⚗️ | Experimenting with larger model architecture.
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@ -2,29 +2,39 @@ import torch
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import torch.nn as nn
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import torch.nn.utils as utils
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def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
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padding = (kernel_size // 2) * dilation
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return nn.Sequential(
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utils.spectral_norm(nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)),
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nn.BatchNorm1d(out_channels),
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nn.LeakyReLU(0.2, inplace=True) # Changed activation to LeakyReLU
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)
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class SISUDiscriminator(nn.Module):
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def __init__(self):
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super(SISUDiscriminator, self).__init__()
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layers = 8
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layers = 32 # Increased base layer count
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self.model = nn.Sequential(
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utils.spectral_norm(nn.Conv1d(1, layers, kernel_size=7, stride=2, padding=3)),
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nn.BatchNorm1d(layers),
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nn.PReLU(),
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nn.Conv1d(layers, layers * 2, kernel_size=7, padding=3),
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nn.BatchNorm1d(layers * 2),
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nn.PReLU(),
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nn.Conv1d(layers * 2, layers * 4, kernel_size=5, padding=2),
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nn.BatchNorm1d(layers * 4),
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nn.PReLU(),
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nn.Conv1d(layers * 4, layers * 8, kernel_size=3, padding=1),
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nn.BatchNorm1d(layers * 8),
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nn.PReLU(),
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nn.Conv1d(layers * 8, 1, kernel_size=3, padding=1),
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# Initial Convolution
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discriminator_block(1, layers, kernel_size=7, stride=2, dilation=1), # Downsample
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# Core Discriminator Blocks with varied kernels and dilations
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discriminator_block(layers, layers * 2, kernel_size=5, stride=2, dilation=1), # Downsample
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discriminator_block(layers * 2, layers * 2, kernel_size=3, dilation=2),
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discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=4),
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discriminator_block(layers * 4, layers * 4, kernel_size=3, dilation=8),
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discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=16),
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discriminator_block(layers * 8, layers * 8, kernel_size=3, dilation=8),
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discriminator_block(layers * 8, layers * 4, kernel_size=5, dilation=4),
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discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=2),
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discriminator_block(layers * 2, layers, kernel_size=5, dilation=1),
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# Final Convolution
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discriminator_block(layers, 1, kernel_size=3, stride=1),
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)
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self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
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def forward(self, x):
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x = x + 0.01 * torch.randn_like(x)
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# Gaussian noise is not necessary here for discriminator as it is already implicit in the training process
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x = self.model(x)
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x = self.global_avg_pool(x)
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x = x.view(-1, 1)
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38
generator.py
38
generator.py
@ -1,31 +1,39 @@
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import torch.nn as nn
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def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
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return nn.Sequential(
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nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, dilation=dilation, padding=(kernel_size // 2) * dilation),
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nn.BatchNorm1d(out_channels),
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nn.PReLU()
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)
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class SISUGenerator(nn.Module):
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def __init__(self):
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super(SISUGenerator, self).__init__()
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layer = 16
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# Convolution layers with BatchNorm and Residuals
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layer = 32 # Increased base layer count
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self.conv1 = nn.Sequential(
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nn.Conv1d(1, layer * 2, kernel_size=7, padding=3),
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nn.BatchNorm1d(layer * 2),
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nn.PReLU(),
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nn.Conv1d(layer * 2, layer * 5, kernel_size=7, padding=3),
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nn.BatchNorm1d(layer * 5),
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nn.PReLU(),
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nn.Conv1d(layer * 5, layer * 5, kernel_size=7, padding=3),
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nn.BatchNorm1d(layer * 5),
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nn.Conv1d(1, layer, kernel_size=7, padding=3),
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nn.BatchNorm1d(layer),
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nn.PReLU(),
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)
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self.conv_blocks = nn.Sequential(
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conv_block(layer, layer, kernel_size=3, dilation=1), # Local details
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conv_block(layer, layer*2, kernel_size=5, dilation=2), # Local Context
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conv_block(layer*2, layer*2, kernel_size=3, dilation=4), # Wider context
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conv_block(layer*2, layer*4, kernel_size=7, dilation=8), # Longer range dependencies
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conv_block(layer*4, layer*4, kernel_size=3, dilation=16), # Longer range dependencies
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conv_block(layer*4, layer*2, kernel_size=5, dilation=8), # Wider context
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conv_block(layer*2, layer*2, kernel_size=3, dilation=4), # Wider context
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conv_block(layer*2, layer, kernel_size=5, dilation=2), # Local Context
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conv_block(layer, layer, kernel_size=3, dilation=1), # Local details
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)
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self.final_layer = nn.Sequential(
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nn.Conv1d(layer * 5, layer * 2, kernel_size=5, padding=2),
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nn.BatchNorm1d(layer * 2),
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nn.PReLU(),
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nn.Conv1d(layer * 2, 1, kernel_size=3, padding=1),
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# nn.Tanh() # Normalize audio... if needed...
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nn.Conv1d(layer, 1, kernel_size=3, padding=1),
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)
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def forward(self, x):
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residual = x
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x = self.conv1(x)
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x = self.conv_blocks(x)
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x = self.final_layer(x)
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return x + residual
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11
training.py
11
training.py
@ -175,16 +175,15 @@ def start_training():
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if generator_epoch % 10 == 0:
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print(f"Saved epoch {generator_epoch}!")
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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 what ever that low_quality had to high_quality
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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.
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torchaudio.save(f"./output/epoch-{generator_epoch}-audio-ai.wav", ai_enhanced_audio[0][0].cpu(), ai_enhanced_audio[1])
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torchaudio.save(f"./output/epoch-{generator_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
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if generator_epoch % 50 == 0:
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torch.save(discriminator.state_dict(), f"models/epoch-{generator_epoch}-discriminator.pt")
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torch.save(generator.state_dict(), f"models/epoch-{generator_epoch}-generator.pt")
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torch.save(discriminator.state_dict(), f"models/current-epoch-discriminator.pt")
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torch.save(generator.state_dict(), f"models/current-epoch-generator.pt")
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torch.save(discriminator.state_dict(), "models/epoch-500-discriminator.pt")
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torch.save(generator.state_dict(), "models/epoch-500-generator.pt")
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torch.save(discriminator.state_dict(), "models/epoch-5000-discriminator.pt")
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torch.save(generator.state_dict(), "models/epoch-5000-generator.pt")
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print("Training complete!")
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start_training()
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