###########################################################
#                FEATURE EXTRACTION SETTING               #
###########################################################
# 其实没用上，其实用的是 16000
sr: 24000
n_fft: 2048
win_length: 1200
hop_length: 300
n_mels: 80
###########################################################
#                       MODEL SETTING                     #
###########################################################
generator_params:
    dim_in: 64
    style_dim: 64
    max_conv_dim: 512
    w_hpf: 0
    F0_channel: 256
mapping_network_params:
    num_domains: 20      # num of speakers in StarGANv2
    latent_dim: 16
    style_dim: 64        # same as style_dim in generator_params
    hidden_dim: 512      # same as max_conv_dim in generator_params
style_encoder_params:
    dim_in: 64           # same as dim_in in generator_params
    style_dim: 64        # same as style_dim in generator_params
    num_domains: 20      # same as num_domains in generator_params
    max_conv_dim: 512    # same as max_conv_dim in generator_params
discriminator_params:
    dim_in: 64           # same as dim_in in generator_params
    num_domains: 20      # same as num_domains in mapping_network_params
    max_conv_dim: 512    # same as max_conv_dim in generator_params
    n_repeat: 4
asr_params:
    input_dim: 80
    hidden_dim: 256
    n_token: 80
    token_embedding_dim: 256

###########################################################
#               ADVERSARIAL LOSS SETTING                  #
###########################################################
loss_params:
    g_loss:
        lambda_sty: 1.
        lambda_cyc: 5.
        lambda_ds: 1.
        lambda_norm: 1.
        lambda_asr: 10.
        lambda_f0: 5.
        lambda_f0_sty: 0.1
        lambda_adv: 2.
        lambda_adv_cls: 0.5
        norm_bias: 0.5
    d_loss:
        lambda_reg: 1.
        lambda_adv_cls: 0.1
        lambda_con_reg: 10.

    adv_cls_epoch: 50
    con_reg_epoch: 30
        

###########################################################
#                  DATA LOADER SETTING                    #
###########################################################
batch_size: 5               # Batch size.
num_workers: 2              # Number of workers in DataLoader.

###########################################################
#             OPTIMIZER & SCHEDULER SETTING               #
###########################################################
generator_optimizer_params:
    beta1: 0.0
    beta2: 0.99
    weight_decay: 1e-4                   
    epsilon: 1e-9
generator_scheduler_params:
    max_learning_rate: 2e-4
    phase_pct: 0.0
    divide_factor: 1
    total_steps: 200000                # train_max_steps
    end_learning_rate: 2e-4
style_encoder_optimizer_params:
    beta1: 0.0
    beta2: 0.99
    weight_decay: 1e-4                 
    epsilon: 1e-9
style_encoder_scheduler_params:
    max_learning_rate: 2e-4
    phase_pct: 0.0
    divide_factor: 1
    total_steps: 200000                # train_max_steps
    end_learning_rate: 2e-4
mapping_network_optimizer_params:
    beta1: 0.0
    beta2: 0.99
    weight_decay: 1e-4                 
    epsilon: 1e-9
mapping_network_scheduler_params:
    max_learning_rate: 2e-6
    phase_pct: 0.0
    divide_factor: 1
    total_steps: 200000                # train_max_steps
    end_learning_rate: 2e-6
discriminator_optimizer_params:
    beta1: 0.0
    beta2: 0.99
    weight_decay: 1e-4                 
    epsilon: 1e-9
discriminator_scheduler_params:
    max_learning_rate: 2e-4
    phase_pct: 0.0
    divide_factor: 1
    total_steps: 200000                # train_max_steps
    end_learning_rate: 2e-4        

###########################################################
#                    TRAINING SETTING                     #
###########################################################
max_epoch: 150
num_snapshots: 5
seed: 1