log_dir: "Models/LibriTTS"

first_stage_path: "first_stage.pth"

save_freq: 1

log_interval: 10

device: "cuda"

epochs_1st: 50 # number of epochs for first stage training (pre-training)

epochs_2nd: 30 # number of peochs for second stage training (joint training)

batch_size: 16

max_len: 300 # maximum number of frames

pretrained_model: ""

second_stage_load_pretrained: true # set to true if the pre-trained model is for 2nd stage

load_only_params: false # set to true if do not want to load epoch numbers and optimizer parameters

F0_path: "Utils/JDC/bst.t7"

ASR_config: "Utils/ASR/config.yml"

ASR_path: "Utils/ASR/epoch_00080.pth"

PLBERT_dir: 'Utils/PLBERT/'

data_params:

train_data: "Data/train_list.txt"

val_data: "Data/val_list.txt"

root_path: ""

OOD_data: "Data/OOD_texts.txt"

min_length: 50 # sample until texts with this size are obtained for OOD texts

preprocess_params:

sr: 24000

spect_params:

n_fft: 2048

win_length: 1200

hop_length: 300

model_params:

multispeaker: true

dim_in: 64

hidden_dim: 512

max_conv_dim: 512

n_layer: 3

n_mels: 80

n_token: 178 # number of phoneme tokens

max_dur: 50 # maximum duration of a single phoneme

style_dim: 128 # style vector size

dropout: 0.2

# config for decoder

decoder:

type: 'hifigan' # either hifigan or istftnet

resblock_kernel_sizes: [3,7,11]

upsample_rates : [10,5,3,2]

upsample_initial_channel: 512

resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]]

upsample_kernel_sizes: [20,10,6,4]

# speech language model config

slm:

model: 'microsoft/wavlm-base-plus'

sr: 16000 # sampling rate of SLM

hidden: 768 # hidden size of SLM

nlayers: 13 # number of layers of SLM

initial_channel: 64 # initial channels of SLM discriminator head

# style diffusion model config

diffusion:

embedding_mask_proba: 0.1

# transformer config

transformer:

num_layers: 3

num_heads: 8

head_features: 64

multiplier: 2

# diffusion distribution config

dist:

sigma_data: 0.2 # placeholder for estimate_sigma_data set to false

estimate_sigma_data: true # estimate sigma_data from the current batch if set to true

mean: -3.0

std: 1.0

loss_params:

lambda_mel: 5. # mel reconstruction loss

lambda_gen: 1. # generator loss

lambda_slm: 1. # slm feature matching loss

lambda_mono: 1. # monotonic alignment loss (1st stage, TMA)

lambda_s2s: 1. # sequence-to-sequence loss (1st stage, TMA)

TMA_epoch: 5 # TMA starting epoch (1st stage)

lambda_F0: 1. # F0 reconstruction loss (2nd stage)

lambda_norm: 1. # norm reconstruction loss (2nd stage)

lambda_dur: 1. # duration loss (2nd stage)

lambda_ce: 20. # duration predictor probability output CE loss (2nd stage)

lambda_sty: 1. # style reconstruction loss (2nd stage)

lambda_diff: 1. # score matching loss (2nd stage)

diff_epoch: 10 # style diffusion starting epoch (2nd stage)

joint_epoch: 15 # joint training starting epoch (2nd stage)

optimizer_params:

lr: 0.0001 # general learning rate

bert_lr: 0.00001 # learning rate for PLBERT

ft_lr: 0.00001 # learning rate for acoustic modules

slmadv_params:

min_len: 400 # minimum length of samples

max_len: 500 # maximum length of samples

batch_percentage: 0.5 # to prevent out of memory, only use half of the original batch size

iter: 20 # update the discriminator every this iterations of generator update

thresh: 5 # gradient norm above which the gradient is scaled

scale: 0.01 # gradient scaling factor for predictors from SLM discriminators

sig: 1.5 # sigma for differentiable duration modeling