Conformer CTC ============= This tutorial shows you how to run a conformer ctc model with the `LibriSpeech `_ 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, with n-gram LM rescoring and attention decoder rescoring - (4) How to use a pre-trained model, provided by us - (5) How to deploy your trained model in C++, without Python dependencies 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 .. HINT:: If you have pre-downloaded the `LibriSpeech `_ dataset and the `musan `_ dataset, say, they are saved in ``/tmp/LibriSpeech`` 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. .. NOTE:: All generated files by ``./prepare.sh``, e.g., features, lexicon, etc, are saved in ``./data`` directory. 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 -------- Configurable options ~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash $ cd egs/librispeech/ASR $ ./conformer_ctc/train.py --help shows you the training options that can be passed from the commandline. The following options are used quite often: - ``--full-libri`` If it's True, the training part uses all the training data, i.e., 960 hours. Otherwise, the training part uses only the subset ``train-clean-100``, which has 100 hours of training data. .. CAUTION:: The training set is perturbed by speed with two factors: 0.9 and 1.1. If ``--full-libri`` is True, each epoch actually processes ``3x960 == 2880`` hours of data. - ``--num-epochs`` It is the number of epochs to train. For instance, ``./conformer_ctc/train.py --num-epochs 30`` trains for 30 epochs and generates ``epoch-0.pt``, ``epoch-1.pt``, ..., ``epoch-29.pt`` in the folder ``./conformer_ctc/exp``. - ``--start-epoch`` It's used to resume training. ``./conformer_ctc/train.py --start-epoch 10`` loads the checkpoint ``./conformer_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/librispeech/ASR $ export CUDA_VISIBLE_DEVICES="0,2" $ ./conformer_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/librispeech/ASR $ ./conformer_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/librispeech/ASR $ export CUDA_VISIBLE_DEVICES="3" $ ./conformer_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 ``200``. .. 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 `conformer_ctc/train.py `_ You don't need to change these pre-configured parameters. If you really need to change them, please modify ``./conformer_ctc/train.py`` directly. Training logs ~~~~~~~~~~~~~ Training logs and checkpoints are saved in ``conformer_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 $ ./conformer_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 conformer_ctc/exp/tensorboard $ tensorboard dev upload --logdir . --description "Conformer CTC training for LibriSpeech 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/lzGnETjwRxC3yghNMd4kPw/ [2021-08-24T16:42:43] Started scanning logdir. Uploading 4540 scalars... Note there is a URL in the above output, click it and you will see the following screenshot: .. figure:: images/librispeech-conformer-ctc-tensorboard-log.png :width: 600 :alt: TensorBoard screenshot :align: center :target: https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/ 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/librispeech/ASR $ ./conformer_ctc/train.py --max-duration 200 --full-libri 0 It uses ``--max-duration`` of 200 to avoid OOM. Also, it uses only a subset of the LibriSpeech data for training. **Case 2** ^^^^^^^^^^ .. code-block:: bash $ cd egs/librispeech/ASR $ export CUDA_VISIBLE_DEVICES="0,3" $ ./conformer_ctc/train.py --world-size 2 It uses GPU 0 and GPU 3 for DDP training. **Case 3** ^^^^^^^^^^ .. code-block:: bash $ cd egs/librispeech/ASR $ ./conformer_ctc/train.py --num-epochs 10 --start-epoch 3 It loads checkpoint ``./conformer_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/librispeech/ASR $ ./conformer_ctc/decode.py --help shows the options for decoding. The commonly used options are: - ``--method`` This specifies the decoding method. This script supports 7 decoding methods. As for ctc decoding, it uses a sentence piece model to convert word pieces to words. And it needs neither a lexicon nor an n-gram LM. For example, the following command uses CTC topology for decoding: .. code-block:: $ cd egs/librispeech/ASR $ ./conformer_ctc/decode.py --method ctc-decoding --max-duration 300 # Caution: The above command is tested with a model with vocab size 500. And the following command uses attention decoder for rescoring: .. code-block:: $ cd egs/librispeech/ASR $ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --nbest-scale 0.5 - ``--nbest-scale`` It is used to scale down lattice scores so that there are more unique paths for rescoring. - ``--max-duration`` It has the same meaning as the one during training. A larger value may cause OOM. Here are some results for CTC decoding with a vocab size of 500: Usage: .. code-block:: bash $ cd egs/librispeech/ASR # NOTE: Tested with a model with vocab size 500. # It won't work for a model with vocab size 5000. $ ./conformer_ctc/decode.py \ --epoch 25 \ --avg 1 \ --max-duration 300 \ --exp-dir conformer_ctc/exp \ --lang-dir data/lang_bpe_500 \ --method ctc-decoding The output is given below: .. code-block:: bash 2021-09-26 12:44:31,033 INFO [decode.py:537] Decoding started 2021-09-26 12:44:31,033 INFO [decode.py:538] {'lm_dir': PosixPath('data/lm'), 'subsampling_factor': 4, 'vgg_frontend': False, 'use_feat_batchnorm': True, 'feature_dim': 80, 'nhead': 8, 'attention_dim': 512, 'num_decoder_layers': 6, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 25, 'avg': 1, 'method': 'ctc-decoding', 'num_paths': 100, 'nbest_scale': 0.5, 'export': False, 'exp_dir': PosixPath('conformer_ctc/exp'), 'lang_dir': PosixPath('data/lang_bpe_500'), 'full_libri': False, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 100, 'bucketing_sampler': False, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2} 2021-09-26 12:44:31,406 INFO [lexicon.py:113] Loading pre-compiled data/lang_bpe_500/Linv.pt 2021-09-26 12:44:31,464 INFO [decode.py:548] device: cuda:0 2021-09-26 12:44:36,171 INFO [checkpoint.py:92] Loading checkpoint from conformer_ctc/exp/epoch-25.pt 2021-09-26 12:44:36,776 INFO [decode.py:652] Number of model parameters: 109226120 2021-09-26 12:44:37,714 INFO [decode.py:473] batch 0/206, cuts processed until now is 12 2021-09-26 12:45:15,944 INFO [decode.py:473] batch 100/206, cuts processed until now is 1328 2021-09-26 12:45:54,443 INFO [decode.py:473] batch 200/206, cuts processed until now is 2563 2021-09-26 12:45:56,411 INFO [decode.py:494] The transcripts are stored in conformer_ctc/exp/recogs-test-clean-ctc-decoding.txt 2021-09-26 12:45:56,592 INFO [utils.py:331] [test-clean-ctc-decoding] %WER 3.26% [1715 / 52576, 163 ins, 128 del, 1424 sub ] 2021-09-26 12:45:56,807 INFO [decode.py:506] Wrote detailed error stats to conformer_ctc/exp/errs-test-clean-ctc-decoding.txt 2021-09-26 12:45:56,808 INFO [decode.py:522] For test-clean, WER of different settings are: ctc-decoding 3.26 best for test-clean 2021-09-26 12:45:57,362 INFO [decode.py:473] batch 0/203, cuts processed until now is 15 2021-09-26 12:46:35,565 INFO [decode.py:473] batch 100/203, cuts processed until now is 1477 2021-09-26 12:47:15,106 INFO [decode.py:473] batch 200/203, cuts processed until now is 2922 2021-09-26 12:47:16,131 INFO [decode.py:494] The transcripts are stored in conformer_ctc/exp/recogs-test-other-ctc-decoding.txt 2021-09-26 12:47:16,208 INFO [utils.py:331] [test-other-ctc-decoding] %WER 8.21% [4295 / 52343, 396 ins, 315 del, 3584 sub ] 2021-09-26 12:47:16,432 INFO [decode.py:506] Wrote detailed error stats to conformer_ctc/exp/errs-test-other-ctc-decoding.txt 2021-09-26 12:47:16,432 INFO [decode.py:522] For test-other, WER of different settings are: ctc-decoding 8.21 best for test-other 2021-09-26 12:47:16,433 INFO [decode.py:680] Done! 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:: bash $ cd egs/librispeech/ASR $ git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09 $ cd icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09 $ git lfs pull .. CAUTION:: You have to use ``git lfs pull`` to download the pre-trained model. Otherwise, you will have the following issue when running ``decode.py``: .. code-block:: _pickle.UnpicklingError: invalid load key, 'v' To fix that issue, please use: .. code-block:: bash cd icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09 git lfs pull .. CAUTION:: In order to use this pre-trained model, your k2 version has to be v1.9 or