This project requires dvc and yarn installed on your system.
After cloning the repository, retrieve the model and test audio files:
dvc fetch
dvc checkout
Then install the project dependencies:
yarn install
Launch the software:
yarn run start
First, install the build tools for your platform. Then, run
yarn run dist
and check the dist folder for the build results.
The Tensorflow.js models in models/<language> are generated from the Keras model files models/<language>.h5 using
the convert-to-tfjs.sh script located in the models folder. It is normally not necessary to re-do this step, but we
include it here for reasons of reproducibility.
The script utilizes tensorflowjs_converter that needs to be installed separately.
Having python and virtualenv installed, it can be done using:
virtualenv --no-site-packages venv
. venv/bin/activate
pip install tensorflowjs==1.3.2
Other tensorflowjs versions might work as well, but will most likely not produce the exact same output files.
Model files, test and training data are managed via dvc. Two remotes are set up for this repository.
The default remote is for pulling only. The s3remote points to the same location, but allows pushing as well.
It is necessary for this remote to provide login credentials:
AWS_ACCESS_KEY_ID=your_s3_id AWS_SECRET_ACCESS_KEY=your_s3_secret dvc push -r s3remote
Note the space before the definition of the environment variables to avoid storing the credentials in your shells history.
Copyright 2020 IMAGINARY gGmbH
Licensed under the Apache License, Version 2.0.
See the LICENSE files for more details.
The following only applies to project versions greater or equal to 0.4.0.
Copyright 2020 Andreas Krug, Jens Johannsmeier and Sebastian Stober at Artificial Intelligence Lab, Otto-von-Guericke-University Magdeburg
Licensed under CC-BY 4.0.
The models have been trained using the following freely available data sets:
- English: LibriSpeech ASR corpus
- German: M-AILABS speech dataset