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

Reviewed-on: #1

AudioUtils.py

@@ -0,0 +1,18 @@
+import torch
+import torch.nn.functional as F
+
+def stereo_tensor_to_mono(waveform):
+    if waveform.shape[0] > 1:
+        # Average across channels
+        mono_waveform = torch.mean(waveform, dim=0, keepdim=True)
+    else:
+        # Already mono
+        mono_waveform = waveform
+    return mono_waveform
+
+def stretch_tensor(tensor, target_length):
+    scale_factor = target_length / tensor.size(1)
+
+    tensor = F.interpolate(tensor, scale_factor=scale_factor, mode='linear', align_corners=False)
+
+    return tensor

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

@@ -1,49 +1,53 @@
 from torch.utils.data import Dataset
 import torch.nn.functional as F
+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, target_duration=None, padding_mode='constant', padding_value=0.0):
-        self.input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.wav')]
-        self.target_duration = target_duration  # Duration in seconds or None if not set
-        self.padding_mode = padding_mode
-        self.padding_value = padding_value
+    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)
 
+        # Generate low-quality audio with random downsampling
         mangled_sample_rate = random.choice(self.audio_sample_rates)
-        resample_transform = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
-        low_quality_audio = resample_transform(high_quality_audio)
+        resample_transform_low = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
+        low_quality_audio = resample_transform_low(high_quality_audio)
 
-        # Calculate target length based on desired duration and 16000 Hz
-        # if self.target_duration is not None:
-        #     target_length = int(self.target_duration * 44100)
-        # else:
-        #     # Calculate duration of original high quality audio
-        #     target_length = high_quality_wav.size(1)
+        resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
+        low_quality_audio = resample_transform_high(low_quality_audio)
 
-        # Pad both to the calculated target length
-        # high_quality_wav = self.stretch_tensor(high_quality_wav, target_length)
-        # low_quality_wav = self.stretch_tensor(low_quality_wav, target_length)
+        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)
-
-    def stretch_tensor(self, tensor, target_length):
-        current_length = tensor.size(1)
-        scale_factor = target_length / current_length
-
-        # Resample the tensor using linear interpolation
-        tensor = F.interpolate(tensor.unsqueeze(0), scale_factor=scale_factor, mode='linear', align_corners=False).squeeze(0)
-
-        return tensor

discriminator.py

@@ -1,24 +1,63 @@
+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, spectral_norm=True, use_instance_norm=True):
+    padding = (kernel_size // 2) * dilation
+    conv_layer = nn.Conv1d(
+        in_channels,
+        out_channels,
+        kernel_size=kernel_size,
+        stride=stride,
+        dilation=dilation,
+        padding=padding
+    )
+
+    if spectral_norm:
+        conv_layer = utils.spectral_norm(conv_layer)
+
+    layers = [conv_layer]
+    layers.append(nn.LeakyReLU(0.2, inplace=True))
+
+    if use_instance_norm:
+        layers.append(nn.InstanceNorm1d(out_channels))
+
+    return nn.Sequential(*layers)
+
+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):
+    def __init__(self, base_channels=16):
         super(SISUDiscriminator, self).__init__()
+        layers = base_channels
         self.model = nn.Sequential(
-            nn.Conv1d(2, 128, kernel_size=3, padding=1),
-            #nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(128, 256, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(256, 128, kernel_size=3, padding=1),
-            #nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(128, 64, kernel_size=3, padding=1),
-            #nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(64, 1, kernel_size=3, padding=1),
-            #nn.LeakyReLU(0.2, inplace=True),
+            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)  # Output size (1,)
+
+        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
 
     def forward(self, x):
         x = self.model(x)
         x = self.global_avg_pool(x)
-        x = x.view(-1, 1)  # Flatten to (batch_size, 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

@@ -1,27 +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.InstanceNorm1d(out_channels),
+        nn.PReLU()
+    )
+
+class AttentionBlock(nn.Module):
+    """
+    Simple Channel Attention Block. Learns to weight channels based on their importance.
+    """
+    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 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.conv_layers(x)
+        x = self.attention(x)
+        return x + residual
+
 class SISUGenerator(nn.Module):
-    def __init__(self, upscale_scale=1): # No noise_dim parameter
+    def __init__(self, channels=16, num_rirb=4, alpha=1.0):
         super(SISUGenerator, self).__init__()
-        self.layers1 = nn.Sequential(
-            nn.Conv1d(2, 128, kernel_size=3, padding=1),
-            # nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(128, 256, kernel_size=3, padding=1),
-            # nn.LeakyReLU(0.2, inplace=True),
+        self.alpha = alpha
+
+        self.conv1 = nn.Sequential(
+            nn.Conv1d(1, channels, kernel_size=7, padding=3),
+            nn.InstanceNorm1d(channels),
+            nn.PReLU(),
         )
 
