input shape of nns

[HaoranWeiUTD]

I trained a model and find its input have shape of [194,1,40]. I think 1 is the batchsize, 40 is he feature size, but how 194 comes from?
Because I set the max_seq_length_train=1500, and max_seq_length_valid=1400
Thanks for help !

[HaoranWeiUTD]

---

Here is my cfg file:

[cfg_proto]
cfg_proto=proto/global.proto
cfg_proto_chunk=proto/global_chunk.proto

[exp]
cmd=
run_nn_script=run_nn
out_folder=exp/libri_GRU_fmllr
seed=1234
use_cuda=True
multi_gpu=True
save_gpumem=False
N_epochs_tr=15

[dataset1]
data_name=train_all
fea:fea_name=fmllr
fea_lst=/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/train_all/feats.scp
fea_opts=apply-cmvn --utt2spk=ark:/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/train_all/utt2spk ark:/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/train_all/data/cmvn_speaker.ark ark:- ark:- | add-deltas --delta-order=0 ark:- ark:- |
cw_left=0
cw_right=0

lab:lab_name=lab_cd
lab_folder=/XXX/Tools/kaldi/egs/multi_cn/s5/exp/tri4a/
lab_opts=ali-to-pdf
lab_count_file=auto
lab_data_folder=/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/train_all/
lab_graph=/XXX/Tools/kaldi/egs/multi_cn/s5/exp/tri4a/graph_tg/

N_chunks=50

[dataset2]
data_name=aidatatang
fea:fea_name=fmllr
fea_lst=/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/aidatatang/feats.scp
fea_opts=apply-cmvn --utt2spk=ark:/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/aidatatang/utt2spk ark:/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/aidatatang/data/cmvn_speaker.ark ark:- ark:- | add-deltas --delta-order=0 ark:- ark:- |
cw_left=0
cw_right=0

lab:lab_name=lab_cd
lab_folder=/XXX/Tools/kaldi/egs/multi_cn/s5/exp/tri4b_ali_aidatatang
lab_opts=ali-to-pdf
lab_count_file=auto
lab_data_folder=/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/aidatatang/
lab_graph=/XXX/Tools/kaldi/egs/multi_cn/s5/exp/tri4a/graph_tg/

N_chunks=5

[dataset3]
data_name=magicdata
fea:fea_name=fmllr
fea_lst=/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/magicdata/feats.scp
fea_opts=apply-cmvn --utt2spk=ark:/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/magicdata/utt2spk ark:/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/magicdata/data/cmvn_speaker.ark ark:- ark:- | add-deltas --delta-order=0 ark:- ark:- |
cw_left=0
cw_right=0

lab:lab_name=lab_cd
lab_folder=/XXX/Tools/kaldi/egs/multi_cn/s5/exp/tri4b_ali_magicdata
lab_opts=ali-to-pdf
lab_count_file=auto
lab_data_folder=/XXX/Tools/kaldi/egs/multi_cn/s5/fmllr/magicdata/
lab_graph=/XXX/Tools/kaldi/egs/multi_cn/s5/exp/tri4a/graph_tg/

N_chunks=5

[data_use]
train_with=train_all
valid_with=aidatatang
forward_with=magicdata

[batches]
batch_size_train=16
max_seq_length_train=1500
increase_seq_length_train=True
start_seq_len_train=300
multply_factor_seq_len_train=5
batch_size_valid=8
max_seq_length_valid=1400

[architecture1]
arch_name = GRU_layers
arch_proto = proto/GRU.proto
arch_library = neural_networks
arch_class = GRU
arch_pretrain_file = none
arch_freeze = False
arch_seq_model = True

gru_lay = 512,512,512,512
gru_drop = 0.2,0.2,0.2,0.2
gru_use_laynorm_inp = False
gru_use_batchnorm_inp = False
gru_use_laynorm = False,False,False,False
gru_use_batchnorm = True,True,True,True
gru_bidir = True
gru_act = tanh,tanh,tanh,tanh
gru_orthinit=True

arch_lr = 0.0004
arch_halving_factor = 0.5
arch_improvement_threshold = 0.001
arch_opt = rmsprop
opt_momentum = 0.0
opt_alpha = 0.95
opt_eps = 1e-8
opt_centered = False
opt_weight_decay = 0.0

[architecture2]
arch_name=MLP_layers
arch_proto=proto/MLP.proto
arch_library=neural_networks
arch_class=MLP
arch_pretrain_file=none
arch_freeze=False
arch_seq_model=False
dnn_lay=N_out_lab_cd
dnn_drop=0.0
dnn_use_laynorm_inp=False
dnn_use_batchnorm_inp=False
dnn_use_batchnorm=False
dnn_use_laynorm=False
dnn_act=softmax

arch_lr=0.0004
arch_halving_factor=0.5
arch_improvement_threshold=0.001
arch_opt=rmsprop
opt_momentum=0.0
opt_alpha=0.95
opt_eps=1e-8
opt_centered=False
opt_weight_decay=0.0

[model]
model_proto=proto/model.proto
model:out_dnn1=compute(GRU_layers,fmllr)
out_dnn2=compute(MLP_layers,out_dnn1)
loss_final=cost_nll(out_dnn2,lab_cd)
err_final=cost_err(out_dnn2,lab_cd)

[forward]
forward_out=out_dnn2
normalize_posteriors=True
normalize_with_counts_from=lab_cd
save_out_file=False
require_decoding=True

[decoding]
decoding_script_folder=kaldi_decoding_scripts/
decoding_script=decode_dnn.sh
decoding_proto=proto/decoding.proto
min_active=200
max_active=7000
max_mem=50000000
beam=20.0
latbeam=12.0
acwt=0.10
max_arcs=-1
skip_scoring=false
scoring_script=/scratch/ravanelm/exp/librispeech/s5/local/score.sh
scoring_opts="--min-lmwt 4 --max-lmwt 23"
norm_vars=False

[TParcollet]

It actually depends where this print comes from. It can be the sequence length (as sequence are ordered, smaller ones start first) or sequence_lenght * batch_size (often done before a linear layer to speedup the training).

[HaoranWeiUTD]

Thanks for your explanation