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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
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>