text: class HParams:
      def __init__(self):
          self.n_mel_channels = None
          self.dynamic_loss_scaling = True
          self.fp16_run = False
          self.distributed_run = False
  
          ################################
          # Data Parameters             #
          ################################
          self.load_mel_from_disk = False
  
          ################################
          # Audio Parameters             #
          ################################
          self.max_wav_value = 32768.0
          self.sampling_rate = 22050
          self.filter_length = 1024
          self.hop_length = 256
          self.win_length = 1024
          self.n_mel_channels = 80
          self.mel_fmin = 0.0
          self.mel_fmax = 8000.0
  
          ################################
          # Model Parameters             #
          ################################
          self.n_symbols = 35
          self.symbols_embedding_dim = 512
  
          # Encoder parameters
          self.encoder_kernel_size = 5
          self.encoder_n_convolutions = 3
          self.encoder_embedding_dim = 512
  
          # Decoder parameters
          self.n_frames_per_step = 1  # currently only 1 is supported
          self.decoder_rnn_dim = 1024
          self.prenet_dim = 256
          self.max_decoder_steps = 1000
          self.gate_threshold = 0.5
          self.p_attention_dropout = 0.1
          self.p_decoder_dropout = 0.1
  
          # Attention parameters
          self.attention_rnn_dim = 1024
          self.attention_dim = 128
  
          # Location Layer parameters
          self.attention_location_n_filters = 32
          self.attention_location_kernel_size = 31
  
          # Mel-post processing network parameters
          self.postnet_embedding_dim = 512
          self.postnet_kernel_size = 5
          self.postnet_n_convolutions = 5
  
          ################################
          # Optimization Hyperparameters #
          ################################
          self.learning_rate = 1e-3
          self.weight_decay = 1e-6
          self.batch_size = 64
          self.mask_padding = True  # set model's padded outputs to padded values
  