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from tfcompat.hparam import HParams
# NOTE: If you want full control for model architecture. please take a look
# at the code and change whatever you want. Some hyper parameters are hardcoded.
# Default hyperparameters:
hparams = HParams(
# sea params
dim_neck_sea = 4,
dim_freq_sea = 14,
dim_enc_sea = 512,
# autopst params
dim_freq = 80,
dim_code = 4,
dim_spk = 82,
dim_sty = 128,
gate_threshold = 0.48,
dec_steps_tx = 640,
dec_steps_sp = 806,
chs_grp = 16,
# onmt params
enc_layers = 4,
enc_rnn_size = 256,
dec_layers = 4,
dec_rnn_size = 256,
transformer_ff = 1024,
heads = 8,
dropout = 0.1,
attention_dropout = 0.1,
max_relative_positions = 0,
copy_attn = False,
self_attn_type = "scaled-dot",
aan_useffn = False,
full_context_alignment = False,
alignment_layer = 0,
alignment_heads = 0,
# pretrained model
pretrained_path = './assets/xxx.ckpt',
# data loader
meta_file = './assets/train_vctk.meta',
feat_dir_1 = './assets/vctk16-train-sp-mel',
feat_dir_2 = './assets/vctk16-train-cep-mel',
feat_dir_3 = './assets/vctk16-train-teacher',
batch_size = 4,
shuffle = True,
num_workers = 0,
samplier = 2,
max_len_pad = 2048,
sampling_params = (0.8, 1.3, 0.1),
)
def hparams_debug_string():
values = hparams.values()
hp = [' %s: %s' % (name, values[name]) for name in values]
return 'Hyperparameters:\n' + '\n'.join(hp) |