mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
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.
78 lines
2.6 KiB
Markdown
78 lines
2.6 KiB
Markdown
<div align="center">
|
|
<img src="https://raw.githubusercontent.com/k2-fsa/icefall/master/docs/source/_static/logo.png" width=168>
|
|
</div>
|
|
|
|
## 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][yesno]
|
|
- [LibriSpeech][librispeech]
|
|
|
|
### 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.
|
|
|
|
[](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
|
|
|
|
|
|
### LibriSpeech
|
|
|
|
We provide two models for this recipe: [conformer CTC model][LibriSpeech_conformer_ctc]
|
|
and [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc].
|
|
|
|
#### 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: [](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
|
|
|
|
#### 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: [](https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd?usp=sharing)
|
|
|
|
|
|
## 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][k2] with C++.
|
|
Please see: [](https://colab.research.google.com/drive/1BIGLWzS36isskMXHKcqC9ysN6pspYXs_?usp=sharing)
|
|
|
|
|
|
[LibriSpeech_tdnn_lstm_ctc]: egs/librispeech/ASR/tdnn_lstm_ctc
|
|
[LibriSpeech_conformer_ctc]: egs/librispeech/ASR/conformer_ctc
|
|
[yesno]: egs/yesno/ASR
|
|
[librispeech]: egs/librispeech/ASR
|
|
[k2]: https://github.com/k2-fsa/k2
|