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from text import symbols
# creating a python dictionary with all hyper parameters
tacotron_params = {'filter_length': 1024, # audio parameters:
'hop_length': 256,
'win_length': 1024,
'n_mel_channels': 80,
'mel_fmin': 0.0,
'mel_fmax': 8000.0,
'sampling_rate': 22050,
'max_wav_value': 32768.0,
'clipping_value': 1e-5,
'C': 1,
# dataset parameters:
'load_mel_from_disk': False,
'sort_by_length': False,
'text_cleaners': ['english_cleaners'],
# embedding parameters:
'symbols_embedding_length': 512,
'n_symbols': len(symbols),
# encoder parameters:
'encoder_embedding_dim': 512,
'encoder_convs': 3,
'conv_kernel_size': 5,
'conv_stride': 1,
'conv_dilation': 1,
'w_init_gain': 'relu',
# decoder parameters:
'number_frames_step': 1,
'decoder_rnn_dim': 1024,
'prenet_dim': 256,
'max_decoder_steps': 1000,
'gate_threshold': 0.5, # Need to be reviewed
'p_attention_dropout': 0.1,
'p_decoder_dropout': 0.1,
# attention parameters:
'attention_rnn_dim': 1024,
'attention_dim': 128,
# location features parameters:
'attention_location_n_filters': 32,
'attention_location_kernel_size': 31,
# postnet parameters:
'postnet_embedding_dim': 512,
'postnet_kernel_size': 5,
'postnet_n_convolutions': 5,
# GST parameters:
'E': 512,
'token_num': 3,
'num_heads': 1,
'seq_ref_enc_filter_size': [3, 7, 11], # phoneme, word/silence, utterance levels respectively
'ref_enc_out_channels': [8, 16, 16],
# optimization parameters:
'use_saved_learning_rate': True,
'batch_size': 32, # 64 should be larger than the number of GPUs. Integer multiple of the num. of GPUs
'learning_rate': 1e-3,
'weight_decay': 1e-6,
'grad_clip_thresh': 1.0,
'mask_padding': False,
# experiment parameters:
'epochs': 300, # 160, 500
'iters_per_checkpoint': 1500, # 1000. How many iterations before validating
'seed': 1234,
'dynamic_loss_scaling': True, # CHECK IT OUT!
'distributed_run': False,
'dist_backend': 'nccl',
'dist_url': "/home/alex/PyTorch_TACOTRON_2/pycharm-tacotron2", # CHECK IT OUT!
'cudnn_enabled': True,
'cudnn_benchmark': False,
'fp16_run': False}
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