Elpis is a tool which allows language workers with minimal computational experience to build their own speech models for use in automatically transcribing audio. It relies on the Kaldi automatic speech recognition (ASR) library. Kaldi is notorious for being difficult to build, use and navigate - even for trained computer scientists. The goal of Elpis is to expose the power of Kaldi to linguists and language workers by abstracting away much of the needless technical complexity.
Currently, the major component of Elpis is kaldi_helpers, a collection of Python and shell scripts designed to prepare data for use with Kaldi and convert between the various time-aligned transcription formats that linguists work with.
Elpis uses Docker, specifically an Ubuntu Linux image, to install Kaldi and its (many) dependencies. It also installs kaldi_helpers and the Task task runner. We have defined a number of tasks which automate many common workflows like data preparation, model creation and inferring transcriptions for new files. You can read about these tasks and how to use them here.
Please check the wiki pages for a step-by-step guide to using Elpis on your data. If you're comfortable using Docker and are aware of the types of data required, you can also consult our more concise advanced guide.
All the required programs should be available in the environment.
- python3 + pip
- git
- npm
Elpis is the Greek goddess of hope. For us Elpis represents our hope that one day everyone will have access to the power of ASR without having to interact directly with the Kaldi codebase. We've also backronymed it to stand for "Endangered Language Pipeline and Inference System," as much of our motivation for this project derives from our desire to assist in documentation and revitalisation efforts for the world's many endangered languages - including many in Australia, where most of Elpis' development has occurred.
This software is the product of academic research funded by the Australian Research Council Centre of Excellence for the Dynamics of Language. If you use the software or code in an academic setting, please be sure to cite it appropriately as follows:
Foley, B., Arnold, J., Coto-Solano, R., Durantin, G., Ellison, T. M., van Esch, D., Heath, S., Kratochvíl, F., Maxwell-Smith, Z., Nash, D., Olsson, O., Richards, M., San, N., Stoakes, H., Thieberger, N. & Wiles, J. (2018). Building Speech Recognition Systems for Language Documentation: The CoEDL Endangered Language Pipeline and Inference System (Elpis). In S. S. Agrawal (Ed.), The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU) (pp. 200–204). Available on https://www.isca-speech.org/archive/SLTU_2018/pdfs/Ben.pdf.