data.py

@@ -4,23 +4,20 @@ import torch
 import torchaudio
 import os
 import random
-
 import torchaudio.transforms as T
 import AudioUtils
 
 class AudioDataset(Dataset):
-    #audio_sample_rates = [8000, 11025, 16000, 22050]
     audio_sample_rates = [11025]
+    MAX_LENGTH = 88200  # Define your desired maximum length here
 
     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)
@@ -33,7 +30,24 @@ class AudioDataset(Dataset):
         resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
         low_quality_audio = resample_transform_high(low_quality_audio)
 
-        high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_quality_audio).to(self.device)
+        high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_quality_audio)
-        low_quality_audio = AudioUtils.stereo_tensor_to_mono(low_quality_audio).to(self.device)
+        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

@@ -2,35 +2,54 @@ import torch
 import torch.nn as nn
 import torch.nn.utils as utils
 
-def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
+def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, spectral_norm=True):
     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)
     return nn.Sequential(
-        utils.spectral_norm(nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)),
+        conv_layer,
         nn.LeakyReLU(0.2, inplace=True),
         nn.BatchNorm1d(out_channels)
     )
 
-class SISUDiscriminator(nn.Module):
+class AttentionBlock(nn.Module):
-    def __init__(self):
+    def __init__(self, channels):
-        super(SISUDiscriminator, self).__init__()
+        super(AttentionBlock, self).__init__()
-        layers = 4 # Increased base layer count
+        self.attention = nn.Sequential(
-        self.model = nn.Sequential(
+            nn.Conv1d(channels, channels // 4, kernel_size=1),
-            discriminator_block(1, layers, kernel_size=7, stride=2),  # Initial downsampling
+            nn.ReLU(),
-            discriminator_block(layers, layers * 2, kernel_size=5, stride=2),  # Downsampling
+            nn.Conv1d(channels // 4, channels, kernel_size=1),
-            discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=2), # Increased dilation
+            nn.Sigmoid()
-            discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=4), # Increased dilation
-            discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=8), # Deeper layer!
-            discriminator_block(layers * 8, layers * 8, kernel_size=5, dilation=1), # Deeper layer!
-            discriminator_block(layers * 8, layers * 4, kernel_size=3, dilation=2), # Reduced dilation
-            discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=1),
-            discriminator_block(layers * 2, layers, kernel_size=3, stride=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
+        attention_weights = self.attention(x)
+        return x * attention_weights
+
+class SISUDiscriminator(nn.Module):
+    def __init__(self, layers=4): #Increased base layer count
+        super(SISUDiscriminator, self).__init__()
+        self.model = nn.Sequential(
+            discriminator_block(1, layers, kernel_size=7, stride=4), #Aggressive downsampling
+            discriminator_block(layers, layers * 2, kernel_size=5, stride=2),
+            discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=2),
+            discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=4),
+            AttentionBlock(layers * 8), #Added attention
+            discriminator_block(layers * 8, layers * 16, kernel_size=5, dilation=8),
+            discriminator_block(layers * 16, layers * 16, kernel_size=3, dilation=1),
+            discriminator_block(layers * 16, layers * 8, kernel_size=3, dilation=2),
+            discriminator_block(layers * 8, layers * 4, kernel_size=3, dilation=1),
+            discriminator_block(layers * 4, layers * 2, kernel_size=3, stride=1),
+            discriminator_block(layers * 2, layers, kernel_size=3, stride=1),
+            discriminator_block(layers, 1, kernel_size=3, stride=1, spectral_norm=False) #last layer no spectral norm.
+        )
+        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
         x = self.model(x)
         x = self.global_avg_pool(x)
         x = x.view(-1, 1)
+        x = self.sigmoid(x)
         return x

generator.py

@@ -7,30 +7,46 @@ def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
         nn.PReLU()
     )
 
+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(),
+            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):
+    def __init__(self, layer=4, num_rirb=4): #increased base layer and rirb amounts
         super(SISUGenerator, self).__init__()
-        layer = 4 # Increased base layer count
         self.conv1 = nn.Sequential(
             nn.Conv1d(1, layer, kernel_size=7, padding=3),
             nn.BatchNorm1d(layer),
             nn.PReLU(),
         )
-        self.conv_blocks = nn.Sequential(
+        self.rir_blocks = nn.Sequential(*[ResidualInResidualBlock(layer) for _ in range(num_rirb)])
-            conv_block(layer, layer, kernel_size=3, dilation=1),      # Local details
+        self.final_layer = nn.Conv1d(layer, 1, kernel_size=3, padding=1)
-            conv_block(layer, layer*2, kernel_size=5, dilation=2),    # Local Context
-            conv_block(layer*2, layer*2, kernel_size=3, dilation=16),   # Longer range dependencies
-            conv_block(layer*2, layer*2, kernel_size=5, dilation=8),    # Wider context
-            conv_block(layer*2, layer, kernel_size=5, dilation=2),    # Local Context
-            conv_block(layer, layer, kernel_size=3, dilation=1),      # Local details
-        )
-        self.final_layer = nn.Sequential(
-            nn.Conv1d(layer, 1, kernel_size=3, padding=1),
-        )
 
     def forward(self, x):
         residual = x
         x = self.conv1(x)
-        x = self.conv_blocks(x)
+        x = self.rir_blocks(x)
         x = self.final_layer(x)
         return x + residual

training.py

@@ -38,7 +38,7 @@ device = torch.device(args.device if torch.cuda.is_available() else "cpu")
 print(f"Using device: {device}")
 
 mfcc_transform = T.MFCC(
-    sample_rate=16000,  # Adjust to your sample rate
+    sample_rate=44100,  # Adjust to your sample rate
     n_mfcc=20,
     melkwargs={'n_fft': 2048, 'hop_length': 512} # adjust n_fft and hop_length to your needs.
 ).to(device)
@@ -97,20 +97,9 @@ debug = args.verbose
 dataset_dir = './dataset/good'
 dataset = AudioDataset(dataset_dir, device)
 
-# ========= MULTIPLE =========
-
-# 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=1, shuffle=True)
+train_data_loader = DataLoader(dataset, batch_size=128, shuffle=True)
 
 # Initialize models and move them to device
 generator = SISUGenerator()
@@ -175,17 +164,17 @@ def start_training():
             scheduler_g.step(combined_loss)
 
             # ========= SAVE LATEST AUDIO =========
-            high_quality_audio = high_quality_clip
+            high_quality_audio = (high_quality_clip[0][0], high_quality_clip[1][0])
-            low_quality_audio = low_quality_clip
+            low_quality_audio = (low_quality_clip[0][0], low_quality_clip[1][0])
-            ai_enhanced_audio = (generator_output, high_quality_clip[1])
+            ai_enhanced_audio = (generator_output[0], high_quality_clip[1][0])
 
         new_epoch = generator_epoch+epoch
 
         if generator_epoch % 10 == 0:
             print(f"Saved epoch {new_epoch}!")
-            torchaudio.save(f"./output/epoch-{new_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"./output/epoch-{new_epoch}-audio-crap.wav", low_quality_audio[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"./output/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0][0].cpu(), ai_enhanced_audio[1])
+            torchaudio.save(f"./output/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu(), ai_enhanced_audio[1])
-            torchaudio.save(f"./output/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
+            torchaudio.save(f"./output/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0].cpu(), high_quality_audio[1])
 
         if debug:
             print(generator.state_dict().keys())