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* add timit recipe for icefall * add shared file * update the docs for timit recipe * Delete shared * update the timit recipe and check style * Update model.py * Do some changes * Update model.py * Update model.py * Add README.md and RESULTS.md * Update RESULTS.md * Update README.md * update the docs for timit recipe
108 lines
3.5 KiB
Markdown
108 lines
3.5 KiB
Markdown
<div align="center">
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<img src="https://raw.githubusercontent.com/k2-fsa/icefall/master/docs/source/_static/logo.png" width=168>
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</div>
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## Installation
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Please refer to <https://icefall.readthedocs.io/en/latest/installation/index.html>
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for installation.
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## Recipes
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Please refer to <https://icefall.readthedocs.io/en/latest/recipes/index.html>
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for more information.
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We provide three recipes at present:
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- [yesno][yesno]
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- [LibriSpeech][librispeech]
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- [TIMIT][timit]
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### yesno
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This is the simplest ASR recipe in `icefall` and can be run on CPU.
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Training takes less than 30 seconds and gives you the following WER:
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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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We do provide a Colab notebook for this recipe.
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[](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
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### LibriSpeech
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We provide two models for this recipe: [conformer CTC model][LibriSpeech_conformer_ctc]
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and [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc].
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#### Conformer CTC Model
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The best WER we currently have is:
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| | test-clean | test-other |
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|-----|------------|------------|
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| WER | 2.42 | 5.73 |
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We provide a Colab notebook to run a pre-trained conformer CTC model: [](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
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#### TDNN LSTM CTC Model
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The WER for this model is:
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| | test-clean | test-other |
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|-----|------------|------------|
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| WER | 6.59 | 17.69 |
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We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd?usp=sharing)
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### TIMIT
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We provide two models for this recipe: [TDNN LSTM CTC model][TIMIT_tdnn_lstm_ctc]
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and [TDNN LiGRU CTC model][TIMIT_tdnn_ligru_ctc].
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#### TDNN LSTM CTC Model
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The best PER we currently have is:
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||TEST|
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|--|--|
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|PER| 19.71% |
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We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1Hs9DA4V96uapw_30uNp32OMJgkuR5VVd?usp=sharing)
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#### TDNN LiGRU CTC Model
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The PER for this model is:
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||TEST|
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|--|--|
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|PER| 17.66% |
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We provide a Colab notebook to run a pre-trained TDNN LiGRU CTC model: [](https://colab.research.google.com/drive/11IT-k4HQIgQngXz1uvWsEYktjqQt7Tmb?usp=sharing)
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## Deployment with C++
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Once you have trained a model in icefall, you may want to deploy it with C++,
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without Python dependencies.
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Please refer to the documentation
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<https://icefall.readthedocs.io/en/latest/recipes/librispeech/conformer_ctc.html#deployment-with-c>
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for how to do this.
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We also provide a Colab notebook, showing you how to run a torch scripted model in [k2][k2] with C++.
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Please see: [](https://colab.research.google.com/drive/1BIGLWzS36isskMXHKcqC9ysN6pspYXs_?usp=sharing)
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[LibriSpeech_tdnn_lstm_ctc]: egs/librispeech/ASR/tdnn_lstm_ctc
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[LibriSpeech_conformer_ctc]: egs/librispeech/ASR/conformer_ctc
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[TIMIT_tdnn_lstm_ctc]: egs/timit/ASR/tdnn_lstm_ctc
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[TIMIT_tdnn_ligru_ctc]: egs/timit/ASR/tdnn_ligru_ctc
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[yesno]: egs/yesno/ASR
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[librispeech]: egs/librispeech/ASR
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[timit]: egs/timit/ASR
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[k2]: https://github.com/k2-fsa/k2
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