TDNN-LSTM CTC ============= This tutorial shows you how to run a tdnn-lstm ctc model with the `Aishell `_ dataset. .. HINT:: We assume you have read the page :ref:`install icefall` and have setup the environment for ``icefall``. .. HINT:: We recommend you to use a GPU or several GPUs to run this recipe. In this tutorial, you will learn: - (1) How to prepare data for training and decoding - (2) How to start the training, either with a single GPU or multiple GPUs - (3) How to do decoding after training. - (4) How to use a pre-trained model, provided by us Data preparation ---------------- .. code-block:: bash $ cd egs/aishell/ASR $ ./prepare.sh The script ``./prepare.sh`` handles the data preparation for you, **automagically**. All you need to do is to run it. The data preparation contains several stages, you can use the following two options: - ``--stage`` - ``--stop-stage`` to control which stage(s) should be run. By default, all stages are executed. For example, .. code-block:: bash $ cd egs/aishell/ASR $ ./prepare.sh --stage 0 --stop-stage 0 means to run only stage 0. To run stage 2 to stage 5, use: .. code-block:: bash $ ./prepare.sh --stage 2 --stop-stage 5 .. HINT:: If you have pre-downloaded the `Aishell `_ dataset and the `musan `_ dataset, say, they are saved in ``/tmp/aishell`` and ``/tmp/musan``, you can modify the ``dl_dir`` variable in ``./prepare.sh`` to point to ``/tmp`` so that ``./prepare.sh`` won't re-download them. .. HINT:: A 3-gram language model will be downloaded from huggingface, we assume you have installed and initialized ``git-lfs``. If not, you could install ``git-lfs`` by .. code-block:: bash $ sudo apt-get install git-lfs $ git-lfs install If you don't have the ``sudo`` permission, you could download the `git-lfs binary `_ here, then add it to you ``PATH``. .. NOTE:: All generated files by ``./prepare.sh``, e.g., features, lexicon, etc, are saved in ``./data`` directory. Training -------- Configurable options ~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash $ cd egs/aishell/ASR $ ./tdnn_lstm_ctc/train.py --help shows you the training options that can be passed from the commandline. The following options are used quite often: - ``--num-epochs`` It is the number of epochs to train. For instance, ``./tdnn_lstm_ctc/train.py --num-epochs 30`` trains for 30 epochs and generates ``epoch-0.pt``, ``epoch-1.pt``, ..., ``epoch-29.pt`` in the folder ``./tdnn_lstm_ctc/exp``. - ``--start-epoch`` It's used to resume training. ``./tdnn_lstm_ctc/train.py --start-epoch 10`` loads the checkpoint ``./tdnn_lstm_ctc/exp/epoch-9.pt`` and starts training from epoch 10, based on the state from epoch 9. - ``--world-size`` It is used for multi-GPU single-machine DDP training. - (a) If it is 1, then no DDP training is used. - (b) If it is 2, then GPU 0 and GPU 1 are used for DDP training. The following shows some use cases with it. **Use case 1**: You have 4 GPUs, but you only want to use GPU 0 and GPU 2 for training. You can do the following: .. code-block:: bash $ cd egs/aishell/ASR $ export CUDA_VISIBLE_DEVICES="0,2" $ ./tdnn_lstm_ctc/train.py --world-size 2 **Use case 2**: You have 4 GPUs and you want to use all of them for training. You can do the following: .. code-block:: bash $ cd egs/aishell/ASR $ ./tdnn_lstm_ctc/train.py --world-size 4 **Use case 3**: You have 4 GPUs but you only want to use GPU 3 for training. You can do the following: .. code-block:: bash $ cd egs/aishell/ASR $ export CUDA_VISIBLE_DEVICES="3" $ ./tdnn_lstm_ctc/train.py --world-size 1 .. CAUTION:: Only multi-GPU single-machine DDP training is implemented at present. Multi-GPU multi-machine DDP training will be added later. - ``--max-duration`` It specifies the number of seconds over all utterances in a batch, before **padding**. If you encounter CUDA OOM, please reduce it. For instance, if your are using V100 NVIDIA GPU, we recommend you to set it to ``2000``. .. HINT:: Due to padding, the number of seconds of all utterances in a batch will usually be larger than ``--max-duration``. A larger value for ``--max-duration`` may cause OOM during training, while a smaller value may increase the training time. You have to tune it. Pre-configured options ~~~~~~~~~~~~~~~~~~~~~~ There are some training