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The Corpusbuilder

Takes a collection of zip or wav files and a JSON-file with metadata to build a corpus according to user criteria.

This is useful if you have a big corpus and would like to extract interviews by speaker characteristics, e.g. all speakers from one location, all speakers born before a certain date, speakers of a certain gender, or a combination of those.

Download the Corpusbuilder here.


The corpusbuilder takes the following arguments:

corpusbuilder (fix_formatting_off, manual_input, json_file, input_folder, output_folder, **criteria)

Basic input:
json_file --- a JSON-formatted file containing informant metadata such as place of residence, age, etc. 
input_folder ---  a folder containing the entire corpus (files in .wav or .zip format). 
output_folder --- a (preferably emtpy) folder where selected corpus files will be copied to. 
json_file, input_folder, output_folder are all set through a graphical interface after the script is started. 
criteria  --- A variable number of criteria (such as place of residence, age, ...) to select which files to copy from input_folder to output_folder. Any criteria contained in the json_file can be used. 
Criteria are specified in the terminal before the script is run. This input needs to be formatted like so: --criterion "operator, condition", e.g. --DOB "> 1900". Detailed description in the notes below. 

Optional input:
fix_formatting_off --- if set, switches off algorithm that fixes faulty formatting in JSON files. 
manual_input --- if set, accepts a text file with a list of directories. That way, more than one directory can be provided as input_folder. The text file needs to consist of one full path per line. The file itself will be chosen through a GUI after the script is started.

All the possible criteria and their options for the specific setting are described in the help file which can be accessed by typing

python --help

How to run it

Run it in a shell. See here for instructions.

The basics.

This example shows the basic setup for the corpusbuilder:

python --gender "=M"

Since we specified that --gender should be "=M", the corpusbuilder will search the metadata file and extract all the data associated with speakers that have gender set to "M", i.e. male speakers. It then copies all matching files from the input_folder to the output_folder.

This input needs to be formatted like this: --criterion "operator, condition". See here for a complete list of operators. The exact criteria you can use depend on the data in yout JSON file (i.e. if it does not have entries for "gender", the above will not work). The command python --help will give you a list of all the criteria possible for your dataset and what input they take.

More refined settings are possible. This example will extract all male speakers who currently live in Fredericksburg.

python --gender "=M" --current_residence "=fredericksburg"

Numerical values allow for more fine-grained comparision. The following example will extract all recordings of male speakers born between 1900 and 1940.

python --gender "=M" --DOB ">=1900" --DOB "<1941"

Advanced settings.

This switches off the algorithm that fixes errors in the json_file's formatting.

python --fix_formatting_off 

This setting lets the user input a text files with a list of directories instead of picking one directory through the GUI.

python --manual_input

All settings are described in more detail in the help file which can be accessed by running python --help.


1. The possible constraints can vary from dataset to dataset.

Type python --help to see which ones are available in your dataset. The Texas German corpus can be sorted by the following categories:

--language_home (e.g. german, unknown, english)

--current_residence (e.g doss, floresville, universal city)

--is_locked (e.g. 1,0)

--gender (e.g. m, f)

--created_at (e.g. 2016-05-05 16:41:00, 2016-05-05 16:58:47)

--updated_at (e.g. 2015-02-04 13:23:59, 2015-03-04 15:16:48)

--childhood_residence (e.g. clifton, geronemo, rural)

--island_id (e.g. 1, 0)

--DOB (e.g. 1908, 1920, 1921)

--language_school (e.g. german, unknown)

--education_complete (e.g. middle school, unknown, elementary school)

--questionnaire (e.g. {"interview_id":"X","upload_status":"none","informant_id":"X","first_name":"X"," [...]}

--informant_id (e.g. 344, 345)

2. The following comparisons are possible:

"<": less than

"<=": less than or equal to

">=": more than or equal to

">": more than

"==": equal to

"=": equal to

"!=": not equal to

3. Capitalization does not matter. Fredericksburg is treated the same as fredericksburg.

4. Missing data can trip you up. If you for example coded missing dates of birth as DOB = 0, this will match searches like --DOB "<1941" although this might not be true.

5. File naming standards. As of now, the corpusbuilder expects files to be named according to the template "X+-X+-X+-.[zip|wav] where X+ stands for one or more numbers. The second X+ is assumed to be the speaker ID.

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