99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
from models.mossformer2_se.mossformer2 import MossFormer_MaskNet
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import torch.nn as nn
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class MossFormer2_SE_48K(nn.Module):
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"""
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The MossFormer2_SE_48K model for speech enhancement.
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This class encapsulates the functionality of the MossFormer MaskNet
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within a higher-level model. It processes input audio data to produce
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enhanced outputs and corresponding masks.
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Arguments
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---------
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args : Namespace
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Configuration arguments that may include hyperparameters
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and model settings (not utilized in this implementation but
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can be extended for flexibility).
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Example
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---------
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>>> model = MossFormer2_SE_48K(args).model
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>>> x = torch.randn(10, 180, 2000) # Example input
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>>> outputs, mask = model(x) # Forward pass
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>>> outputs.shape, mask.shape # Check output shapes
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"""
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def __init__(self, args):
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super(MossFormer2_SE_48K, self).__init__()
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# Initialize the TestNet model, which contains the MossFormer MaskNet
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self.model = TestNet() # Instance of TestNet
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def forward(self, x):
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"""
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Forward pass through the model.
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Arguments
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---------
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x : torch.Tensor
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Input tensor of dimension [B, N, S], where B is the batch size,
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N is the number of channels (180 in this case), and S is the
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sequence length (e.g., time frames).
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Returns
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-------
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outputs : torch.Tensor
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Enhanced audio output tensor from the model.
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mask : torch.Tensor
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Mask tensor predicted by the model for speech separation.
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"""
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outputs, mask = self.model(x) # Get outputs and mask from TestNet
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return outputs, mask # Return the outputs and mask
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class TestNet(nn.Module):
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"""
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The TestNet class for testing the MossFormer MaskNet implementation.
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This class builds a model that integrates the MossFormer_MaskNet
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for processing input audio and generating masks for source separation.
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Arguments
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---------
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n_layers : int
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The number of layers in the model. It determines the depth
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of the model architecture, we leave this para unused at this moment.
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"""
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def __init__(self, n_layers=18):
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super(TestNet, self).__init__()
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self.n_layers = n_layers # Set the number of layers
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# Initialize the MossFormer MaskNet with specified input and output channels
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self.mossformer = MossFormer_MaskNet(in_channels=180, out_channels=512, out_channels_final=961)
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def forward(self, input):
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"""
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Forward pass through the TestNet model.
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Arguments
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---------
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input : torch.Tensor
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Input tensor of dimension [B, N, S], where B is the batch size,
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N is the number of input channels (180), and S is the sequence length.
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Returns
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-------
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out_list : list
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List containing the mask tensor predicted by the MossFormer_MaskNet.
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"""
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out_list = [] # Initialize output list to store outputs
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# Transpose input to match expected shape for MaskNet
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x = input.transpose(1, 2) # Change shape from [B, N, S] to [B, S, N]
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# Get the mask from the MossFormer MaskNet
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mask = self.mossformer(x) # Forward pass through the MossFormer_MaskNet
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out_list.append(mask) # Append the mask to the output list
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return out_list # Return the list containing the mask
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