🐛 | Fix loading wrong model.

.gitignore

@@ -166,3 +166,4 @@ dataset/
 old-output/
 output/
 *.wav
+models/

AudioUtils.py

@@ -0,0 +1,71 @@
+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
+
+def pad_tensor(audio_tensor: torch.Tensor, target_length: int = 128):
+    current_length = audio_tensor.shape[-1]
+
+    if current_length < target_length:
+        padding_needed = target_length - current_length
+
+        padding_tuple = (0, padding_needed)
+        padded_audio_tensor = F.pad(audio_tensor, padding_tuple, mode='constant', value=0)
+    else:
+        padded_audio_tensor = audio_tensor
+
+    return padded_audio_tensor
+
+def split_audio(audio_tensor: torch.Tensor, chunk_size: int = 128) -> list[torch.Tensor]:
+    if not isinstance(chunk_size, int) or chunk_size <= 0:
+        raise ValueError("chunk_size must be a positive integer.")
+
+    # Handle scalar tensor edge case if necessary
+    if audio_tensor.dim() == 0:
+        return [audio_tensor] if audio_tensor.numel() > 0 else []
+
+    # Identify the dimension to split (usually the last one, representing time/samples)
+    split_dim = -1
+    num_samples = audio_tensor.shape[split_dim]
+
+    if num_samples == 0:
+        return [] # Return empty list if the dimension to split is empty
+
+    # Use torch.split to divide the tensor into chunks
+    # It handles the last chunk being potentially smaller automatically.
+    chunks = list(torch.split(audio_tensor, chunk_size, dim=split_dim))
+
+    return chunks
+
+def reconstruct_audio(chunks: list[torch.Tensor]) -> torch.Tensor:
+    if not chunks:
+        return torch.empty(0)
+
+    if len(chunks) == 1 and chunks[0].dim() == 0:
+        return chunks[0]
+
+    concat_dim = -1
+
+    try:
+        reconstructed_tensor = torch.cat(chunks, dim=concat_dim)
+    except RuntimeError as e:
+        raise RuntimeError(
+            f"Failed to concatenate audio chunks. Ensure chunks have compatible shapes "
+            f"for concatenation along dimension {concat_dim}. Original error: {e}"
+        )
+
+    return reconstructed_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.

app.py

@@ -0,0 +1,60 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+import torch.nn.functional as F
+import torchaudio
+import tqdm
+
+import argparse
+import math
+import os
+
+import AudioUtils
+from generator import SISUGenerator
+
+
+# Init script argument parser
+parser = argparse.ArgumentParser(description="Training script")
+parser.add_argument("--device", type=str, default="cpu",  help="Select device")
+parser.add_argument("--model", type=str, help="Model to use for upscaling")
+parser.add_argument("--clip_length", type=int, default=1024, help="Internal clip length, leave unspecified if unsure")
+parser.add_argument("-i", "--input", type=str, help="Input audio file")
+parser.add_argument("-o", "--output", type=str, help="Output audio file")
+
+args = parser.parse_args()
+
+device = torch.device(args.device if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+generator = SISUGenerator()
+
+models_dir = args.model
+clip_length = args.clip_length
+input_audio = args.input
+output_audio = args.output
+
+if models_dir:
+    generator.load_state_dict(torch.load(f"{models_dir}", map_location=device, weights_only=True))
+else:
+    print(f"Generator model (--model) isn't specified. Do you have the trained model? If not you need to train it OR acquire it from somewhere (DON'T ASK ME, YET!)")
+
+generator = generator.to(device)
+
+def start():
+    # To Mono!
+    audio, original_sample_rate = torchaudio.load(input_audio, normalize=True)
+    audio = AudioUtils.stereo_tensor_to_mono(audio)
+
+    splitted_audio = AudioUtils.split_audio(audio, clip_length)
+    splitted_audio_on_device = [t.to(device) for t in splitted_audio]
+    processed_audio = []
+
+    for clip in tqdm.tqdm(splitted_audio_on_device, desc="Processing..."):
+        processed_audio.append(generator(clip))
+
+    reconstructed_audio = AudioUtils.reconstruct_audio(processed_audio)
+    print(f"Saving {output_audio}!")
+    torchaudio.save(output_audio, reconstructed_audio.cpu().detach(), original_sample_rate)
+
+start()

data.py

@@ -1,50 +1,46 @@
 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 tqdm
+import AudioUtils
 
 class AudioDataset(Dataset):
-    audio_sample_rates = [8000, 11025, 16000, 22050]
+    audio_sample_rates = [11025]
 
