docstr: Computes the forward pass
  Arguments
  ---------
  batch: str
      a single batch
  stage: speechbrain.Stage
      the training stage
  Returns
  -------
  the model output
docstr_tok: ["Computes","the","forward","pass","Arguments","--","--","--","--","-","batch",":","str","a","single","batch","stage",":","speechbrain.Stage","the","training","stage","Returns","--","--","--","-","the","model","output"]
code: def compute_forward(self, batch, stage):
      """Computes the forward pass
      Arguments
      ---------
      batch: str
          a single batch
      stage: speechbrain.Stage
          the training stage
      Returns
      -------
      the model output
      """
      inputs, _ = self.batch_to_device(batch)
  
      (
          tokens,
          durations,
          pitch,
          energy,
          no_spn_seqs,
          last_phonemes,
      ) = inputs
  
      # Forward pass for the silent token predictor module
      if (
          self.hparams.epoch_counter.current
          > self.hparams.train_spn_predictor_epochs
      ):
          self.hparams.modules["spn_predictor"].eval()
          with torch.no_grad():
              spn_preds = self.hparams.modules["spn_predictor"](
                  no_spn_seqs, last_phonemes
              )
      else:
          spn_preds = self.hparams.modules["spn_predictor"](
              no_spn_seqs, last_phonemes
          )
  
      # Forward pass for the FastSpeech2 module
      (
          predict_mel_post,
          predict_postnet_output,
          predict_durations,
          predict_pitch,
          predict_avg_pitch,
          predict_energy,
          predict_avg_energy,
          predict_mel_lens,
      ) = self.hparams.model(tokens, durations, pitch, energy)
  
      return (
          predict_mel_post,
          predict_postnet_output,
          predict_durations,
          predict_pitch,
          predict_avg_pitch,
          predict_energy,
          predict_avg_energy,
          predict_mel_lens,
          spn_preds,
      )
  
imports: import os
  import sys
  import torch
  import logging
  import torchaudio
  import numpy
  import speechbrain
  from speechbrain.inference.vocoders import HIFIGAN
  from pathlib import Path
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.data_utils import scalarize
  from speechbrain.inference.text import GraphemeToPhoneme
label_desc: Use image node_id 2 for calling the FastSpeech2Brain obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: predict_mel_post,  predict_postnet_output,  predict_durations,  predict_pitch,  predict_avg_pitch,  predict_energy,  predict_avg_energy,  predict_mel_lens,  spn_preds