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log_dir: ./Models/Finetune
save_freq: 1
log_interval: 10
device: cuda
epochs: 50
batch_size: 2
max_len: 310 # maximum number of frames
pretrained_model: ./Models/Finetune/base_model.pth
load_only_params: false # set to true if do not want to load epoch numbers and optimizer parameters
debug: true
data_params:
train_data: "../../Data_Speech/viVoice/train.txt"
val_data: "../../Data_Speech/combine/combine_val.txt"
root_path: "../../Data_Speech/"
symbol: #Total 189 symbols
pad: "$"
punctuation: ';:,.!?¡¿—…"«»“” '
letters: "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
letters_ipa: "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
extend: "∫̆ăη͡123456" #ADD MORE SYMBOLS HERE
preprocess_params:
sr: 24000
spect_params:
n_fft: 2048
win_length: 1200
hop_length: 300
training_strats:
#All modules: 'decoder', 'predictor', 'text_encoder', 'style_encoder', 'text_aligner', 'pitch_extractor', 'mpd', 'msd'
freeze_modules: [''] # Not updated when training.
ignore_modules: [''] # Not loading => fresh start. IMPORTANT: 'text_aligner' and 'pitch_extractor' are util pretraineds DO NOT ignore them.
model_params:
dim_in: 64
hidden_dim: 512
max_conv_dim: 512
n_layer: 3
n_mels: 80
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_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 |