21096e99d8
* Update RESULTS using vocab size 500, att rate 0.8 * Update README. * Refactoring. Since FSAs in an Nbest object are linear in structure, we can add the scores of a path to compute the total scores. * Update documentation. * Change default vocab size from 5000 to 500.
Installation
Please refer to https://icefall.readthedocs.io/en/latest/installation/index.html for installation.
Recipes
Please refer to https://icefall.readthedocs.io/en/latest/recipes/index.html for more information.
We provide two recipes at present:
yesno
This is the simplest ASR recipe in icefall and can be run on CPU.
Training takes less than 30 seconds and gives you the following WER:
[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
We do provide a Colab notebook for this recipe.
LibriSpeech
We provide two models for this recipe: conformer CTC model and TDNN LSTM CTC model.
Conformer CTC Model
The best WER we currently have is:
| test-clean | test-other | |
|---|---|---|
| WER | 2.42 | 5.73 |
We provide a Colab notebook to run a pre-trained conformer CTC model:
TDNN LSTM CTC Model
The WER for this model is:
| test-clean | test-other | |
|---|---|---|
| WER | 6.59 | 17.69 |
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model:
Deployment with C++
Once you have trained a model in icefall, you may want to deploy it with C++, without Python dependencies.
Please refer to the documentation https://icefall.readthedocs.io/en/latest/recipes/librispeech/conformer_ctc.html#deployment-with-c for how to do this.
We also provide a Colab notebook, showing you how to run a torch scripted model in k2 with C++.
Please see: