Files
SISU/training.py
2025-02-10 19:35:50 +02:00

165 lines
6.5 KiB
Python

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchaudio
import tqdm
import argparse
import math
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import AudioUtils
from data import AudioDataset
from generator import SISUGenerator
from discriminator import SISUDiscriminator
def perceptual_loss(y_true, y_pred):
return torch.mean((y_true - y_pred) ** 2)
def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
optimizer_d.zero_grad()
# Forward pass for real samples
discriminator_decision_from_real = discriminator(high_quality[0])
d_loss_real = criterion_d(discriminator_decision_from_real, real_labels)
# Forward pass for fake samples (from generator output)
generator_output = generator(low_quality[0])
discriminator_decision_from_fake = discriminator(generator_output.detach())
d_loss_fake = criterion_d(discriminator_decision_from_fake, fake_labels)
# Combine real and fake losses
d_loss = (d_loss_real + d_loss_fake) / 2.0
# Backward pass and optimization
d_loss.backward()
nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0) # Gradient Clipping
optimizer_d.step()
return d_loss
def generator_train(low_quality, real_labels):
optimizer_g.zero_grad()
# Forward pass for fake samples (from generator output)
generator_output = generator(low_quality[0])
discriminator_decision = discriminator(generator_output)
g_loss = criterion_g(discriminator_decision, real_labels)
g_loss.backward()
optimizer_g.step()
return generator_output
def first(objects):
if len(objects) >= 1:
return objects[0]
return objects
# Init script argument parser
parser = argparse.ArgumentParser(description="Training script")
parser.add_argument("--generator", type=str, default=None,
help="Path to the generator model file")
parser.add_argument("--discriminator", type=str, default=None,
help="Path to the discriminator model file")
args = parser.parse_args()
# 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)
# ========= SINGLE =========
train_data_loader = DataLoader(dataset, batch_size=16, shuffle=True)
# Initialize models and move them to device
generator = SISUGenerator()
discriminator = SISUDiscriminator()
if args.generator is not None:
generator.load_state_dict(torch.load(args.generator, weights_only=True))
if args.discriminator is not None:
discriminator.load_state_dict(torch.load(args.discriminator, weights_only=True))
generator = generator.to(device)
discriminator = discriminator.to(device)
# Loss
criterion_g = nn.MSELoss()
criterion_d = nn.BCELoss()
# Optimizers
optimizer_g = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
# Scheduler
scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_g, mode='min', factor=0.5, patience=5)
scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_d, mode='min', factor=0.5, patience=5)
def start_training():
generator_epochs = 5000
for generator_epoch in range(generator_epochs):
low_quality_audio = (torch.empty((1)), 1)
high_quality_audio = (torch.empty((1)), 1)
ai_enhanced_audio = (torch.empty((1)), 1)
times_correct = 0
# ========= TRAINING =========
for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Epoch {generator_epoch+1}/{generator_epochs}"):
# for high_quality_clip, low_quality_clip in train_data_loader:
high_quality_sample = (high_quality_clip[0].to(device), high_quality_clip[1])
low_quality_sample = (low_quality_clip[0].to(device), low_quality_clip[1])
# ========= LABELS =========
batch_size = high_quality_clip[0].size(0)
real_labels = torch.ones(batch_size, 1).to(device)
fake_labels = torch.zeros(batch_size, 1).to(device)
# ========= DISCRIMINATOR =========
discriminator.train()
discriminator_train(high_quality_sample, low_quality_sample, real_labels, fake_labels)
# ========= GENERATOR =========
generator.train()
generator_output = generator_train(low_quality_sample, real_labels)
# ========= SAVE LATEST AUDIO =========
high_quality_audio = (first(high_quality_clip[0]), high_quality_clip[1][0])
low_quality_audio = (first(low_quality_clip[0]), low_quality_clip[1][0])
ai_enhanced_audio = (first(generator_output[0]), high_quality_clip[1][0])
print(high_quality_audio)
print(f"Saved epoch {generator_epoch}!")
torchaudio.save(f"./output/epoch-{generator_epoch}-audio-crap.wav", low_quality_audio[0][0].cpu(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
torchaudio.save(f"./output/epoch-{generator_epoch}-audio-ai.wav", ai_enhanced_audio[0][0].cpu(), ai_enhanced_audio[1])
torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
#metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
#print(f"Generator metric {metric}!")
#scheduler_g.step(metric)
if generator_epoch % 10 == 0:
print(f"Saved epoch {generator_epoch}!")
torchaudio.save(f"./output/epoch-{generator_epoch}-audio-crap.wav", low_quality_audio[0][0].cpu(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
torchaudio.save(f"./output/epoch-{generator_epoch}-audio-ai.wav", ai_enhanced_audio[0][0].cpu(), ai_enhanced_audio[1])
torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
torch.save(discriminator.state_dict(), f"models/current-epoch-discriminator.pt")
torch.save(generator.state_dict(), f"models/current-epoch-generator.pt")
torch.save(discriminator.state_dict(), "models/epoch-5000-discriminator.pt")
torch.save(generator.state_dict(), "models/epoch-5000-generator.pt")
print("Training complete!")
start_training()