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https://github.com/k2-fsa/icefall.git
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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.
81 lines
2.4 KiB
Markdown
81 lines
2.4 KiB
Markdown
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# Table of Contents
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- [Installation](#installation)
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* [Install k2](#install-k2)
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* [Install lhotse](#install-lhotse)
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* [Install icefall](#install-icefall)
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- [Run recipes](#run-recipes)
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## Installation
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`icefall` depends on [k2][k2] for FSA operations and [lhotse][lhotse] for
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data preparations. To use `icefall`, you have to install its dependencies first.
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The following subsections describe how to setup the environment.
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CAUTION: There are various ways to setup the environment. What we describe
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here is just one alternative.
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### Install k2
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Please refer to [k2's installation documentation][k2-install] to install k2.
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If you have any issues about installing k2, please open an issue at
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<https://github.com/k2-fsa/k2/issues>.
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### Install lhotse
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Please refer to [lhotse's installation documentation][lhotse-install] to install
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lhotse.
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### Install icefall
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`icefall` is a set of Python scripts. What you need to do is just to set
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the environment variable `PYTHONPATH`:
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```bash
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cd $HOME/open-source
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git clone https://github.com/k2-fsa/icefall
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cd icefall
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pip install -r requirements.txt
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export PYTHONPATH=$HOME/open-source/icefall:$PYTHONPATHON
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```
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To verify `icefall` was installed successfully, you can run:
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```bash
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python3 -c "import icefall; print(icefall.__file__)"
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```
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It should print the path to `icefall`.
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## Recipes
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At present, two recipes are provided:
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- [LibriSpeech][LibriSpeech]
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- [yesno][yesno] [](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
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### Yesno
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For the yesno recipe, training with 50 epochs takes less than 2 minutes using **CPU**.
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The WER is
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```
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[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
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```
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## Use Pre-trained models
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See [egs/librispeech/ASR/conformer_ctc/README.md](egs/librispeech/ASR/conformer_ctc/README.md)
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for how to use pre-trained models.
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[](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
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[yesno]: egs/yesno/ASR/README.md
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[LibriSpeech]: egs/librispeech/ASR/README.md
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[k2-install]: https://k2.readthedocs.io/en/latest/installation/index.html#
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[k2]: https://github.com/k2-fsa/k2
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[lhotse]: https://github.com/lhotse-speech/lhotse
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[lhotse-install]: https://lhotse.readthedocs.io/en/latest/getting-started.html#installation
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