# This is the hyperparameter configuration file for MelGAN.
# Please make sure this is adjusted for the LJSpeech dataset. If you want to
# apply to the other dataset, you might need to carefully change some parameters.
# This configuration requires ~4 GB GPU memory and takes ~15 hours on TITAN V.

###########################################################
#                FEATURE EXTRACTION SETTING               #
###########################################################
sampling_rate: 22050     # Sampling rate.
fft_size: 1024           # FFT size.
hop_size: 256            # Hop size.
win_length: null         # 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: true       # 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: 2048    # Frame size in trimming.
trim_hop_size: 512       # Hop size in trimming.
format: "hdf5"           # Feature file format. "npy" or "hdf5" is supported.

###########################################################
#         GENERATOR NETWORK ARCHITECTURE SETTING          #
###########################################################
generator_type: "MelGANGenerator" # Generator type.
generator_params:
    in_channels: 80               # Number of input channels.
    out_channels: 1               # Number of output channels.
    kernel_size: 7                # Kernel size of initial and final conv layers.
    channels: 512                 # Initial number of channels for conv layers.
    upsample_scales: [8, 8, 2, 2] # List of Upsampling scales.
    stack_kernel_size: 3          # Kernel size of dilated conv layers in residual stack.
    stacks: 3                     # Number of stacks in a single residual stack module.
    use_weight_norm: True         # Whether to use weight normalization.
    use_causal_conv: False        # Whether to use causal convolution.

###########################################################
#       DISCRIMINATOR NETWORK ARCHITECTURE SETTING        #
###########################################################
discriminator_params:
    in_channels: 1        # Number of input channels.
    out_channels: 1       # Number of output channels.
    kernel_size: 3        # Number of output channels.
    layers: 10            # Number of conv layers.
    conv_channels: 64     # Number of chnn layers.
    bias: true            # Whether to use bias parameter in conv.
    use_weight_norm: true # Whether to use weight norm.
                          # If set to true, it will be applied to all of the conv layers.
    nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv.
    nonlinear_activation_params:      # Nonlinear function parameters
        negative_slope: 0.2           # Alpha in LeakyReLU.

###########################################################
#                   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

###########################################################
#               ADVERSARIAL LOSS SETTING                  #
###########################################################
lambda_adv: 4.0  # Loss balancing coefficient.

###########################################################
#                  DATA LOADER SETTING                    #
###########################################################
batch_size: 8              # Batch size.
batch_max_steps: 25600     # 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: true # 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_params:
    lr: 0.0001             # Generator's learning rate.
    eps: 1.0e-6            # Generator's epsilon.
    weight_decay: 0.0      # Generator's weight decay coefficient.
generator_scheduler_params:
    step_size: 200000      # Generator's scheduler step size.
    gamma: 0.5             # Generator's scheduler gamma.
                           # At each step size, lr will be multiplied by this parameter.
generator_grad_norm: 10    # Generator's gradient norm.
discriminator_optimizer_params:
    lr: 0.00005            # Discriminator's learning rate.
    eps: 1.0e-6            # Discriminator's epsilon.
    weight_decay: 0.0      # Discriminator's weight decay coefficient.
discriminator_scheduler_params:
    step_size: 200000      # Discriminator's scheduler step size.
    gamma: 0.5             # Discriminator's scheduler gamma.
                           # At each step size, lr will be multiplied by this parameter.
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: 400000                 # Number of training steps.
save_interval_steps: 5000               # 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.