load local model

.gitattributes

@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*ckpt filter=lfs diff=lfs merge=lfs -text
+*best filter=lfs diff=lfs merge=lfs -text

DeepFilterNet2/checkpoints/model_96.ckpt.best

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb5eccb429e675bb4ec5ec9e280f048bfff9787b40bd3eb835fd11509eb14a3e
+size 9397209

DeepFilterNet2/config.ini

@@ -0,0 +1,109 @@
+[train]
+seed = 43
+device = 
+model = deepfilternet2
+jit = false
+mask_only = false
+df_only = false
+batch_size = 96
+batch_size_eval = 128
+num_workers = 16
+max_sample_len_s = 3.0
+p_atten_lim = 0.0
+p_reverb = 0.1
+overfit = false
+max_epochs = 100
+log_freq = 100
+log_timings = False
+validation_criteria = loss
+validation_criteria_rule = min
+early_stopping_patience = 15
+global_ds_sampling_f = 1
+num_prefetch_batches = 8
+dataloader_snrs = -5,0,5,10,20,40
+detect_anomaly = false
+batch_size_scheduling = 0/8,1/16,2/24,5/32,10/64,20/128,40/9999
+start_eval = true
+validation_set_caching = false
+
+[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 = input_specf
+
+[deepfilternet]
+conv_lookahead = 2
+conv_ch = 64
+conv_depthwise = True
+emb_hidden_dim = 256
+emb_num_layers = 3
+gru_groups = 8
+linear_groups = 8
+conv_dec_mode = transposed
+convt_depthwise = True
+mask_pf = False
+df_order = 5
+df_lookahead = 2
+df_hidden_dim = 256
+df_num_layers = 2
+dfop_method = df
+group_shuffle = False
+conv_kernel = 1,3
+df_gru_skip = none
+df_output_layer = groupedlinear
+gru_type = squeeze
+df_pathway_kernel_size_t = 5
+df_n_iter = 1
+enc_concat = True
+conv_kernel_inp = 3,3
+
+[localsnrloss]
+factor = 1e-3
+
+[maskloss]
+factor = 0
+mask = iam
+gamma = 0.6
+gamma_pred = 0.6
+f_under = 1
+
+[spectralloss]
+factor_magnitude = 1000
+factor_complex = 1000
+gamma = 0.3
+
+[dfalphaloss]
+factor = 0.0
+
+[multiresspecloss]
+factor = 500
+factor_complex = 500
+gamma = 0.3
+fft_sizes = 256,512,1024
+
+[optim]
+lr = 0.001
+momentum = 0
+weight_decay = 1e-12
+weight_decay_end = 0.05
+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
+

app.py

@@ -18,7 +18,7 @@ from df.enhance import enhance, init_df, load_audio, save_audio
 from df.utils import resample
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model, df, _ = init_df(config_allow_defaults=True)
+model, df, _ = init_df("./DeepFilterNet2", config_allow_defaults=True)
 model = model.to(device=device).eval()
 
 NOISES = {