pushing the model files

encoder.ckpt

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+size 1059081

encoder_out.ckpt

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+oid sha256:faaea7a87e2ef529e100b39b3402108a299a4623e9c89f1173eecb043e20fcf4
+size 266067

hyperparams.yaml

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+# Generated 2021-09-17 from:
+# /home/mila/s/subakany/speechbrain_new/recipes/WSJ0Mix/separation/snrestimator_yamls/timedom_convnet_whamr_v2_stnorm_manyseparators.yaml
+# yamllint disable
+# ################################
+# Model: SepFormer for source separation
+# https://arxiv.org/abs/2010.13154
+# Dataset : WSJ0-2mix and WSJ0-3mix
+# ################################
+#
+# 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
+
+# e.g. '/yourpath/wsj0-mix/2speakers'
+# end with 2speakers for wsj0-2mix or 3speakers for wsj0-3mix
+data_folder: /miniscratch/subakany/LibriMixData_new/Libri2Mix/
+
+# the path for wsj0/si_tr_s/ folder -- only needed if dynamic mixing is used
+# e.g. /yourpath/wsj0-processed/si_tr_s/
+# you need to convert the original wsj0 to 8k
+# you can do this conversion with the script ../meta/preprocess_dynamic_mixing.py
+base_folder_dm: /miniscratch/subakany/LibriMixData_new/LibriSpeech/train-clean-360_processed/
+rir_path: /miniscratch/subakany/whamr_rirs_wav
+
+experiment_name: snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators
+output_folder: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234
+train_log: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/train_log.txt
+save_folder: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/save
+train_data: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/save/libri2mix_train-360.csv
+valid_data: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/save/libri2mix_dev.csv
+test_data: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/save/libri2mix_test.csv
+
+wsj_data_folder: /network/tmp1/subakany/wham_original
+train_wsj_data: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/save/wham_tr.csv
+test_wsj_data: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/save/wham_tt.csv
+base_folder_dm_whamr: /network/tmp1/subakany/wsj0-processed/si_tr_s
+use_whamr_train: true
+whamr_proportion: 0.6
+
+test_onwsj: false
+
+skip_prep: false
+
+ckpt_interval_minutes: 60
+
+# Experiment params
+auto_mix_prec: false # Set it to True for mixed precision
+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: 0.0001
+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: 32000000
+
+# Set it to True to dynamically create mixtures at training time
+dynamic_mixing: true
+use_wham_noise: true
+use_reverb_augment: true
+
+# Parameters for data augmentation
+use_wavedrop: false
+use_speedperturb: true
+use_speedperturb_sameforeachsource: false
+use_rand_shift: false
+min_shift: -8000
+max_shift: 8000
+
+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
+
+# loss thresholding -- this thresholds the training loss
+threshold_byloss: true
+threshold: -30
+
+# Encoder parameters
+N_encoder_out: 256
+out_channels: 256
+kernel_size: 16
+kernel_stride: 8
+
+# Dataloader options
+dataloader_opts:
+  batch_size: 1
+  num_workers: 0
+
+
+# Specifying the network
+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
+
+snrmin: 0
+snrmax: 10
+out_n_neurons: 16
+use_snr_compression: true
+separation_norm_type: stnorm
+
+# compute_features: !new:speechbrain.lobes.features.Fbank
+#     n_mels: !ref <n_mels>
+#     left_frames: 0
+#     right_frames: 0
+#     deltas: False
+
+latent_dim: 128
+n_inp: 256
+encoder: &id006 !new:speechbrain.nnet.containers.Sequential
+  input_shape: [!!null '', 2, !!null '']
+  cnn1: !new:speechbrain.nnet.CNN.Conv1d
+    in_channels: 2
+    kernel_size: 4
+    out_channels: 128
+    stride: 1
+    skip_transpose: true
+    padding: valid
+  relu1: !new:torch.nn.ReLU
+  cnn2: !new:speechbrain.nnet.CNN.Conv1d
+    in_channels: 128
+    kernel_size: 4
+    out_channels: 128
+    stride: 2
+    skip_transpose: true
+    padding: valid
+  relu2: !new:torch.nn.ReLU
+  cnn3: !new:speechbrain.nnet.CNN.Conv1d
+    in_channels: 128
+    kernel_size: 4
+    out_channels: 128
+    stride: 2
+    skip_transpose: true
+    padding: valid
+  relu3: !new:torch.nn.ReLU
+  cnn4: !new:speechbrain.nnet.CNN.Conv1d
+    in_channels: 128
+    kernel_size: 4
+    out_channels: 128
+    stride: 2
+    skip_transpose: true
+    padding: valid
+  relu4: !new:torch.nn.ReLU
+  cnn5: !new:speechbrain.nnet.CNN.Conv1d
+    in_channels: 128
+    kernel_size: 4
+    out_channels: 128
+    stride: 2
+    skip_transpose: true
+    padding: valid
+
+stat_pooling: !new:speechbrain.nnet.pooling.StatisticsPooling
+
+
+        # classifier_enc: !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
+        #     input_size: !ref <n_inp>
+        #     channels: [1024, 1024, 1024, 1024, 3072]
+        #     kernel_sizes: [5, 3, 3, 3, 1]
+        #     dilations: [1, 2, 3, 4, 1]
+        #     attention_channels: 128
+        #     lin_neurons: 192
+
+#classifier_out: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
+#    input_size: 192
+#    out_neurons: !ref <out_n_neurons>
+#
+# classifier_out: !new:speechbrain.nnet.linear.Linear
+#     input_size: 256
+#     n_neurons: 1
+
+encoder_out: &id007 !new:speechbrain.nnet.containers.Sequential
+        # lr_scheduler: !ref <lr_scheduler>
+
+  input_shape: [!!null '', 256]
+  layer1: !new:speechbrain.nnet.linear.Linear
+    input_size: 256
+    n_neurons: 256
+  relu: !new:torch.nn.ReLU
+  layer2: !new:speechbrain.nnet.linear.Linear
+    input_size: 256
+    n_neurons: 1
+  sigm: !new:torch.nn.Sigmoid
+
+
+
+classifier_loss: !new:torch.nn.CrossEntropyLoss
+
+optimizer: !name:torch.optim.Adam
+  lr: 0.0001
+  weight_decay: 0
+
+loss: !name:speechbrain.nnet.losses.get_si_snr_with_pitwrapper
+
+lr_scheduler: !new:speechbrain.nnet.schedulers.ReduceLROnPlateau
+  factor: 0.5
+  patience: 2
+  dont_halve_until_epoch: 95
+
+epoch_counter: &id008 !new:speechbrain.utils.epoch_loop.EpochCounter
+  limit: 200
+
+modules:
+  encoder: *id003
+  decoder: *id004
+  masknet: *id005
+  encoder: *id006
+  encoder_out: *id007
+checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
+  checkpoints_dir: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/save
+  recoverables:
+    counter: *id008
+    encoder: *id006
+    encoder_out: *id007
+train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
+  save_file: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/train_log.txt
+
+num_separators_per_model: 3
+separator_base_folder: /home/mila/s/subakany/speechbrain_new/recipes/WHAMandWHAMR/separation/results/
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+    loadables:
+        encoder: !ref <encoder>
+        encoder_out: !ref <encoder_out>