text: 
  # CONFIG -----------------------------------------------------------------------------------------------------------#
  
  # Here are the input and output data paths (Note: you can override wav_path in preprocess.py)
  wav_path = '/home/dawna/tts/data/LJSpeech-1.1/webData/wavs/'
  data_path = 'data/'
  
  # model ids are separate - that way you can use a new tts with an old wavernn and vice versa
  # NB: expect undefined behaviour if models were trained on different DSP settings
  exp_id = 'lj_pretrainGold'
  voc_model_id = exp_id + ''
  tts_model_id = exp_id + ''
  
  # set this to True if you are only interested in WaveRNN
  ignore_tts = False
  
  
  # DSP --------------------------------------------------------------------------------------------------------------#
  
  # Settings for all models
  sample_rate = 22050
  n_fft = 2048
  fft_bins = n_fft // 2 + 1
  num_mels = 80
  hop_length = 275                    # 12.5ms - in line with Tacotron 2 paper
  win_length = 1100                   # 50ms - same reason as above
  fmin = 40
  min_level_db = -100
  ref_level_db = 20
  bits = 9                            # bit depth of signal
  mu_law = True                       # Recommended to suppress noise if using raw bits in hp.voc_mode below
  peak_norm = False                   # Normalise to the peak of each wav file
  
  
  # WAVERNN / VOCODER ------------------------------------------------------------------------------------------------#
  
  
  # Model Hparams
  voc_mode = 'MOL'                    # either 'RAW' (softmax on raw bits) or 'MOL' (sample from mixture of logistics)
  voc_upsample_factors = (5, 5, 11)   # NB - this needs to correctly factorise hop_length
  voc_rnn_dims = 512
  voc_fc_dims = 512
  voc_compute_dims = 128
  voc_res_out_dims = 128
  voc_res_blocks = 10
  
  # Training
  voc_batch_size = 32
  voc_lr = 1e-4
  voc_checkpoint_every = 25_000
  voc_gen_at_checkpoint = 5           # number of samples to generate at each checkpoint
  voc_total_steps = 50_000         # Total number of training steps
  voc_test_samples = 50               # How many unseen samples to put aside for testing
  voc_pad = 2                         # this will pad the input so that the resnet can 'see' wider than input length
  voc_seq_len = hop_length * 5        # must be a multiple of hop_length
  voc_clip_grad_norm = 4              # set to None if no gradient clipping needed
  voc_init_weights_path = '/home/dawna/tts/qd212/models/WaveRNN/quick_start/voc_weights/latest_weights.pyt' # initial weights, usually from a pretrained model
  
  # Generating / Synthesizing
  voc_gen_batched = True              # very fast (realtime+) single utterance batched generation
  voc_target = 11_000                 # target number of samples to be generated in each batch entry
  voc_overlap = 550                   # number of samples for crossfading between batches
  
  
  # TACOTRON/TTS -----------------------------------------------------------------------------------------------------#
  
  
  # Model Hparams
  tts_embed_dims = 256                # embedding dimension for the graphemes/phoneme inputs
  tts_encoder_dims = 128
  tts_decoder_dims = 256
  tts_postnet_dims = 128
  tts_encoder_K = 16
  tts_lstm_dims = 512
  tts_postnet_K = 8
  tts_num_highways = 4
  tts_dropout = 0.5
  tts_cleaner_names = ['english_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
  
  # Training
  
  tts_schedule = [(7,  1e-3,  10_000,  32),   # progressive training schedule
                  (5,  1e-4, 20_000,  32),   # (r, lr, step, batch_size)
                  (2,  1e-4, 40_000,  16),
                  (2,  1e-4, 80_000,  8)]
  
  tts_max_mel_len = 1250              # if you have a couple of extremely long spectrograms you might want to use this
  tts_bin_lengths = True              # bins the spectrogram lengths before sampling in data loader - speeds up training
  tts_clip_grad_norm = 1.0            # clips the gradient norm to prevent explosion - set to None if not needed
  tts_checkpoint_every = 2_000        # checkpoints the model every X steps
  tts_init_weights_path = '/home/dawna/tts/qd212/models/WaveRNN/quick_start/tts_weights/latest_weights.pyt' # initial weights, usually from a pretrained model
  # TODO: tts_phoneme_prob = 0.0              # [0 <-> 1] probability for feeding model phonemes vrs graphemes
  
  # mode = 'teacher_forcing'
  
  
  # ------------------------------------------------------------------------------------------------------------------#
  
  