoch [{epoch+1}/{num_epochs}]')
+
+torch.save(generator.state_dict(), "generator.pt")
+torch.save(discriminator.state_dict(), "discriminator.pt")
+
+print("Training complete!")