108 lines
3.4 KiB
Python
108 lines
3.4 KiB
Python
import torch
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import torch.nn as nn
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import torch.optim as optim
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import torchaudio
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import tqdm
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from torch.utils.data import random_split
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from torch.utils.data import DataLoader
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from data import AudioDataset
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from generator import SISUGenerator
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from discriminator import SISUDiscriminator
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# Check for CUDA availability
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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# Initialize dataset and dataloader
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dataset_dir = './dataset/good'
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dataset = AudioDataset(dataset_dir, target_duration=2.0)
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dataset_size = len(dataset)
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train_size = int(dataset_size * .9)
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val_size = int(dataset_size-train_size)
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train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
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train_data_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
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val_data_loader = DataLoader(val_dataset, batch_size=1, shuffle=True)
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# Initialize models and move them to device
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generator = SISUGenerator()
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discriminator = SISUDiscriminator()
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generator = generator.to(device)
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discriminator = discriminator.to(device)
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# Loss
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criterion_g = nn.L1Loss()
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criterion_d = nn.BCEWithLogitsLoss()
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# Optimizers
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optimizer_g = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
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optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
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# Training loop
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num_epochs = 500
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for epoch in range(num_epochs):
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low_quality_audio = torch.empty((1))
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high_quality_audio = torch.empty((1))
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ai_enhanced_audio = torch.empty((1))
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total_d_loss = 0
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total_g_loss = 0
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# Training
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for low_quality, high_quality in tqdm.tqdm(train_data_loader, desc=f"Epoch {epoch+1}/{num_epochs}"):
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high_quality = high_quality.to(device)
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low_quality = low_quality.to(device)
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batch_size = 1
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real_labels = torch.ones(batch_size, 1).to(device)
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fake_labels = torch.zeros(batch_size, 1).to(device)
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###### Train Discriminator ######
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discriminator.train()
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optimizer_d.zero_grad()
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# 1. Real data
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real_outputs = discriminator(high_quality)
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d_loss_real = criterion_d(real_outputs, real_labels)
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# 2. Fake data
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fake_audio = generator(low_quality)
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fake_outputs = discriminator(fake_audio.detach())
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d_loss_fake = criterion_d(fake_outputs, fake_labels)
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d_loss = (d_loss_real + d_loss_fake) / 2.0 # Without gradient penalty
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d_loss.backward()
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optimizer_d.step()
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total_d_loss += d_loss.item()
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generator.train()
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optimizer_g.zero_grad()
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# Generator loss: how well fake data fools the discriminator
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fake_outputs = discriminator(fake_audio) # No detach here
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g_loss = criterion_g(fake_outputs, real_labels) # Train generator to produce real-like outputs
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g_loss.backward()
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optimizer_g.step()
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total_g_loss += g_loss.item()
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low_quality_audio = low_quality
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high_quality_audio = high_quality
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ai_enhanced_audio = fake_audio
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if epoch % 10 == 0:
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print(f"Saved epoch {epoch}!")
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torchaudio.save(f"./output/epoch-{epoch}-audio-crap.wav", low_quality_audio[0].cpu(), 44100)
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torchaudio.save(f"./output/epoch-{epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu(), 44100)
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torchaudio.save(f"./output/epoch-{epoch}-audio-orig.wav", high_quality_audio[0].cpu(), 44100)
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torch.save(generator.state_dict(), "generator.pt")
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torch.save(discriminator.state_dict(), "discriminator.pt")
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print("Training complete!")
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