text: import os
  
  # Dataset
  dataset = "LJSpeech"
  data_path = "./LJSpeech-1.1"
  #dataset = "Blizzard2013"
  #data_path = "./Blizzard-2013/train/segmented/"
  
  
  # Text
  text_cleaners = []
  
  
  # Audio and mel
  ### for LJSpeech ###
  sampling_rate = 22050
  filter_length = 1024
  hop_length = 256
  win_length = 1024
  ### for Blizzard2013 ###
  #sampling_rate = 16000
  #filter_length = 800
  #hop_length = 200
  #win_length = 800
  
  max_wav_value = 32768.0
  n_mel_channels = 80
  mel_fmin = 0.0
  mel_fmax = 8000.0
  
  
  # FastSpeech 2
  encoder_layer = 4
  encoder_head = 2
  encoder_hidden = 256
  decoder_layer = 4
  decoder_head = 2
  decoder_hidden = 256
  fft_conv1d_filter_size = 1024
  fft_conv1d_kernel_size = (9, 1)
  encoder_dropout = 0.2
  decoder_dropout = 0.2
  
  variance_predictor_filter_size = 256
  variance_predictor_kernel_size = 3
  variance_predictor_dropout = 0.5
  
  max_seq_len = 1000
  
  
  # Quantization for F0 and energy
  ### for LJSpeech ###
  f0_min = 71.0
  f0_max = 795.8
  energy_min = 17.76
  energy_max = 91.42
  ### for Blizzard2013 ###
  #f0_min = 71.0
  #f0_max = 786.7
  #energy_min = 21.23
  #energy_max = 101.02
  
  n_bins = 256
  
  
  # Checkpoints and synthesis path
  preprocessed_path = os.path.join("./preprocessed/", dataset)
  checkpoint_path = os.path.join("./ckpt/", dataset)
  synth_path = os.path.join("./synth/", dataset)
  eval_path = os.path.join("./eval/", dataset)
  logger_path = os.path.join("./log/", dataset)
  test_path = "./results"
  waveglow_path = "./waveglow/pretrained_model/waveglow_256channels.pt"
  
  
  # Optimizer
  batch_size = 16
  epochs = 1000
  n_warm_up_step = 4000
  grad_clip_thresh = 1.0
  
  betas = (0.9, 0.98)
  eps = 1e-9
  weight_decay = 1e-6
  
  
  # Save, log and synthesis
  save_step = 10000
  synth_step = 1000
  eval_size = 256
  log_step = 50
  clear_Time = 20
  