options, e.g., weight decay, number of warmup steps, results dir, etc, that are not passed from the commandline. They are pre-configured by the function ``get_params()`` in `tdnn_lstm_ctc/train.py `_ You don't need to change these pre-configured parameters. If you really need to change them, please modify ``./tdnn_lstm_ctc/train.py`` directly. .. CAUTION:: The training set is perturbed by speed with two factors: 0.9 and 1.1. Each epoch actually processes ``3x150 == 450`` hours of data. Training logs ~~~~~~~~~~~~~ Training logs and checkpoints are saved in ``tdnn_lstm_ctc/exp``. You will find the following files in that directory: - ``epoch-0.pt``, ``epoch-1.pt``, ... These are checkpoint files, containing model ``state_dict`` and optimizer ``state_dict``. To resume training from some checkpoint, say ``epoch-10.pt``, you can use: .. code-block:: bash $ ./tdnn_lstm_ctc/train.py --start-epoch 11 - ``tensorboard/`` This folder contains TensorBoard logs. Training loss, validation loss, learning rate, etc, are recorded in these logs. You can visualize them by: .. code-block:: bash $ cd tdnn_lstm_ctc/exp/tensorboard $ tensorboard dev upload --logdir . --description "TDNN-LSTM CTC training for Aishell with icefall" It will print something like below: .. code-block:: TensorFlow installation not found - running with reduced feature set. Upload started and will continue reading any new data as it's added to the logdir. To stop uploading, press Ctrl-C. New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/LJI9MWUORLOw3jkdhxwk8A/ [2021-09-13T11:59:23] Started scanning logdir. [2021-09-13T11:59:24] Total uploaded: 4454 scalars, 0 tensors, 0 binary objects Listening for new data in logdir... Note there is a URL in the above output, click it and you will see the following screenshot: .. figure:: images/aishell-tdnn-lstm-ctc-tensorboard-log.jpg :width: 600 :alt: TensorBoard screenshot :align: center :target: https://tensorboard.dev/experiment/LJI9MWUORLOw3jkdhxwk8A/ TensorBoard screenshot. - ``log/log-train-xxxx`` It is the detailed training log in text format, same as the one you saw printed to the console during training. Usage examples ~~~~~~~~~~~~~~ The following shows typical use cases: **Case 1** ^^^^^^^^^^ .. code-block:: bash $ cd egs/aishell/ASR $ export CUDA_VISIBLE_DEVICES="0,3" $ ./tdnn_lstm_ctc/train.py --world-size 2 It uses GPU 0 and GPU 3 for DDP training. **Case 2** ^^^^^^^^^^ .. code-block:: bash $ cd egs/aishell/ASR $ ./tdnn_lstm_ctc/train.py --num-epochs 10 --start-epoch 3 It loads checkpoint ``./tdnn_lstm_ctc/exp/epoch-2.pt`` and starts training from epoch 3. Also, it trains for 10 epochs. Decoding -------- The decoding part uses checkpoints saved by the training part, so you have to run the training part first. .. code-block:: bash $ cd egs/aishell/ASR $ ./tdnn_lstm_ctc/decode.py --help shows the options for decoding. The commonly used options are: - ``--method`` This specifies the decoding method. The following command uses attention decoder for rescoring: .. code-block:: $ cd egs/aishell/ASR $ ./tdnn_lstm_ctc/decode.py --method 1best --max-duration 100 - ``--max-duration`` It has the same meaning as the one during training. A larger value may cause OOM. Pre-trained Model ----------------- We have uploaded a pre-trained model to ``_. We describe how to use the pre-trained model to transcribe a sound file or multiple sound files in the following. Install kaldifeat ~~~~~~~~~~~~~~~~~ `kaldifeat `_ is used to extract features for a single sound file or multiple sound files at the same time. Please refer to ``_ for installation. Download the pre-trained model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The following commands describe how to download the pre-trained model: .. code-block:: $ cd egs/aishell/ASR $ mkdir tmp $ cd tmp $ git lfs install $ git clone https://huggingface.co/pkufool/icefall_asr_aishell_tdnn_lstm_ctc .. CAUTION:: You have to use ``git lfs`` to download the pre-trained model. .. CAUTION:: In order to use this pre-trained model, your k2 version has to be v1.7 or later. After downloading, you will have the following files: .. code-block:: bash $ cd egs/aishell/ASR $ tree tmp .. code-block:: bash tmp/ `-- icefall_asr_aishell_tdnn_lstm_ctc |-- README.md |-- data | `-- lang_phone | |-- HLG.pt | |-- tokens.txt | `-- words.txt |-- exp | `-- pretrained.pt `-- test_waves |-- BAC009S0764W0121.wav |-- BAC009S0764W0122.wav |-- BAC009S0764W0123.wav `-- trans.txt 5 directories, 9 files **File descriptions**: - ``data/lang_phone/HLG.pt`` It is the decoding graph. - ``data/lang_phone/tokens.txt`` It contains tokens and their IDs. Provided only for convenience so that you can look up the SOS/EOS ID easily. - ``data/lang_phone/words.txt`` It contains words and their IDs. - ``exp/pretrained.pt`` It contains pre-trained model parameters, obtained by averaging checkpoints from ``epoch-18.pt`` to ``epoch-40.pt``. Note: We have removed optimizer ``state_dict`` to reduce file size. - ``test_waves/*.wav`` It contains some test sound files from Aishell ``test`` dataset. - ``test_waves/trans.txt`` It contains the reference transcripts for the sound files in `test_waves/`. The information of the test sound files is listed below: .. code-block:: bash $ soxi tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/*.wav Input File : 'tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0121.wav' Channels : 1 Sample Rate : 16000 Precision : 16-bit Duration : 00:00:04.20 = 67263 samples ~ 315.295 CDDA sectors File Size : 135k Bit Rate : 256k Sample Encoding: 16-bit Signed Integer PCM Input File : 'tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0122.wav' Channels : 1 Sample Rate : 16000 Precision : 16-bit Duration : 00:00:04.12 = 65840 samples ~ 308.625 CDDA sectors File Size : 132k Bit Rate : 256k Sample Encoding: 16-bit Signed Integer PCM Input File : 'tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0123.wav' Channels : 1 Sample Rate : 16000 Precision : 16-bit Duration : 00:00:04.00 = 64000 samples ~ 300 CDDA sectors File Size : 128k Bit Rate : 256k Sample Encoding: 16-bit Signed Integer PCM Total Duration of 3 files: 00:00:12.32 Usage ~~~~~ .. code-block:: $ cd egs/aishell/ASR $ ./tdnn_lstm_ctc/pretrained.py --help displays the help information. HLG decoding ^^^^^^^^^^^^ HLG decoding uses the best path of the decoding lattice as the decoding result. The command to run HLG decoding is: .. code-block:: bash $ cd egs/aishell/ASR $ ./tdnn_lstm_ctc/pretrained.py \ --checkpoint ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/exp/pretrained.pt \ --words-file ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/data/lang_phone/words.txt \ --HLG ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/data/lang_phone/HLG.pt \ --method 1best \ ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0121.wav \ ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0122.wav \ ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0123.wav The output is given below: .. code-block:: 2021-09-13 15:00:55,858 INFO [pretrained.py:140] device: cuda:0 2021-09-13 15:00:55,858 INFO [pretrained.py:142] Creating model 2021-09-13 15:01:05,389 INFO [pretrained.py:154] Loading HLG from ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/data/lang_phone/HLG.pt 2021-09-13 15:01:06,531 INFO [pretrained.py:161] Constructing Fbank computer 2021-09-13 15:01:06,536 INFO [pretrained.py:171] Reading sound files: ['./tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0121.wav', './tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0122.wav', './tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0123.wav'] 2021-09-13 15:01:06,539 INFO [pretrained.py:177] Decoding started 2021-09-13 15:01:06,917 INFO [pretrained.py:207] Use HLG decoding 2021-09-13 15:01:07,129 INFO [pretrained.py:220] ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0121.wav: 甚至 出现 交易 几乎 停滞 的 情况 ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0122.wav: 一二 线 城市 虽然 也 处于 调整 中 ./tmp/icefall_asr_aishell_tdnn_lstm_ctc/test_waves/BAC009S0764W0123.wav: 但 因为 聚集 了 过多 公共 资源 2021-09-13 15:01:07,129 INFO [pretrained.py:222] Decoding Done Colab notebook -------------- We do provide a colab notebook for this recipe showing how to use a pre-trained model. |aishell asr conformer ctc colab notebook| .. |aishell asr conformer ctc colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/drive/1jbyzYq3ytm6j2nlEt-diQm-6QVWyDDEa?usp=sharing **Congratulations!** You have finished the aishell ASR recipe with TDNN-LSTM CTC models in ``icefall``.