# This configuration tested on 4 GPUs (V100) with 32GB GPU
# memory. It takes around 2 weeks to finish the training
# but 100k iters model should generate reasonable results.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
n_mels: 80
fs: 22050 # sr
n_fft: 1024 # FFT size (samples).
n_shift: 256 # Hop size (samples). 12.5ms
win_length: null # Window length (samples). 50ms
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
fmin: 0 # minimum frequency for Mel basis
fmax: null # maximum frequency for Mel basis
f0min: 80 # Minimum f0 for pitch extraction.
f0max: 400 # Maximum f0 for pitch extraction.
##########################################################
# TTS MODEL SETTING #
##########################################################
model:
# generator related
generator_type: jets_generator
generator_params:
adim: 256 # attention dimension
aheads: 2 # number of attention heads
elayers: 4 # number of encoder layers
eunits: 1024 # number of encoder ff units
dlayers: 4 # number of decoder layers
dunits: 1024 # number of decoder ff units
positionwise_layer_type: conv1d # type of position-wise layer
positionwise_conv_kernel_size: 3 # kernel size of position wise conv layer
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
use_masking: True # whether to apply masking for padded part in loss calculation
encoder_normalize_before: True # whether to perform layer normalization before the input
decoder_normalize_before: True # whether to perform layer normalization before the input
encoder_type: transformer # encoder type
decoder_type: transformer # decoder type
conformer_rel_pos_type: latest # relative positional encoding type
conformer_pos_enc_layer_type: rel_pos # conformer positional encoding type
conformer_self_attn_layer_type: rel_selfattn # conformer self-attention type
conformer_activation_type: swish # conformer activation type
use_macaron_style_in_conformer: true # whether to use macaron style in conformer
use_cnn_in_conformer: true # whether to use CNN in conformer
conformer_enc_kernel_size: 7 # kernel size in CNN module of conformer-based encoder
conformer_dec_kernel_size: 31 # kernel size in CNN module of conformer-based decoder
init_type: xavier_uniform # initialization type
init_enc_alpha: 1.0 # initial value of alpha for encoder
init_dec_alpha: 1.0 # initial value of alpha for decoder
transformer_enc_dropout_rate: 0.2 # dropout rate for transformer encoder layer
transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding
transformer_enc_attn_dropout_rate: 0.2 # dropout rate for transformer encoder attention layer
transformer_dec_dropout_rate: 0.2 # dropout rate for transformer decoder layer
transformer_dec_positional_dropout_rate: 0.2 # dropout rate for transformer decoder positional encoding
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: true # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy
stop_gradient_from_energy_predictor: false # whether to stop the gradient from energy predictor to encoder
generator_out_channels: 1
generator_channels: 512
generator_global_channels: -1
generator_kernel_size: 7
generator_upsample_scales: [8, 8, 2, 2]
generator_upsample_kernel_sizes: [16, 16, 4, 4]
generator_resblock_kernel_sizes: [3, 7, 11]
generator_resblock_dilations: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
generator_use_additional_convs: true
generator_bias: true
generator_nonlinear_activation: "leakyrelu"
generator_nonlinear_activation_params:
negative_slope: 0.1
generator_use_weight_norm: true
segment_size: 64 # segment size for random windowed discriminator
# discriminator related
discriminator_type: hifigan_multi_scale_multi_period_discriminator
discriminator_params:
scales: 1
scale_downsample_pooling: "AvgPool1D"
scale_downsample_pooling_params:
kernel_size: 4
stride: 2
padding: 2
scale_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes: [15, 41, 5, 3]
channels: 128
max_downsample_channels: 1024
max_groups: 16
bias: True
downsample_scales: [2, 2, 4, 4, 1]
nonlinear_activation: "leakyrelu"
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: True
use_spectral_norm: False
follow_official_norm: False
periods: [2, 3, 5, 7, 11]
period_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes: [5, 3]
channels: 32
downsample_scales: [3, 3, 3, 3, 1]
max_downsample_channels: 1024
bias: True
nonlinear_activation: "leakyrelu"
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: True
use_spectral_norm: False
# others
sampling_rate: 22050 # needed in the inference for saving wav
cache_generator_outputs: True # whether to cache generator outputs in the training
use_alignment_module: False # whether to use alignment module
###########################################################
# LOSS SETTING #
###########################################################
# loss function related
generator_adv_loss_params:
average_by_discriminators: False # whether to average loss value by #discriminators
loss_type: mse # loss type, "mse" or "hinge"
discriminator_adv_loss_params:
average_by_discriminators: False # whether to average loss value by #discriminators
loss_type: mse # loss type, "mse" or "hinge"
feat_match_loss_params:
average_by_discriminators: False # whether to average loss value by #discriminators
average_by_layers: False # whether to average loss value by #layers of each discriminator
include_final_outputs: True # whether to include final outputs for loss calculation
mel_loss_params:
fs: 22050 # must be the same as the training data
fft_size: 1024 # fft points
hop_size: 256 # hop size
win_length: null # window length
window: hann # window type
num_mels: 80 # number of Mel basis
fmin: 0 # minimum frequency for Mel basis
fmax: null # maximum frequency for Mel basis
log_base: null # null represent natural log
###########################################################
# ADVERSARIAL LOSS SETTING #
###########################################################
lambda_adv: 1.0 # loss scaling coefficient for adversarial loss
lambda_mel: 45.0 # loss scaling coefficient for Mel loss
lambda_feat_match: 2.0 # loss scaling coefficient for feat match loss
lambda_var: 1.0 # loss scaling coefficient for duration loss
lambda_align: 2.0 # loss scaling coefficient for KL divergence loss
# others
sampling_rate: 22050 # needed in the inference for saving wav
cache_generator_outputs: True # whether to cache generator outputs in the training
# extra module for additional inputs
pitch_extract: dio # pitch extractor type
pitch_extract_conf:
reduction_factor: 1
use_token_averaged_f0: false
pitch_normalize: global_mvn # normalizer for the pitch feature
energy_extract: energy # energy extractor type
energy_extract_conf:
reduction_factor: 1
use_token_averaged_energy: false
energy_normalize: global_mvn # normalizer for the energy feature
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 32 # Batch size.
num_workers: 4 # Number of workers in DataLoader.
##########################################################
# OPTIMIZER & SCHEDULER SETTING #
##########################################################
# optimizer setting for generator
generator_optimizer_params:
beta1: 0.8
beta2: 0.99
epsilon: 1.0e-9
weight_decay: 0.0
generator_scheduler: exponential_decay
generator_scheduler_params:
learning_rate: 2.0e-4
gamma: 0.999875
# optimizer setting for discriminator
discriminator_optimizer_params:
beta1: 0.8
beta2: 0.99
epsilon: 1.0e-9
weight_decay: 0.0
discriminator_scheduler: exponential_decay
discriminator_scheduler_params:
learning_rate: 2.0e-4
gamma: 0.999875
generator_first: True # whether to start updating generator first
##########################################################
# OTHER TRAINING SETTING #
##########################################################
num_snapshots: 10 # max number of snapshots to keep while training
train_max_steps: 350000 # Number of training steps. == total_iters / ngpus, total_iters = 1000000
save_interval_steps: 1000 # Interval steps to save checkpoint.
eval_interval_steps: 250 # Interval steps to evaluate the network.
seed: 777 # random seed number