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####
#### Sample DNN Configuration for DNN-HMM Decoding (-dnnconf)
####
####
#### Feature Extraction
####
# feature type, in HTK parameter specification format
feature_type FBANK_D_A_Z
# julius options to configure the acoustic parameter extraction.
#
# The example below indicates that:
# 1. parameters should be loaded from an HTK config file,
# 2. use CMN/CVN,
# 3. load ceptral mean and variance from the specified file,
# 4. keep the cepstral mean/variance static, not update while processing
#
# the specified string will be expanded inline at the point where this
# dnnconf file is specified by "-dnnconf", and passed to Julius.
# As the same as other options in Julius, the later option will override
# former. Please check the start-up messages to check if the
# feature extraction are correctly set up.
#
feature_options -htkconf model/dnn/config.lmfb.40ch.jnas -cvn -cmnload model/dnn/norm.jnas -cmnstatic
# feature vector length (including delta or accel, before splicing)
feature_len 120
# splicing length
context_len 11
####
#### NN Definition
####
# number of input nodes (should be equal to (feature_len * context_len))
input_nodes 1320
# number of output nodes (num and order should correspond to HMM definition)
output_nodes 2004
# number of nodes in hidden layers
hidden_nodes 2048
# number of hidden layers (layers excluding input and output)
hidden_layers 5
# weights W and biases b for hidden layers, in numpy np.save() format
# dtype of these file should be '<f4' (32-bit float little indian)!
W1 model/dnn/W_l1.npy
W2 model/dnn/W_l2.npy
W3 model/dnn/W_l3.npy
W4 model/dnn/W_l4.npy
W5 model/dnn/W_l5.npy
B1 model/dnn/bias_l1.npy
B2 model/dnn/bias_l2.npy
B3 model/dnn/bias_l3.npy
B4 model/dnn/bias_l4.npy
B5 model/dnn/bias_l5.npy
# also weights and biases for output layer
output_W model/dnn/W_output.npy
output_B model/dnn/bias_output.npy
# state prior in 'state_id(%d) prior(%e)' format
state_prior model/dnn/prior.dnn
# state prior factor
state_prior_factor 1.0
# batch size (not used)
batch_size 64