You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
139 lines
7.3 KiB
139 lines
7.3 KiB
3 years ago
|
# This is the hyperparameter configuration file for MelGAN.
|
||
|
|
# Please make sure this is adjusted for the CSMSC dataset. If you want to
|
||
|
|
# apply to the other dataset, you might need to carefully change some parameters.
|
||
|
|
# This configuration requires ~ 8GB memory and will finish within 7 days on Titan V.
|
||
|
|
|
||
|
|
# This configuration is based on full-band MelGAN but the hop size and sampling
|
||
|
|
# rate is different from the paper (16kHz vs 24kHz). The number of iteraions
|
||
|
|
# is not shown in the paper so currently we train 1M iterations (not sure enough
|
||
|
|
# to converge). The optimizer setting is based on @dathudeptrai advice.
|
||
|
|
# https://github.com/kan-bayashi/ParallelWaveGAN/issues/143#issuecomment-632539906
|
||
|
|
|
||
|
|
###########################################################
|
||
|
|
# FEATURE EXTRACTION SETTING #
|
||
|
|
###########################################################
|
||
|
|
fs: 24000 # Sampling rate.
|
||
|
|
n_fft: 2048 # FFT size. (in samples)
|
||
|
|
n_shift: 300 # Hop size. (in samples)
|
||
|
|
win_length: 1200 # Window length. (in samples)
|
||
|
|
# 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: 80 # Number of input channels.
|
||
|
|
out_channels: 4 # Number of output channels.
|
||
|
|
kernel_size: 7 # Kernel size of initial and final conv layers.
|
||
|
|
channels: 384 # Initial number of channels for conv layers.
|
||
|
|
upsample_scales: [5, 5, 3] # List of Upsampling scales.
|
||
|
|
stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack.
|
||
|
|
stacks: 4 # 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.
|
||
|
|
use_final_nonlinear_activation: False # If True, spectral_convergence_loss and sub_spectral_convergence_loss will be too large (eg.30)
|
||
|
|
|
||
|
|
|
||
|
|
###########################################################
|
||
|
|
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
|
||
|
|
###########################################################
|
||
|
|
discriminator_params:
|
||
|
|
in_channels: 1 # Number of input channels.
|
||
|
|
out_channels: 1 # Number of output channels.
|
||
|
|
scales: 3 # Number of multi-scales.
|
||
|
|
downsample_pooling: "AvgPool1D" # Pooling type for the input downsampling.
|
||
|
|
downsample_pooling_params: # Parameters of the above pooling function.
|
||
|
|
kernel_size: 4
|
||
|
|
stride: 2
|
||
|
|
padding: 1
|
||
|
|
exclusive: True
|
||
|
|
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.
|
||
|
|
downsample_scales: [4, 4, 4] # 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
|
||
|
|
use_subband_stft_loss: true
|
||
|
|
subband_stft_loss_params:
|
||
|
|
fft_sizes: [384, 683, 171] # List of FFT size for STFT-based loss.
|
||
|
|
hop_sizes: [30, 60, 10] # List of hop size for STFT-based loss
|
||
|
|
win_lengths: [150, 300, 60] # List of window length for STFT-based loss.
|
||
|
|
window: "hann" # Window function for STFT-based loss
|
||
|
|
|
||
|
|
###########################################################
|
||
|
|
# ADVERSARIAL LOSS SETTING #
|
||
|
|
###########################################################
|
||
|
|
use_feat_match_loss: false # Whether to use feature matching loss.
|
||
|
|
lambda_adv: 2.5 # Loss balancing coefficient for adversarial loss.
|
||
|
|
|
||
|
|
###########################################################
|
||
|
|
# DATA LOADER SETTING #
|
||
|
|
###########################################################
|
||
|
|
batch_size: 64 # Batch size.
|
||
|
|
batch_max_steps: 16200 # Length of each audio in batch. Make sure dividable by hop_size.
|
||
|
|
num_workers: 4 # Number of workers in DataLoader.
|
||
|
|
|
||
|
|
###########################################################
|
||
|
|
# OPTIMIZER & SCHEDULER SETTING #
|
||
|
|
###########################################################
|
||
|
|
generator_optimizer_params:
|
||
|
|
epsilon: 1.0e-7 # Generator's epsilon.
|
||
|
|
weight_decay: 0.0 # Generator's weight decay coefficient.
|
||
|
|
|
||
|
|
generator_grad_norm: -1 # Generator's gradient norm.
|
||
|
|
generator_scheduler_params:
|
||
|
|
learning_rate: 1.0e-3 # Generator's learning rate.
|
||
|
|
gamma: 0.5 # Generator's scheduler gamma.
|
||
|
|
milestones: # At each milestone, lr will be multiplied by gamma.
|
||
|
|
- 100000
|
||
|
|
- 200000
|
||
|
|
- 300000
|
||
|
|
- 400000
|
||
|
|
- 500000
|
||
|
|
- 600000
|
||
|
|
discriminator_optimizer_params:
|
||
|
|
epsilon: 1.0e-7 # Discriminator's epsilon.
|
||
|
|
weight_decay: 0.0 # Discriminator's weight decay coefficient.
|
||
|
|
|
||
|
|
discriminator_grad_norm: -1 # Discriminator's gradient norm.
|
||
|
|
discriminator_scheduler_params:
|
||
|
|
learning_rate: 1.0e-3 # Discriminator's learning rate.
|
||
|
|
gamma: 0.5 # Discriminator's scheduler gamma.
|
||
|
|
milestones: # At each milestone, lr will be multiplied by gamma.
|
||
|
|
- 100000
|
||
|
|
- 200000
|
||
|
|
- 300000
|
||
|
|
- 400000
|
||
|
|
- 500000
|
||
|
|
- 600000
|
||
|
|
|
||
|
|
###########################################################
|
||
|
|
# INTERVAL SETTING #
|
||
|
|
###########################################################
|
||
|
|
discriminator_train_start_steps: 200000 # Number of steps to start to train discriminator.
|
||
|
|
train_max_steps: 1000000 # Number of training steps.
|
||
|
|
save_interval_steps: 50000 # 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
|