TDNN-LSTM-CTC ============= This tutorial shows you how to run a TDNN-LSTM-CTC model with the `LibriSpeech `_ dataset. .. HINT:: We assume you have read the page :ref:`install icefall` and have setup the environment for ``icefall``. Data preparation ---------------- .. code-block:: bash $ cd egs/librispeech/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/librispeech/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 We provide the following YouTube video showing how to run ``./prepare.sh``. .. note:: To get the latest news of `next-gen Kaldi `_, please subscribe the following YouTube channel by `Nadira Povey `_: ``_ .. youtube:: ofEIoJL-mGM Training -------- Now describing the training of TDNN-LSTM-CTC model, contained in the `tdnn_lstm_ctc `_ folder. The command to run the training part is: .. code-block:: bash $ cd egs/librispeech/ASR $ export CUDA_VISIBLE_DEVICES="0,1,2,3" $ ./tdnn_lstm_ctc/train.py --world-size 4 By default, it will run ``20`` epochs. Training logs and checkpoints are saved in ``tdnn_lstm_ctc/exp``. In ``tdnn_lstm_ctc/exp``, you will find the following files: - ``epoch-0.pt``, ``epoch-1.pt``, ..., ``epoch-19.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 training for librispeech with icefall" - ``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. To see available training options, you can use: .. code-block:: bash $ ./tdnn_lstm_ctc/train.py --help Other training options, e.g., learning rate, results dir, etc., are pre-configured in the function ``get_params()`` in `tdnn_lstm_ctc/train.py `_. Normally, you don't need to change them. You can change them by modifying the code, if you want. Decoding -------- The decoding part uses checkpoints saved by the training part, so you have to run the training part first. The command for decoding is: .. code-block:: bash $ export CUDA_VISIBLE_DEVICES="0" $ ./tdnn_lstm_ctc/decode.py You will see the WER in the output log. Decoded results are saved in ``tdnn_lstm_ctc/exp``. .. code-block:: bash $ ./tdnn_lstm_ctc/decode.py --help shows you the available decoding options. Some commonly used options are: - ``--epoch`` You can select which checkpoint to be used for decoding. For instance, ``./tdnn_lstm_ctc/decode.py --epoch 10`` means to use ``./tdnn_lstm_ctc/exp/epoch-10.pt`` for decoding. - ``--avg`` It's related to model averaging. It specifies number of checkpoints to be averaged. The averaged model is used for decoding. For example, the following command: .. code-block:: bash $ ./tdnn_lstm_ctc/decode.py --epoch 10 --avg 3 uses the average of ``epoch-8.pt``, ``epoch-9.pt`` and ``epoch-10.pt`` for decoding. - ``--export`` If it is ``True``, i.e., ``./tdnn_lstm_ctc/decode.py --export 1``, the code will save the averaged model to ``tdnn_lstm_ctc/exp/pretrained.pt``. See :ref:`tdnn_lstm_ctc use a pre-trained model` for how to use it. .. _tdnn_lstm_ctc use a pre-trained model: Pre-trained Model ----------------- We have uploaded the pre-trained model to ``_. The following shows you how to use the pre-trained model. 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash $ cd egs/librispeech/ASR $ mkdir tmp $ cd tmp $ git lfs install $ git clone https://huggingface.co/pkufool/icefall_asr_librispeech_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/librispeech/ASR $ tree tmp .. code-block:: bash tmp/ `-- icefall_asr_librispeech_tdnn-lstm_ctc |-- README.md |-- data | |-- lang_phone | | |-- HLG.pt | | |-- tokens.txt | | `-- words.txt | `-- lm | `-- G_4_gram.pt |-- exp | `-- pretrained.pt `-- test_wavs |-- 1089-134686-0001.flac |-- 1221-135766-0001.flac |-- 1221-135766-0002.flac `-- trans.txt 6 directories, 10 files **File descriptions**: - ``data/lang_phone/HLG.pt`` It is the decoding graph. - ``data/lang_phone/tokens.txt`` It contains tokens and their IDs. - ``data/lang_phone/words.txt`` It contains words and their IDs. - ``data/lm/G_4_gram.pt`` It is a 4-gram LM, useful for LM rescoring. - ``exp/pretrained.pt`` It contains pre-trained model parameters, obtained by averaging checkpoints from ``epoch-14.pt`` to ``epoch-19.pt``. Note: We have removed optimizer ``state_dict`` to reduce file size. - ``test_waves/*.flac`` It contains some test sound files from LibriSpeech ``test-clean`` 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_librispeech_tdnn-lstm_ctc/test_wavs/*.flac