## Installation
Please refer to
for installation.
## Recipes
Please refer to
for more information.
We provide four recipes at present:
- [yesno][yesno]
- [LibriSpeech][librispeech]
- [Aishell][aishell]
- [TIMIT][timit]
### 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 4 models for this recipe:
- [conformer CTC model][LibriSpeech_conformer_ctc]
- [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc]
- [Transducer: Conformer encoder + LSTM decoder][LibriSpeech_transducer]
- [Transducer: Conformer encoder + Embedding decoder][LibriSpeech_transducer_stateless]
#### 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)
#### Transducer: Conformer encoder + LSTM decoder
Using Conformer as encoder and LSTM as decoder.
The best WER with greedy search is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 3.07 | 7.51 |
We provide a Colab notebook to run a pre-trained RNN-T conformer model: [](https://colab.research.google.com/drive/1_u6yK9jDkPwG_NLrZMN2XK7Aeq4suMO2?usp=sharing)
#### Transducer: Conformer encoder + Embedding decoder
Using Conformer as encoder. The decoder consists of 1 embedding layer
and 1 convolutional layer.
The best WER using beam search with beam size 4 is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 2.68 | 6.72 |
Note: No auxiliary losses are used in the training and no LMs are used
in the decoding.
We provide a Colab notebook to run a pre-trained transducer conformer + stateless decoder model: [](https://colab.research.google.com/drive/1Rc4Is-3Yp9LbcEz_Iy8hfyenyHsyjvqE?usp=sharing)
### Aishell
We provide two models for this recipe: [conformer CTC model][Aishell_conformer_ctc]
and [TDNN LSTM CTC model][Aishell_tdnn_lstm_ctc].
#### Conformer CTC Model
The best CER we currently have is:
| | test |
|-----|------|
| CER | 4.26 |
We provide a Colab notebook to run a pre-trained conformer CTC model: [](https://colab.research.google.com/drive/1WnG17io5HEZ0Gn_cnh_VzK5QYOoiiklC?usp=sharing)
#### Transducer Stateless Model
The best CER we currently have is:
| | test |
|-----|------|
| CER | 5.7 |
We provide a Colab notebook to run a pre-trained TransducerStateless model: [](https://colab.research.google.com/drive/14XaT2MhnBkK-3_RqqWq3K90Xlbin-GZC?usp=sharing)
#### TDNN LSTM CTC Model
The CER for this model is:
| | test |
|-----|-------|
| CER | 10.16 |
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1qULaGvXq7PCu_P61oubfz9b53JzY4H3z?usp=sharing)
### TIMIT
We provide two models for this recipe: [TDNN LSTM CTC model][TIMIT_tdnn_lstm_ctc]
and [TDNN LiGRU CTC model][TIMIT_tdnn_ligru_ctc].
#### TDNN LSTM CTC Model
The best PER we currently have is:
||TEST|
|--|--|
|PER| 19.71% |
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1Hs9DA4V96uapw_30uNp32OMJgkuR5VVd?usp=sharing)
#### TDNN LiGRU CTC Model
The PER for this model is:
||TEST|
|--|--|
|PER| 17.66% |
We provide a Colab notebook to run a pre-trained TDNN LiGRU CTC model: [](https://colab.research.google.com/drive/11IT-k4HQIgQngXz1uvWsEYktjqQt7Tmb?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
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
[LibriSpeech_transducer]: egs/librispeech/ASR/transducer
[LibriSpeech_transducer_stateless]: egs/librispeech/ASR/transducer_stateless
[Aishell_tdnn_lstm_ctc]: egs/aishell/ASR/tdnn_lstm_ctc
[Aishell_conformer_ctc]: egs/aishell/ASR/conformer_ctc
[TIMIT_tdnn_lstm_ctc]: egs/timit/ASR/tdnn_lstm_ctc
[TIMIT_tdnn_ligru_ctc]: egs/timit/ASR/tdnn_ligru_ctc
[yesno]: egs/yesno/ASR
[librispeech]: egs/librispeech/ASR
[aishell]: egs/aishell/ASR
[timit]: egs/timit/ASR
[k2]: https://github.com/k2-fsa/k2