Merge pull request 'Merge new-arch, because it has proven to give the best results' (#1) from new-arch into main

Reviewed-on: #1

README.md

@@ -18,6 +18,7 @@ SISU (Super Ingenious Sound Upscaler) is a project that uses GANs (Generative Ad
 1. **Set Up**:
    - Make sure you have Python installed (version 3.8 or higher).
    - Install needed packages: `pip install -r requirements.txt`
+   - Install current version of PyTorch (CUDA/ROCm/What ever your device supports)
 
 2. **Prepare Audio Data**:
    - Put your audio files in the `dataset/good` folder.

data.py

@@ -4,22 +4,20 @@ import torch
 import torchaudio
 import os
 import random
-
 import torchaudio.transforms as T
 import AudioUtils
 
 class AudioDataset(Dataset):
-    #audio_sample_rates = [8000, 11025, 16000, 22050]
     audio_sample_rates = [11025]
+    MAX_LENGTH = 44100  # Define your desired maximum length here
 
-    def __init__(self, input_dir):
+    def __init__(self, input_dir, device):
         self.input_files = [os.path.join(root, f) for root, _, files in os.walk(input_dir) for f in files if f.endswith('.wav')]
-
+        self.device = device
 
     def __len__(self):
         return len(self.input_files)
 
-
     def __getitem__(self, idx):
         # Load high-quality audio
         high_quality_audio, original_sample_rate = torchaudio.load(self.input_files[idx], normalize=True)
@@ -32,4 +30,24 @@ class AudioDataset(Dataset):
         resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
         low_quality_audio = resample_transform_high(low_quality_audio)
 
-        return (AudioUtils.stereo_tensor_to_mono(high_quality_audio), original_sample_rate), (AudioUtils.stereo_tensor_to_mono(low_quality_audio), mangled_sample_rate)
+        high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_quality_audio)
+        low_quality_audio = AudioUtils.stereo_tensor_to_mono(low_quality_audio)
+
+        # Pad or truncate high-quality audio
+        if high_quality_audio.shape[1] < self.MAX_LENGTH:
+            padding = self.MAX_LENGTH - high_quality_audio.shape[1]
+            high_quality_audio = F.pad(high_quality_audio, (0, padding))
+        elif high_quality_audio.shape[1] > self.MAX_LENGTH:
+            high_quality_audio = high_quality_audio[:, :self.MAX_LENGTH]
+
+        # Pad or truncate low-quality audio
+        if low_quality_audio.shape[1] < self.MAX_LENGTH:
+            padding = self.MAX_LENGTH - low_quality_audio.shape[1]
+            low_quality_audio = F.pad(low_quality_audio, (0, padding))
+        elif low_quality_audio.shape[1] > self.MAX_LENGTH:
+            low_quality_audio = low_quality_audio[:, :self.MAX_LENGTH]
+
+        high_quality_audio = high_quality_audio.to(self.device)
+        low_quality_audio = low_quality_audio.to(self.device)
+
+        return (high_quality_audio, original_sample_rate), (low_quality_audio, mangled_sample_rate)

discriminator.py

@@ -2,36 +2,62 @@ import torch
 import torch.nn as nn
 import torch.nn.utils as utils
 
-def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
+def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, spectral_norm=True, use_instance_norm=True):
     padding = (kernel_size // 2) * dilation
-    return nn.Sequential(
+    conv_layer = nn.Conv1d(
-        utils.spectral_norm(nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)),
+        in_channels,
-        nn.BatchNorm1d(out_channels),
+        out_channels,
-        nn.LeakyReLU(0.2, inplace=True) # Changed activation to LeakyReLU
+        kernel_size=kernel_size,
+        stride=stride,
+        dilation=dilation,
+        padding=padding
     )
 
