[train]
seed = 43
device = 
model = deepfilternet3
jit = false
mask_only = false
df_only = false
batch_size = 64
batch_size_eval = 64
num_workers = 16
max_sample_len_s = 3.0
p_atten_lim = 0.0
overfit = false
max_epochs = 120
log_freq = 100
log_timings = True
validation_criteria = loss
validation_criteria_rule = min
early_stopping_patience = 25
global_ds_sampling_f = 1
num_prefetch_batches = 8
dataloader_snrs = -100,-5,0,5,10,20,40
detect_anomaly = false
batch_size_scheduling = 0/16,2/24,5/32,10/64,20/128,40/9999
start_eval = false
validation_set_caching = false
cp_blacklist = 

[distortion]
p_reverb = 0.1
p_bandwidth_ext = 0.0
p_clipping = 0.0
p_air_absorption = 0.0
p_zeroing = 0.0
p_interfer_sp = 0.0

[df]
sr = 48000
fft_size = 960
hop_size = 480
nb_erb = 32
nb_df = 96
norm_tau = 1
lsnr_max = 35
lsnr_min = -15
min_nb_erb_freqs = 2
pad_mode = output
df_order = 5
df_lookahead = 2

[deepfilternet]
conv_lookahead = 2
conv_ch = 64
conv_depthwise = True
emb_hidden_dim = 256
emb_num_layers = 3
enc_linear_groups = 32
linear_groups = 16
conv_dec_mode = transposed
convt_depthwise = False
mask_pf = False
df_hidden_dim = 256
df_num_layers = 2
dfop_method = df
group_shuffle = False
conv_kernel = 1,3
df_gru_skip = groupedlinear
df_pathway_kernel_size_t = 5
enc_concat = False
conv_kernel_inp = 3,3
emb_gru_skip = none
df_n_iter = 1
convt_kernel = 1,3
lsnr_dropout = False

[localsnrloss]
factor = 1e-3

[maskloss]
factor = 0
mask = spec
gamma = 0.3
gamma_pred = 0.3
f_under = 1

[spectralloss]
factor_magnitude = 0
factor_complex = 0
gamma = 0.3
factor_under = 1

[dfalphaloss]
factor = 0.0

[multiresspecloss]
factor = 500
factor_complex = 500
gamma = 0.3
fft_sizes = 256,512,1024,2048

[optim]
lr = 0.001
momentum = 0
weight_decay = 1e-12
weight_decay_end = 0.01
optimizer = adamw
lr_min = 1e-06
lr_warmup = 0.0001
warmup_epochs = 3
lr_cycle_mul = 1.0
lr_cycle_decay = 0.5
lr_cycle_limit = 1
lr_update_per_epoch = False
lr_cycle_epochs = -1

[sdrloss]
factor = 0.0
segmental_ws = 0

[asrloss]
factor = 0
factor_lm = 0
loss_lm = CrossEntropy
model = base.en