text: import tensorflow as tf
  from text.symbols import symbols
  
  
  def create_hparams(hparams_string=None, verbose=False):
      """Create model hyperparameters. Parse nondefault from given string."""
  
      hparams = tf.contrib.training.HParams(
          ################################
          # Experiment Parameters        #
          ################################
          epochs=50000,
          iters_per_checkpoint=500,
          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=['speaker_embedding.weight', 'embedding.weight'],
  
          ################################
          # Data Parameters             #
          ################################
          training_files='filelists/brspeech_train_filelist.txt',
          validation_files='filelists/brspeech_test_filelist.txt',
          text_cleaners=['basic_cleaners'],
          p_arpabet=1.0,
          #cmudict_path="data/cmu_dictionary",
          cmudict_path=None,
  
          ################################
          # 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,
          f0_min=80,
          f0_max=880,
          harm_thresh=0.25,
  
          ################################
          # 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,
          prenet_f0_n_layers=1,
          prenet_f0_dim=1,
          prenet_f0_kernel_size=1,
          prenet_rms_dim=0,
          prenet_rms_kernel_size=1,
          max_decoder_steps=1000,
          gate_threshold=0.5,
          p_attention_dropout=0.1,
          p_decoder_dropout=0.1,
          p_teacher_forcing=1.0,
  
          # 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,
  
          # Speaker embedding
          n_speakers=123,
          speaker_embedding_dim=128,
  
          # Reference encoder
          with_gst=True,
          ref_enc_filters=[32, 32, 64, 64, 128, 128],
          ref_enc_size=[3, 3],
          ref_enc_strides=[2, 2],
          ref_enc_pad=[1, 1],
          ref_enc_gru_size=128,
  
          # Style Token Layer
          token_embedding_size=256,
          token_num=10,
          num_heads=8,
  
          ################################
          # Optimization Hyperparameters #
          ################################
          use_saved_learning_rate=False,
          learning_rate=1e-3,
          learning_rate_min=1e-5,
          learning_rate_anneal=50000,
          weight_decay=1e-6,
          grad_clip_thresh=1.0,
          batch_size=12,
          mask_padding=True,  # set model's padded outputs to padded values
  
      )
  
      if hparams_string:
          tf.compat.v1.logging.info('Parsing command line hparams: %s', hparams_string)
          hparams.parse(hparams_string)
  
      if verbose:
          tf.compat.v1.logging.info('Final parsed hparams: %s', hparams.values())
  
      return hparams
  