-class SISUDiscriminator(nn.Module):
+    if spectral_norm:
-    def __init__(self):
+        conv_layer = utils.spectral_norm(conv_layer)
-        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
+    layers = [conv_layer]
-            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, dilation=1), # Downsample
+    layers.append(nn.LeakyReLU(0.2, inplace=True))
-            discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=4),
+
-            discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=16),
+    if use_instance_norm:
-            discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=8),
+        layers.append(nn.InstanceNorm1d(out_channels))
-            discriminator_block(layers * 2, layers, kernel_size=3, dilation=1),
+
-            # Final Convolution
+    return nn.Sequential(*layers)
-            discriminator_block(layers, 1, kernel_size=3, stride=1),
+
+class AttentionBlock(nn.Module):
+    def __init__(self, channels):
+        super(AttentionBlock, self).__init__()
+        self.attention = nn.Sequential(
+            nn.Conv1d(channels, channels // 4, kernel_size=1),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(channels // 4, channels, kernel_size=1),
+            nn.Sigmoid()
         )
+
+    def forward(self, x):
+        attention_weights = self.attention(x)
+        return x * attention_weights
+
+class SISUDiscriminator(nn.Module):
+    def __init__(self, base_channels=16):
+        super(SISUDiscriminator, self).__init__()
+        layers = base_channels
+        self.model = nn.Sequential(
+            discriminator_block(1, layers, kernel_size=7, stride=1, spectral_norm=True, use_instance_norm=False),
+            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers * 2, layers * 4, kernel_size=5, stride=1, dilation=2, spectral_norm=True, use_instance_norm=True),
+            AttentionBlock(layers * 4),
+            discriminator_block(layers * 4, layers * 8, kernel_size=5, stride=1, dilation=4, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers * 8, layers * 4, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers * 4, layers * 2, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers * 2, layers, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(layers, 1, kernel_size=3, stride=1, spectral_norm=False, use_instance_norm=False)
+        )
+
         self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
 
     def forward(self, x):
-      # Gaussian noise is not necessary here for discriminator as it is already implicit in the training process
         x = self.model(x)
         x = self.global_avg_pool(x)
-        x = x.view(-1, 1)
+        x = x.view(x.size(0), -1)
         return x

file_utils.py

@@ -0,0 +1,28 @@
+import json
+
+filepath = "my_data.json"
+
+def write_data(filepath, data):
+  try:
+    with open(filepath, 'w') as f:
+      json.dump(data, f, indent=4)  # Use indent for pretty formatting
+    print(f"Data written to '{filepath}'")
+  except Exception as e:
+    print(f"Error writing to file: {e}")
+
+
+def read_data(filepath):
+  try:
+    with open(filepath, 'r') as f:
+      data = json.load(f)
+    print(f"Data read from '{filepath}'")
+    return data
+  except FileNotFoundError:
+    print(f"File not found: {filepath}")
+    return None
+  except json.JSONDecodeError:
+    print(f"Error decoding JSON from file: {filepath}")
+    return None
+  except Exception as e:
+    print(f"Error reading from file: {e}")
+    return None

generator.py

@@ -1,36 +1,74 @@
+import torch
 import torch.nn as nn
 
 def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
     return nn.Sequential(
-        nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, dilation=dilation, padding=(kernel_size // 2) * dilation),
+        nn.Conv1d(
-        nn.BatchNorm1d(out_channels),
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            dilation=dilation,
+            padding=(kernel_size // 2) * dilation
+        ),
+        nn.InstanceNorm1d(out_channels),
         nn.PReLU()
     )
 
-class SISUGenerator(nn.Module):
+class AttentionBlock(nn.Module):
-    def __init__(self):
+    """
-        super(SISUGenerator, self).__init__()
+    Simple Channel Attention Block. Learns to weight channels based on their importance.
-        layer = 4 # Increased base layer count
+    """
-        self.conv1 = nn.Sequential(
+    def __init__(self, channels):
-            nn.Conv1d(1, layer, kernel_size=7, padding=3),
+        super(AttentionBlock, self).__init__()
-            nn.BatchNorm1d(layer),
+        self.attention = nn.Sequential(
-            nn.PReLU(),
+            nn.Conv1d(channels, channels // 4, kernel_size=1),
-        )
+            nn.ReLU(inplace=True),
-        self.conv_blocks = nn.Sequential(
+            nn.Conv1d(channels // 4, channels, kernel_size=1),
-            conv_block(layer, layer, kernel_size=3, dilation=1),      # Local details
+            nn.Sigmoid()
-            conv_block(layer, layer*2, kernel_size=5, dilation=2),    # Local Context
-            conv_block(layer*2, layer*2, kernel_size=3, dilation=16),   # Longer range dependencies
-            conv_block(layer*2, layer*2, kernel_size=5, dilation=8),    # Wider context
-            conv_block(layer*2, layer, kernel_size=5, dilation=2),    # Local Context
-            conv_block(layer, layer, kernel_size=3, dilation=1),      # Local details
-        )
-        self.final_layer = nn.Sequential(
-            nn.Conv1d(layer, 1, kernel_size=3, padding=1),
         )
 