-    def __init__(self, input_dir, target_duration=None, padding_mode='constant', padding_value=0.0):
+    def __init__(self, input_dir, device, clip_length = 1024):
-        self.input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.wav')]
+        self.device = device
-        self.target_duration = target_duration  # Duration in seconds or None if not set
+        input_files = [os.path.join(root, f) for root, _, files in os.walk(input_dir) for f in files if f.endswith('.wav') or f.endswith('.mp3') or f.endswith('.flac')]
-        self.padding_mode = padding_mode
+
-        self.padding_value = padding_value
+        data = []
+        for audio_clip in tqdm.tqdm(input_files, desc=f"Processing {len(input_files)} audio file(s)"):
+            audio, original_sample_rate = torchaudio.load(audio_clip, normalize=True)
+            audio = AudioUtils.stereo_tensor_to_mono(audio)
+
+            # Generate low-quality audio with random downsampling
+            mangled_sample_rate = random.choice(self.audio_sample_rates)
+            resample_transform_low = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
+            resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
+
+            low_audio = resample_transform_low(audio)
+            low_audio = resample_transform_high(low_audio)
+
+            splitted_high_quality_audio = AudioUtils.split_audio(audio, clip_length)
+            splitted_high_quality_audio[-1] = AudioUtils.pad_tensor(splitted_high_quality_audio[-1], clip_length)
+
+            splitted_low_quality_audio = AudioUtils.split_audio(low_audio, clip_length)
+            splitted_low_quality_audio[-1] = AudioUtils.pad_tensor(splitted_low_quality_audio[-1], clip_length)
+
+            for high_quality_sample, low_quality_sample in zip(splitted_high_quality_audio, splitted_low_quality_audio):
+                data.append(((high_quality_sample, low_quality_sample), (original_sample_rate, mangled_sample_rate)))
+
+        self.audio_data = data
 
     def __len__(self):
-        return len(self.input_files)
+        return len(self.audio_data)
-
 
     def __getitem__(self, idx):
-        high_quality_wav, sr_original = torchaudio.load(self.input_files[idx], normalize=True)
+        return self.audio_data[idx]
-
-        sample_rate = random.choice(self.audio_sample_rates)
-        resample_transform = torchaudio.transforms.Resample(sr_original, sample_rate)
-        low_quality_wav = resample_transform(high_quality_wav)
-        low_quality_wav = low_quality_wav
-
-        # 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)
-
-        # 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)
-
-
-        return low_quality_wav, high_quality_wav
-
-    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),
+            discriminator_block(1, layers, kernel_size=7, stride=1, spectral_norm=True, use_instance_norm=False),
-            nn.LeakyReLU(0.2, inplace=True),
+            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
-            nn.Conv1d(128, 256, kernel_size=3, padding=1),
+            discriminator_block(layers * 2, layers * 4, kernel_size=5, stride=1, dilation=2, spectral_norm=True, use_instance_norm=True),
-            nn.LeakyReLU(0.2, inplace=True),
+            AttentionBlock(layers * 4),
-            nn.Conv1d(256, 128, kernel_size=3, padding=1),
+            discriminator_block(layers * 4, layers * 8, kernel_size=5, stride=1, dilation=4, spectral_norm=True, use_instance_norm=True),
-            nn.LeakyReLU(0.2, inplace=True),
+            discriminator_block(layers * 8, layers * 4, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
-            nn.Conv1d(128, 64, kernel_size=3, padding=1),
+            discriminator_block(layers * 4, layers * 2, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
-            nn.LeakyReLU(0.2, inplace=True),
+            discriminator_block(layers * 2, layers, kernel_size=3, stride=1, spectral_norm=True, use_instance_norm=True),
-            nn.Conv1d(64, 1, kernel_size=3, padding=1),
+            discriminator_block(layers, 1, kernel_size=3, stride=1, spectral_norm=False, use_instance_norm=False)
-            nn.LeakyReLU(0.2, inplace=True),
         )
-        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 +1,30 @@
+import json
+
+filepath = "my_data.json"
+
+def write_data(filepath, data, debug=False):
+  try:
+    with open(filepath, 'w') as f:
+      json.dump(data, f, indent=4)  # Use indent for pretty formatting
+    if debug:
+        print(f"Data written to '{filepath}'")
+  except Exception as e:
+    print(f"Error writing to file: {e}")
+
+
+def read_data(filepath, debug=False):
+  try:
+    with open(filepath, 'r') as f:
+      data = json.load(f)
+    if debug:
+        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