Input File : 'tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac' Channels : 1 Sample Rate : 16000 Precision : 16-bit Duration : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors File Size : 116k Bit Rate : 140k Sample Encoding: 16-bit FLAC Input File : 'tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac' Channels : 1 Sample Rate : 16000 Precision : 16-bit Duration : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors File Size : 343k Bit Rate : 164k Sample Encoding: 16-bit FLAC Input File : 'tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac' Channels : 1 Sample Rate : 16000 Precision : 16-bit Duration : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors File Size : 105k Bit Rate : 174k Sample Encoding: 16-bit FLAC Total Duration of 3 files: 00:00:28.16 Inference with a pre-trained model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash $ cd egs/librispeech/ASR $ ./tdnn_lstm_ctc/pretrained.py --help shows the usage information of ``./tdnn_lstm_ctc/pretrained.py``. To decode with ``1best`` method, we can use: .. code-block:: bash ./tdnn_lstm_ctc/pretrained.py \ --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \ --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \ --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \ ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \ ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \ ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac The output is: .. code-block:: 2021-08-24 16:57:13,315 INFO [pretrained.py:168] device: cuda:0 2021-08-24 16:57:13,315 INFO [pretrained.py:170] Creating model 2021-08-24 16:57:18,331 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt 2021-08-24 16:57:27,581 INFO [pretrained.py:199] Constructing Fbank computer 2021-08-24 16:57:27,584 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac'] 2021-08-24 16:57:27,599 INFO [pretrained.py:215] Decoding started 2021-08-24 16:57:27,791 INFO [pretrained.py:245] Use HLG decoding 2021-08-24 16:57:28,098 INFO [pretrained.py:266] ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac: AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac: GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac: YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION 2021-08-24 16:57:28,099 INFO [pretrained.py:268] Decoding Done To decode with ``whole-lattice-rescoring`` methond, you can use .. code-block:: bash ./tdnn_lstm_ctc/pretrained.py \ --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \ --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \ --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \ --method whole-lattice-rescoring \ --G ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt \ --ngram-lm-scale 0.8 \ ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \ ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \ ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac The decoding output is: .. code-block:: 2021-08-24 16:39:24,725 INFO [pretrained.py:168] device: cuda:0 2021-08-24 16:39:24,725 INFO [pretrained.py:170] Creating model 2021-08-24 16:39:29,403 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt 2021-08-24 16:39:40,631 INFO [pretrained.py:190] Loading G from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt 2021-08-24 16:39:53,098 INFO [pretrained.py:199] Constructing Fbank computer 2021-08-24 16:39:53,107 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac'] 2021-08-24 16:39:53,121 INFO [pretrained.py:215] Decoding started 2021-08-24 16:39:53,443 INFO [pretrained.py:250] Use HLG decoding + LM rescoring 2021-08-24 16:39:54,010 INFO [pretrained.py:266] ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac: AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac: GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac: YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION 2021-08-24 16:39:54,010 INFO [pretrained.py:268] Decoding Done Colab notebook -------------- We provide a colab notebook for decoding with pre-trained model. |librispeech tdnn_lstm_ctc colab notebook| .. |librispeech tdnn_lstm_ctc colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/drive/1-iSfQMp2So-We_Uu49N4AAcMInB72u9z?usp=sharing **Congratulations!** You have finished the TDNN-LSTM-CTC recipe on librispeech in ``icefall``.