text: import ast
  import pprint
  
  class HParams(object):
      def __init__(self, **kwargs): self.__dict__.update(kwargs)
      def __setitem__(self, key, value): setattr(self, key, value)
      def __getitem__(self, key): return getattr(self, key)
      def __repr__(self): return pprint.pformat(self.__dict__)
  
      def parse(self, string):
          # Overrides hparams from a comma-separated string of name=value pairs
          if len(string) > 0:
              overrides = [s.split("=") for s in string.split(",")]
              keys, values = zip(*overrides)
              keys = list(map(str.strip, keys))
              values = list(map(str.strip, values))
              for k in keys:
                  self.__dict__[k] = ast.literal_eval(values[keys.index(k)])
          return self
  
  hparams = HParams(
          ### Signal Processing (used in both synthesizer and vocoder)
          sample_rate = 16000,
          n_fft = 800,
          num_mels = 80,
          hop_size = 200,                             # Tacotron uses 12.5 ms frame shift (set to sample_rate * 0.0125)
          win_size = 800,                             # Tacotron uses 50 ms frame length (set to sample_rate * 0.050)
          fmin = 55,
          min_level_db = -100,
          ref_level_db = 20,
          max_abs_value = 4.,                         # Gradient explodes if too big, premature convergence if too small.
          preemphasis = 0.97,                         # Filter coefficient to use if preemphasize is True
          preemphasize = True,
  
          ### Tacotron Text-to-Speech (TTS)
          tts_embed_dims = 512,                       # Embedding dimension for the graphemes/phoneme inputs
          tts_encoder_dims = 256,
          tts_decoder_dims = 128,
          tts_postnet_dims = 512,
          tts_encoder_K = 5,
          tts_lstm_dims = 1024,
          tts_postnet_K = 5,
          tts_num_highways = 4,
          tts_dropout = 0.5,
          tts_cleaner_names = ["basic_cleaners"],
          tts_stop_threshold = -3.4,                  # Value below which audio generation ends.
                                                      # For example, for a range of [-4, 4], this
                                                      # will terminate the sequence at the first
                                                      # frame that has all values < -3.4
  
          ### Tacotron Training
          tts_schedule = [(2,  1e-3,  20_000,  8),   # Progressive training schedule
                          (2,  5e-4,  40_000,  8),   # (r, lr, step, batch_size)
                          (2,  2e-4,  80_000,  8),   #
                          (2,  1e-4, 160_000,  8),   # r = reduction factor (# of mel frames
                          (2,  3e-5, 320_000,  8),   #     synthesized for each decoder iteration)
                          (2,  1e-5, 640_000,  8)],  # lr = learning rate
  
          tts_clip_grad_norm = 1.0,                   # clips the gradient norm to prevent explosion - set to None if not needed
          tts_eval_interval = 500,                    # Number of steps between model evaluation (sample generation)
                                                      # Set to -1 to generate after completing epoch, or 0 to disable
  
          tts_eval_num_samples = 1,                   # Makes this number of samples
  
          ### Data Preprocessing
          max_mel_frames = 900,
          rescale = True,
          rescaling_max = 0.9,
          synthesis_batch_size = 8,                  # For vocoder preprocessing and inference.
  
          ### Mel Visualization and Griffin-Lim
          signal_normalization = True,
          power = 1.5,
          griffin_lim_iters = 60,
  
          ### Audio processing options
          fmax = 7600,                                # Should not exceed (sample_rate // 2)
          allow_clipping_in_normalization = True,     # Used when signal_normalization = True
          clip_mels_length = True,                    # If true, discards samples exceeding max_mel_frames
          use_lws = False,                            # "Fast spectrogram phase recovery using local weighted sums"
          symmetric_mels = True,                      # Sets mel range to [-max_abs_value, max_abs_value] if True,
                                                      #               and [0, max_abs_value] if False
          trim_silence = True,                        # Use with sample_rate of 16000 for best results
  
          ### SV2TTS
          speaker_embedding_size = 256,               # Dimension for the speaker embedding
          silence_min_duration_split = 0.4,           # Duration in seconds of a silence for an utterance to be split
          utterance_min_duration = 1.6,               # Duration in seconds below which utterances are discarded
          )
  