Delete Twilight0/config.yml

Twilight0/config.yml

@@ -1,117 +0,0 @@
-log_dir: "Models/Twilight0"
-first_stage_path: "first_stage.pth"
-save_freq: 1
-log_interval: 10
-device: "cuda"
-epochs_1st: 200 # number of epochs for first stage training (pre-training)
-epochs_2nd: 100 # number of peochs for second stage training (joint training)
-batch_size: 2
-max_len: 400 # maximum number of frames
-  #pretrained_model: "Models/LibriTTS/epochs_2nd_00020.pth"
-pretrained_model: "Models/Twilight0/epoch_1st_00063.pth"
-second_stage_load_pretrained: false # 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: "twilight_data"
-  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: false
-
-  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: '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: '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: 50 # 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: 20 # style diffusion starting epoch (2nd stage)
-    joint_epoch: 50 # 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: 10 # 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
-