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# 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.
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