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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ # yamllint disable
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+ end-of-epoch: true
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+ si-snr: -7.369511792677621
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+ unixtime: 1621680451.6093628
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+ # Generated 2021-05-22 from:
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+ # /home/mila/s/subakany/speechbrain_new/recipes/WSJ0Mix/separation/yamls/sepformer-whamr-16k.yaml
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+ # yamllint disable
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+ # ################################
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+ # Model: SepFormer for source separation
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+ # https://arxiv.org/abs/2010.13154
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+ #
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+ # Dataset : WSJ0-2mix and WSJ0-3mix
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+ # ################################
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+ # Basic parameters
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+ # Seed needs to be set at top of yaml, before objects with parameters are made
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+ #
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+ seed: 1234
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+ __set_seed: !apply:torch.manual_seed [1234]
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+
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+ # Data params
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+
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+ # the data folder for the wham dataset
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+ # data_folder needs to follow the format: /yourpath/whamr.
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+ # make sure to use the name whamr at your top folder for the dataset!
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+ data_folder: /network/tmp1/subakany/whamr_16k
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+
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+ # the path for wsj0/si_tr_s/ folder -- only needed if dynamic mixing is used
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+ # e.g. /yourpath/wsj0-processed/si_tr_s/
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+ # you need to convert the original wsj0 to 8k
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+ # you can do this conversion with the script ../meta/preprocess_dynamic_mixing.py
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+ wsj0_tr: /yourpath/wsj0-processed/si_tr_s/
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+
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+ experiment_name: sepformer-whamr-randomreverb-16k
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+ output_folder: results/sepformer-whamr-randomreverb-16k/1234
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+ train_log: results/sepformer-whamr-randomreverb-16k/1234/train_log.txt
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+ save_folder: results/sepformer-whamr-randomreverb-16k/1234/save
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+
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+ # the file names should start with whamr instead of whamorg
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+ train_data: results/sepformer-whamr-randomreverb-16k/1234/save/whamr_tr.csv
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+ valid_data: results/sepformer-whamr-randomreverb-16k/1234/save/whamr_cv.csv
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+ test_data: results/sepformer-whamr-randomreverb-16k/1234/save/whamr_tt.csv
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+ skip_prep: false
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+
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+ # Experiment params
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+ auto_mix_prec: false # Set it to True for mixed precision
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+ test_only: true
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+ num_spks: 2 # set to 3 for wsj0-3mix
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+ progressbar: true
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+ save_audio: false # Save estimated sources on disk
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+ sample_rate: 16000
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+
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+ # Training parameters
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+ N_epochs: 200
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+ batch_size: 1
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+ lr: 0.00015
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+ clip_grad_norm: 5
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+ loss_upper_lim: 999999 # this is the upper limit for an acceptable loss
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+ # if True, the training sequences are cut to a specified length
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+ limit_training_signal_len: true
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+ # this is the length of sequences if we choose to limit
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+ # the signal length of training sequences
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+ training_signal_len: 64000
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+
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+ # Set it to True to dynamically create mixtures at training time
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+ dynamic_mixing: false
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+
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+ # Parameters for data augmentation
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+
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+ # rir_path variable points to the directory of the room impulse responses
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+ # e.g. /miniscratch/subakany/rir_wavs
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+ # If the path does not exist, it is created automatically.
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+ rir_path: /network/tmp1/subakany/rir_wavs_16k
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+
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+ use_wavedrop: false
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+ use_speedperturb: true
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+ use_speedperturb_sameforeachsource: false
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+ use_rand_shift: false
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+ min_shift: -8000
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+ max_shift: 8000
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+
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+ speedperturb: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
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+ perturb_prob: 1.0
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+ drop_freq_prob: 0.0
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+ drop_chunk_prob: 0.0
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+ sample_rate: 16000
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+ speeds: [95, 100, 105]
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+
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+ wavedrop: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
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+ perturb_prob: 0.0
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+ drop_freq_prob: 1.0
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+ drop_chunk_prob: 1.0
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+ sample_rate: 16000
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+
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+ # loss thresholding -- this thresholds the training loss
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+ threshold_byloss: true
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+ threshold: -30
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+
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+ # Encoder parameters
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+ N_encoder_out: 256
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+ out_channels: 256
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+ kernel_size: 16
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+ kernel_stride: 8
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+
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+ # Dataloader options
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+ dataloader_opts:
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+ batch_size: 1
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+ num_workers: 3
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+
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+ # Specifying the network
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+ Encoder: &id003 !new:speechbrain.lobes.models.dual_path.Encoder
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+ kernel_size: 16
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+ out_channels: 256
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+
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+
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+ SBtfintra: &id001 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
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+ num_layers: 8
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+ d_model: 256
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+ nhead: 8
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+ d_ffn: 1024
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+ dropout: 0
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+ use_positional_encoding: true
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+ norm_before: true
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+
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+ SBtfinter: &id002 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
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+ num_layers: 8
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+ d_model: 256
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+ nhead: 8
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+ d_ffn: 1024
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+ dropout: 0
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+ use_positional_encoding: true
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+ norm_before: true
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+
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+ MaskNet: &id005 !new:speechbrain.lobes.models.dual_path.Dual_Path_Model
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+
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+ num_spks: 2
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+ in_channels: 256
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+ out_channels: 256
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+ num_layers: 2
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+ K: 250
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+ intra_model: *id001
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+ inter_model: *id002
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+ norm: ln
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+ linear_layer_after_inter_intra: false
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+ skip_around_intra: true
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+
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+ Decoder: &id004 !new:speechbrain.lobes.models.dual_path.Decoder
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+ in_channels: 256
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+ out_channels: 1
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+ kernel_size: 16
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+ stride: 8
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+ bias: false
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+
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+ optimizer: !name:torch.optim.Adam
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+ lr: 0.00015
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+ weight_decay: 0
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+
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+ loss: !name:speechbrain.nnet.losses.get_si_snr_with_pitwrapper
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+
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+ lr_scheduler: &id007 !new:speechbrain.nnet.schedulers.ReduceLROnPlateau
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+
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+ factor: 0.5
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+ patience: 2
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+ dont_halve_until_epoch: 85
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+
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+ epoch_counter: &id006 !new:speechbrain.utils.epoch_loop.EpochCounter
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+ limit: 200
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+
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+ modules:
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+ encoder: *id003
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+ decoder: *id004
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+ masknet: *id005
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+ checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
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+ checkpoints_dir: results/sepformer-whamr-randomreverb-16k/1234/save
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+ recoverables:
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+ encoder: *id003
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+ decoder: *id004
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+ masknet: *id005
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+ counter: *id006
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+ lr_scheduler: *id007
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+ train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
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+ save_file: results/sepformer-whamr-randomreverb-16k/1234/train_log.txt
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