class_docstr: Zero-pads model inputs and targets based on number of frames per step
  Arguments
  ---------
  speaker_embeddings_pickle : str
      Path to the file containing speaker embeddings
  n_frames_per_step: int
      The number of output frames per step
  Returns
  -------
  result: tuple
      a tuple inputs/targets
      (
          text_padded,
          input_lengths,
          mel_padded,
          gate_padded,
          output_lengths,
          len_x,
          labels,
          wavs,
          spk_embs,
          spk_ids
      )
code: def __init__(
      self,
      speaker_embeddings_pickle,
      n_frames_per_step=1,
  ):
      self.n_frames_per_step = n_frames_per_step
      self.speaker_embeddings_pickle = speaker_embeddings_pickle
  
imports: from math import sqrt
  from speechbrain.nnet.loss.guidedattn_loss import GuidedAttentionLoss
  import torch
  from torch import nn
  from torch.nn import functional
  from collections import namedtuple
  import pickle
  from speechbrain.lobes.models.Tacotron2 import LinearNorm, Postnet, Encoder, Decoder, get_mask_from_lengths
label_desc: Use image node_id 1 to create a new TextMelCollate object  with example: obj = TextMelCollate(speaker_embeddings_pickle, n_frames_per_step)