# Generated 2021-02-26 from: # /scratch/csubakan/speechbrain_new/recipes/WSJ2Mix/separation/yamls/dptransformer78.yaml # yamllint disable # ################################ # Model: SepFormer for source separation # https://arxiv.org/abs/2010.13154 # # Dataset : WSJ0-mix # ################################ # Basic parameters # Seed needs to be set at top of yaml, before objects with parameters are made # seed: 1234 __set_seed: !apply:torch.manual_seed [1234] # Data params data_folder: /localscratch/csubakan.62709298.0/wsj0-mix/2speakers # wsj2mix or wsj3mix experiment_name: 78-speedchange-dynamicmix-hardcodegaussian output_folder: results/78-speedchange-dynamicmix-hardcodegaussian/1234 train_log: results/78-speedchange-dynamicmix-hardcodegaussian/1234/train_log.txt save_folder: results/78-speedchange-dynamicmix-hardcodegaussian/1234/save train_data: results/78-speedchange-dynamicmix-hardcodegaussian/1234/save/wsj_tr.csv valid_data: results/78-speedchange-dynamicmix-hardcodegaussian/1234/save/wsj_cv.csv test_data: results/78-speedchange-dynamicmix-hardcodegaussian/1234/save/wsj_tt.csv wsj0_tr: /localscratch/csubakan.62709298.0/wsj0-processed/si_tr_s/ # Experiment params auto_mix_prec: true test_only: false num_spks: 2 # set to 3 for wsj0-3mix progressbar: true save_audio: false # Save estimated sources on disk sample_rate: 8000 # Training parameters N_epochs: 200 batch_size: 1 lr: 0.00015 clip_grad_norm: 5 loss_upper_lim: 999999 # this is the upper limit for an acceptable loss # if True, the training sequences are cut to a specified length limit_training_signal_len: false # this is the length of sequences if we choose to limit # the signal length of training sequences training_signal_len: 128000 dynamic_mixing: regular # Augment parameters use_wavedrop: false use_speedperturb: true use_speedperturb_sameforeachsource: false use_rand_shift: false min_shift: -8000 max_shift: 8000 # Neural parameters N_encoder_out: 256 out_channels: 256 kernel_size: 16 kernel_stride: 8 threshold_byloss: true threshold: -30 # Dataloader options dataloader_opts: batch_size: 1 num_workers: 3 speedperturb: !new:speechbrain.lobes.augment.TimeDomainSpecAugment perturb_prob: 1.0 drop_freq_prob: 0.0 drop_chunk_prob: 0.0 sample_rate: 8000 speeds: [95, 100, 105] wavedrop: !new:speechbrain.lobes.augment.TimeDomainSpecAugment perturb_prob: 0.0 drop_freq_prob: 1.0 drop_chunk_prob: 1.0 sample_rate: 8000 Encoder: &id003 !new:speechbrain.lobes.models.dual_path.Encoder kernel_size: 16 out_channels: 256 SBtfintra: &id001 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock num_layers: 8 d_model: 256 nhead: 8 d_ffn: 1024 dropout: 0 use_positional_encoding: true norm_before: true SBtfinter: &id002 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock num_layers: 8 d_model: 256 nhead: 8 d_ffn: 1024 dropout: 0 use_positional_encoding: true norm_before: true MaskNet: &id005 !new:speechbrain.lobes.models.dual_path.Dual_Path_Model num_spks: 2 in_channels: 256 out_channels: 256 num_layers: 2 K: 250 intra_model: *id001 inter_model: *id002 norm: ln linear_layer_after_inter_intra: false skip_around_intra: true Decoder: &id004 !new:speechbrain.lobes.models.dual_path.Decoder in_channels: 256 out_channels: 1 kernel_size: 16 stride: 8 bias: false optimizer: !name:torch.optim.Adam lr: 0.00015 weight_decay: 0 loss: !name:speechbrain.nnet.losses.get_si_snr_with_pitwrapper lr_scheduler: &id007 !new:speechbrain.nnet.schedulers.ReduceLROnPlateau factor: 0.5 patience: 4 dont_halve_until_epoch: 100 epoch_counter: &id006 !new:speechbrain.utils.epoch_loop.EpochCounter limit: 200 modules: encoder: *id003 decoder: *id004 masknet: *id005 checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer checkpoints_dir: results/78-speedchange-dynamicmix-hardcodegaussian/1234/save recoverables: encoder: *id003 decoder: *id004 masknet: *id005 counter: *id006 lr_scheduler: *id007 train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger save_file: results/78-speedchange-dynamicmix-hardcodegaussian/1234/train_log.txt pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer loadables: MaskNet: !ref Encoder: !ref Decoder: !ref