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| # Table of Contents | ||
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| - [Installation](#installation) | ||
| * [Install k2](#install-k2) | ||
| * [Install lhotse](#install-lhotse) | ||
| * [Install icefall](#install-icefall) | ||
| - [Run recipes](#run-recipes) | ||
| <div align="center"> | ||
| <img src="https://raw.githubusercontent.com/k2-fsa/icefall/master/docs/source/_static/logo.png" width=168> | ||
| </div> | ||
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| ## Installation | ||
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| `icefall` depends on [k2][k2] for FSA operations and [lhotse][lhotse] for | ||
| data preparations. To use `icefall`, you have to install its dependencies first. | ||
| The following subsections describe how to setup the environment. | ||
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| CAUTION: There are various ways to setup the environment. What we describe | ||
| here is just one alternative. | ||
| Please refer to <https://icefall.readthedocs.io/en/latest/installation/index.html> | ||
| for installation. | ||
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| ### Install k2 | ||
| ## Recipes | ||
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| Please refer to [k2's installation documentation][k2-install] to install k2. | ||
| If you have any issues about installing k2, please open an issue at | ||
| <https://github.com/k2-fsa/k2/issues>. | ||
| Please refer to <https://icefall.readthedocs.io/en/latest/recipes/index.html> | ||
| for more information. | ||
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| ### Install lhotse | ||
| We provide two recipes at present: | ||
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| Please refer to [lhotse's installation documentation][lhotse-install] to install | ||
| lhotse. | ||
| - [yesno][yesno] | ||
| - [LibriSpeech][librispeech] | ||
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| ### Install icefall | ||
| ### yesno | ||
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| `icefall` is a set of Python scripts. What you need to do is just to set | ||
| the environment variable `PYTHONPATH`: | ||
| 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: | ||
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| ```bash | ||
| cd $HOME/open-source | ||
| git clone https://github.com/k2-fsa/icefall | ||
| cd icefall | ||
| pip install -r requirements.txt | ||
| export PYTHONPATH=$HOME/open-source/icefall:$PYTHONPATHON | ||
| ``` | ||
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| To verify `icefall` was installed successfully, you can run: | ||
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| ```bash | ||
| python3 -c "import icefall; print(icefall.__file__)" | ||
| [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ] | ||
| ``` | ||
| We do provide a Colab notebook for this recipe. | ||
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| It should print the path to `icefall`. | ||
| [](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing) | ||
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| ## Recipes | ||
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| At present, two recipes are provided: | ||
| ### LibriSpeech | ||
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| - [LibriSpeech][LibriSpeech] | ||
| - [yesno][yesno] [](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing) | ||
| We provide two models for this recipe: [conformer CTC model][LibriSpeech_conformer_ctc] | ||
| and [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc]. | ||
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| ### Yesno | ||
| #### Conformer CTC Model | ||
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| For the yesno recipe, training with 50 epochs takes less than 2 minutes using **CPU**. | ||
| The best WER we currently have is: | ||
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| The WER is | ||
| ||test-clean|test-other| | ||
| |--|--|--| | ||
| |WER| 2.57% | 5.94% | | ||
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| ``` | ||
| [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ] | ||
| ``` | ||
| 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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| ## Use Pre-trained models | ||
| The WER for this model is: | ||
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| See [egs/librispeech/ASR/conformer_ctc/README.md](egs/librispeech/ASR/conformer_ctc/README.md) | ||
| for how to use pre-trained models. | ||
| [](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing) | ||
| ||test-clean|test-other| | ||
| |--|--|--| | ||
| |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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| [yesno]: egs/yesno/ASR/README.md | ||
| [LibriSpeech]: egs/librispeech/ASR/README.md | ||
| [k2-install]: https://k2.readthedocs.io/en/latest/installation/index.html# | ||
| [k2]: https://github.com/k2-fsa/k2 | ||
| [lhotse]: https://github.com/lhotse-speech/lhotse | ||
| [lhotse-install]: https://lhotse.readthedocs.io/en/latest/getting-started.html#installation | ||
| [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 |
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| ## Data preparation | ||
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| If you want to use `./prepare.sh` to download everything for you, | ||
| you can just run | ||
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| ``` | ||
| ./prepare.sh | ||
| ``` | ||
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| If you have pre-downloaded the LibriSpeech dataset, please | ||
| read `./prepare.sh` and modify it to point to the location | ||
| of your dataset so that it won't re-download it. After modification, | ||
| please run | ||
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| ``` | ||
| ./prepare.sh | ||
| ``` | ||
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| The script `./prepare.sh` prepares features, lexicon, LMs, etc. | ||
| All generated files are saved in the folder `./data`. | ||
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| **HINT:** `./prepare.sh` supports options `--stage` and `--stop-stage`. | ||
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| ## TDNN-LSTM CTC training | ||
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| The folder `tdnn_lstm_ctc` contains scripts for CTC training | ||
| with TDNN-LSTM models. | ||
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| Pre-configured parameters for training and decoding are set in the function | ||
| `get_params()` within `tdnn_lstm_ctc/train.py` | ||
| and `tdnn_lstm_ctc/decode.py`. | ||
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| Parameters that can be passed from the command-line can be found by | ||
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| ``` | ||
| ./tdnn_lstm_ctc/train.py --help | ||
| ./tdnn_lstm_ctc/decode.py --help | ||
| ``` | ||
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| If you have 4 GPUs on a machine and want to use GPU 0, 2, 3 for | ||
| mutli-GPU training, you can run | ||
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| ``` | ||
| export CUDA_VISIBLE_DEVICES="0,2,3" | ||
| ./tdnn_lstm_ctc/train.py \ | ||
| --master-port 12345 \ | ||
| --world-size 3 | ||
| ``` | ||
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| If you want to decode by averaging checkpoints `epoch-8.pt`, | ||
| `epoch-9.pt` and `epoch-10.pt`, you can run | ||
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| ``` | ||
| ./tdnn_lstm_ctc/decode.py \ | ||
| --epoch 10 \ | ||
| --avg 3 | ||
| ``` | ||
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| ## Conformer CTC training | ||
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| The folder `conformer-ctc` contains scripts for CTC training | ||
| with conformer models. The steps of running the training and | ||
| decoding are similar to `tdnn_lstm_ctc`. | ||
| Please refer to <https://icefall.readthedocs.io/en/latest/recipes/librispeech.html> | ||
| for how to run models in this recipe. |