113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
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!")
|