README.md

@@ -18,7 +18,6 @@ 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

@@ -4,20 +4,22 @@ 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, device):
+    def __init__(self, input_dir):
         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)
@@ -30,24 +32,4 @@ class AudioDataset(Dataset):
         resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
         low_quality_audio = resample_transform_high(low_quality_audio)
 
-        high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_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)
-        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

@@ -2,62 +2,40 @@ 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):
+def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
     padding = (kernel_size // 2) * dilation
-    conv_layer = nn.Conv1d(
+    return nn.Sequential(
-        in_channels,
+        utils.spectral_norm(nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)),
-        out_channels,
+        nn.BatchNorm1d(out_channels),
-        kernel_size=kernel_size,
+        nn.LeakyReLU(0.2, inplace=True) # Changed activation to LeakyReLU
-        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, base_channels=16):
+    def __init__(self):
         super(SISUDiscriminator, self).__init__()
-        layers = base_channels
+        layers = 32 # Increased base layer count
         self.model = nn.Sequential(
-            discriminator_block(1, layers, kernel_size=7, stride=1, spectral_norm=True, use_instance_norm=False),
+            # Initial Convolution
-            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, spectral_norm=True, use_instance_norm=True),
+            discriminator_block(1, layers, kernel_size=7, stride=2, dilation=1), # Downsample
-            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)
-        )
 
+            # Core Discriminator Blocks with varied kernels and dilations
+            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, dilation=1), # Downsample
+            discriminator_block(layers * 2, layers * 2, kernel_size=3, dilation=2),
+            discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=4),
+            discriminator_block(layers * 4, layers * 4, kernel_size=3, dilation=8),
+            discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=16),
+            discriminator_block(layers * 8, layers * 8, kernel_size=3, dilation=8),
+            discriminator_block(layers * 8, layers * 4, kernel_size=5, dilation=4),
+            discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=2),
+            discriminator_block(layers * 2, layers, kernel_size=5, dilation=1),
+            # Final Convolution
+            discriminator_block(layers, 1, kernel_size=3, stride=1),
+        )
         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(x.size(0), -1)
+        x = x.view(-1, 1)
         return x

file_utils.py

@@ -1,28 +0,0 @@
-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

@@ -1,74 +1,39 @@
-import torch
 import torch.nn as nn
 
 def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
     return nn.Sequential(
-        nn.Conv1d(
+        nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, dilation=dilation, padding=(kernel_size // 2) * dilation),
-            in_channels,
+        nn.BatchNorm1d(out_channels),
-            out_channels,
-            kernel_size=kernel_size,
-            dilation=dilation,
-            padding=(kernel_size // 2) * dilation
-        ),
-        nn.InstanceNorm1d(out_channels),
         nn.PReLU()
     )
 
-class AttentionBlock(nn.Module):
+class SISUGenerator(nn.Module):
-    """
+    def __init__(self):
-    Simple Channel Attention Block. Learns to weight channels based on their importance.
+        super(SISUGenerator, self).__init__()
-    """
+        layer = 32 # Increased base layer count
-    def __init__(self, channels):
+        self.conv1 = nn.Sequential(
-        super(AttentionBlock, self).__init__()
+            nn.Conv1d(1, layer, kernel_size=7, padding=3),
-        self.attention = nn.Sequential(
+            nn.BatchNorm1d(layer),
-            nn.Conv1d(channels, channels // 4, kernel_size=1),
+            nn.PReLU(),
-            nn.ReLU(inplace=True),
-            nn.Conv1d(channels // 4, channels, kernel_size=1),
-            nn.Sigmoid()
         )
-
+        self.conv_blocks = nn.Sequential(
-    def forward(self, x):
+            conv_block(layer, layer, kernel_size=3, dilation=1),      # Local details
-        attention_weights = self.attention(x)
+            conv_block(layer, layer*2, kernel_size=5, dilation=2),    # Local Context
-        return x * attention_weights
+            conv_block(layer*2, layer*2, kernel_size=3, dilation=4),    # Wider context
-
+            conv_block(layer*2, layer*4, kernel_size=7, dilation=8),    # Longer range dependencies
-class ResidualInResidualBlock(nn.Module):
+            conv_block(layer*4, layer*4, kernel_size=3, dilation=16),   # Longer range dependencies
-    def __init__(self, channels, num_convs=3):
+            conv_block(layer*4, layer*2, kernel_size=5, dilation=8),    # Wider context
-        super(ResidualInResidualBlock, self).__init__()
+            conv_block(layer*2, layer*2, kernel_size=3, dilation=4),    # Wider context
-
+            conv_block(layer*2, layer, kernel_size=5, dilation=2),    # Local Context
-        self.conv_layers = nn.Sequential(
+            conv_block(layer, layer, kernel_size=3, dilation=1),      # Local details
-            *[conv_block(channels, channels) for _ in range(num_convs)]
+        )
+        self.final_layer = nn.Sequential(
+            nn.Conv1d(layer, 1, kernel_size=3, padding=1),
         )
-
-        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)
+        x = self.conv_blocks(x)
-        learned_residual = self.final_layer(x_rirb_out)
+        x = self.final_layer(x)
-        output = residual_input + self.alpha * learned_residual
+        return x + residual
-
-        return output

requirements.txt

@@ -4,9 +4,11 @@ Jinja2==3.1.4
 MarkupSafe==2.1.5
 mpmath==1.3.0
 networkx==3.4.2
-numpy==2.2.3
+numpy==2.2.1
-pillow==11.0.0
+pytorch-triton-rocm==3.2.0+git0d4682f0
 setuptools==70.2.0
-sympy==1.13.3
+sympy==1.13.1
+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

