class_docstr: Predict melspectrogram from aligned phonemes
class_docstr_tok: ["Predict","melspectrogram","from","aligned","phonemes"]
code: def __init__(self, is_training=True):
      super().__init__()
      self.is_training = is_training
      self.encoder = TokenEncoder(
          FLAGS.vocab_size, FLAGS.acoustic_encoder_dim, 0.5, is_training
      )
      self.decoder = hk.deep_rnn_with_skip_connections(
          [
              hk.LSTM(FLAGS.acoustic_decoder_dim),
              hk.LSTM(FLAGS.acoustic_decoder_dim),
          ]
      )
      self.projection = hk.Linear(FLAGS.mel_dim)
  
      # prenet
      self.prenet_fc1 = hk.Linear(256, with_bias=False)
      self.prenet_fc2 = hk.Linear(256, with_bias=False)
      # posnet
      self.postnet_convs = [
          hk.Conv1D(FLAGS.postnet_dim, 5) for _ in range(4)
      ]
      self.postnet_convs.append(hk.Conv1D(FLAGS.mel_dim, 5))
      self.postnet_bns = [
          hk.BatchNorm(True, True, 0.9) for _ in range(4)
      ] + [None]
  
imports: import haiku
  import jax
  import jax.numpy
  from jax.numpy import ndarray
  from .config import FLAGS, AcousticInput, DurationInput
label_desc: Use image node_id 1 to create a new AcousticModel object from inherited base classes: hk with example: obj = AcousticModel(is_training)