NameError: name 'get_data_path_list' is not defined when running fine tuning code

[rikabi89]

(StyleTTS2) H:\StyleTTS2>python train_finetune.py --config_path ./Configs/config_ft.yml
Traceback (most recent call last):
File "train_finetune.py", line 707, in
main()
File "H:\anaconda3\envs\StyleTTS2\lib\site-packages\click\core.py", line 1157, in call
return self.main(*args, **kwargs)
File "H:\anaconda3\envs\StyleTTS2\lib\site-packages\click\core.py", line 1078, in main
rv = self.invoke(ctx)
File "H:\anaconda3\envs\StyleTTS2\lib\site-packages\click\core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "H:\anaconda3\envs\StyleTTS2\lib\site-packages\click\core.py", line 783, in invoke
return __callback(*args, **kwargs)
File "train_finetune.py", line 90, in main
train_list, val_list = get_data_path_list(train_path, val_path)
NameError: name 'get_data_path_list' is not defined

--

This is my config.yml:

log_dir: "Models/LJSpeech"
first_stage_path: "first_stage.pth"
save_freq: 2
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: 16
max_len: 400 # maximum number of frames
pretrained_model: ""
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

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: "H:/StyleTTS2/Data/train_list.txt"
val_data: "H:/StyleTTS2/Data/val_list.txt"
root_path: "H:/ai-voice-cloning/training/ben_shirpo/audio"
OOD_data: "H:/StyleTTS2/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

On Win11 Anaconda