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@ -12,10 +12,11 @@ for installation.
Please refer to <https://icefall.readthedocs.io/en/latest/recipes/index.html>
for more information.
We provide two recipes at present:
We provide three recipes at present:
- [yesno][yesno]
- [LibriSpeech][librispeech]
- [TIMIT][timit]
### yesno
@ -56,6 +57,32 @@ The WER for this model is:
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd?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: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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++,
@ -71,6 +98,9 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
[LibriSpeech_tdnn_lstm_ctc]: egs/librispeech/ASR/tdnn_lstm_ctc
[LibriSpeech_conformer_ctc]: egs/librispeech/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
[timit]: egs/timit/ASR
[k2]: https://github.com/k2-fsa/k2