# This is the configuration file for JSUT 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.

###########################################################
#                FEATURE EXTRACTION SETTING               #
###########################################################
sampling_rate: 24000     # Sampling rate.
fft_size: 2048           # FFT size.
hop_size: 300            # Hop size.
win_length: 1200         # Window length.
                         # If set to null, it will be the same as fft_size.
window: "hann"           # Window function.
num_mels: 80             # Number of mel basis.
fmin: 80                 # Minimum freq in mel basis calculation.
fmax: 7600               # Maximum frequency in mel basis calculation.
global_gain_scale: 1.0   # Will be multiplied to all of waveform.
trim_silence: false      # Whether to trim the start and end of silence.
trim_threshold_in_db: 60 # Need to tune carefully if the recording is not good.
trim_frame_size: 1024    # Frame size in trimming.
trim_hop_size: 256       # Hop size in trimming.
format: "hdf5"           # Feature file format. " npy " or " hdf5 " is supported.

###########################################################
#         GENERATOR NETWORK ARCHITECTURE SETTING          #
###########################################################
generator_type: "StyleMelGANGenerator" # Generator type.
generator_params:
    in_channels: 128
    aux_channels: 80
    channels: 64
    out_channels: 1
    kernel_size: 9
    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]
    upsample_mode: "nearest"
    gated_function: "softmax"
    use_weight_norm: True

###########################################################
#       DISCRIMINATOR NETWORK ARCHITECTURE SETTING        #
###########################################################
discriminator_type: "StyleMelGANDiscriminator" # Discriminator type.
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
        kernel_sizes: [5, 3]
        channels: 16
        max_downsample_channels: 512
        bias: True
        downsample_scales: [4, 4, 4, 1]
        nonlinear_activation: "LeakyReLU"
        nonlinear_activation_params:
            negative_slope: 0.2
    use_weight_norm: True

###########################################################
#                   STFT LOSS SETTING                     #
###########################################################
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"         # 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      # Length of each audio in batch. Make sure dividable by hop_size.
pin_memory: true            # Whether to pin memory in Pytorch DataLoader.
num_workers: 2              # Number of workers in Pytorch DataLoader.
remove_short_samples: false # Whether to remove samples the length of which are less than batch_max_steps.
allow_cache: true           # Whether to allow cache in dataset. If true, it requires cpu memory.

###########################################################
#             OPTIMIZER & SCHEDULER SETTING               #
###########################################################
generator_optimizer_type: Adam
generator_optimizer_params:
    lr: 1.0e-4
    betas: [0.5, 0.9]
    weight_decay: 0.0
generator_scheduler_type: MultiStepLR
generator_scheduler_params:
    gamma: 0.5
    milestones:
        - 100000
        - 300000
        - 500000
        - 700000
        - 900000
generator_grad_norm: -1
discriminator_optimizer_type: Adam
discriminator_optimizer_params:
    lr: 2.0e-4
    betas: [0.5, 0.9]
    weight_decay: 0.0
discriminator_scheduler_type: MultiStepLR
discriminator_scheduler_params:
    gamma: 0.5
    milestones:
        - 200000
        - 400000
        - 600000
        - 800000
discriminator_grad_norm: -1

###########################################################
#                    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: 50000              # Interval steps to save checkpoint.
eval_interval_steps: 1000              # Interval steps to evaluate the network.
log_interval_steps: 100                # Interval steps to record the training log.

###########################################################
#                     OTHER SETTING                       #
###########################################################
num_save_intermediate_results: 4  # Number of results to be saved as intermediate results.