Running the training procedure for the models should be straightforward. Make sure that the WAV, Flux, CuArrays, JLD, and BSON packages are installed. As well, install the fork I've made of the MFCC package (which only updates one line to make a function run on Julia 0.6). Start by cloning the Git repository for the project:
$ git clone https://github.com/maetshju/gsoc2018.git
The user will need to download the TIMIT speech corpus from the Linguistic Data Consortium, as I discussed in the first section of this previous blog post.
Navigate into the speech-cnn folder. To extract the data from the TIMIT corpus, use the 00-data.jl script. More information on this script can be found in the blog post dedicated to it.
$ julia 00-data.jl
Now, to train the network, run the 02-speech-cnn.jl script.
$ julia 02-speech-cnn.jl
Note that it is essentially necessary to have a GPU to train the network on because the training process is extremely slow on just the CPU. Additionally, the script calls out to the GPU implementation of the CTC algorithm, which will fail without a GPU. The script will likely take over a day to run, so come back to it later. After the script finishes, the model should be trained and ready for use in making predictions.
Navigate into the speehch-blstm folder. To extract the data from the TIMIT corpus, use the 00-data.jl script.
$ julia 00-data.jl
Now, to train the network, run the 01-speech-blstm.jl script.
$ julia 01-speech-blstm.jl
This network trains reasonably fast on the CPU, so GPU functionality was not implemented.