class_docstr: Upsampler for spectrograms with Transposed Conv
  Only the upsamling is done here, the layer-specific Conv can be found
  in residual bloack to map the mel bands into 2× residual channels
  
  Example
  -------
  >>> from speechbrain.lobes.models.DiffWave import SpectrogramUpsampler
  >>> spec_upsampler = SpectrogramUpsampler()
  >>> mel_input = torch.rand(3, 80, 100)
  >>> upsampled_mel = spec_upsampler(mel_input)
  >>> upsampled_mel.shape
  torch.Size([3, 80, 25600])
class_docstr_tok: ["Upsampler","for","spectrograms","with","Transposed","Conv","Only","the","upsamling","is","done","here",",","the","layer-specific","Conv","can","be","found","in","residual","bloack","to","map","the","mel","bands","into","2\u00d7","residual","channels","Example","--","--","--","-",">",">",">","from","speechbrain.lobes.models.DiffWave","import","SpectrogramUpsampler",">",">",">","spec_upsampler","=","SpectrogramUpsampler","(",")",">",">",">","mel_input","=","torch.rand","(","3",",","80",",","100",")",">",">",">","upsampled_mel","=","spec_upsampler","(","mel_input",")",">",">",">","upsampled_mel.shape","torch.Size","(","[","3",",","80",",","25600","]",")"]
code: def __init__(self):
      super().__init__()
      self.conv1 = ConvTranspose2d(
          1, 1, [3, 32], stride=[1, 16], padding=[1, 8]
      )
      self.conv2 = ConvTranspose2d(
          1, 1, [3, 32], stride=[1, 16], padding=[1, 8]
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from speechbrain.nnet.CNN import Conv1d
  from speechbrain.nnet import linear
  from speechbrain.nnet.diffusion import DenoisingDiffusion
  from math import sqrt
  from torchaudio import transforms
label_desc: Use image node_id 1 to create a new SpectrogramUpsampler object from inherited base classes: nn with example: obj = SpectrogramUpsampler()