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nx_denoise
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model_name: "dfnet"

# spec
sample_rate: 8000
nfft: 512
win_size: 200
hop_size: 80

spec_bins: 256
erb_bins: 32
min_freq_bins_for_erb: 2
use_ema_norm: true

# model
conv_channels: 64
conv_kernel_size_input:
  - 3
  - 3
conv_kernel_size_inner:
  - 1
  - 3
convt_kernel_size_inner:
  - 1
  - 3

embedding_hidden_size: 256
encoder_combine_op: "concat"

encoder_emb_skip_op: "none"
encoder_emb_linear_groups: 16
encoder_emb_hidden_size: 256

encoder_linear_groups: 32

decoder_emb_num_layers: 3
decoder_emb_skip_op: "none"
decoder_emb_linear_groups: 16
decoder_emb_hidden_size: 256

df_decoder_hidden_size: 256
df_num_layers: 2
df_order: 5
df_bins: 96
df_gru_skip: "grouped_linear"
df_decoder_linear_groups: 16
df_pathway_kernel_size_t: 5
df_lookahead: 2

# lsnr
n_frame: 3
lsnr_max: 30
lsnr_min: -15
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: 8
batch_size: 64
eval_steps: 10000

# runtime
use_post_filter: true