⚗️ | Experimenting...

data.py

@@ -4,32 +4,49 @@ import torch
 import torchaudio
 import os
 import random
-
-import torchaudio.transforms as T
-import AudioUtils
+from AudioUtils import stereo_tensor_to_mono, stretch_tensor
 
 class AudioDataset(Dataset):
-    #audio_sample_rates = [8000, 11025, 16000, 22050]
     audio_sample_rates = [11025]
 
     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.input_files = [
+            os.path.join(root, f)
+            for root, _, files in os.walk(input_dir)
+            for f in files if f.endswith('.wav')
+        ]
 
     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)
+        high_quality_path = self.input_files[idx]
+        high_quality_audio, original_sample_rate = torchaudio.load(high_quality_path)
+        high_quality_audio = stereo_tensor_to_mono(high_quality_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)
-        low_quality_audio = resample_transform_low(high_quality_audio)
+        resample_low = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
+        low_quality_audio = resample_low(high_quality_audio)
 
-        resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
-        low_quality_audio = resample_transform_high(low_quality_audio)
+        resample_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
+        low_quality_audio = resample_high(low_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)
+        # Pad or truncate to match a fixed length
+        target_length = 44100  # Adjust this based on your data
+        high_quality_audio = self.pad_or_truncate(high_quality_audio, target_length)
+        low_quality_audio = self.pad_or_truncate(low_quality_audio, target_length)
+
+        return (high_quality_audio, original_sample_rate), (low_quality_audio, mangled_sample_rate)
+
+    def pad_or_truncate(self, tensor, target_length):
+        current_length = tensor.size(1)
+        if current_length < target_length:
+            # Pad with zeros
+            padding = target_length - current_length
+            tensor = F.pad(tensor, (0, padding))
+        else:
+            # Truncate to target length
+            tensor = tensor[:, :target_length]
+        return tensor

discriminator.py

@@ -5,33 +5,34 @@ import torch.nn.utils as utils
 def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
     padding = (kernel_size // 2) * dilation
     return nn.Sequential(
-        utils.spectral_norm(nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)),
+        utils.spectral_norm(
+            nn.Conv1d(in_channels, out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=padding
+            )
+        ),
         nn.BatchNorm1d(out_channels),
-        nn.LeakyReLU(0.2, inplace=True) # Changed activation to LeakyReLU
+        nn.LeakyReLU(0.2, inplace=True)
     )
 
 class SISUDiscriminator(nn.Module):
     def __init__(self):
         super(SISUDiscriminator, self).__init__()
-        layers = 4 # Increased base layer count
+        layers = 4
         self.model = nn.Sequential(
-            # Initial Convolution
-            discriminator_block(1, layers, kernel_size=7, stride=2, dilation=1), # Downsample
-
-            # 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 * 4, kernel_size=5, dilation=4),
-            discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=16),
-            discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=8),
-            discriminator_block(layers * 2, layers, kernel_size=3, dilation=1),
-            # Final Convolution
-            discriminator_block(layers, 1, kernel_size=3, stride=1),
+            discriminator_block(1, layers, kernel_size=7, stride=2, dilation=1),
+            discriminator_block(layers, layers * 2, kernel_size=5, stride=2, dilation=1),
+            discriminator_block(layers * 2, layers * 4, kernel_size=3, dilation=4),
+            discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=8),
+            discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=16),
+            discriminator_block(layers * 2, layers, kernel_size=5, dilation=2),
+            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(-1, 1)
-        return x
+        return x.view(-1, 1)

generator.py

@@ -1,36 +1,41 @@
 import torch.nn as nn
 
-def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
+def conv_residual_block(in_channels, out_channels, kernel_size=3, dilation=1):
+    padding = (kernel_size // 2) * dilation
     return nn.Sequential(
-        nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, dilation=dilation, padding=(kernel_size // 2) * dilation),
+        nn.Conv1d(in_channels, out_channels, kernel_size, dilation=dilation, padding=padding),
         nn.BatchNorm1d(out_channels),
-        nn.PReLU()
+        nn.PReLU(),
+        nn.Conv1d(out_channels, out_channels, kernel_size, dilation=dilation, padding=padding),
+        nn.BatchNorm1d(out_channels)
     )
 
 class SISUGenerator(nn.Module):
     def __init__(self):
         super(SISUGenerator, self).__init__()
-        layer = 4 # Increased base layer count
+        layers = 4
         self.conv1 = nn.Sequential(
-            nn.Conv1d(1, layer, kernel_size=7, padding=3),
-            nn.BatchNorm1d(layer),
-            nn.PReLU(),
+            nn.Conv1d(1, layers, kernel_size=7, padding=3),
+            nn.BatchNorm1d(layers),
+            nn.PReLU()
         )
+
         self.conv_blocks = nn.Sequential(
-            conv_block(layer, layer, kernel_size=3, dilation=1),      # Local details
-            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
+            conv_residual_block(layers, layers, kernel_size=3, dilation=1),
+            conv_residual_block(layers, layers * 2, kernel_size=5, dilation=2),
+            conv_residual_block(layers * 2, layers * 4, kernel_size=3, dilation=16),
+            conv_residual_block(layers * 4, layers * 2, kernel_size=5, dilation=8),
+            conv_residual_block(layers * 2, layers, kernel_size=5, dilation=2),
+            conv_residual_block(layers, layers, kernel_size=3, dilation=1)
         )
+
         self.final_layer = nn.Sequential(
-            nn.Conv1d(layer, 1, kernel_size=3, padding=1),
+            nn.Conv1d(layers, 1, kernel_size=3, padding=1)
         )
 
     def forward(self, x):
         residual = x
         x = self.conv1(x)
-        x = self.conv_blocks(x)
+        x = self.conv_blocks(x) + x  # Adding residual connection after blocks
         x = self.final_layer(x)
         return x + residual

training.py

@@ -55,6 +55,11 @@ def generator_train(low_quality, real_labels):
     optimizer_g.step()
     return generator_output
 
+def first(objects):
+    if len(objects) >= 1:
+        return objects[0]
+    return objects
+
 # Init script argument parser
 parser = argparse.ArgumentParser(description="Training script")
 parser.add_argument("--generator", type=str, default=None,
@@ -72,17 +77,6 @@ print(f"Using device: {device}")
 dataset_dir = './dataset/good'
 dataset = AudioDataset(dataset_dir)
 
-# ========= 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=16, shuffle=True)
@@ -112,31 +106,6 @@ 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)
@@ -165,9 +134,15 @@ def start_training():
             generator_output = generator_train(low_quality_sample, real_labels)
 
             # ========= 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 = (first(high_quality_clip[0]), high_quality_clip[1][0])
+            low_quality_audio = (first(low_quality_clip[0]), low_quality_clip[1][0])
+            ai_enhanced_audio = (first(generator_output[0]), high_quality_clip[1][0])
+            print(high_quality_audio)
+
+            print(f"Saved epoch {generator_epoch}!")
+            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"./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])
 
         #metric = snr(high_quality_audio[0].to(device), ai_enhanced_audio[0])
         #print(f"Generator metric {metric}!")