import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F 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 # Mel Spectrogram Loss class MelSpectrogramLoss(nn.Module): def __init__(self, sample_rate=44100, n_fft=2048, hop_length=512, n_mels=128): super(MelSpectrogramLoss, self).__init__() self.mel_transform = torchaudio.transforms.MelSpectrogram( sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length, n_mels=n_mels ).to(device) # Move to device def forward(self, y_pred, y_true): mel_pred = self.mel_transform(y_pred) mel_true = self.mel_transform(y_true) return F.l1_loss(mel_pred, mel_true) def snr(y_true, y_pred): noise = y_true - y_pred signal_power = torch.mean(y_true ** 2) noise_power = torch.mean(noise ** 2) snr_db = 10 * torch.log10(signal_power / noise_power) return snr_db def discriminator_train(high_quality, low_quality, scale, real_labels, fake_labels): optimizer_d.zero_grad() discriminator_decision_from_real = discriminator(high_quality) # TODO: Experiment with criterions HERE! d_loss_real = criterion_d(discriminator_decision_from_real, real_labels) generator_output = generator(low_quality, scale) discriminator_decision_from_fake = discriminator(generator_output.detach()) # TODO: Experiment with criterions HERE! d_loss_fake = criterion_d(discriminator_decision_from_fake, fake_labels) d_loss = (d_loss_real + d_loss_fake) / 2.0 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, scale, real_labels): optimizer_g.zero_grad() generator_output = generator(low_quality, scale) discriminator_decision = discriminator(generator_output) # TODO: Fix this shit g_loss = criterion_g(discriminator_decision, real_labels) g_loss.backward() optimizer_g.step() return generator_output # 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) 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=1, shuffle=True) val_data_loader = DataLoader(val_dataset, batch_size=1, shuffle=True) # Initialize models and move them to device generator = SISUGenerator() discriminator = SISUDiscriminator() generator = generator.to(device) discriminator = discriminator.to(device) # Loss criterion_g = nn.L1Loss() criterion_g_mel = MelSpectrogramLoss().to(device) criterion_d = nn.BCEWithLogitsLoss() # 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(): # Training loop # ========= DISCRIMINATOR PRE-TRAINING ========= discriminator_epochs = 1 for discriminator_epoch in range(discriminator_epochs): # ========= TRAINING ========= for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Epoch {discriminator_epoch+1}/{discriminator_epochs}"): high_quality_sample = high_quality_clip[0].to(device) low_quality_sample = low_quality_clip[0].to(device) scale = high_quality_clip[0].shape[2]/low_quality_clip[0].shape[2] # ========= LABELS ========= batch_size = high_quality_sample.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, scale, real_labels, fake_labels) torch.save(discriminator.state_dict(), "models/discriminator-single-shot-pre-train.pt") generator_epochs = 500 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) # ========= TRAINING ========= for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Epoch {generator_epoch+1}/{generator_epochs}"): high_quality_sample = high_quality_clip[0].to(device) low_quality_sample = low_quality_clip[0].to(device) scale = high_quality_clip[0].shape[2]/low_quality_clip[0].shape[2] # ========= 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() for _ in range(3): discriminator_train(high_quality_sample, low_quality_sample, scale, real_labels, fake_labels) # ========= GENERATOR ========= generator.train() generator_output = generator_train(low_quality_sample, scale, real_labels) # ========= SAVE LATEST AUDIO ========= high_quality_audio = high_quality_clip low_quality_audio = low_quality_clip ai_enhanced_audio = (generator_output, high_quality_clip[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(), low_quality_audio[1]) 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]) if generator_epoch % 50 == 0: torch.save(discriminator.state_dict(), f"models/epoch-{generator_epoch}-discriminator.pt") torch.save(generator.state_dict(), f"models/epoch-{generator_epoch}-generator.pt") torch.save(discriminator.state_dict(), "models/epoch-500-discriminator.pt") torch.save(generator.state_dict(), "models/epoch-500-generator.pt") print("Training complete!") start_training()