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melgan.v3.yaml
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melgan.v3.yaml
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# 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.
# Both generator and discriminator are based on MelGAN but we also use
# STFT-based auxiliary loss with fixed lr. This configuration requires ~4 GB
# GPU memory and takes ~4 days on TITAN V.
# The discriminator loss is not stable as v1, i.e., gradually decreasing both
# real and fake losses (Also, feature matching loss keeps increasing). But in
# terms of naturalness, this model is better than v1.
# If you get unstable results, please increase train_max_steps or use v3.long.
###########################################################
# 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_type: "MelGANMultiScaleDiscriminator" # Discriminator type.
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
count_include_pad: False
kernel_sizes: [5, 3] # List of kernel size.
channels: 16 # Number of channels of the initial conv layer.
max_downsample_channels: 1024 # Maximum number of channels of downsampling layers.
downsample_scales: [4, 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 #
###########################################################
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 #
###########################################################
use_feat_match_loss: true # Whether to use feature matching loss.
lambda_feat_match: 25.0 # Loss balancing coefficient for feature matching loss.
lambda_adv: 4.0 # Loss balancing coefficient for adversarial loss.
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 16 # Batch size.
batch_max_steps: 8192 # 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: 2000000 # 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: 2000000 # 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: 2000000 # 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.