# This is the hyperparameter configuration file for Parallel WaveGAN.
# Please make sure this is adjusted for the VCTK corpus. If you want to
# apply to the other dataset, you might need to carefully change some parameters.
# This configuration requires 12 GB GPU memory and takes ~3 days on RTX TITAN.
###########################################################
# FEATURE EXTRACTION SETTING #
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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 #
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generator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_size: 3 # Kernel size of dilated convolution.
layers: 30 # Number of residual block layers.
stacks: 3 # Number of stacks i.e., dilation cycles.
residual_channels: 64 # Number of channels in residual conv.
gate_channels: 128 # Number of channels in gated conv.
skip_channels: 64 # Number of channels in skip conv.
aux_channels: 80 # Number of channels for auxiliary feature conv.
# Must be the same as num_mels.
aux_context_window: 2 # Context window size for auxiliary feature.
# If set to 2, previous 2 and future 2 frames will be considered.
dropout: 0.0 # Dropout rate. 0.0 means no dropout applied.
use_weight_norm: True # Whether to use weight norm.
# If set to true, it will be applied to all of the conv layers.
upsample_scales: [4, 5, 3, 5] # Upsampling scales. prod(upsample_scales) == n_shift
###########################################################
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
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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 #
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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
###########################################################
# ADVERSARIAL LOSS SETTING #
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lambda_adv: 4.0 # Loss balancing coefficient.
###########################################################
# DATA LOADER SETTING #
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batch_size: 8 # Batch size.
batch_max_steps: 24000 # Length of each audio in batch. Make sure dividable by n_shift.
num_workers: 2 # Number of workers in DataLoader.
###########################################################
# OPTIMIZER & SCHEDULER SETTING #
###########################################################
generator_optimizer_params:
epsilon: 1.0e-6 # Generator's epsilon.
weight_decay: 0.0 # Generator's weight decay coefficient.
generator_scheduler_params:
learning_rate: 0.0001 # Generator's learning rate.
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:
epsilon: 1.0e-6 # Discriminator's epsilon.
weight_decay: 0.0 # Discriminator's weight decay coefficient.
discriminator_scheduler_params:
learning_rate: 0.00005 # Discriminator's learning rate.
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: 1000000 # 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_save_intermediate_results: 4 # Number of results to be saved as intermediate results.
num_snapshots: 10 # max number of snapshots to keep while training
seed: 42 # random seed for paddle, random, and np.random