text: # -*- coding: utf-8 -*-
  #/usr/bin/python2
  '''
  By kyubyong park. kbpark.linguist@gmail.com. 
  https://www.github.com/kyubyong/tacotron
  '''
  class Hyperparams:
      '''Hyper parameters'''
      
      # pipeline
      prepro = False  # if True, run `python prepro.py` first before running `python train.py`.
  
      vocab = "PE abcdefghijklmnopqrstuvwxyz'.?" # P: Padding E: End of Sentence
  
      # data
      data = "/data/private/voice/LJSpeech-1.0"
      # data = "/data/private/voice/nick"
      test_data = 'harvard_sentences.txt'
      max_duration = 10.0
  
      # signal processing
      sr = 22050 # Sample rate.
      n_fft = 2048 # fft points (samples)
      frame_shift = 0.0125 # seconds
      frame_length = 0.05 # seconds
      hop_length = int(sr*frame_shift) # samples.
      win_length = int(sr*frame_length) # samples.
      n_mels = 80 # Number of Mel banks to generate
      power = 1.2 # Exponent for amplifying the predicted magnitude
      n_iter = 50 # Number of inversion iterations
      preemphasis = .97 # or None
      max_db = 100
      ref_db = 20
  
      # model
      embed_size = 256 # alias = E
      encoder_num_banks = 16
      decoder_num_banks = 8
      num_highwaynet_blocks = 4
      r = 5 # Reduction factor. Paper => 2, 3, 5
      dropout_rate = .5
  
      # training scheme
      lr = 0.001 # Initial learning rate.
      logdir = "logdir/01"
      sampledir = 'samples'
      batch_size = 32
  
  
  
  
  