6c2c9b9d74
* Add recipe for the yes_no dataset. * Refactoring: Remove unused code. * Add Colab notebook for the yesno dataset. * Add GitHub actions to run yesno. * Fix a typo. * Minor fixes. * Train more epochs for GitHub actions. * Minor fixes. * Minor fixes. * Fix style issues.
Table of Contents
Installation
icefall depends on k2 for FSA operations and lhotse for
data preparations. To use icefall, you have to install its dependencies first.
The following subsections describe how to setup the environment.
CAUTION: There are various ways to setup the environment. What we describe here is just one alternative.
Install k2
Please refer to k2's installation documentation to install k2. If you have any issues about installing k2, please open an issue at https://github.com/k2-fsa/k2/issues.
Install lhotse
Please refer to lhotse's installation documentation to install lhotse.
Install icefall
icefall is a set of Python scripts. What you need to do is just to set
the environment variable PYTHONPATH:
cd $HOME/open-source
git clone https://github.com/k2-fsa/icefall
cd icefall
pip install -r requirements.txt
export PYTHONPATH=$HOME/open-source/icefall:$PYTHONPATHON
To verify icefall was installed successfully, you can run:
python3 -c "import icefall; print(icefall.__file__)"
It should print the path to icefall.
Recipes
At present, two recipes are provided:
Yesno
For the yesno recipe, training with 50 epochs takes less than 2 minutes using CPU.
The WER is
[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
Use Pre-trained models
See egs/librispeech/ASR/conformer_ctc/README.md
for how to use pre-trained models.