@@ -1,23 +1,74 @@
+import torch
 import torch.nn as nn
 
-class SISUGenerator(nn.Module):
+def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
-    def __init__(self, upscale_scale=1): # No noise_dim parameter
+    return nn.Sequential(
-        super(SISUGenerator, self).__init__()
+        nn.Conv1d(
-        self.model = nn.Sequential(
+            in_channels,
-            nn.Conv1d(2, 128, kernel_size=3, padding=1),
+            out_channels,
-            nn.LeakyReLU(0.2, inplace=True),
+            kernel_size=kernel_size,
-            nn.Conv1d(128, 256, kernel_size=3, padding=1),
+            dilation=dilation,
-            nn.LeakyReLU(0.2, inplace=True),
+            padding=(kernel_size // 2) * dilation
+        ),
+        nn.InstanceNorm1d(out_channels),
+        nn.PReLU()
+    )
 
-            nn.Upsample(scale_factor=upscale_scale, mode='nearest'),
+class AttentionBlock(nn.Module):
-
+    """
-            nn.Conv1d(256, 128, kernel_size=3, padding=1),
+    Simple Channel Attention Block. Learns to weight channels based on their importance.
-            nn.LeakyReLU(0.2, inplace=True),
+    """
-            nn.Conv1d(128, 64, kernel_size=3, padding=1),
+    def __init__(self, channels):
-            nn.LeakyReLU(0.2, inplace=True),
+        super(AttentionBlock, self).__init__()
-            nn.Conv1d(64, 2, kernel_size=3, padding=1),
+        self.attention = nn.Sequential(
-            nn.Tanh()
+            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):
-        return self.model(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, 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

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

@@ -6,39 +6,114 @@ 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
 
-# Check for CUDA availability
+from training_utils import discriminator_train, generator_train
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+import file_utils as Data
+
+import torchaudio.transforms as T
+
+# 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")
+
+args = parser.parse_args()
+
+device = torch.device(args.device if torch.cuda.is_available() else "cpu")
 print(f"Using device: {device}")
 
+# Parameters
+sample_rate = 44100
+n_fft = 1024
+win_length = n_fft
+hop_length = n_fft // 4
+n_mels = 40
+n_mfcc = 13
+
+mfcc_transform = T.MFCC(
+    sample_rate=sample_rate,
+    n_mfcc=n_mfcc,
+    melkwargs={
+        'n_fft': n_fft,
+        'hop_length': hop_length,
+        'win_length': win_length,
+        'n_mels': n_mels,
+        'power': 1.0,
+    }
+).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)
+# ========= SINGLE =========
-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(dataset, batch_size=2048, shuffle=True, num_workers=24)
 
-train_data_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
-val_data_loader = DataLoader(val_dataset, batch_size=8, shuffle=True)
 
-# Initialize models and move them to device
+# ========= MODELS =========
+
 generator = SISUGenerator()
 discriminator = SISUDiscriminator()
 
+epoch: int = args.epoch
+
+if args.continue_training:
+    if args.generator is not None:
+        generator.load_state_dict(torch.load(args.generator, map_location=device, weights_only=True))
+    elif args.discriminator is not None:
+        discriminator.load_state_dict(torch.load(args.discriminator, map_location=device, weights_only=True))
+    else:
+        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_discriminator.pt", map_location=device, weights_only=True))
+
+        epoch_from_file = Data.read_data(f"{models_dir}/epoch_data.json")
+        epoch = epoch_from_file["epoch"] + 1
+
 generator = generator.to(device)
 discriminator = discriminator.to(device)
 
 # Loss
-criterion_g = nn.L1Loss()
+criterion_g = nn.BCEWithLogitsLoss()
 criterion_d = nn.BCEWithLogitsLoss()
 
 # Optimizers
@@ -49,87 +124,81 @@ 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)
 
-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
-
-def discriminator_train(discriminator, optimizer, criterion, generator, real_labels, fake_labels, high_quality, low_quality):
-    optimizer.zero_grad()
-
-    discriminator_decision_from_real = discriminator(high_quality)
-    d_loss_real = criterion(discriminator_decision_from_real, real_labels)
-
-    generator_output = generator(low_quality)
-    discriminator_decision_from_fake = discriminator(generator_output.detach())
-    d_loss_fake = criterion(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.step()
-    # print(f"Discriminator Loss: {d_loss.item():.4f}, Mean Real Logit: {discriminator_decision_from_real.mean().item():.2f}, Mean Fake Logit: {discriminator_decision_from_fake.mean().item():.2f}")
-
 def start_training():
-
+    generator_epochs = 5000
-    # Training loop
-    # discriminator_epochs = 1000
-    generator_epochs = 500
     for generator_epoch in range(generator_epochs):
-        low_quality_audio = torch.empty((1))
+        high_quality_audio = ([torch.empty((1))], 1)
-        high_quality_audio = torch.empty((1))
+        low_quality_audio = ([torch.empty((1))], 1)
-        ai_enhanced_audio = torch.empty((1))
+        ai_enhanced_audio = ([torch.empty((1))], 1)
 