later. After downloading, you will have the following files: .. code-block:: bash $ cd egs/librispeech/ASR $ tree icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09 .. code-block:: bash icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09 |-- README.md |-- data | |-- lang_bpe_500 | | |-- HLG.pt | | |-- HLG_modified.pt | | |-- bpe.model | | |-- tokens.txt | | `-- words.txt | `-- lm | `-- G_4_gram.pt |-- exp | |-- cpu_jit.pt | `-- pretrained.pt |-- log | `-- log-decode-2021-11-09-17-38-28 `-- test_wavs |-- 1089-134686-0001.wav |-- 1221-135766-0001.wav |-- 1221-135766-0002.wav `-- trans.txt **File descriptions**: - ``data/lang_bpe_500/HLG.pt`` It is the decoding graph. - ``data/lang_bpe_500/HLG_modified.pt`` It uses a modified CTC topology while building HLG. - ``data/lang_bpe_500/bpe.model`` It is a sentencepiece model. You can use it to reproduce our results. - ``data/lang_bpe_500/tokens.txt`` It contains tokens and their IDs, generated from ``bpe.model``. Provided only for convenience so that you can look up the SOS/EOS ID easily. - ``data/lang_bpe_500/words.txt`` It contains words and their IDs. - ``data/lm/G_4_gram.pt`` It is a 4-gram LM, used for n-gram LM rescoring. - ``exp/pretrained.pt`` It contains pre-trained model parameters, obtained by averaging checkpoints from ``epoch-23.pt`` to ``epoch-77.pt``. Note: We have removed optimizer ``state_dict`` to reduce file size. - ``exp/cpu_jit.pt`` It contains torch scripted model that can be deployed in C++. - ``test_wavs/*.wav`` It contains some test sound files from LibriSpeech ``test-clean`` dataset. - ``test_wavs/trans.txt`` It contains the reference transcripts for the sound files in ``test_wavs/``. The information of the test sound files is listed below: .. code-block:: bash $ soxi icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/*.wav Input File : 'icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav' Channels : 1 Sample Rate : 16000 Precision : 16-bit Duration : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors File Size : 212k Bit Rate : 256k Sample Encoding: 16-bit Signed Integer PCM Input File : 'icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav' Channels : 1 Sample Rate : 16000 Precision : 16-bit Duration : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors File Size : 535k Bit Rate : 256k Sample Encoding: 16-bit Signed Integer PCM Input File : 'icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav' Channels : 1 Sample Rate : 16000 Precision : 16-bit Duration : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors File Size : 154k Bit Rate : 256k Sample Encoding: 16-bit Signed Integer PCM Total Duration of 3 files: 00:00:28.16 Usage ~~~~~ .. code-block:: $ cd egs/librispeech/ASR $ ./conformer_ctc/pretrained.py --help displays the help information. It supports 4 decoding methods: - CTC decoding - HLG decoding - HLG + n-gram LM rescoring - HLG + n-gram LM rescoring + attention decoder rescoring CTC decoding ^^^^^^^^^^^^ CTC decoding uses the best path of the decoding lattice as the decoding result without any LM or lexicon. The command to run CTC decoding is: .. code-block:: bash $ cd egs/librispeech/ASR $ ./conformer_ctc/pretrained.py \ --checkpoint ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/pretrained.pt \ --bpe-model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/bpe.model \ --method ctc-decoding \ --num-classes 500 \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav The output is given below: .. code-block:: 2021-11-10 12:12:29,554 INFO [pretrained.py:260] {'sample_rate': 16000, 'subsampling_factor': 4, 'vgg_frontend': False, 'use_feat_batchnorm': True, 'feature_dim': 80, 'nhead': 8, 'attention_dim': 512, 'num_decoder_layers': 0, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/pretrained.pt', 'words_file': None, 'HLG': None, 'bpe_model': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/bpe.model', 'method': 'ctc-decoding', 'G': None, 'num_paths': 100, 'ngram_lm_scale': 1.3, 'attention_decoder_scale': 1.2, 'nbest_scale': 0.5, 'sos_id': 1, 'num_classes': 500, 'eos_id': 1, 'sound_files': ['./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav'], 'env_info': {'k2-version': '1.9', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '7178d67e594bc7fa89c2b331ad7bd1c62a6a9eb4', 