@@ -10,8 +10,6 @@ import argparse
 
 import math
 
-import os
-
 from torch.utils.data import random_split
 from torch.utils.data import DataLoader
 
@@ -20,10 +18,42 @@ from data import AudioDataset
 from generator import SISUGenerator
 from discriminator import SISUDiscriminator
 
-from training_utils import discriminator_train, generator_train
+def perceptual_loss(y_true, y_pred):
-import file_utils as Data
+    return torch.mean((y_true - y_pred) ** 2)
 
-import torchaudio.transforms as T
+def discriminator_train(high_quality, low_quality, real_labels, fake_labels):
+    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")
@@ -31,78 +61,47 @@ 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")
+# Check for CUDA availability
+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, device)
+dataset = AudioDataset(dataset_dir)
-models_dir = "models"
+
-os.makedirs(models_dir, exist_ok=True)
+# ========= MULTIPLE =========
-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)
+
+#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=64, shuffle=True)
+train_data_loader = DataLoader(dataset, batch_size=1, shuffle=True)
-
-
-# ========= MODELS =========
 
+# Initialize models and move them to device
 generator = SISUGenerator()
 discriminator = SISUDiscriminator()
 
-epoch: int = args.epoch
+if args.generator is not None:
-epoch_from_file = Data.read_data(f"{models_dir}/epoch_data.json")
+    generator.load_state_dict(torch.load(args.generator, weights_only=True))
-
+if args.discriminator is not None:
-if args.continue_training:
+    discriminator.load_state_dict(torch.load(args.discriminator, weights_only=True))
-    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.BCEWithLogitsLoss()
+criterion_g = nn.MSELoss()
-criterion_d = nn.BCEWithLogitsLoss()
+criterion_d = nn.BCELoss()
 
 # Optimizers
 optimizer_g = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
@@ -113,6 +112,31 @@ 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)
@@ -122,10 +146,10 @@ def start_training():
         times_correct = 0
 
         # ========= 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 tqdm.tqdm(train_data_loader, desc=f"Epoch {generator_epoch+1}/{generator_epochs}"):
         # for high_quality_clip, low_quality_clip in train_data_loader:
-            high_quality_sample = (high_quality_clip[0], high_quality_clip[1])
+            high_quality_sample = (high_quality_clip[0].to(device), high_quality_clip[1])
-            low_quality_sample = (low_quality_clip[0], low_quality_clip[1])
+            low_quality_sample = (low_quality_clip[0].to(device), low_quality_clip[1])
 
             # ========= LABELS =========
             batch_size = high_quality_clip[0].size(0)
@@ -134,61 +158,32 @@ def start_training():
 
             # ========= DISCRIMINATOR =========
             discriminator.train()
-            d_loss = discriminator_train(
+            discriminator_train(high_quality_sample, low_quality_sample, real_labels, fake_labels)
-                high_quality_sample,
-                low_quality_sample,
-                real_labels,
-                fake_labels,
-                discriminator,
-                generator,
-                criterion_d,
-                optimizer_d
-            )
 
             # ========= GENERATOR =========
             generator.train()
-            generator_output, combined_loss, adversarial_loss, mel_l1_tensor, log_stft_l1_tensor, mfcc_l_tensor = generator_train(
+            generator_output = generator_train(low_quality_sample, real_labels)
-                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[0][0], high_quality_clip[1][0])
+            high_quality_audio = high_quality_clip
-            low_quality_audio = (low_quality_clip[0][0], low_quality_clip[1][0])
+            low_quality_audio = low_quality_clip
-            ai_enhanced_audio = (generator_output[0], high_quality_clip[1][0])
+            ai_enhanced_audio = (generator_output, high_quality_clip[1])
 
-        new_epoch = generator_epoch+epoch
+        #metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
+        #print(f"Generator metric {metric}!")
+        #scheduler_g.step(metric)
 
-        if generator_epoch % 25 == 0:
+        if generator_epoch % 10 == 0:
-            print(f"Saved epoch {new_epoch}!")
+            print(f"Saved epoch {generator_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"./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-ai.wav", ai_enhanced_audio[0].cpu().detach(), ai_enhanced_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"{audio_output_dir}/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0].cpu().detach(), high_quality_audio[1])
+            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
 
-        #if debug:
+        torch.save(discriminator.state_dict(), f"models/current-epoch-discriminator.pt")
-        #    print(generator.state_dict().keys())
+        torch.save(generator.state_dict(), f"models/current-epoch-generator.pt")
-        #    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(discriminator, "models/epoch-5000-discriminator.pt")
+    torch.save(generator.state_dict(), "models/epoch-5000-generator.pt")
-    torch.save(generator, "models/epoch-5000-generator.pt")
     print("Training complete!")
 
 start_training()

training_utils.py

@@ -1,144 +0,0 @@
-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