-        # Training
+        times_correct = 0
-        for low_quality, high_quality in tqdm.tqdm(train_data_loader, desc=f"Epoch {generator_epoch+1}/{generator_epochs}"):
-            high_quality = high_quality.to(device)
-            low_quality = low_quality.to(device)
 
-            batch_size = high_quality.size(0)
+        # ========= TRAINING =========
+        for training_data in tqdm.tqdm(train_data_loader, desc=f"Training epoch {generator_epoch+1}/{generator_epochs}, Current epoch {epoch+1}"):
+            ## Data structure:
+            # [[[float..., float..., float...], [float..., float..., float...]], [original_sample_rate, mangled_sample_rate]]
+
+            # ========= LABELS =========
+            good_quality_data = training_data[0][0].to(device)
+            bad_quality_data = training_data[0][1].to(device)
+            original_sample_rate = training_data[1][0]
+            mangled_sample_rate = training_data[1][1]
+
+            batch_size = good_quality_data.size(0)
             real_labels = torch.ones(batch_size, 1).to(device)
             fake_labels = torch.zeros(batch_size, 1).to(device)
 
-            # Train Discriminator
+            high_quality_audio = (good_quality_data, original_sample_rate)
+            low_quality_audio = (bad_quality_data, mangled_sample_rate)
+
+            # ========= DISCRIMINATOR =========
             discriminator.train()
+            d_loss = discriminator_train(
+                good_quality_data,
+                bad_quality_data,
+                real_labels,
+                fake_labels,
+                discriminator,
+                generator,
+                criterion_d,
+                optimizer_d
+            )
 
-            for _ in range(3):
+            # ========= GENERATOR =========
-                discriminator_train(discriminator, optimizer_d, criterion_d, generator, real_labels, fake_labels, high_quality, low_quality)
-
-            # Train Generator
             generator.train()
-            optimizer_g.zero_grad()
+            generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor = generator_train(
+                bad_quality_data,
+                good_quality_data,
+                real_labels,
+                generator,
+                discriminator,
+                criterion_d,
+                optimizer_g,
+                device,
+                mel_transform,
+                stft_transform,
+                mfcc_transform
+            )
 
-            # Generator loss: how well fake data fools the discriminator
+            if debug:
-            generator_output = generator(low_quality)
+                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}")
-            discriminator_decision = discriminator(generator_output)  # No detach here
+            scheduler_d.step(d_loss.detach())
-            g_loss = criterion_g(discriminator_decision, real_labels)  # Train generator to produce real-like outputs
+            scheduler_g.step(adversarial_loss.detach())
 
-            g_loss.backward()
+            # ========= SAVE LATEST AUDIO =========
-            optimizer_g.step()
+            high_quality_audio = (good_quality_data, original_sample_rate)
+            low_quality_audio = (bad_quality_data, original_sample_rate)
+            ai_enhanced_audio = (generator_output, original_sample_rate)
 
-            low_quality_audio = low_quality
+        torch.save(discriminator.state_dict(), f"{models_dir}/temp_discriminator.pt")
-            high_quality_audio = high_quality
+        torch.save(generator.state_dict(), f"{models_dir}/temp_generator.pt")
-            ai_enhanced_audio = generator_output
 
-        metric = snr(high_quality_audio, ai_enhanced_audio)
+        new_epoch = generator_epoch+epoch
-        print(f"Generator metric {metric}!")
+        Data.write_data(f"{models_dir}/epoch_data.json", {"epoch": new_epoch})
-        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].cpu(), 44100)
-            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu(), 44100)
-            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0].cpu(), 44100)
 
-        if generator_epoch % 50 == 0:
+    torch.save(discriminator, "models/epoch-5000-discriminator.pt")
-            torch.save(discriminator.state_dict(), "discriminator.pt")
+    torch.save(generator, "models/epoch-5000-generator.pt")
-            torch.save(generator.state_dict(), "generator.pt")
-
-    torch.save(discriminator.state_dict(), "discriminator.pt")
-    torch.save(generator.state_dict(), "generator.pt")
     print("Training complete!")
 
 start_training()

training_utils.py

@@ -0,0 +1,142 @@
+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)
+
+    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(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)
+    d_loss_real = criterion(discriminator_decision_from_real, real_labels)
+
+    with torch.no_grad():
+        generator_output = generator(low_quality)
+    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)
+
+    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, 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, generator_output)
+
+    # Calculate MFCC Loss if weight is positive
+    if lambda_mfcc > 0:
+        mfcc_l = gpu_mfcc_loss(mfcc_transform, high_quality, 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