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log_dir: "/home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade_v02"
first_stage_path: "/home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade_v02/epoch_1st_00026.pth"
save_freq: 1
log_interval: 10
device: "cuda"
epochs_1st: 30 # number of epochs for first stage training (pre-training)
epochs_2nd: 20 # number of peochs for second stage training (joint training)
batch_size: 64
max_len: 4000 # maximum number of frames
pretrained_model: "/home/austin/disk2/llmvcs/tt/stylekan/Models/Style_Kanade_v02/epoch_2nd_00007.pth"
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
# CUDA_VISIBLE_DEVICES=1,2,3 accelerate launch train_first.py --config_path ./Configs/config_kanade.yml
# CUDA_VISIBLE_DEVICES=6,7 accelerate launch accelerate_train_second.py --config_path ./Configs/config_kanade_test.yml
F0_path: "/home/austin/disk2/llmvcs/tt/stylekan/Utils/JDC/bst.t7"
ASR_config: "/home/austin/disk2/llmvcs/tt/stylekan/Utils/ASR/config.yml"
ASR_path: "/home/austin/disk2/llmvcs/tt/stylekan/Utils/ASR/bst_00080.pth"
PLBERT_dir: 'Utils/PLBERT/'
data_params:
train_data: "/home/austin/disk2/llmvcs/tt/stylekan/Data/metadata_cleanest/filtered_train_list_no_nsp_plus.csv"
val_data: "/home/austin/disk2/llmvcs/tt/stylekan/Data/mg_valid.txt"
root_path: ""
OOD_data: "/home/austin/disk2/llmvcs/tt/stylekan/Data/OOD_LargeScale_.csv"
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
decoder:
type: 'istftnet' # either hifigan or istftnet
resblock_kernel_sizes: [3,7,11]
upsample_rates : [10, 6]
upsample_initial_channel: 512
resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]]
upsample_kernel_sizes: [20, 12]
gen_istft_n_fft: 20
gen_istft_hop_size: 5
# speech language model config
slm:
model: 'Respair/Whisper_Large_v2_Encoder_Block' # The model itself is hardcoded, change it through -> losses.py
sr: 16000 # sampling rate of SLM
hidden: 1280 # hidden size of SLM
nlayers: 33 # 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: 10. # 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: 4 # style diffusion starting epoch (2nd stage)
joint_epoch: 999 # 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
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