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Running
on
Zero
File size: 1,738 Bytes
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from models.mossformer2_sr.generator import Mossformer, Generator
import torch.nn as nn
class MossFormer2_SR_48K(nn.Module):
"""
The MossFormer2_SR_48K model for speech super-resolution.
This class encapsulates the functionality of the MossFormer2 and HiFi-Gan
Generator within a higher-level model. It processes input audio data to produce
higher-resolution outputs.
Arguments
---------
args : Namespace
Configuration arguments that may include hyperparameters
and model settings (not utilized in this implementation but
can be extended for flexibility).
Example
---------
>>> model = MossFormer2_SR_48K(args).model
>>> x = torch.randn(10, 180, 2000) # Example input
>>> outputs = model(x) # Forward pass
>>> outputs.shape, mask.shape # Check output shapes
"""
def __init__(self, args):
super(MossFormer2_SR_48K, self).__init__()
# Initialize the TestNet model, which contains the MossFormer MaskNet
self.model_m = Mossformer() # Instance of TestNet
self.model_g = Generator(args)
def forward(self, x):
"""
Forward pass through the model.
Arguments
---------
x : torch.Tensor
Input tensor of dimension [B, N, S], where B is the batch size,
N is the number of mel bins (80 in this case), and S is the
sequence length (e.g., time frames).
Returns
-------
outputs : torch.Tensor
Bandwidth expanded audio output tensor from the model.
"""
x = self.model_m(x) # Get outputs and mask from TestNet
outpus = self.model_g(x)
return outputs # Return the outputs
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