text: import json
  
  import torch
  
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # sets device for model and PyTorch tensors
  
  meta_file = 'data/BZNSYP/ProsodyLabeling/000001-010000.txt'
  wave_folder = 'data/BZNSYP/Wave'
  vacab_file = 'data/vacab.json'
  
  with open(vacab_file, 'r', encoding='utf-8') as file:
      data = json.load(file)
  
  VOCAB = data['VOCAB']
  IVOCAB = data['IVOCAB']
  
  num_train = 9900
  num_valid = 100
  
  ################################
  # Experiment Parameters        #
  ################################
  epochs = 500
  iters_per_checkpoint = 1000
  seed = 1234
  dynamic_loss_scaling = True
  fp16_run = False
  distributed_run = False
  
  ################################
  # Data Parameters             #
  ################################
  load_mel_from_disk = False
  training_files = 'filelists/bznsyp_audio_text_train_filelist.txt'
  validation_files = 'filelists/bznsyp_audio_text_valid_filelist.txt'
  
  ################################
  # Audio Parameters             #
  ################################
  max_wav_value = 32768.0
  sampling_rate = 22050
  filter_length = 1024
  hop_length = 256
  win_length = 1024
  n_mel_channels = 80
  mel_fmin = 0.0
  mel_fmax = 8000.0
  
  ################################
  # Model Parameters             #
  ################################
  n_symbols = 35
  symbols_embedding_dim = 512
  
  # Encoder parameters
  encoder_kernel_size = 5
  encoder_n_convolutions = 3
  encoder_embedding_dim = 512
  
  # Decoder parameters
  n_frames_per_step = 1  # currently only 1 is supported
  decoder_rnn_dim = 1024
  prenet_dim = 256
  max_decoder_steps = 1000
  gate_threshold = 0.5
  p_attention_dropout = 0.1
  p_decoder_dropout = 0.1
  
  # Attention parameters
  attention_rnn_dim = 1024
  attention_dim = 128
  
  # Location Layer parameters
  attention_location_n_filters = 32
  attention_location_kernel_size = 31
  
  # Mel-post processing network parameters
  postnet_embedding_dim = 512
  postnet_kernel_size = 5
  postnet_n_convolutions = 5
  
  ################################
  # Optimization Hyperparameters #
  ################################
  learning_rate = 1e-3
  weight_decay = 1e-6
  batch_size = 64
  mask_padding = True  # set model's padded outputs to padded values
  