⚗️ | Increase discriminator size and implement mfcc_loss for generator.

discriminator.py

@@ -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),
-        nn.LeakyReLU(0.2, inplace=True) # Changed activation to LeakyReLU
+        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),  # Initial downsampling
-            discriminator_block(1, layers, kernel_size=7, stride=2, dilation=1), # Downsample
+            discriminator_block(layers, layers * 2, kernel_size=5, stride=2),  # Downsampling
-
+            discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=2), # Increased dilation
-            # Core Discriminator Blocks with varied kernels and dilations
+            discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=4), # Increased dilation
-            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, dilation=1), # Downsample
+            discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=8), # Deeper layer!
-            discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=4),
+            discriminator_block(layers * 8, layers * 8, kernel_size=5, dilation=1), # Deeper layer!
-            discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=16),
+            discriminator_block(layers * 8, layers * 4, kernel_size=3, dilation=2), # Reduced dilation
-            discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=8),
+            discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=1),
-            discriminator_block(layers * 2, layers, kernel_size=3, dilation=1),
+            discriminator_block(layers * 2, layers, kernel_size=3, stride=1),  # Final convolution
-            # Final Convolution
+            discriminator_block(layers, 1, kernel_size=3, stride=1)
-            discriminator_block(layers, 1, kernel_size=3, stride=1),
         )
         self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
 

training.py

@@ -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):
+import librosa
-    return torch.mean((y_true - y_pred) ** 2)
+
+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(discriminator.state_dict(), f"{models_dir}/discriminator_epoch_{generator_epoch}.pt")
-        torch.save(generator.state_dict(), f"models/current-epoch-generator.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(discriminator, "models/epoch-5000-discriminator.pt")
-    torch.save(generator.state_dict(), "models/epoch-5000-generator.pt")
+    torch.save(generator, "models/epoch-5000-generator.pt")
     print("Training complete!")
 
 start_training()