File size: 4,403 Bytes
f0ce3b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
# Generated 2021-09-18 from:
# /home/mila/s/subakany/speechbrain_new/recipes/WHAMandWHAMR/separation/hparams/dprnn-whamr.yaml
# yamllint disable
# ################################
# Model: SepFormer for source separation
# https://arxiv.org/abs/2010.13154
#
# Dataset : WHAMR!
# ################################
# Basic parameters
# Seed needs to be set at top of yaml, before objects with parameters are made
#
seed: 3
__set_seed: !apply:torch.manual_seed [3]
# 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/
# 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: /network/tmp1/subakany/wsj0-processed/si_tr_s/
experiment_name: dprnn-whamr
output_folder: results/dprnn-whamr/3
train_log: results/dprnn-whamr/3/train_log.txt
save_folder: results/dprnn-whamr/3/save
# the file names should start with whamr instead of whamorg
train_data: results/dprnn-whamr/3/save/whamr_tr.csv
valid_data: results/dprnn-whamr/3/save/whamr_cv.csv
test_data: results/dprnn-whamr/3/save/whamr_tt.csv
skip_prep: false
# Experiment params
auto_mix_prec: true # 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: 0.00015
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: 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
# 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: /miniscratch/subakany/whamr_rirs_wav
# 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
intra: &id001 !new:speechbrain.lobes.models.dual_path.SBRNNBlock
num_layers: 1
input_size: 256
hidden_channels: 256
dropout: 0
bidirectional: true
inter: &id002 !new:speechbrain.lobes.models.dual_path.SBRNNBlock
num_layers: 1
input_size: 256
hidden_channels: 256
dropout: 0
bidirectional: true
MaskNet: &id005 !new:speechbrain.lobes.models.dual_path.Dual_Path_Model
num_spks: 2
in_channels: 256
out_channels: 256
num_layers: 6
K: 250
intra_model: *id001
inter_model: *id002
norm: ln
linear_layer_after_inter_intra: true
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
save_all_checkpoints: true
checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
checkpoints_dir: results/dprnn-whamr/3/save
recoverables:
encoder: *id003
decoder: *id004
masknet: *id005
counter: *id006
lr_scheduler: *id007
train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
save_file: results/dprnn-whamr/3/train_log.txt
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
loadables:
encoder: !ref <Encoder>
masknet: !ref <MaskNet>
decoder: !ref <Decoder>
|