-        self.layers2 = nn.Sequential(
-            nn.Conv1d(256, 128, kernel_size=3, padding=1),
-            # nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(128, 64, kernel_size=3, padding=1),
-            # nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv1d(64, 2, kernel_size=3, padding=1),
-            # nn.Tanh()
+        self.rir_blocks = nn.Sequential(
+            *[ResidualInResidualBlock(channels) for _ in range(num_rirb)]
         )
 
-    def forward(self, x, scale):
-        x = self.layers1(x)
-        upsample = nn.Upsample(scale_factor=scale, mode='nearest')
-        x = upsample(x)
-        x = self.layers2(x)
-        return x
+        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

@@ -1,12 +1,12 @@
-filelock>=3.16.1
-fsspec>=2024.10.0
-Jinja2>=3.1.4
-MarkupSafe>=2.1.5
-mpmath>=1.3.0
-networkx>=3.4.2
-numpy>=2.1.2
-pillow>=11.0.0
-setuptools>=70.2.0
-sympy>=1.13.1
-tqdm>=4.67.1
-typing_extensions>=4.12.2
+filelock==3.16.1
+fsspec==2024.10.0
+Jinja2==3.1.4
+MarkupSafe==2.1.5
+mpmath==1.3.0
+networkx==3.4.2
+numpy==2.2.3
+pillow==11.0.0
+setuptools==70.2.0
+sympy==1.13.3
+tqdm==4.67.1
+typing_extensions==4.12.2

test.py

@@ -1,10 +0,0 @@
-import torch.nn as nn
-import torch
-from discriminator import SISUDiscriminator
-
-
-discriminator = SISUDiscriminator()
-test_input = torch.randn(1, 2, 1000) # Example input (batch_size, channels, frames)
-output = discriminator(test_input)
-print(output)
-print("Output shape:", output.shape)

training.py

@@ -6,94 +6,102 @@ import torch.nn.functional as F
 import torchaudio
 import tqdm
 
+import argparse
+
+import math
+
+import os
+
 from torch.utils.data import random_split
 from torch.utils.data import DataLoader
 
+import AudioUtils
 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
+from training_utils import discriminator_train, generator_train
+import file_utils as Data
 
-    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)
+import torchaudio.transforms as T
 
-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
+# Init script argument parser
+parser = argparse.ArgumentParser(description="Training script")
+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")
 
-def discriminator_train(high_quality, low_quality, scale, real_labels, fake_labels):
-    optimizer_d.zero_grad()
+args = parser.parse_args()
 
-    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")
+device = torch.device(args.device 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, target_duration=2.0)
+dataset = AudioDataset(dataset_dir, device)
+models_dir = "models"
+os.makedirs(models_dir, exist_ok=True)
+audio_output_dir = "output"
+os.makedirs(audio_output_dir, exist_ok=True)
 
-dataset_size = len(dataset)
-train_size = int(dataset_size * .9)
-val_size = int(dataset_size-train_size)
+# ========= SINGLE =========
 
-train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
+train_data_loader = DataLoader(dataset, batch_size=64, shuffle=True)
 
-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
+# ========= MODELS =========
+
 generator = SISUGenerator()
 discriminator = SISUDiscriminator()
 
+epoch: int = args.epoch
+epoch_from_file = Data.read_data(f"{models_dir}/epoch_data.json")
+
+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.L1Loss()
-criterion_g_mel = MelSpectrogramLoss().to(device)
+criterion_g = nn.BCEWithLogitsLoss()
 criterion_d = nn.BCEWithLogitsLoss()
 
 # Optimizers
@@ -105,43 +113,19 @@ 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 = 500
+    generator_epochs = 5000
     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)
+        times_correct = 0
 
-            scale = high_quality_clip[0].shape[2]/low_quality_clip[0].shape[2]
+        # ========= TRAINING =========
+        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], high_quality_clip[1])
+            low_quality_sample = (low_quality_clip[0], low_quality_clip[1])
 
             # ========= LABELS =========
             batch_size = high_quality_clip[0].size(0)
@@ -150,34 +134,61 @@ def start_training():
 
             # ========= DISCRIMINATOR =========
             discriminator.train()
-            for _ in range(3):
-                discriminator_train(high_quality_sample, low_quality_sample, scale, 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, scale, 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
-            low_quality_audio = low_quality_clip
-            ai_enhanced_audio = (generator_output, high_quality_clip[1])
+            high_quality_audio = (high_quality_clip[0][0], high_quality_clip[1][0])
+            low_quality_audio = (low_quality_clip[0][0], low_quality_clip[1][0])
+            ai_enhanced_audio = (generator_output[0], high_quality_clip[1][0])
 
-        metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
-        print(f"Generator metric {metric}!")
-        scheduler_g.step(metric)
+        new_epoch = generator_epoch+epoch
 
-        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 % 25 == 0:
+            print(f"Saved epoch {new_epoch}!")
+            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"{audio_output_dir}/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu().detach(), ai_enhanced_audio[1])
+            torchaudio.save(f"{audio_output_dir}/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0].cpu().detach(), 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")
+        #if debug:
+        #    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-500-discriminator.pt")
-    torch.save(generator.state_dict(), "models/epoch-500-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