193 lines
7.1 KiB
Python
193 lines
7.1 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 torch.nn.functional as F
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import torchaudio
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import tqdm
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import argparse
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import math
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import os
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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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import AudioUtils
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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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import torchaudio.transforms as T
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# Init script argument parser
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parser = argparse.ArgumentParser(description="Training script")
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parser.add_argument("--generator", type=str, default=None,
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help="Path to the generator model file")
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parser.add_argument("--discriminator", type=str, default=None,
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help="Path to the discriminator model file")
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parser.add_argument("--device", type=str, default="cpu", help="Select device")
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parser.add_argument("--epoch", type=int, default=0, help="Current epoch for model versioning")
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parser.add_argument("--debug", action="store_true", help="Print debug logs")
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args = parser.parse_args()
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device = torch.device(args.device if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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mfcc_transform = T.MFCC(
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sample_rate=44100,
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n_mfcc=20,
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melkwargs={'n_fft': 2048, 'hop_length': 256}
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).to(device)
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def gpu_mfcc_loss(y_true, y_pred):
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mfccs_true = mfcc_transform(y_true)
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mfccs_pred = mfcc_transform(y_pred)
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min_len = min(mfccs_true.shape[2], mfccs_pred.shape[2])
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mfccs_true = mfccs_true[:, :, :min_len]
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mfccs_pred = mfccs_pred[:, :, :min_len]
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loss = torch.mean((mfccs_true - mfccs_pred)**2)
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return loss
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def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
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optimizer_d.zero_grad()
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# Forward pass for real samples
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discriminator_decision_from_real = discriminator(high_quality[0])
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d_loss_real = criterion_d(discriminator_decision_from_real, real_labels)
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# Forward pass for fake samples (from generator output)
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generator_output = generator(low_quality[0])
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discriminator_decision_from_fake = discriminator(generator_output.detach())
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d_loss_fake = criterion_d(discriminator_decision_from_fake, fake_labels)
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# Combine real and fake losses
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d_loss = (d_loss_real + d_loss_fake) / 2.0
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# Backward pass and optimization
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d_loss.backward()
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nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0) # Gradient Clipping
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optimizer_d.step()
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return d_loss
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def generator_train(low_quality, high_quality, real_labels):
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optimizer_g.zero_grad()
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# Forward pass for fake samples (from generator output)
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generator_output = generator(low_quality[0])
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#mfcc_l = gpu_mfcc_loss(high_quality[0], generator_output)
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discriminator_decision = discriminator(generator_output)
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adversarial_loss = criterion_g(discriminator_decision, real_labels)
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#combined_loss = adversarial_loss + 0.5 * mfcc_l
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adversarial_loss.backward()
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optimizer_g.step()
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#return (generator_output, combined_loss, adversarial_loss, mfcc_l)
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return (generator_output, adversarial_loss)
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debug = args.debug
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# Initialize dataset and dataloader
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dataset_dir = './dataset/good'
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dataset = AudioDataset(dataset_dir, device)
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# ========= SINGLE =========
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train_data_loader = DataLoader(dataset, batch_size=8, 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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epoch: int = args.epoch
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generator = generator.to(device)
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discriminator = discriminator.to(device)
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if args.generator is not None:
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generator.load_state_dict(torch.load(args.generator, map_location=device, weights_only=True))
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if args.discriminator is not None:
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discriminator.load_state_dict(torch.load(args.discriminator, map_location=device, weights_only=True))
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# Loss
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criterion_g = nn.MSELoss()
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criterion_d = nn.BCELoss()
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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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# Scheduler
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scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_g, mode='min', factor=0.5, patience=5)
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scheduler_d = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_d, mode='min', factor=0.5, patience=5)
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models_dir = "models"
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os.makedirs(models_dir, exist_ok=True)
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def start_training():
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generator_epochs = 5000
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for generator_epoch in range(generator_epochs):
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low_quality_audio = (torch.empty((1)), 1)
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high_quality_audio = (torch.empty((1)), 1)
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ai_enhanced_audio = (torch.empty((1)), 1)
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times_correct = 0
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# ========= TRAINING =========
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for high_quality_clip, low_quality_clip in tqdm.tqdm(train_data_loader, desc=f"Training epoch {generator_epoch+1}/{generator_epochs}, Current epoch {epoch+1}"):
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# for high_quality_clip, low_quality_clip in train_data_loader:
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high_quality_sample = (high_quality_clip[0], high_quality_clip[1])
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low_quality_sample = (low_quality_clip[0], low_quality_clip[1])
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# ========= LABELS =========
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batch_size = high_quality_clip[0].size(0)
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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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# ========= DISCRIMINATOR =========
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discriminator.train()
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d_loss = discriminator_train(high_quality_sample, low_quality_sample, real_labels, fake_labels)
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# ========= GENERATOR =========
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generator.train()
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#generator_output, combined_loss, adversarial_loss, mfcc_l = generator_train(low_quality_sample, high_quality_sample, real_labels)
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generator_output, adversarial_loss = generator_train(low_quality_sample, high_quality_sample, real_labels)
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if debug:
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print(d_loss, adversarial_loss)
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scheduler_d.step(d_loss)
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scheduler_g.step(adversarial_loss)
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# ========= SAVE LATEST AUDIO =========
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high_quality_audio = (high_quality_clip[0][0], high_quality_clip[1][0])
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low_quality_audio = (low_quality_clip[0][0], low_quality_clip[1][0])
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ai_enhanced_audio = (generator_output[0], high_quality_clip[1][0])
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new_epoch = generator_epoch+epoch
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if generator_epoch % 10 == 0:
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print(f"Saved epoch {new_epoch}!")
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torchaudio.save(f"./output/epoch-{new_epoch}-audio-crap.wav", low_quality_audio[0].cpu(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
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torchaudio.save(f"./output/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu(), ai_enhanced_audio[1])
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torchaudio.save(f"./output/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0].cpu(), high_quality_audio[1])
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if debug:
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print(generator.state_dict().keys())
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print(discriminator.state_dict().keys())
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torch.save(discriminator.state_dict(), f"{models_dir}/discriminator_epoch_{new_epoch}.pt")
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torch.save(generator.state_dict(), f"{models_dir}/generator_epoch_{new_epoch}.pt")
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torch.save(discriminator, "models/epoch-5000-discriminator.pt")
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torch.save(generator, "models/epoch-5000-generator.pt")
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print("Training complete!")
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start_training()
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