'k2-git-date': 'Tue Oct 26 22:12:54 2021', 'lhotse-version': '0.11.0.dev+missing.version.file', 'torch-cuda-available': True, 'torch-cuda-version': '10.1', 'python-version': '3.8', 'icefall-git-branch': 'bpe-500', 'icefall-git-sha1': '8d93169-dirty', 'icefall-git-date': 'Wed Nov 10 11:52:44 2021', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-fix', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-bpe-500/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-bpe-500/lhotse/__init__.py'}} 2021-11-10 12:12:29,554 INFO [pretrained.py:266] device: cuda:0 2021-11-10 12:12:29,554 INFO [pretrained.py:268] Creating model 2021-11-10 12:12:35,600 INFO [pretrained.py:285] Constructing Fbank computer 2021-11-10 12:12:35,601 INFO [pretrained.py:295] Reading sound files: ['./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav'] 2021-11-10 12:12:35,758 INFO [pretrained.py:301] Decoding started 2021-11-10 12:12:36,025 INFO [pretrained.py:319] Use CTC decoding 2021-11-10 12:12:36,204 INFO [pretrained.py:425] ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav: AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROFFELS ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav: 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 B OSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav: YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION 2021-11-10 12:12:36,204 INFO [pretrained.py:427] Decoding Done 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/librispeech/ASR $ ./conformer_ctc/pretrained.py \ --checkpoint ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/pretrained.pt \ --words-file ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt \ --method 1best \ --num-classes 500 \ --HLG ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav The output is given below: .. code-block:: 2021-11-10 13:33:03,723 INFO [pretrained.py:260] {'sample_rate': 16000, 'subsampling_factor': 4, 'vgg_frontend': False, 'use_feat_batchnorm': True, 'feature_dim': 80, 'nhead': 8, 'attention_dim': 512, 'num_decoder_layers': 0, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/pretrained.pt', 'words_file': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt', 'HLG': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt', 'bpe_model': None, 'method': '1best', 'G': None, 'num_paths': 100, 'ngram_lm_scale': 1.3, 'attention_decoder_scale': 1.2, 'nbest_scale': 0.5, 'sos_id': 1, 'num_classes': 500, 'eos_id': 1, 'sound_files': ['./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav'], 'env_info': {'k2-version': '1.9', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '7178d67e594bc7fa89c2b331ad7bd1c62a6a9eb4', 'k2-git-date': 'Tue Oct 26 22:12:54 2021', 'lhotse-version': '0.11.0.dev+missing.version.file', 'torch-cuda-available': True, 'torch-cuda-version': '10.1', 'python-version': '3.8', 'icefall-git-branch': 'bpe-500', 'icefall-git-sha1': '8d93169-dirty', 'icefall-git-date': 'Wed Nov 10 11:52:44 2021', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-fix', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-bpe-500/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-bpe-500/lhotse/__init__.py'}} 2021-11-10 13:33:03,723 INFO [pretrained.py:266] device: cuda:0 2021-11-10 13:33:03,723 INFO [pretrained.py:268] Creating model 2021-11-10 13:33:09,775 INFO [pretrained.py:285] Constructing Fbank computer 2021-11-10 13:33:09,776 INFO [pretrained.py:295] Reading sound files: ['./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav'] 2021-11-10 13:33:09,881 INFO [pretrained.py:301] Decoding started 2021-11-10 13:33:09,951 INFO [pretrained.py:352] Loading HLG from ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt 2021-11-10 13:33:13,234 INFO [pretrained.py:384] Use HLG decoding 2021-11-10 13:33:13,571 INFO [pretrained.py:425] ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav: AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav: 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 ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav: YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION 2021-11-10 13:33:13,571 INFO [pretrained.py:427] Decoding Done HLG decoding + LM rescoring ^^^^^^^^^^^^^^^^^^^^^^^^^^^ It uses an n-gram LM to