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@@ -49,12 +49,12 @@ The main developer / maintainer is Akinobu Lee ([email protected]). | |
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# Download Julius | ||
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The latest release version is [4.3.1](https://github.com/julius-speech/julius/releases), released on January 15, 2014. | ||
The latest release version is [4.4](https://github.com/julius-speech/julius/releases), released on August 30, 2016. | ||
You can get the released package from the [Release page](https://github.com/julius-speech/julius/releases). | ||
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Version 4.3.1 is a bug fix release. Several bugs has been fixed. | ||
See the "Release.txt" file for the full list of updates. | ||
Run with "-help" to see full list of options. | ||
Version 4.4 supports stand-alone DNN-HMM support, and several new | ||
tools and bug fixes are included. See the "Release.txt" file for the | ||
full list of updates. Run with "-help" to see full list of options. | ||
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# Toolkit and Assets | ||
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#### | ||
#### Sample DNN Configuration for DNN-HMM Decoding (-dnnconf) | ||
#### | ||
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#### | ||
#### Feature Extraction | ||
#### | ||
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# feature type, in HTK parameter specification format | ||
feature_type FBANK_D_A_Z | ||
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# 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 | ||
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# feature vector length (including delta or accel, before splicing) | ||
feature_len 120 | ||
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# splicing length | ||
context_len 11 | ||
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#### | ||
#### NN Definition | ||
#### | ||
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# number of input nodes (should be equal to (feature_len * context_len)) | ||
input_nodes 1320 | ||
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# number of output nodes (num and order should correspond to HMM definition) | ||
output_nodes 2004 | ||
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# number of nodes in hidden layers | ||
hidden_nodes 2048 | ||
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# number of hidden layers (layers excluding input and output) | ||
hidden_layers 5 | ||
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# 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 | ||
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# also weights and biases for output layer | ||
output_W model/dnn/W_output.npy | ||
output_B model/dnn/bias_output.npy | ||
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# state prior in 'state_id(%d) prior(%e)' format | ||
state_prior model/dnn/prior.dnn | ||
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# state prior factor | ||
state_prior_factor 1.0 | ||
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# batch size (not used) | ||
batch_size 64 |
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