NikkeDoy/SISU
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

⚗️ | Increase discriminator size and implement mfcc_loss for generator.

Browse Source
This commit is contained in:
NikkeDoy 2025-02-23 13:52:01 +02:00
parent fb7b624c87
commit 741dcce7b4
2 changed files with 65 additions and 56 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

25
discriminator.py
View File

@ -6,8 +6,8 @@ def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dila
padding = (kernel_size // 2) * dilation
return nn.Sequential(
utils.spectral_norm(nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)),
nn.BatchNorm1d(out_channels),
nn.LeakyReLU(0.2, inplace=True) # Changed activation to LeakyReLU
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm1d(out_channels)
)
class SISUDiscriminator(nn.Module):
@ -15,17 +15,16 @@ class SISUDiscriminator(nn.Module):
super(SISUDiscriminator, self).__init__()
layers = 4 # Increased base layer count
self.model = nn.Sequential(
# Initial Convolution
discriminator_block(1, layers, kernel_size=7, stride=2, dilation=1), # Downsample
# Core Discriminator Blocks with varied kernels and dilations
discriminator_block(layers, layers * 2, kernel_size=5, stride=2, dilation=1), # Downsample
discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=4),
discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=16),
discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=8),
discriminator_block(layers * 2, layers, kernel_size=3, dilation=1),
# Final Convolution
discriminator_block(layers, 1, kernel_size=3, stride=1),
discriminator_block(1, layers, kernel_size=7, stride=2), # Initial downsampling
discriminator_block(layers, layers * 2, kernel_size=5, stride=2), # Downsampling
discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=2), # Increased dilation
discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=4), # Increased dilation
discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=8), # Deeper layer!
discriminator_block(layers * 8, layers * 8, kernel_size=5, dilation=1), # Deeper layer!
discriminator_block(layers * 8, layers * 4, kernel_size=3, dilation=2), # Reduced dilation
discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=1),
discriminator_block(layers * 2, layers, kernel_size=3, stride=1), # Final convolution
discriminator_block(layers, 1, kernel_size=3, stride=1)
)
self.global_avg_pool = nn.AdaptiveAvgPool1d(1)

96
training.py
View File

@ -10,6 +10,8 @@ import argparse
import math
import os
from torch.utils.data import random_split
from torch.utils.data import DataLoader
@ -18,8 +20,26 @@ 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)
import librosa
def mfcc_loss(y_true, y_pred, sr):
# 1. Ensure sr is a NumPy scalar (not a Tensor)
if isinstance(sr, torch.Tensor): # Check if it's a Tensor
sr = sr.item() # Extract the value as a Python number
# 2. Convert y_true and y_pred to NumPy arrays
y_true_np = y_true.cpu().detach().numpy()[0] # .cpu() is crucial!
y_pred_np = y_pred.cpu().detach().numpy()[0]
mfccs_true = librosa.feature.mfcc(y=y_true_np, sr=sr, n_mfcc=20)
mfccs_pred = librosa.feature.mfcc(y=y_pred_np, sr=sr, n_mfcc=20)
# 3. Convert MFCCs back to PyTorch tensors and ensure correct device
mfccs_true = torch.tensor(mfccs_true, device=y_true.device, dtype=torch.float32)
mfccs_pred = torch.tensor(mfccs_pred, device=y_pred.device, dtype=torch.float32)
return torch.mean((mfccs_true - mfccs_pred)**2)
def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
optimizer_d.zero_grad()
@ -43,17 +63,23 @@ def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
return d_loss
def generator_train(low_quality, real_labels):
def generator_train(low_quality, high_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()
mfcc_l = mfcc_loss(high_quality[0], generator_output, high_quality[1])
discriminator_decision = discriminator(generator_output)
adversarial_loss = criterion_g(discriminator_decision, real_labels)
combined_loss = adversarial_loss + 0.5 * mfcc_l
combined_loss.backward()
optimizer_g.step()
return generator_output
return (generator_output, combined_loss, adversarial_loss, mfcc_l)
# Init script argument parser
parser = argparse.ArgumentParser(description="Training script")
@ -61,6 +87,7 @@ 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")
parser.add_argument("--verbose", action="store_true", help="Increase output verbosity")
args = parser.parse_args()
@ -68,6 +95,8 @@ args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
debug = args.verbose
# Initialize dataset and dataloader
dataset_dir = './dataset/good'
dataset = AudioDataset(dataset_dir)
@ -85,7 +114,7 @@ dataset = AudioDataset(dataset_dir)
# ========= SINGLE =========
train_data_loader = DataLoader(dataset, batch_size=16, shuffle=True)
train_data_loader = DataLoader(dataset, batch_size=1, shuffle=True)
# Initialize models and move them to device
generator = SISUGenerator()
@ -111,32 +140,10 @@ optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0001, betas=(0.5, 0.99
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)
models_dir = "models"
os.makedirs(models_dir, exist_ok=True)
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 = 5000
for generator_epoch in range(generator_epochs):
low_quality_audio = (torch.empty((1)), 1)
@ -158,32 +165,35 @@ def start_training():
# ========= DISCRIMINATOR =========
discriminator.train()
discriminator_train(high_quality_sample, low_quality_sample, real_labels, fake_labels)
d_loss = discriminator_train(high_quality_sample, low_quality_sample, real_labels, fake_labels)
# ========= GENERATOR =========
generator.train()
generator_output = generator_train(low_quality_sample, real_labels)
generator_output, combined_loss, adversarial_loss, mfcc_l = generator_train(low_quality_sample, high_quality_sample, real_labels)
if debug:
print(d_loss, combined_loss, adversarial_loss, mfcc_l)
scheduler_d.step(d_loss)
scheduler_g.step(combined_loss)
# ========= 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(), 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(), f"{models_dir}/discriminator_epoch_{generator_epoch}.pt")
torch.save(generator.state_dict(), f"{models_dir}/generator_epoch_{generator_epoch}.pt")
torch.save(discriminator, f"{models_dir}/discriminator_epoch_{generator_epoch}_full.pt")
torch.save(generator, f"{models_dir}/generator_epoch_{generator_epoch}_full.pt")
torch.save(discriminator.state_dict(), "models/epoch-5000-discriminator.pt")
torch.save(generator.state_dict(), "models/epoch-5000-generator.pt")
torch.save(discriminator, "models/epoch-5000-discriminator.pt")
torch.save(generator, "models/epoch-5000-generator.pt")
print("Training complete!")
start_training()