class_docstr: A ready-to-use inference wrapper for DiffWave as vocoder.
  The wrapper allows to perform generative tasks:
      locally-conditional generation: mel_spec -> waveform
  Arguments
  ---------
  hparams
      Hyperparameters (from HyperPyYAML)
class_docstr_tok: ["A","ready-to-use","inference","wrapper","for","DiffWave","as","vocoder",".","The","wrapper","allows","to","perform","generative","tasks",":","locally-conditional","generation",":","mel_spec","-",">","waveform","Arguments","--","--","--","--","-","hparams","Hyperparameters","(","from","HyperPyYAML",")"]
docstr: Decodes the input spectrograms
docstr_tok: ["Decodes","the","input","spectrograms"]
code: def forward(self, spectrogram):
      """Decodes the input spectrograms"""
      return self.decode_batch(spectrogram)
  
imports: import logging
  import torch
  from speechbrain.dataio.dataio import length_to_mask
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 5 for calling the DiffWaveVocoder obj's underlying member method code with example usage: obj.forward(spectrogram) and returns: self