# This is the configuration file for CSMSC dataset.This configuration is based 
# on StyleMelGAN paper but uses MSE loss instead of Hinge loss. And I found that
# batch_size = 8 is also working good. So maybe if you want to accelerate the training, 
# you can reduce the batch size (e.g. 8 or 16). Upsampling scales is modified to 
# fit the shift size 300 pt.
# NOTE: batch_max_steps(24000) == prod(noise_upsample_scales)(80) * prod(upsample_scales)(300)

###########################################################
#                FEATURE EXTRACTION SETTING               #
###########################################################
fs: 24000                # Sampling rate.
n_fft: 2048              # FFT size (samples).
n_shift: 300             # Hop size (samples). 12.5ms
win_length: 1200         # Window length (samples). 50ms
                         # If set to null, it will be the same as fft_size.
window: "hann"           # Window function.
n_mels: 80               # Number of mel basis.
fmin: 80                 # Minimum freq in mel basis calculation. (Hz)
fmax: 7600               # Maximum frequency in mel basis calculation. (Hz)

###########################################################
#         GENERATOR NETWORK ARCHITECTURE SETTING          #
###########################################################
generator_params:
    in_channels: 128              # Number of input channels.
    aux_channels: 80
    channels: 64                  # Initial number of channels for conv layers.
    out_channels: 1               # Number of output channels.
    kernel_size: 9                # Kernel size of initial and final conv layers.
    dilation: 2
    bias: True
    noise_upsample_scales: [10, 2, 2, 2]
    noise_upsample_activation: "leakyrelu"
    noise_upsample_activation_params:
        negative_slope: 0.2
    upsample_scales: [5, 1, 5, 1, 3, 1, 2, 2, 1] # List of Upsampling scales. prod(upsample_scales) == n_shift
    upsample_mode: "nearest"
    gated_function: "softmax"
    use_weight_norm: True                        # Whether to use weight normalization.

###########################################################
#       DISCRIMINATOR NETWORK ARCHITECTURE SETTING        #
###########################################################
discriminator_params:
    repeats: 4
    window_sizes: [512, 1024, 2048, 4096]
    pqmf_params:
        - [1, None, None, None]
        - [2, 62, 0.26700, 9.0]
        - [4, 62, 0.14200, 9.0]
        - [8, 62, 0.07949, 9.0]
    discriminator_params:
        out_channels: 1               # Number of output channels.
        kernel_sizes: [5, 3]          # List of kernel size.
        channels: 16                  # Number of channels of the initial conv layer.
        max_downsample_channels: 512  # Maximum number of channels of downsampling layers.
        bias: True
        downsample_scales: [4, 4, 4, 1]   # List of downsampling scales.
        nonlinear_activation: "leakyrelu" # Nonlinear activation function.
        nonlinear_activation_params:      # Parameters of nonlinear activation function.
            negative_slope: 0.2
    use_weight_norm: True                 # Whether to use weight norm.
    

###########################################################
#                   STFT LOSS SETTING                     #
###########################################################
use_stft_loss: true
stft_loss_params:
    fft_sizes: [1024, 2048, 512]  # List of FFT size for STFT-based loss.
    hop_sizes: [120, 240, 50]     # List of hop size for STFT-based loss
    win_lengths: [600, 1200, 240] # List of window length for STFT-based loss.
    window: "hann"                # Window function for STFT-based loss
lambda_aux: 1.0                   # Loss balancing coefficient for aux loss.

###########################################################
#               ADVERSARIAL LOSS SETTING                  #
###########################################################
lambda_adv: 1.0 # Loss balancing coefficient for adv loss.
generator_adv_loss_params:
    average_by_discriminators: false # Whether to average loss by #discriminators.
discriminator_adv_loss_params:
    average_by_discriminators: false # Whether to average loss by #discriminators.

###########################################################
#                  DATA LOADER SETTING                    #
###########################################################
batch_size: 32              # Batch size.
# batch_max_steps(24000) == prod(noise_upsample_scales)(80) * prod(upsample_scales)(300, n_shift)
batch_max_steps: 24000      # Length of each audio in batch. Make sure dividable by n_shift.
num_workers: 2              # Number of workers in Pytorch DataLoader.

###########################################################
#             OPTIMIZER & SCHEDULER SETTING               #
###########################################################
generator_optimizer_params:
    beta1: 0.5
    beta2: 0.9
    weight_decay: 0.0                       # Generator's weight decay coefficient.
generator_scheduler_params:  
    learning_rate: 1.0e-4                   # Generator's learning rate.
    gamma: 0.5                              # Generator's scheduler gamma.
    milestones:                             # At each milestone, lr will be multiplied by gamma.
        - 100000
        - 300000
        - 500000
        - 700000
        - 900000
generator_grad_norm: -1                     # Generator's gradient norm.
discriminator_optimizer_params:
    beta1: 0.5
    beta2: 0.9
    weight_decay: 0.0                       # Discriminator's weight decay coefficient.
discriminator_scheduler_params:
    learning_rate: 2.0e-4                   # Discriminator's learning rate.
    gamma: 0.5                              # Discriminator's scheduler gamma.
    milestones:                             # At each milestone, lr will be multiplied by gamma.
        - 200000
        - 400000
        - 600000
        - 800000
discriminator_grad_norm: -1                 # Discriminator's gradient norm.

###########################################################
#                    INTERVAL SETTING                     #
###########################################################
discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator.
train_max_steps: 1500000                # Number of training steps.
save_interval_steps: 5000               # Interval steps to save checkpoint.
eval_interval_steps: 1000               # Interval steps to evaluate the network.

###########################################################
#                     OTHER SETTING                       #
###########################################################
num_snapshots: 10                 # max number of snapshots to keep while training
seed: 42                          # random seed for paddle, random, and np.random