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# This configuration is for ESPnet2 to train VITS, which
# is truely end-to-end text-to-waveform model. To run
# this config, you need to specify "--tts_task gan_tts"
# option for tts.sh at least and use 22050 hz audio as
# the training data (mainly tested on LJspeech).
# 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.
##########################################################
# TTS MODEL SETTING #
##########################################################
tts: vits
tts_conf:
# generator related
generator_type: vits_generator
generator_params:
hidden_channels: 192
spks: -1
spk_embed_dim: 192
global_channels: 256
segment_size: 32
text_encoder_attention_heads: 2
text_encoder_ffn_expand: 4
text_encoder_blocks: 6
text_encoder_positionwise_layer_type: "conv1d"
text_encoder_positionwise_conv_kernel_size: 3
text_encoder_positional_encoding_layer_type: "rel_pos"
text_encoder_self_attention_layer_type: "rel_selfattn"
text_encoder_activation_type: "swish"
text_encoder_normalize_before: true
text_encoder_dropout_rate: 0.1
text_encoder_positional_dropout_rate: 0.0
text_encoder_attention_dropout_rate: 0.1
use_macaron_style_in_text_encoder: true
# NOTE(kan-bayashi): Conformer conv requires BatchNorm1d which causes
# errors when multiple GPUs in pytorch 1.7.1. Therefore, we disable
# it as a default. We need to consider the alternative normalization
# or different version pytorch may solve this issue.
use_conformer_conv_in_text_encoder: false
text_encoder_conformer_kernel_size: -1
decoder_kernel_size: 7
decoder_channels: 512
decoder_upsample_scales: [8, 8, 2, 2]
decoder_upsample_kernel_sizes: [16, 16, 4, 4]
decoder_resblock_kernel_sizes: [3, 7, 11]
decoder_resblock_dilations: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
use_weight_norm_in_decoder: true
posterior_encoder_kernel_size: 5
posterior_encoder_layers: 16
posterior_encoder_stacks: 1
posterior_encoder_base_dilation: 1
posterior_encoder_dropout_rate: 0.0
use_weight_norm_in_posterior_encoder: true
flow_flows: 4
flow_kernel_size: 5
flow_base_dilation: 1
flow_layers: 4
flow_dropout_rate: 0.0
use_weight_norm_in_flow: true
use_only_mean_in_flow: true
stochastic_duration_predictor_kernel_size: 3
stochastic_duration_predictor_dropout_rate: 0.5
stochastic_duration_predictor_flows: 4
stochastic_duration_predictor_dds_conv_layers: 3
# 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
# 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
n_fft: 1024 # fft points
hop_length: 256 # hop size
win_length: null # window length
window: hann # window type
n_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
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_dur: 1.0 # loss scaling coefficient for duration loss
lambda_kl: 1.0 # 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
##########################################################
# OPTIMIZER & SCHEDULER SETTING #
##########################################################
# optimizer setting for generator
optim: adamw
optim_conf:
lr: 2.0e-4
betas: [0.8, 0.99]
eps: 1.0e-9
weight_decay: 0.0
scheduler: exponentiallr
scheduler_conf:
gamma: 0.999875
# optimizer setting for discriminator
optim2: adamw
optim2_conf:
lr: 2.0e-4
betas: [0.8, 0.99]
eps: 1.0e-9
weight_decay: 0.0
scheduler2: exponentiallr
scheduler2_conf:
gamma: 0.999875
generator_first: false # whether to start updating generator first
##########################################################
# OTHER TRAINING SETTING #
##########################################################
#num_iters_per_epoch: 1000 # number of iterations per epoch
max_epoch: 1000 # number of epochs
accum_grad: 1 # gradient accumulation
batch_bins: 1500000 # batch bins (feats_type=raw)
batch_type: numel # how to make batch
grad_clip: -1 # gradient clipping norm
grad_noise: false # whether to use gradient noise injection
sort_in_batch: descending # how to sort data in making batch
sort_batch: descending # how to sort created batches
num_workers: 4 # number of workers of data loader
use_amp: false # whether to use pytorch amp
log_interval: 50 # log interval in iterations
keep_nbest_models: 10 # number of models to keep
num_att_plot: 3 # number of attention figures to be saved in every check
seed: 3407 # random seed number
patience: null # patience for early stopping
unused_parameters: true # needed for multi gpu case
best_model_criterion: # criterion to save the best models
- - train
- total_count
- max
cudnn_deterministic: false # setting to false accelerates the training speed but makes it non-deterministic
# in the case of GAN-TTS training, we strongly recommend setting to false
cudnn_benchmark: false # setting to true might acdelerate the training speed but sometimes decrease it
# therefore, we set to false as a default (recommend trying both cases)
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