Delete config.py

config.py

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-# Ke Chen
-# [email protected]
-# Zero-shot Audio Source Separation via Query-based Learning from Weakly-labeled Data
-# The configuration file
-
-# for model training
-exp_name = "exp_zs_asp_full" # the saved ckpt prefix name of the model 
-workspace = "/home/Research/ZS_ASP/" # the folder of your code
-dataset_path = "/home/Research/ZS_ASP/data/audioset" # the dataset path
-index_type = "full_train"
-idc_path = "/home/Research/ZS_ASP/" # the folder of audioset class count files
-balanced_data = True
-
-# trained from a checkpoint, or evaluate a single model 
-resume_checkpoint = None
-# "/home/Research/ZS_ASP/model_backup/zeroshot_asp_full.ckpt"
-
-loss_type = "mae"
-
-gather_mode = False
-debug = False
-
-classes_num = 527
-eval_list = [] # left blank to preserve all classes, otherwise will filter the specified classes
-# [15, 63, 81, 184, 335, 449, 474, 348, 486, 4] # randomly generated from the 527-classes for held-out evaludation
-
-
-batch_size = 16 * 8   # batch size per GPU x GPU number , default is 16 x 8 = 128
-learning_rate = 1e-3 # 3e-4 is also workable
-max_epoch = 100
-num_workers = 3
-lr_scheduler_epoch = [90, 110]
-latent_dim = 2048
-
-# for signal processing
-sample_rate = 32000
-clip_samples = sample_rate * 10 # audio_set 10-sec clip
-segment_frames = 200 
-hop_samples = 320
-random_seed = 12412 # 444612 1536123 12412
-random_mode = "one_class" # "no_random, one_class, random, order", one class is the best
-
-# for evaluation
-musdb_path = "/home/Research/ZS_ASP/data/musdb-wav/" # musdb download folder
-testavg_path = "/home/Research/ZS_ASP/data/musdb30-train-32000fs.npy" # the processed training set (to get the latent query)
-testset_path = "/home/Research/ZS_ASP/data/musdb-test-32000fs.npy" # the processed testing set (to calculate the performance)
-test_key = ["vocals", "drums", "bass", "other"] # four tracks for musdb, and your named track for other inference
-test_type = "mix"
-infer_type = "mean"
-energy_thres = 0.1
-wave_output_path = "/home/Research/ZS_ASP/wavoutput" # output folder
-using_wiener = True # use wiener filter or not (default: True)
-using_whiting = False # use whiting or not (default: False)
-
-# weight average
-wa_model_folder = "/home/Research/ZS_ASP/version_3/checkpoints/"
-wa_model_path = "zs_wa.ckpt"
-
-# for inference
-inference_file = "/home/Research/ZS_ASP/data/pagenini.wav" # an audio file to separate
-inference_query = "/home/Research/ZS_ASP/data/query" # a folder containing all samples for obtaining the query
-overlap_rate = 0.0 # [0.0, 1.0), 0 to disabled, recommand 0.5 for 50% overlap. Overlap will increase computation time and improve result quality