cleaned inference hyparam file

hyperparams.yaml

@@ -1,114 +1,21 @@
-# Generated 2021-05-22 from:
-# /home/mila/s/subakany/speechbrain_new/recipes/WSJ0Mix/separation/yamls/sepformer-whamr-16k.yaml
-# yamllint disable
 # ################################
-# Model: SepFormer for source separation
+# Model: Inference for source separation with SepFormer
 # 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
-
-# the data folder for the wham dataset
-# data_folder needs to follow the format:  /yourpath/whamr.
-# make sure to use the name whamr at your top folder for the dataset!
-data_folder: /network/tmp1/subakany/whamr_16k
+# Generated from speechbrain/recipes/WSJ0Mix/separation/train/hparams/sepformer-whamr-16khz.yaml
+# Dataset : Whamr-16kHz
+# ###############################
 
-# 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
-wsj0_tr: /yourpath/wsj0-processed/si_tr_s/
 
-experiment_name: sepformer-whamr-randomreverb-16k
-output_folder: results/sepformer-whamr-randomreverb-16k/1234
-train_log: results/sepformer-whamr-randomreverb-16k/1234/train_log.txt
-save_folder: results/sepformer-whamr-randomreverb-16k/1234/save
-
-# the file names should start with whamr instead of whamorg
-train_data: results/sepformer-whamr-randomreverb-16k/1234/save/whamr_tr.csv
-valid_data: results/sepformer-whamr-randomreverb-16k/1234/save/whamr_cv.csv
-test_data: results/sepformer-whamr-randomreverb-16k/1234/save/whamr_tt.csv
-skip_prep: false
-
-# Experiment params
-auto_mix_prec: false # Set it to True for mixed precision
-test_only: true
-num_spks: 2 # set to 3 for wsj0-3mix
-progressbar: true
-save_audio: false # Save estimated sources on disk
+# Parameters
 sample_rate: 16000
-
-# 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: true
-# this is the length of sequences if we choose to limit
-# the signal length of training sequences
-training_signal_len: 64000
-
-# Set it to True to dynamically create mixtures at training time
-dynamic_mixing: false
-
-# Parameters for data augmentation
-
-# rir_path variable points to the directory of the room impulse responses
-# e.g. /miniscratch/subakany/rir_wavs
-# If the path does not exist, it is created automatically.
-rir_path: /network/tmp1/subakany/rir_wavs_16k
-
-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: 16000
-  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: 16000
-
-# 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: 3
+num_spks: 2
 
 # Specifying the network
-Encoder: &id003 !new:speechbrain.lobes.models.dual_path.Encoder
+Encoder: !new:speechbrain.lobes.models.dual_path.Encoder
   kernel_size: 16
   out_channels: 256
 
-
-SBtfintra: &id001 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
+SBtfintra: !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
   num_layers: 8
   d_model: 256
   nhead: 8
@@ -117,7 +24,7 @@ SBtfintra: &id001 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
   use_positional_encoding: true
   norm_before: true
 
-SBtfinter: &id002 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
+SBtfinter: !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
   num_layers: 8
   d_model: 256
   nhead: 8
@@ -126,59 +33,33 @@ SBtfinter: &id002 !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
   use_positional_encoding: true
   norm_before: true
 
-MaskNet: &id005 !new:speechbrain.lobes.models.dual_path.Dual_Path_Model
-
-  num_spks: 2
+MaskNet: !new:speechbrain.lobes.models.dual_path.Dual_Path_Model
+  num_spks: !ref <num_spks>
   in_channels: 256
   out_channels: 256
   num_layers: 2
   K: 250
-  intra_model: *id001
-  inter_model: *id002
+  intra_model: !ref <SBtfintra>
+  inter_model: !ref <SBtfinter>
   norm: ln
   linear_layer_after_inter_intra: false
   skip_around_intra: true
 
-Decoder: &id004 !new:speechbrain.lobes.models.dual_path.Decoder
+Decoder: !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: 2
-  dont_halve_until_epoch: 85
-
-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/sepformer-whamr-randomreverb-16k/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/sepformer-whamr-randomreverb-16k/1234/train_log.txt
-
+  encoder: !ref <Encoder>
+  decoder: !ref <Decoder>
+  masknet: !ref <MaskNet>
 
 pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
-   loadables:
-      masknet: !ref <MaskNet>
-      encoder: !ref <Encoder>
-      decoder: !ref <Decoder>
+  loadables:
+    masknet: !ref <MaskNet>
+    encoder: !ref <Encoder>
+    decoder: !ref <Decoder>
+

hyperparams_train.yaml

@@ -0,0 +1,184 @@
+# Generated 2021-05-22 from:
+# /home/mila/s/subakany/speechbrain_new/recipes/WSJ0Mix/separation/yamls/sepformer-whamr-16k.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
+
+# the data folder for the wham dataset
+# data_folder needs to follow the format:  /yourpath/whamr.
+# make sure to use the name whamr at your top folder for the dataset!
+data_folder: /network/tmp1/subakany/whamr_16k
+
+# 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
+wsj0_tr: /yourpath/wsj0-processed/si_tr_s/
+
+experiment_name: sepformer-whamr-randomreverb-16k
+output_folder: results/sepformer-whamr-randomreverb-16k/1234
+train_log: results/sepformer-whamr-randomreverb-16k/1234/train_log.txt
+save_folder: results/sepformer-whamr-randomreverb-16k/1234/save
+
+# the file names should start with whamr instead of whamorg
+train_data: results/sepformer-whamr-randomreverb-16k/1234/save/whamr_tr.csv
+valid_data: results/sepformer-whamr-randomreverb-16k/1234/save/whamr_cv.csv
+test_data: results/sepformer-whamr-randomreverb-16k/1234/save/whamr_tt.csv
+skip_prep: false
+
+# Experiment params
+auto_mix_prec: false # Set it to True for mixed precision
+test_only: true
+num_spks: 2 # set to 3 for wsj0-3mix
+progressbar: true
+save_audio: false # Save estimated sources on disk
+sample_rate: 16000
+
+# 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: true
+# this is the length of sequences if we choose to limit
+# the signal length of training sequences
+training_signal_len: 64000
+
+# Set it to True to dynamically create mixtures at training time
+dynamic_mixing: false
+
+# Parameters for data augmentation
+
+# rir_path variable points to the directory of the room impulse responses
+# e.g. /miniscratch/subakany/rir_wavs
+# If the path does not exist, it is created automatically.
+rir_path: /network/tmp1/subakany/rir_wavs_16k
+
+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: 16000
+  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: 16000
+
+# 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: 3
+
+# 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
+
+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: 2
+  dont_halve_until_epoch: 85
+
+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/sepformer-whamr-randomreverb-16k/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/sepformer-whamr-randomreverb-16k/1234/train_log.txt
+
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+   loadables:
+      masknet: !ref <MaskNet>
+      encoder: !ref <Encoder>
+      decoder: !ref <Decoder>