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"""
U-Net Autoencoder for Speech Enhancement
Processes mel-spectrograms to remove noise
Architecture: Encoder-Decoder with skip connections
Input: Noisy mel-spectrogram (128 x T)
Output: Clean mel-spectrogram (128 x T)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class DoubleConv(nn.Module):
"""
Double Convolution block: Conv -> BatchNorm -> ReLU -> Conv -> BatchNorm -> ReLU
Used as basic building block in U-Net
"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""
Downsampling block: MaxPool -> DoubleConv
Reduces spatial dimensions, increases channels
"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
"""
Upsampling block: Upsample -> Concat with skip connection -> DoubleConv
Increases spatial dimensions, decreases channels
"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
"""
Args:
x1: Feature map from decoder path
x2: Feature map from encoder path (skip connection)
"""
x1 = self.up(x1)
# Handle size mismatch due to padding
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
# Concatenate skip connection
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class UNetAudioEnhancer(nn.Module):
"""
U-Net model for audio enhancement
Architecture:
Encoder: 4 downsampling stages (64 -> 128 -> 256 -> 512)
Bottleneck: 1024 channels
Decoder: 4 upsampling stages (512 -> 256 -> 128 -> 64)
Output: 1 channel (clean spectrogram)
Args:
in_channels: Number of input channels (1 for single spectrogram)
out_channels: Number of output channels (1 for single spectrogram)
"""
def __init__(self, in_channels=1, out_channels=1):
super().__init__()
# Initial convolution
self.inc = DoubleConv(in_channels, 64)
# Encoder (downsampling path)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
self.down4 = Down(512, 1024)
# Decoder (upsampling path)
self.up1 = Up(1024, 512)
self.up2 = Up(512, 256)
self.up3 = Up(256, 128)
self.up4 = Up(128, 64)
# Output convolution
self.outc = nn.Conv2d(64, out_channels, kernel_size=1)
def forward(self, x):
"""
Forward pass through U-Net
Args:
x: Input tensor (batch_size, 1, height, width)
For mel-spectrograms: (B, 1, 128, T)
Returns:
Output tensor (batch_size, 1, height, width)
"""
# Encoder path (save features for skip connections)
x1 = self.inc(x) # 64 channels
x2 = self.down1(x1) # 128 channels
x3 = self.down2(x2) # 256 channels
x4 = self.down3(x3) # 512 channels
x5 = self.down4(x4) # 1024 channels (bottleneck)
# Decoder path (with skip connections)
x = self.up1(x5, x4) # 512 channels
x = self.up2(x, x3) # 256 channels
x = self.up3(x, x2) # 128 channels
x = self.up4(x, x1) # 64 channels
# Output
x = self.outc(x) # 1 channel
return x
def count_parameters(self):
"""Count trainable parameters"""
return sum(p.numel() for p in self.parameters() if p.requires_grad)
def test_model():
"""
Test the model with dummy input
Verifies input/output dimensions
"""
print("="*70)
print("Testing U-Net Audio Enhancer Model")
print("="*70)
# Create model
model = UNetAudioEnhancer(in_channels=1, out_channels=1)
# Print model info
print(f"\nModel Parameters: {model.count_parameters():,}")
print(f" (~{model.count_parameters() / 1e6:.2f}M parameters)")
# Test with dummy input
# Mel-spectrogram size: (batch, channels, mels, time)
# Time frames = (3 seconds * 16000 Hz) / 256 hop_length = 187.5 ≈ 188
batch_size = 4
mel_bins = 128
time_frames = 188
dummy_input = torch.randn(batch_size, 1, mel_bins, time_frames)
print(f"\n🔍 Input shape: {dummy_input.shape}")
# Forward pass
with torch.no_grad():
output = model(dummy_input)
print(f"Output shape: {output.shape}")
# Verify shapes match
assert output.shape == dummy_input.shape, "Output shape mismatch!"
print("\nModel test passed!")
print("="*70)
if __name__ == "__main__":
test_model()
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