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log_dir: "Models/Finetune_Extend"
save_freq: 1
log_interval: 5
device: "cuda"
epochs: 50
batch_size: 3
max_len: 210 # maximum number of frames
pretrained_model: "Models/Finetune_Extend/current_model.pth"
load_only_params: false # set to true if do not want to load epoch numbers and optimizer parameters
data_params:
train_data: "../../Data_Speech/viVoice/train.txt"
val_data: "../../Data_Speech/combine/combine_val.txt"
root_path: "../../Data_Speech/"
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:
dim_in: 64
hidden_dim: 512
max_conv_dim: 512
n_layer: 3
n_mels: 80
n_token: 189 # number of phoneme tokens
max_dur: 50 # maximum duration of a single phoneme
style_dim: 128 # style vector size
dropout: 0.2
ASR_params:
input_dim: 80
hidden_dim: 256
n_token: 189 # number of phoneme tokens
n_layers: 6
token_embedding_dim: 512
JDC_params:
num_class: 1
seq_len: 192
# 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]
loss_params:
lambda_mel: 5. # mel reconstruction loss
lambda_gen: 1. # generator loss
lambda_mono: 1. # monotonic alignment loss (TMA)
lambda_s2s: 1. # sequence-to-sequence loss (TMA)
lambda_F0: 1. # F0 reconstruction loss
lambda_norm: 1. # norm reconstruction loss
lambda_dur: 1. # duration loss
lambda_ce: 20. # duration predictor probability output CE loss
optimizer_params:
lr: 0.0001 # general learning rate
ft_lr: 0.00001 # learning rate for acoustic modules |