text: """Hyper parameters."""
  __author__ = 'Erdene-Ochir Tuguldur'
  
  
  class HParams:
      """Hyper parameters"""
  
      disable_progress_bar = False  # set True if you don't want the progress bar in the console
  
      logdir = "logdir"  # log dir where the checkpoints and tensorboard files are saved
  
      # audio.py options, these values are from https://github.com/Kyubyong/dc_tts/blob/master/hyperparams.py
      reduction_rate = 4  # melspectrogram reduction rate, don't change because SSRN is using this rate
      n_fft = 2048 # fft points (samples)
      n_mels = 80  # Number of Mel banks to generate
      power = 1.5  # Exponent for amplifying the predicted magnitude
      n_iter = 50  # Number of inversion iterations
      preemphasis = .97
      max_db = 100
      ref_db = 20
      sr = 22050  # Sampling rate
      frame_shift = 0.0125  # seconds
      frame_length = 0.05  # seconds
      hop_length = int(sr * frame_shift)  # samples. =276.
      win_length = int(sr * frame_length)  # samples. =1102.
      max_N = 300  # Maximum number of characters.
      max_T = 400  # Maximum number of mel frames.
  
      e = 128  # embedding dimension
      d = 256  # Text2Mel hidden unit dimension
      c = 512+128  # SSRN hidden unit dimension
  
      dropout_rate = 0.05  # dropout
  
      dataset_dir = '/m/home/home4/48/kausiar1/unix/tts_data/esfan_vid'
  
     # Text2Mel network options
      text2mel_lr = 0.0003  # learning rate
      text2mel_max_iteration = 600000  # max train step
      text2mel_weight_init = 'none'  # 'kaiming', 'xavier' or 'none'
      text2mel_normalization = 'layer'  # 'layer', 'weight' or 'none'
      text2mel_basic_block = 'gated_conv'  # 'highway', 'gated_conv' or 'residual'
      text2mel_save_every = 1500
      text2mel_lr_decay = False
  
  
      # Synthesize
      synthesize_samples_dir = 'esfan_vid'
  
     # SSRN network options
      ssrn_lr = 0.0005  # learning rate
      ssrn_max_iteration = 150000  # max train step
      ssrn_weight_init = 'kaiming'  # 'kaiming', 'xavier' or 'none'
      ssrn_normalization = 'weight'  # 'layer', 'weight' or 'none'
      ssrn_basic_block = 'residual'  # 'highway', 'gated_conv' or 'residual'
  