text: import tensorflow as tf
  from text import symbols
  
  class AttrDict(dict):
      def __init__(self,*args,**kwargs):
          super(AttrDict, self).__init__(*args,**kwargs)
          self.__dict__=self
  
  def create_hparams(hparams_string=None, verbose=False):
      """Create model hyperparameters. Parse nondefault from given string."""
  
      hparams = AttrDict({
          ################################
          # Experiment Parameters        #
          ################################
          "epochs":1000,
          "iters_per_checkpoint":1000,
          "seed":1234,
          "dynamic_loss_scaling":True,
          "fp16_run":False,
          "distributed_run":False,
          "dist_backend":"nccl",
          "dist_url":"tcp://localhost:54321",
          "cudnn_enabled":True,
          "cudnn_benchmark":False,
          "ignore_layers":['embedding.weight'],
  
          ################################
          # Data Parameters             #
          ################################
         "load_mel_from_disk":False,
         "training_files":'/home/Ciro/Desktop/sansone-english-finetuning/tacotron2/filelists/filelists.txt',
         "validation_files":'/home/Ciro/Desktop/sansone-english-finetuning/tacotron2/filelists/filelists.txt',
         "text_cleaners":['english_cleaners'],
  
          ################################
          # Audio Parameters             #
          ################################
          "max_wav_value":60768.0,
          "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":len(symbols),
          "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":10000,
          "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 #
          ################################
          "use_saved_learning_rate":False,
          "learning_rate":1e-3,
          "weight_decay":1e-6,
          "grad_clip_thresh":1.0,
          "batch_size":8,
          "mask_padding":True
      }
    # set model's padded outputs to padded values
      )
  
      if hparams_string:
          tf.logging.info('Parsing command line hparams: %s', hparams_string)
          hparams.parse(hparams_string)
  
      if verbose:
          tf.logging.info('Final parsed hparams: %s', hparams.values())
  
      return hparams
  