+    def forward(self, x):
+        attention_weights = self.attention(x)
+        return x * attention_weights
+
+class ResidualInResidualBlock(nn.Module):
+    def __init__(self, channels, num_convs=3):
+        super(ResidualInResidualBlock, self).__init__()
+
+        self.conv_layers = nn.Sequential(
+            *[conv_block(channels, channels) for _ in range(num_convs)]
+        )
+
+        self.attention = AttentionBlock(channels)
+
     def forward(self, x):
         residual = x
-        x = self.conv1(x)
+        x = self.conv_layers(x)
-        x = self.conv_blocks(x)
+        x = self.attention(x)
-        x = self.final_layer(x)
         return x + residual
+
+class SISUGenerator(nn.Module):
+    def __init__(self, channels=16, num_rirb=4, alpha=1.0):
+        super(SISUGenerator, self).__init__()
+        self.alpha = alpha
+
+        self.conv1 = nn.Sequential(
+            nn.Conv1d(1, channels, kernel_size=7, padding=3),
+            nn.InstanceNorm1d(channels),
+            nn.PReLU(),
+        )
+
+        self.rir_blocks = nn.Sequential(
+            *[ResidualInResidualBlock(channels) for _ in range(num_rirb)]
+        )
+
+        self.final_layer = nn.Conv1d(channels, 1, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        residual_input = x
+        x = self.conv1(x)
+        x_rirb_out = self.rir_blocks(x)
+        learned_residual = self.final_layer(x_rirb_out)
+        output = residual_input + self.alpha * learned_residual
+
+        return output

requirements.txt

@@ -4,11 +4,9 @@ Jinja2==3.1.4
 MarkupSafe==2.1.5
 mpmath==1.3.0
 networkx==3.4.2
-numpy==2.2.1
+numpy==2.2.3
-pytorch-triton-rocm==3.2.0+git0d4682f0
+pillow==11.0.0
 setuptools==70.2.0
-sympy==1.13.1
+sympy==1.13.3
-torch==2.6.0.dev20241222+rocm6.2.4
-torchaudio==2.6.0.dev20241222+rocm6.2.4
 tqdm==4.67.1
 typing_extensions==4.12.2

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
 
@@ -18,42 +20,10 @@ from data import AudioDataset
 from generator import SISUGenerator
 from discriminator import SISUDiscriminator
 
-def perceptual_loss(y_true, y_pred):
+from training_utils import discriminator_train, generator_train
-    return torch.mean((y_true - y_pred) ** 2)
+import file_utils as Data
 
-def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
+import torchaudio.transforms as T
-    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
 
 # Init script argument parser
 parser = argparse.ArgumentParser(description="Training script")
@@ -61,47 +31,78 @@ 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("--device", type=str, default="cpu",  help="Select device")
+parser.add_argument("--epoch", type=int, default=0, help="Current epoch for model versioning")
+parser.add_argument("--debug", action="store_true",  help="Print debug logs")
+parser.add_argument("--continue_training", action="store_true", help="Continue training using temp_generator and temp_discriminator models")
 
 args = parser.parse_args()
 
-# Check for CUDA availability
+device = torch.device(args.device if torch.cuda.is_available() else "cpu")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"Using device: {device}")
 
