----- Welcome to KWS -----

This program is an implementation of the small footprint keyword spotter system described by google.
It attempts to ease the process of formatting audios and extracting useful training information from them, 
as well as training of a convolutional neural network.

--------------------------

REQUIREMENTS

The program was run and tested on the following platforms:

A. Ubuntu Linux			18.04

B. Python 			3.6.7

Package                       Version       
----------------------------- --------------
argcomplete                   1.9.4         
argparse			1.1
auditok                       0.1.5         
dash                          0.39.0        
dash-core-components          0.44.0        
dash-daq                      0.1.0         
dash-html-components          0.14.0        
dash-renderer                 0.20.0        
dash-table                    3.6.0         
h5py                          2.9.0         
Keras                         2.2.4         
Keras-Applications            1.0.7         
Keras-Preprocessing           1.0.9         
numpy                         1.16.2        
PyAudio                       0.2.11        
pydub                         0.23.1        
PyQt5                         5.11.3        
PyQt5-sip                     4.19.13       
qtconsole                     4.4.3         
scikit-learn                  0.20.2        
scipy                         1.2.1         
sox                           1.3.7         
tensorflow                    1.13.1        
tensorflow-estimator          1.13.0        

--------------------------

HOW TO USE

A. PREPARING THE AUDIO DATA

1. Copy all audios to be used for training/testing into kws/tokens folder (create it if it doesn't exist).
2. Split these audios into training and test folders, with each word in its own folder.
	- Copy tokens into kws/tokens/{label}/test and kws/tokens/{label}/train.
	**The label used for training the audios is the name of the keyword.
3. Make sure the folder names or the audio files do not have any whitespace (replace with underscores "_")
	- There is a utility program in kws/scripts/sh called renamer.sh that can rename all files by their folder name
	- To use it, simply copy it to the folder containing the keywords and run it 
	.e.g for a keyword folder in kws/tokens/{keyword}, copy to kws/tokens/ and run.
4. Run kws/dump.sh. 
	- Dumped npy files will be located in kws/data/{label}.
	** If you already have converted audios, simply copy them to the data folder directly.
	** Files stored in tokens folder should be removed after dumping, unless otherwise desired.
5. Run kws/train.sh.
6. When training completes, run kws/run.sh to open the interface.

B. USING THE INTERFACE

1. The interface is simple to use, with two checkboxes to signify which type of audio is required.
	- If audio files is selected, locate the audio using the dialog accessed via the "open" button.
	- If microphone is selected, simply proceed to the next step.
2. After selecting an option, press the "start" button.
3. When detection has completed, press the "execute command" button to run the associated command. 
	** Note that not all keywords have associated commands.

--------------------------