rescore the decoding lattice and the best path of the rescored lattice is the decoding result. The command to run HLG decoding + LM rescoring is: .. code-block:: bash $ cd egs/librispeech/ASR ./conformer_ctc/pretrained.py \ --checkpoint ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/pretrained.pt \ --words-file ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt \ --method whole-lattice-rescoring \ --num-classes 500 \ --HLG ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt \ --G ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt \ --ngram-lm-scale 1.0 \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav Its output is: .. code-block:: 2021-11-10 13:39:55,857 INFO [pretrained.py:260] {'sample_rate': 16000, 'subsampling_factor': 4, 'vgg_frontend': False, 'use_feat_batchnorm': True, 'feature_dim': 80, 'nhead': 8, 'attention_dim': 512, 'num_decoder_layers': 0, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/pretrained.pt', 'words_file': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt', 'HLG': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt', 'bpe_model': None, 'method': 'whole-lattice-rescoring', 'G': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt', 'num_paths': 100, 'ngram_lm_scale': 1.0, 'attention_decoder_scale': 1.2, 'nbest_scale': 0.5, 'sos_id': 1, 'num_classes': 500, 'eos_id': 1, 'sound_files': ['./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav'], 'env_info': {'k2-version': '1.9', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-$it-sha1': '7178d67e594bc7fa89c2b331ad7bd1c62a6a9eb4', 'k2-git-date': 'Tue Oct 26 22:12:54 2021', 'lhotse-version': '0.11.0.dev+missing.version.file', 'torch-cuda-available': True, 'torch-cuda-version': '10.1', 'python-version': '3.8', 'icefall-git-branch': 'bpe-500', 'icefall-git-sha1': '8d93169-dirty', 'icefall-git-date': 'Wed Nov 10 11:52:44 2021', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-fix', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-bpe-500/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-bpe-500/lhotse/__init__.py'}} 2021-11-10 13:39:55,858 INFO [pretrained.py:266] device: cuda:0 2021-11-10 13:39:55,858 INFO [pretrained.py:268] Creating model 2021-11-10 13:40:01,979 INFO [pretrained.py:285] Constructing Fbank computer 2021-11-10 13:40:01,980 INFO [pretrained.py:295] Reading sound files: ['./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav'] 2021-11-10 13:40:02,055 INFO [pretrained.py:301] Decoding started 2021-11-10 13:40:02,117 INFO [pretrained.py:352] Loading HLG from ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt 2021-11-10 13:40:05,051 INFO [pretrained.py:363] Loading G from ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt 2021-11-10 13:40:18,959 INFO [pretrained.py:389] Use HLG decoding + LM rescoring 2021-11-10 13:40:19,546 INFO [pretrained.py:425] ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav: AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav: 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 ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav: YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION 2021-11-10 13:40:19,546 INFO [pretrained.py:427] Decoding Done HLG decoding + LM rescoring + attention decoder rescoring ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ It uses an n-gram LM to rescore the decoding lattice, extracts n paths from the rescored lattice, recores the extracted paths with an attention decoder. The path with the highest score is the decoding result. The command to run HLG decoding + LM rescoring + attention decoder rescoring is: .. code-block:: bash $ cd egs/librispeech/ASR $ ./conformer_ctc/pretrained.py \ --checkpoint ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/pretrained.pt \ --words-file ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt \ --method attention-decoder \ --num-classes 500 \ --HLG ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt \ --G ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt \ --ngram-lm-scale 2.0 \ --attention-decoder-scale 2.0 \ --nbest-scale 0.5 \ --num-paths 100 \ --sos-id 1 \ --eos-id 1 \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav The output is below: .. code-block:: 2021-11-10 13:43:45,598 INFO [pretrained.py:260] {'sample_rate': 