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model_name: "cnn_vad"
# spec
sample_rate: 8000
nfft: 512
win_size: 240
hop_size: 80
win_type: hann
# model
conv2d_block_param_list:
- batch_norm: true
in_channels: 1
out_channels: 8
kernel_size: 3
padding: "same"
dilation: 3
activation: relu
dropout: 0.1
- in_channels: 8
out_channels: 8
kernel_size: 5
padding: "same"
dilation: 3
activation: relu
dropout: 0.1
- in_channels: 8
out_channels: 8
kernel_size: 3
padding: "same"
dilation: 2
activation: relu
dropout: 0.1
encoder_output_size: 2056
# lsnr
n_frame: 3
min_local_snr_db: -15
max_local_snr_db: 30
norm_tau: 1.
# data
min_snr_db: -10
max_snr_db: 20
# train
lr: 0.001
lr_scheduler: "CosineAnnealingLR"
lr_scheduler_kwargs:
T_max: 250000
eta_min: 0.0001
max_epochs: 100
clip_grad_norm: 10.0
seed: 1234
num_workers: 4
batch_size: 128
eval_steps: 25000
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