+# Parameters
+sample_rate = 44100
+n_fft = 2048
+hop_length = 256
+win_length = n_fft
+n_mels = 128
+n_mfcc = 20 # If using MFCC
+
+mfcc_transform = T.MFCC(
+    sample_rate,
+    n_mfcc,
+    melkwargs = {'n_fft': n_fft, 'hop_length': hop_length}
+).to(device)
+
+mel_transform = T.MelSpectrogram(
+    sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length,
+    win_length=win_length, n_mels=n_mels, power=1.0 # Magnitude Mel
+).to(device)
+
+stft_transform = T.Spectrogram(
+    n_fft=n_fft, win_length=win_length, hop_length=hop_length
+).to(device)
+
+debug = args.debug
+
 # Initialize dataset and dataloader
 dataset_dir = './dataset/good'
-dataset = AudioDataset(dataset_dir)
+dataset = AudioDataset(dataset_dir, device)
-
+models_dir = "models"
-# ========= MULTIPLE =========
+os.makedirs(models_dir, exist_ok=True)
-
+audio_output_dir = "output"
-# dataset_size = len(dataset)
+os.makedirs(audio_output_dir, exist_ok=True)
-# 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)
 
 # ========= SINGLE =========
 
-train_data_loader = DataLoader(dataset, batch_size=16, shuffle=True)
+train_data_loader = DataLoader(dataset, batch_size=64, shuffle=True)
+
+
+# ========= MODELS =========
 
-# Initialize models and move them to device
 generator = SISUGenerator()
 discriminator = SISUDiscriminator()
 
-if args.generator is not None:
+epoch: int = args.epoch
-    generator.load_state_dict(torch.load(args.generator, weights_only=True))
+epoch_from_file = Data.read_data(f"{models_dir}/epoch_data.json")
-if args.discriminator is not None:
+
-    discriminator.load_state_dict(torch.load(args.discriminator, weights_only=True))
+if args.continue_training:
+    generator.load_state_dict(torch.load(f"{models_dir}/temp_generator.pt", map_location=device, weights_only=True))
+    discriminator.load_state_dict(torch.load(f"{models_dir}/temp_generator.pt", map_location=device, weights_only=True))
+    epoch = epoch_from_file["epoch"] + 1
+else:
+    if args.generator is not None:
+        generator.load_state_dict(torch.load(args.generator, map_location=device, weights_only=True))
+    if args.discriminator is not None:
+        discriminator.load_state_dict(torch.load(args.discriminator, map_location=device, weights_only=True))
 
 generator = generator.to(device)
 discriminator = discriminator.to(device)
 
 # Loss
-criterion_g = nn.MSELoss()
+criterion_g = nn.BCEWithLogitsLoss()
-criterion_d = nn.BCELoss()
+criterion_d = nn.BCEWithLogitsLoss()
 
 # Optimizers
 optimizer_g = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
@@ -112,31 +113,6 @@ scheduler_g = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_g, mode='min'
 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 = 5000
     for generator_epoch in range(generator_epochs):
         low_quality_audio = (torch.empty((1)), 1)
@@ -146,10 +122,10 @@ def start_training():
         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 tqdm.tqdm(train_data_loader, desc=f"Training epoch {generator_epoch+1}/{generator_epochs}, Current epoch {epoch+1}"):
         # for high_quality_clip, low_quality_clip in train_data_loader:
-            high_quality_sample = (high_quality_clip[0].to(device), high_quality_clip[1])
+            high_quality_sample = (high_quality_clip[0], high_quality_clip[1])
-            low_quality_sample = (low_quality_clip[0].to(device), low_quality_clip[1])
+            low_quality_sample = (low_quality_clip[0], low_quality_clip[1])
 
             # ========= LABELS =========
             batch_size = high_quality_clip[0].size(0)
@@ -158,32 +134,61 @@ 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,
+                discriminator,
+                generator,
+                criterion_d,
+                optimizer_d
+            )
 
             # ========= GENERATOR =========
             generator.train()
-            generator_output = generator_train(low_quality_sample, real_labels)
+            generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor = generator_train(
+                low_quality_sample,
+                high_quality_sample,
+                real_labels,
+                generator,
+                discriminator,
+                criterion_d,
+                optimizer_g,
+                device,
+                mel_transform,
+                stft_transform,
+                mfcc_transform
+            )
+
+            if debug:
+                print(f"D_LOSS: {d_loss.item():.4f}, COMBINED_LOSS: {combined_loss.item():.4f}, ADVERSARIAL_LOSS: {adversarial_loss.item():.4f}, MEL_L1_LOSS: {mel_l1_tensor.item():.4f}, LOG_STFT_L1_LOSS: {log_stft_l1_tensor.item():.4f}, MFCC_LOSS: {mfcc_l_tensor.item():.4f}")
+            scheduler_d.step(d_loss.detach())
+            scheduler_g.step(adversarial_loss.detach())
 