16000, 'subsampling_factor': 4, 'vgg_frontend': False, 'use_feat_batchnorm': True, 'feature_dim': 80, 'nhead': 8, 'attention_dim': 512, 'num_decoder_layers': 6, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/pretrained.pt', 'words_file': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt', 'HLG': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt', 'bpe_model': None, 'method': 'attention-decoder', 'G': './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt', 'num_paths': 100, 'ngram_lm_scale': 2.0, 'attention_decoder_scale': 2.0, 'nbest_scale': 0.5, 'sos_id': 1, 'num_classes': 500, 'eos_id': 1, 'sound_files': ['./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav'], 'env_info': {'k2-version': '1.9', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '7178d67e594bc7fa89c2b331ad7bd1c62a6a9eb4', 'k2-git-date': 'Tue Oct 26 22:12:54 2021', 'lhotse-version': '0.11.0.dev+missing.version.file', 'torch-cuda-available': True, 'torch-cuda-version': '10.1', 'python-version': '3.8', 'icefall-git-branch': 'bpe-500', 'icefall-git-sha1': '8d93169-dirty', 'icefall-git-date': 'Wed Nov 10 11:52:44 2021', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-fix', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-bpe-500/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-bpe-500/lhotse/__init__.py'}} 2021-11-10 13:43:45,599 INFO [pretrained.py:266] device: cuda:0 2021-11-10 13:43:45,599 INFO [pretrained.py:268] Creating model 2021-11-10 13:43:51,833 INFO [pretrained.py:285] Constructing Fbank computer 2021-11-10 13:43:51,834 INFO [pretrained.py:295] Reading sound files: ['./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav', './icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav'] 2021-11-10 13:43:51,915 INFO [pretrained.py:301] Decoding started 2021-11-10 13:43:52,076 INFO [pretrained.py:352] Loading HLG from ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt 2021-11-10 13:43:55,110 INFO [pretrained.py:363] Loading G from ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt 2021-11-10 13:44:09,329 INFO [pretrained.py:397] Use HLG + LM rescoring + attention decoder rescoring 2021-11-10 13:44:10,192 INFO [pretrained.py:425] ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav: AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav: 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 ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav: YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION 2021-11-10 13:44:10,192 INFO [pretrained.py:427] Decoding Done Compute WER with the pre-trained model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To check the WER of the pre-trained model on the test datasets, run: .. code-block:: bash $ cd egs/librispeech/ASR $ cd icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/ $ ln -s pretrained.pt epoch-999.pt $ cd ../.. $ ./conformer_ctc/decode.py \ --exp-dir ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp \ --lang-dir ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500 \ --lm-dir ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm \ --epoch 999 \ --avg 1 \ --concatenate-cuts 0 \ --bucketing-sampler 1 \ --max-duration 30 \ --num-paths 1000 \ --method attention-decoder \ --nbest-scale 0.5 Colab notebook -------------- We do provide a colab notebook for this recipe showing how to use a pre-trained model. |librispeech asr conformer ctc colab notebook| .. |librispeech asr conformer ctc colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing .. HINT:: Due to limited memory provided by Colab, you have to upgrade to Colab Pro to run ``HLG decoding + LM rescoring`` and ``HLG decoding + LM rescoring + attention decoder rescoring``. Otherwise, you can only run ``HLG decoding`` with Colab. **Congratulations!