hyperparams.yaml

# 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
classifier_enc: &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

classifier_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
  classifier_enc: *id006
  classifier_out: *id007
checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
  checkpoints_dir: results/snrtrain-timedomain-sbpooling-wwhamr-lessstride-stnorm-manyseparators/1234/save
  recoverables:
    counter: *id008
    classifier_enc: *id006
    classifier_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:
        classifier_enc: !ref <classifier_enc>
        classifier_out: !ref <classifier_out>