             # ========= SAVE LATEST AUDIO =========
-            high_quality_audio = high_quality_clip
+            high_quality_audio = (high_quality_clip[0][0], high_quality_clip[1][0])
-            low_quality_audio = low_quality_clip
+            low_quality_audio = (low_quality_clip[0][0], low_quality_clip[1][0])
-            ai_enhanced_audio = (generator_output, high_quality_clip[1])
+            ai_enhanced_audio = (generator_output[0], high_quality_clip[1][0])
 
-        #metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
+        new_epoch = generator_epoch+epoch
-        #print(f"Generator metric {metric}!")
-        #scheduler_g.step(metric)
 
-        if generator_epoch % 10 == 0:
+        if generator_epoch % 25 == 0:
-            print(f"Saved epoch {generator_epoch}!")
+            print(f"Saved epoch {new_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"{audio_output_dir}/epoch-{new_epoch}-audio-crap.wav", low_quality_audio[0].cpu().detach(), 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"{audio_output_dir}/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu().detach(), ai_enhanced_audio[1])
-            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
+            torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0].cpu().detach(), high_quality_audio[1])
 
-        torch.save(discriminator.state_dict(), f"models/current-epoch-discriminator.pt")
+        #if debug:
-        torch.save(generator.state_dict(), f"models/current-epoch-generator.pt")
+        #    print(generator.state_dict().keys())
+        #    print(discriminator.state_dict().keys())
+        torch.save(discriminator.state_dict(), f"{models_dir}/temp_discriminator.pt")
+        torch.save(generator.state_dict(), f"{models_dir}/temp_generator.pt")
+        Data.write_data(f"{models_dir}/epoch_data.json", {"epoch": new_epoch})
 
-    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()

