argument
|
meaning/value
|
comments
|
| --train-data |
training
data specification |
required |
| --valid-data |
valid
data specification |
required |
| --si-nnet-spec
|
--si-nnet-spec="dF:h(1):h(2):...:h(n):s"
Eg.250:1024:1024:1024:1024:1920 |
required.
Specifies structure of the SI model. dF-feature
dimension; h(i)-size of the
i-th hidden layers; s-number
of targets |
| --adapt-nnet-spec
|
--si-nnet-spec="dI:ha(1):ha(2):...:ha(m)"
Eg.100:512:512 |
required.
Specifies structure of the Adaptation model. dI-i-vector dimension; ha(i)-size of the
i-th adaptation layers |
| --init-model |
path
to the initial DNN model
|
required.
A well-trained DNN model which serves as the initialization of the SI
model
|
--wdir
|
working
directory |
required
|
|
--param-output-file
|
(prefix)
path to
save model parameters in the PDNN format
|
by
default
"": doesn't output PDNN-formatted model. Filenames for
the SI and Adaptation models are appended with the suffix ".si" and
".adapt" respectively
|
--cfg-output-file
|
(prefix)
path to
save model config
|
by
default
"": doesn't output model config. Filenames for the SI
and Adaptation models are appended with the suffix ".si" and ".adapt"
respectively |
--kaldi-output-file
|
(prefix)
path to
save the Kaldi-formatted model
|
by
default
"": doesn't output Kaldi-formatted model. Filenames for
the SI and Adaptation models are appended with the suffix ".si" and
".adapt" respectively |
--model-save-step
|
number
of
epochs between model saving
|
by
default
1: save the tmp model after each epoch
|
|
--ptr-file
|
pre-trained
model
file
|
by
default
"": no pre-training
|
| --ptr-layer-number |
how
many
layers to be initialized with the pre-trained model
|
required
if
--pre-file is provided |
|
| --lrate |
learning
rate |
by
default
D:0.08:0.5:0.05,0.05:15 |
| --batch-size |
mini-batch
size for SGD |
by default 256
|
| --momentum |
the momentum |
by default 0.5 |
|
| --activation |
the
same as dnn
|
by
default
sigmoid
|
|
--input-dropout-factor
|
the
same as dnn |
by
default 0: no dropout is applied to the input features
|
--dropout-factor
|
the
same as dnn |
by
default "": no dropout is applied.
|
|
| --l1-reg |
l1
norm regularization weight
train_objective = cross_entropy + l1_reg * [l1 norm of all weight
matrices]
|
by
default 0
|
| --l2-reg |
l2
norm regularization weight
train_objective = cross_entropy + l2_reg * [l2 norm of all weight
matrices]
|
by
default 0
|
| --max-col-norm |
the
max value of norm of gradients; usually used in dropout and maxout
|
by
default
none: not applied
|