End-to-end spoken language identification out of the box.
tf.keras
.DataFrames
.tf.data
tf.signal
.C_avg
) implemented as a tf.keras.metrics.Metric
subclass.tf.keras.losses.Loss
subclass.tf.data.Dataset
extraction pipeline to also be on the GPUPython 3.7 or 3.8 is required.
python3 -m pip install https://github.com/py-lidbox/lidbox/archive/master.zip
python3 -m pip install 'lidbox==1.0.0rc0'
TensorFlow 2 is not included in the package requirements because you might want to do custom configuration to get the GPU working etc.
If you don't want to customize anything and instead prefer something that just works for now, the following should be enough:
python3 -m pip install tensorflow
If you plan on making changes to the code, it is easier to install lidbox
as a Python package in setuptools develop mode:
git clone --depth 1 https://github.com/py-lidbox/lidbox.git
python3 -m pip install --editable ./lidbox
Then, if you make changes to the code, there's no need to reinstall the package since the changes are reflected immediately.
Just be careful not to make changes when lidbox
is running, because TensorFlow will use its autograph
package to convert some of the Python functions to TF graphs, which might fail if the code changes suddenly.
lidbox
@inproceedings{Lindgren2020,
author={Matias Lindgren and Tommi Jauhiainen and Mikko Kurimo},
title={{Releasing a Toolkit and Comparing the Performance of Language Embeddings Across Various Spoken Language Identification Datasets}},
year=2020,
booktitle={Proc. Interspeech 2020},
pages={467--471},
doi={10.21437/Interspeech.2020-2706},
url={http://dx.doi.org/10.21437/Interspeech.2020-2706}
}