training_utils.py

@@ -0,0 +1,144 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+import torchaudio
+import torchaudio.transforms as T
+
+def gpu_mfcc_loss(mfcc_transform, y_true, y_pred):
+    mfccs_true = mfcc_transform(y_true)
+    mfccs_pred = mfcc_transform(y_pred)
+
+    min_len = min(mfccs_true.shape[2], mfccs_pred.shape[2])
+    mfccs_true = mfccs_true[:, :, :min_len]
+    mfccs_pred = mfccs_pred[:, :, :min_len]
+
+    loss = torch.mean((mfccs_true - mfccs_pred)**2)
+    return loss
+
+def mel_spectrogram_l1_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
+    mel_spec_true = mel_transform(y_true)
+    mel_spec_pred = mel_transform(y_pred)
+
+    # Ensure same time dimension length (due to potential framing differences)
+    min_len = min(mel_spec_true.shape[-1], mel_spec_pred.shape[-1])
+    mel_spec_true = mel_spec_true[..., :min_len]
+    mel_spec_pred = mel_spec_pred[..., :min_len]
+
+    # L1 Loss (Mean Absolute Error)
+    loss = torch.mean(torch.abs(mel_spec_true - mel_spec_pred))
+    return loss
+
+def mel_spectrogram_l2_loss(mel_transform: T.MelSpectrogram, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
+    mel_spec_true = mel_transform(y_true)
+    mel_spec_pred = mel_transform(y_pred)
+
+    min_len = min(mel_spec_true.shape[-1], mel_spec_pred.shape[-1])
+    mel_spec_true = mel_spec_true[..., :min_len]
+    mel_spec_pred = mel_spec_pred[..., :min_len]
+
+    loss = torch.mean((mel_spec_true - mel_spec_pred)**2)
+    return loss
+
+def log_stft_magnitude_loss(stft_transform: T.Spectrogram, y_true: torch.Tensor, y_pred: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    stft_mag_true = stft_transform(y_true)
+    stft_mag_pred = stft_transform(y_pred)
+
+    min_len = min(stft_mag_true.shape[-1], stft_mag_pred.shape[-1])
+    stft_mag_true = stft_mag_true[..., :min_len]
+    stft_mag_pred = stft_mag_pred[..., :min_len]
+
+    loss = torch.mean(torch.abs(torch.log(stft_mag_true + eps) - torch.log(stft_mag_pred + eps)))
+    return loss
+
+def spectral_convergence_loss(stft_transform: T.Spectrogram, y_true: torch.Tensor, y_pred: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    stft_mag_true = stft_transform(y_true)
+    stft_mag_pred = stft_transform(y_pred)
+
+    min_len = min(stft_mag_true.shape[-1], stft_mag_pred.shape[-1])
+    stft_mag_true = stft_mag_true[..., :min_len]
+    stft_mag_pred = stft_mag_pred[..., :min_len]
+
+    norm_true = torch.linalg.norm(stft_mag_true, ord='fro', dim=(-2, -1))
+    norm_diff = torch.linalg.norm(stft_mag_true - stft_mag_pred, ord='fro', dim=(-2, -1))
+
+    loss = torch.mean(norm_diff / (norm_true + eps))
+    return loss
+
+def discriminator_train(high_quality, low_quality, real_labels, fake_labels, discriminator, generator, criterion, optimizer):
+    optimizer.zero_grad()
+
+    # Forward pass for real samples
+    discriminator_decision_from_real = discriminator(high_quality[0])
+    d_loss_real = criterion(discriminator_decision_from_real, real_labels)
+
+    with torch.no_grad():
+        generator_output = generator(low_quality[0])
+    discriminator_decision_from_fake = discriminator(generator_output)
+    d_loss_fake = criterion(discriminator_decision_from_fake, fake_labels.expand_as(discriminator_decision_from_fake))
+
+    d_loss = (d_loss_real + d_loss_fake) / 2.0
+
+    d_loss.backward()
+    # Optional: Gradient Clipping (can be helpful)
+    # nn.utils.clip_grad_norm_(discriminator.parameters(), max_norm=1.0)  # Gradient Clipping
+    optimizer.step()
+
+    return d_loss
+
+def generator_train(
+    low_quality,
+    high_quality,
+    real_labels,
+    generator,
+    discriminator,
+    adv_criterion,
+    g_optimizer,
+    device,
+    mel_transform: T.MelSpectrogram,
+    stft_transform: T.Spectrogram,
+    mfcc_transform: T.MFCC,
+    lambda_adv: float = 1.0,
+    lambda_mel_l1: float = 10.0,
+    lambda_log_stft: float = 1.0,
+    lambda_mfcc: float = 1.0
+):
+    g_optimizer.zero_grad()
+
+    generator_output = generator(low_quality[0])
+
+    discriminator_decision = discriminator(generator_output)
+    adversarial_loss = adv_criterion(discriminator_decision, real_labels.expand_as(discriminator_decision))
+
+    mel_l1 = 0.0
+    log_stft_l1 = 0.0
+    mfcc_l = 0.0
+
+    # Calculate Mel L1 Loss if weight is positive
+    if lambda_mel_l1 > 0:
+        mel_l1 = mel_spectrogram_l1_loss(mel_transform, high_quality[0], generator_output)
+
+    # Calculate Log STFT L1 Loss if weight is positive
+    if lambda_log_stft > 0:
+        log_stft_l1 = log_stft_magnitude_loss(stft_transform, high_quality[0], generator_output)
+
+    # Calculate MFCC Loss if weight is positive
+    if lambda_mfcc > 0:
+        mfcc_l = gpu_mfcc_loss(mfcc_transform, high_quality[0], generator_output)
+
+    mel_l1_tensor = torch.tensor(mel_l1, device=device) if isinstance(mel_l1, float) else mel_l1
+    log_stft_l1_tensor = torch.tensor(log_stft_l1, device=device) if isinstance(log_stft_l1, float) else log_stft_l1
+    mfcc_l_tensor = torch.tensor(mfcc_l, device=device) if isinstance(mfcc_l, float) else mfcc_l
+
+    combined_loss = (lambda_adv * adversarial_loss) + \
+                    (lambda_mel_l1 * mel_l1_tensor) + \
+                    (lambda_log_stft * log_stft_l1_tensor) + \
+                    (lambda_mfcc * mfcc_l_tensor)
+
+    combined_loss.backward()
+    # Optional: Gradient Clipping
+    # nn.utils.clip_grad_norm_(generator.parameters(), max_norm=1.0)
+    g_optimizer.step()
+
+    # 6. Return values for logging
+    return generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor