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Upload new model: ellie-german-v3

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.gitattributes CHANGED
@@ -62,3 +62,4 @@ ellie-italian-v1/default.wav filter=lfs diff=lfs merge=lfs -text
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  ember-italian-v1/default.wav filter=lfs diff=lfs merge=lfs -text
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  PL-BERT-MULTILINGUAL/step_1100000.t7 filter=lfs diff=lfs merge=lfs -text
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  ellie-german-v2/default.wav filter=lfs diff=lfs merge=lfs -text
 
 
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  ember-italian-v1/default.wav filter=lfs diff=lfs merge=lfs -text
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  PL-BERT-MULTILINGUAL/step_1100000.t7 filter=lfs diff=lfs merge=lfs -text
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  ellie-german-v2/default.wav filter=lfs diff=lfs merge=lfs -text
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+ ellie-german-v3/default.wav filter=lfs diff=lfs merge=lfs -text
ellie-german-v3/config_ellie_german_v3.yml ADDED
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+ log_dir: "FinetunedModels/ellie-german-v3"
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+ save_freq: 10
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+ log_interval: 10
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+ device: "cuda"
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+ epochs: 50 # number of finetuning epoch (1 hour of data)
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+ batch_size: 12
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+ max_len: 800 # maximum number of frames
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+ pretrained_model: "Model/epoch_2nd_00049.pth" # from german-base-model
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+ second_stage_load_pretrained: true # set to true if the pre-trained model is for 2nd stage
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+ load_only_params: true # set to true if do not want to load epoch numbers and optimizer parameters
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+
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+ F0_path: "Utils/JDC/bst.t7"
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+ ASR_config: "Utils/ASR/config.yml"
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+ ASR_path: "Utils/ASR/epoch_00080.pth"
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+ PLBERT_dir: 'Utils/PLBERT/'
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+
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+ data_params:
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+ train_data: "Data/ellie_train_list_cleaned.txt"
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+ val_data: "Data/ellie_val_list_cleaned.txt"
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+ root_path: "/root/src/StyleTTS2/Data"
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+ OOD_data: "Data/OOD_texts.txt"
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+ min_length: 50 # sample until texts with this size are obtained for OOD texts
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+
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+ preprocess_params:
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+ sr: 24000
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+ spect_params:
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+ n_fft: 2048
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+ win_length: 1200
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+ hop_length: 300
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+
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+ model_params:
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+ multispeaker: true
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+
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+ dim_in: 64
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+ hidden_dim: 512
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+ max_conv_dim: 512
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+ n_layer: 3
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+ n_mels: 80
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+
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+ n_token: 178 # number of phoneme tokens
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+ max_dur: 50 # maximum duration of a single phoneme
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+ style_dim: 128 # style vector size
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+
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+ dropout: 0.2
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+
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+ # config for decoder
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+ decoder:
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+ type: 'hifigan' # either hifigan or istftnet
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+ resblock_kernel_sizes: [3,7,11]
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+ upsample_rates : [10,5,3,2]
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+ upsample_initial_channel: 512
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+ resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]]
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+ upsample_kernel_sizes: [20,10,6,4]
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+
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+ # speech language model config
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+ slm:
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+ model: 'microsoft/wavlm-base-plus'
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+ sr: 16000 # sampling rate of SLM
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+ hidden: 768 # hidden size of SLM
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+ nlayers: 13 # number of layers of SLM
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+ initial_channel: 64 # initial channels of SLM discriminator head
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+
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+ # style diffusion model config
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+ diffusion:
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+ embedding_mask_proba: 0.1
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+ # transformer config
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+ transformer:
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+ num_layers: 3
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+ num_heads: 8
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+ head_features: 64
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+ multiplier: 2
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+
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+ # diffusion distribution config
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+ dist:
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+ sigma_data: 0.20860986499241868 # placeholder for estimate_sigma_data set to false
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+ estimate_sigma_data: false # estimate sigma_data from the current batch if set to true
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+ mean: -3.0
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+ std: 1.0
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+
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+ loss_params:
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+ lambda_mel: 5. # mel reconstruction loss
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+ lambda_gen: 1. # generator loss
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+ lambda_slm: 1. # slm feature matching loss
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+
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+ lambda_mono: 1. # monotonic alignment loss (TMA)
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+ lambda_s2s: 1. # sequence-to-sequence loss (TMA)
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+
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+ lambda_F0: 1. # F0 reconstruction loss
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+ lambda_norm: 1. # norm reconstruction loss
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+ lambda_dur: 1. # duration loss
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+ lambda_ce: 20. # duration predictor probability output CE loss
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+ lambda_sty: 1. # style reconstruction loss
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+ lambda_diff: 1. # score matching loss
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+
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+ diff_epoch: 10 # style diffusion starting epoch
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+ joint_epoch: 30 # joint training starting epoch
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+
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+ optimizer_params:
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+ lr: 0.0001 # general learning rate
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+ bert_lr: 0.00001 # learning rate for PLBERT
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+ ft_lr: 0.0001 # learning rate for acoustic modules
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+
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+ slmadv_params:
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+ min_len: 640 # minimum length of samples
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+ max_len: 800 # maximum length of samples
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+ batch_percentage: 0.5 # to prevent out of memory, only use half of the original batch size
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+ iter: 10 # update the discriminator every this iterations of generator update
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+ thresh: 5 # gradient norm above which the gradient is scaled
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+ scale: 0.01 # gradient scaling factor for predictors from SLM discriminators
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+ sig: 1.5 # sigma for differentiable duration modeling
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