train.yaml

data_trn: train_data_folder_npy_files
data_eval: eval_data_folder_npy_files


lab_trn: labels_folde_for_trn 
lab_eval: Labels_for_eval



gpu_idx: [0] 
nb_proc_db: 4
save_dir:  folder_for_models 

optimizer: Adam #sgd
lr: 0.0005
opt_mom: 0.9
wd: 0.0001
epoch: 400
pretrn_epoch: 100

batch_size: 8
 

input_dim: 25000  #samples
c_loss_lr: 0.5
lrdec_milestones: [120, 150]
lrdec: 0.2



#flags
comet_disable: 1
nesterov: 0 #for sgd optim
amsgrad: 1 #for adam optim
save_best_only: 0

 
vanilla_kl: 1
only_eval: 0
use_ode: 0
non_vanilla_target: 1
inp_data: 1
ivector_dim: 400
w_point_fp: 0.001
test_ldc: 0
load_model: 0
#sinc
fs: 8000 #remain sinc with g_extract!!!

cw_len: 25000
cw_shift: 10
use_penalty: 0
use_balance: 1
#model-related
model:
  in_channels: 1
  filts: [16, [16, 32], [32, 64], [64, 64], [64, 128], [128, 128]]

  kernels: [[3, 7], [3, 7], [3, 7], [3, 7], [3, 7], [3, 7]]

  strides: [1, 1, [2, 4], [2, 4], [2, 4], [2, 4]]

  blocks: [1, 2, 2, 2]
  nb_fc_node: 64 #4096 #64
  nb_classes: 2
  gru_node: 512 #org: 512
  nb_gru_layer: 1