🔥 | Removed unnecessary models.

.gitignore

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

data.py

@@ -19,26 +19,25 @@ class AudioDataset(Dataset):
 
 
     def __getitem__(self, idx):
-        high_quality_wav, sr_original = torchaudio.load(self.input_files[idx], normalize=True)
+        high_quality_audio, original_sample_rate = torchaudio.load(self.input_files[idx], normalize=True)
 
-        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
+        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)
 
         # 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)
+        # 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)
+        # 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
+        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)

discriminator.py

@@ -5,15 +5,15 @@ class SISUDiscriminator(nn.Module):
         super(SISUDiscriminator, self).__init__()
         self.model = nn.Sequential(
             nn.Conv1d(2, 128, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
+            #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.LeakyReLU(0.2, inplace=True),
             nn.Conv1d(128, 64, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
+            #nn.LeakyReLU(0.2, inplace=True),
             nn.Conv1d(64, 1, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
+            #nn.LeakyReLU(0.2, inplace=True),
         )
         self.global_avg_pool = nn.AdaptiveAvgPool1d(1)  # Output size (1,)
 

generator.py

@@ -3,21 +3,25 @@ import torch.nn as nn
 class SISUGenerator(nn.Module):
     def __init__(self, upscale_scale=1): # No noise_dim parameter
         super(SISUGenerator, self).__init__()
-        self.model = nn.Sequential(
+        self.layers1 = nn.Sequential(
             nn.Conv1d(2, 128, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
+            # nn.LeakyReLU(0.2, inplace=True),
             nn.Conv1d(128, 256, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, inplace=True),
-
-            nn.Upsample(scale_factor=upscale_scale, mode='nearest'),
-
-            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()
+            # nn.LeakyReLU(0.2, inplace=True),
         )
 
-    def forward(self, x):
-        return self.model(x)
+        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()
+        )
+
+    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

training.py

@@ -13,6 +13,60 @@ 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
+
+    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)
+
+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(high_quality, low_quality, scale, real_labels, fake_labels):
+    optimizer_d.zero_grad()
+
+    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")
 print(f"Using device: {device}")
@@ -27,8 +81,8 @@ 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=8, shuffle=True)
-val_data_loader = DataLoader(val_dataset, batch_size=8, 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
 generator = SISUGenerator()
@@ -39,6 +93,7 @@ discriminator = discriminator.to(device)
 
 # Loss
 criterion_g = nn.L1Loss()
+criterion_g_mel = MelSpectrogramLoss().to(device)
 criterion_d = nn.BCEWithLogitsLoss()
 
 # Optimizers
@@ -49,87 +104,80 @@ 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():
 
     # 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))
-        ai_enhanced_audio = torch.empty((1))
 
-        # Training
-        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)
+    # ========= DISCRIMINATOR PRE-TRAINING =========
+    discriminator_epochs = 1
+    for discriminator_epoch in range(discriminator_epochs):
 
-            batch_size = high_quality.size(0)
+        # ========= 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)
 
-            # Train Discriminator
+            # ========= 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
+    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)
+
+            scale = high_quality_clip[0].shape[2]/low_quality_clip[0].shape[2]
+
+            # ========= LABELS =========
+            batch_size = high_quality_clip[0].size(0)
+            real_labels = torch.ones(batch_size, 1).to(device)
+            fake_labels = torch.zeros(batch_size, 1).to(device)
+
+            # ========= DISCRIMINATOR =========
+            discriminator.train()
             for _ in range(3):
-                discriminator_train(discriminator, optimizer_d, criterion_d, generator, real_labels, fake_labels, high_quality, low_quality)
+                discriminator_train(high_quality_sample, low_quality_sample, scale, real_labels, fake_labels)
 
-            # Train Generator
+            # ========= GENERATOR =========
             generator.train()
-            optimizer_g.zero_grad()
+            generator_output = generator_train(low_quality_sample, scale, real_labels)
 
-            # Generator loss: how well fake data fools the discriminator
-            generator_output = generator(low_quality)
-            discriminator_decision = discriminator(generator_output)  # No detach here
-            g_loss = criterion_g(discriminator_decision, real_labels)  # Train generator to produce real-like outputs
+            # ========= SAVE LATEST AUDIO =========
+            high_quality_audio = high_quality_clip
+            low_quality_audio = low_quality_clip
+            ai_enhanced_audio = (generator_output, high_quality_clip[1])
 
-            g_loss.backward()
-            optimizer_g.step()
-
-            low_quality_audio = low_quality
-            high_quality_audio = high_quality
-            ai_enhanced_audio = generator_output
-
-        metric = snr(high_quality_audio, ai_enhanced_audio)
+        metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
         print(f"Generator metric {metric}!")
         scheduler_g.step(metric)
 
         if generator_epoch % 10 == 0:
             print(f"Saved epoch {generator_epoch}!")
-            torchaudio.save(f"./output/epoch-{generator_epoch}-audio-crap.wav", low_quality_audio[0].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)
+            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 % 50 == 0:
-            torch.save(discriminator.state_dict(), "discriminator.pt")
-            torch.save(generator.state_dict(), "generator.pt")
+            torch.save(discriminator.state_dict(), f"models/epoch-{generator_epoch}-discriminator.pt")
+            torch.save(generator.state_dict(), f"models/epoch-{generator_epoch}-generator.pt")
 
-    torch.save(discriminator.state_dict(), "discriminator.pt")
-    torch.save(generator.state_dict(), "generator.pt")
+    torch.save(discriminator.state_dict(), "models/epoch-500-discriminator.pt")
+    torch.save(generator.state_dict(), "models/epoch-500-generator.pt")
     print("Training complete!")
 
 start_training()