docstr: Init for multi-speaker training.
docstr_tok: ["Init","for","multi-speaker","training","."]
code: def init_multispeaker(
      self, args: Coqpit
  ):  # pylint: disable=unused-argument
      """Init for multi-speaker training."""
      self.embedded_speaker_dim = 0
      self.num_speakers = self.args.num_speakers
      self.audio_transform = None
  
      if self.speaker_manager:
          self.num_speakers = self.speaker_manager.num_speakers
  
      if self.args.use_speaker_embedding:
          self._init_speaker_embedding()
  
      if self.args.use_d_vector_file:
          self._init_d_vector()
  
imports: from typing import Callable, Dict, Tuple
  import torch
  import torch.nn.functional
  from coqpit import Coqpit
  from torch import nn
  from TTS.tts.layers.delightful_tts.conformer import Conformer
  from TTS.tts.layers.delightful_tts.encoders import PhonemeLevelProsodyEncoder, UtteranceLevelProsodyEncoder, get_mask_from_lengths
  from TTS.tts.layers.delightful_tts.energy_adaptor import EnergyAdaptor
  from TTS.tts.layers.delightful_tts.networks import EmbeddingPadded, positional_encoding
  from TTS.tts.layers.delightful_tts.phoneme_prosody_predictor import PhonemeProsodyPredictor
  from TTS.tts.layers.delightful_tts.pitch_adaptor import PitchAdaptor
  from TTS.tts.layers.delightful_tts.variance_predictor import VariancePredictor
  from TTS.tts.layers.generic.aligner import AlignmentNetwork
  from TTS.tts.utils.helpers import generate_path, maximum_path, sequence_mask
label_desc: Use image node_id 2 for calling the AcousticModel obj's underlying member method code with example usage: obj.init_multispeaker(args) without return types