Kathabhidhana consists of a few Free/Libre and Open Source Software, documentation to learn and use it, and open datasets that you can customize and shape your metadata.
Kathabhidhana: An Open Source toolkit to record a large number of words in any language
Almost half of the 6909 living languages of the world are dying in a century’s time. In India alone, over 220 Indian languages out of the total of 780 languages have died only in last 50 years. With these languages, there dies a wealth of knowledge forever
”
Kathabhidhana is an open toolkit to record a large number of words. It consists of a few free/libre and open source software, open datasets, methodologies and documentations. It can be used to record pronunciations of words to make a talking dictionary to record phonemes to create a text-to-speech software.
We truly believe in openness and the FLOSS philosophy. So every single component of this toolkit is open. It also contains other dependency FLOSS tools that are built by many kind people in the open source movement.
A tool with many faces
Wikipedia has a sister project called Wiktionary, a multilingual dictionary where you can not just find meaning of words from your own language but also equivalent meanings of foreign language words. Unlike many available dictionaries that help learn proununciations, Wiktionary does not have pronunciations of all words in all the languages. Kathabhidhana was originally started by Subhashish Panigrahi to add pronunciations to the Odia-language Wiktionary. It is adopted from a free software created by Shrinivasan T. It works both on Linux and Mac. The iOS version of Kathabhidhana was created by Prateek Pattanaik. You can certainly create pronunciations and add them to Wiktionary. But you can use Kathabhidhana beyond that by making a large library of pronunciations that can be used to build any machine learning or Natural Language Processing (NLP) tool.
Instruction manual to set up the hardware and software (TBD)
Dependency tools
Audacity for a post-recording batch clean up (Download, you can also check this tutorial in English, and Odia to clean up vocals for individual recordings)
Pattypan for batch uploading recorded and edited audio files on Wikimedia Commons
Open dataset: CSV, .ods for reference while creating meta data for your recordings
Odia→International Phonetic Alphabet converter (IPA)/Roman converter for adding phonetic signs in the metadata while uploading. Thiis converter works only the Odia alphabet. But you can fork and create one for your writing system too.
1. Go to the file called "file". Replace all words with the words you want to record
2. Run the command below (it will record both as .wav and .ogg) python voice-record.py 2> err</code)
3. Go to the path by using cd command in your terminal For instance, in my computer, it is the "Kathabhidhana" folder under "Documents". Then run:
python voice-record.py
The next steps are quite self-explanatory. You need to choose "Y" for yes and "N" for no in the following options inside your terminal.
To upload all the ogg files to Wikimedia Commons This will record the sounds in .ogg and .wav formats. You can then use a tool like Pattypan to batch-upload either the .WAV or the .ogg files on Wikimedia Commons.
• It takes about 20-25 mins to record 100 words; A batch processing to convert and do overall auto-cleanup using Audacity will take about 5 mins for a 100-word-batch; It takes an average of 30 secs for 1 word to manually clean up, check quality, trim extra portions and other such editing work (meaning it will take about 45 mins to clean up a batch of 100 words) using Audacity; It takes about 5-10 mins for setting up Pattypan to upload the cleaned up words on Wikimedia Commons; On an average one would spend roughly about 1.5 hrs from recording to cleaning up to uploading for a batch of 100 words
Kathabhidhana helps you create a pronunciation library with a large number of words in any given language. These recordings can be used to better
and create a library of pronunciation of words by native speaker that can be used to better:
machine learning tools like speech-to-text and text-to-speech as audio libraries are building blocks of both these kind of software developement
the available/nonexistent screenreader app in any language that is extremely vital for visually disabled people
any open source smart home or personal assistant app help people with disability and everyone else
dictionaries with native pronunciation support
document the contemporary version of the intonation, accent and speech pattern of a language that can be useful in the future for any linguistic research
Project led by Subhashish Panigrahi and the iOS tool is led by Prateek Pattanaik. All the media and text content are available under a CC-BY-SA 4.0 license
All the software component is licensed under GNU General Public License (GPL) version 3 (read the License page for more details)
This project and part of the documentation are based on the Voice recorder for Tawiktionary project created by Shrinivasan T (please attribute Shrinivasan T if you're making a derivative of the software)
