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DATA:
dataset: multi
data_root: sample_dataset
wav_path: wav
vertices_path: npy
template_file: templates.pkl
read_audio: False
train_subjects: Arabic English French German Greek Italian Portuguese Russian Spanish Korean Mandarin Japanese
val_subjects: Arabic English French German Greek Italian Portuguese Russian Spanish Korean Mandarin Japanese
test_subjects: Arabic English French German Greek Italian Portuguese Russian Spanish Korean Mandarin Japanese
LOSS:
quant_loss_weight: 1.0
NETWORK:
arch: stage1_vocaset
in_dim: 15069
hidden_size: 1024
num_hidden_layers: 6
num_attention_heads: 8
intermediate_size: 1536
window_size: 1
quant_factor: 0
face_quan_num: 16
neg: 0.2
INaffine: False
VQuantizer:
n_embed: 256
zquant_dim: 64
TRAIN:
use_sgd: False
sync_bn: False # adopt sync_bn or not
train_gpu: [0]
workers: 10 # data loader workers
batch_size: 1 # batch size for training
batch_size_val: 1 # batch size for validation during training, memory and speed tradeoff
base_lr: 0.0001
StepLR: True
warmup_steps: 1
adaptive_lr: False
factor: 0.3
patience: 3
threshold: 0.0001
poly_lr: False
epochs: 200
step_size: 20
gamma: 0.5
start_epoch: 0
power: 0.9
momentum: 0.9
weight_decay: 0.002
manual_seed: 131
print_freq: 10
save_freq: 1
save_path:
# weight:
weight:
resume:
evaluate: True # evaluate on validation set, extra gpu memory needed and small batch_size_val is recommend
eval_freq: 10
Distributed:
dist_url: tcp://127.0.0.1:6701
dist_backend: 'nccl'
multiprocessing_distributed: True
world_size: 1
rank: 0
TEST:
test_workers: 0
test_gpu: [0]
test_batch_size: 1
save: True
model_path:
save_folder:
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