** You have finished the LibriSpeech ASR recipe with conformer CTC models in ``icefall``. If you want to deploy your trained model in C++, please read the following section. Deployment with C++ ------------------- This section describes how to deploy the pre-trained model in C++, without Python dependencies. .. HINT:: At present, it does NOT support streaming decoding. First, let us compile k2 from source: .. code-block:: bash $ cd $HOME $ git clone https://github.com/k2-fsa/k2 $ cd k2 $ git checkout v2.0-pre .. CAUTION:: You have to switch to the branch ``v2.0-pre``! .. code-block:: bash $ mkdir build-release $ cd build-release $ cmake -DCMAKE_BUILD_TYPE=Release .. $ make -j ctc_decode hlg_decode ngram_lm_rescore attention_rescore # You will find four binaries in `./bin`, i.e., # ./bin/ctc_decode, ./bin/hlg_decode, # ./bin/ngram_lm_rescore, and ./bin/attention_rescore Now you are ready to go! Assume you have run: .. code-block:: bash $ cd k2/build-release $ ln -s /path/to/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09 ./ To view the usage of ``./bin/ctc_decode``, run: .. code-block:: $ ./bin/ctc_decode It will show you the following message: .. code-block:: bash Please provide --nn_model This file implements decoding with a CTC topology, without any kinds of LM or lexicons. Usage: ./bin/ctc_decode \ --use_gpu true \ --nn_model \ --bpe_model \ \ \ To see all possible options, use ./bin/ctc_decode --help Caution: - Only sound files (*.wav) with single channel are supported. - It assumes the model is conformer_ctc/transformer.py from icefall. If you use a different model, you have to change the code related to `model.forward` in this file. CTC decoding ~~~~~~~~~~~~ .. code-block:: bash ./bin/ctc_decode \ --use_gpu true \ --nn_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/cpu_jit.pt \ --bpe_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/bpe.model \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav Its output is: .. code-block:: 2021-11-10 13:57:55.316 [I] k2/torch/bin/ctc_decode.cu:105:int main(int, char**) Use GPU 2021-11-10 13:57:55.316 [I] k2/torch/bin/ctc_decode.cu:109:int main(int, char**) Device: cuda:0 2021-11-10 13:57:55.316 [I] k2/torch/bin/ctc_decode.cu:118:int main(int, char**) Load wave files 2021-11-10 13:58:01.221 [I] k2/torch/bin/ctc_decode.cu:125:int main(int, char**) Build Fbank computer 2021-11-10 13:58:01.222 [I] k2/torch/bin/ctc_decode.cu:136:int main(int, char**) Compute features 2021-11-10 13:58:01.228 [I] k2/torch/bin/ctc_decode.cu:144:int main(int, char**) Load neural network model 2021-11-10 13:58:02.19 [I] k2/torch/bin/ctc_decode.cu:159:int main(int, char**) Compute nnet_output 2021-11-10 13:58:02.543 [I] k2/torch/bin/ctc_decode.cu:174:int main(int, char**) Build CTC topo 2021-11-10 13:58:02.547 [I] k2/torch/bin/ctc_decode.cu:177:int main(int, char**) Decoding 2021-11-10 13:58:02.708 [I] k2/torch/bin/ctc_decode.cu:207:int main(int, char**) Decoding result: ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROFFELS ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav 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 ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION HLG decoding ~~~~~~~~~~~~ .. code-block:: bash ./bin/hlg_decode \ --use_gpu true \ --nn_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/cpu_jit.pt \ --hlg ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt \ --word_table ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav The output is: .. code-block:: 2021-11-10 13:59:04.729 [I] k2/torch/bin/hlg_decode.cu:111:int main(int, char**) Use GPU 2021-11-10 13:59:04.729 [I] k2/torch/bin/hlg_decode.cu:115:int main(int, char**) Device: cuda:0 2021-11-10 13:59:04.729 [I] k2/torch/bin/hlg_decode.cu:124:int main(int, char**) Load wave files 2021-11-10 13:59:10.702 [I] k2/torch/bin/hlg_decode.cu:131:int main(int, char**) Build Fbank computer 2021-11-10 13:59:10.703 [I] k2/torch/bin/hlg_decode.cu:142:int main(int, char**) Compute features 2021-11-10 13:59:10.707 [I] k2/torch/bin/hlg_decode.cu:150:int main(int, char**) Load neural network model 2021-11-10 13:59:11.545 [I] k2/torch/bin/hlg_decode.cu:165:int main(int, char**) Compute nnet_output 2021-11-10 13:59:12.72 [I] k2/torch/bin/hlg_decode.cu:180:int main(int, char**) Load ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt 2021-11-10 13:59:12.994 [I] k2/torch/bin/hlg_decode.cu:185:int main(int, char**) Decoding 2021-11-10 13:59:13.268 [I] k2/torch/bin/hlg_decode.cu:216:int main(int, char**) Decoding result: ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav 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 ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION HLG decoding + n-gram LM rescoring ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash ./bin/ngram_lm_rescore \ --use_gpu true \ --nn_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/cpu_jit.pt \ --hlg ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt \ --g ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt \ --ngram_lm_scale 1.0 \ --word_table ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav The output is: .. code-block:: 2021-11-10 14:00:55.279 [I] k2/torch/bin/ngram_lm_rescore.cu:122:int main(int, char**) Use GPU 2021-11-10 14:00:55.280 [I] k2/torch/bin/ngram_lm_rescore.cu:126:int main(int, char**) Device: cuda:0 2021-11-10 14:00:55.280 [I] k2/torch/bin/ngram_lm_rescore.cu:135:int main(int, char**) Load wave files 2021-11-10 14:01:01.214 [I] k2/torch/bin/ngram_lm_rescore.cu:142:int main(int, char**) Build Fbank computer 2021-11-10 14:01:01.215 [I] k2/torch/bin/ngram_lm_rescore.cu:153:int main(int, char**) Compute features 2021-11-10 14:01:01.219 [I] k2/torch/bin/ngram_lm_rescore.cu:161:int main(int, char**) Load neural network model 2021-11-10 14:01:01.945 [I] k2/torch/bin/ngram_lm_rescore.cu:176:int main(int, char**) Compute nnet_output 2021-11-10 14:01:02.475 [I] k2/torch/bin/ngram_lm_rescore.cu:191:int main(int, char**) Load ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt 2021-11-10 14:01:03.398 [I] k2/torch/bin/ngram_lm_rescore.cu:199:int main(int, char**) Decoding 2021-11-10 14:01:03.515 [I] k2/torch/bin/ngram_lm_rescore.cu:205:int main(int, char**) Load n-gram LM: ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt 2021-11-10 14:01:07.432 [W] k2/torch/csrc/deserialization.cu:441:k2::FsaClass k2::LoadFsa(const string&, c10::optional) Ignore non tensor attribute: 'dummy' of type: Int 2021-11-10 14:01:07.589 [I] k2/torch/bin/ngram_lm_rescore.cu:214:int main(int, char**) Rescore with an n-gram LM 2021-11-10 14:01:08.68 [I] k2/torch/bin/ngram_lm_rescore.cu:242:int main(int, char**) Decoding result: ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav 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 ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION HLG decoding + n-gram LM rescoring + attention decoder rescoring ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: bash ./bin/attention_rescore \ --use_gpu true \ --nn_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/cpu_jit.pt \ --hlg ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt \ --g ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt \ --ngram_lm_scale 2.0 \ --attention_scale 2.0 \ --num_paths 100 \ --nbest_scale 0.5 \ --word_table ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt \ --sos_id 1 \ --eos_id 1 \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \ ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav The output is: .. code-block:: 2021-11-10 14:02:43.656 [I] k2/torch/bin/attention_rescore.cu:149:int main(int, char**) Use GPU 2021-11-10 14:02:43.656 [I] k2/torch/bin/attention_rescore.cu:153:int main(int, char**) Device: cuda:0 2021-11-10 14:02:43.656 [I] k2/torch/bin/attention_rescore.cu:162:int main(int, char**) Load wave files 2021-11-10 14:02:49.216 [I] k2/torch/bin/attention_rescore.cu:169:int main(int, char**) Build Fbank computer 2021-11-10 14:02:49.217 [I] k2/torch/bin/attention_rescore.cu:180:int main(int, char**) Compute features 2021-11-10 14:02:49.222 [I] k2/torch/bin/attention_rescore.cu:188:int main(int, char**) Load neural network model 2021-11-10 14:02:49.984 [I] k2/torch/bin/attention_rescore.cu:203:int main(int, char**) Compute nnet_output 2021-11-10 14:02:50.624 [I] k2/torch/bin/attention_rescore.cu:220:int main(int, char**) Load ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt 2021-11-10 14:02:51.519 [I] k2/torch/bin/attention_rescore.cu:228:int main(int, char**) Decoding 2021-11-10 14:02:51.632 [I] k2/torch/bin/attention_rescore.cu:234:int main(int, char**) Load n-gram LM: ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt 2021-11-10 14:02:55.537 [W] k2/torch/csrc/deserialization.cu:441:k2::FsaClass k2::LoadFsa(const string&, c10::optional) Ignore non tensor attribute: 'dummy' of type: Int 2021-11-10 14:02:55.645 [I] k2/torch/bin/attention_rescore.cu:243:int main(int, char**) Rescore with an n-gram LM 2021-11-10 14:02:55.970 [I] k2/torch/bin/attention_rescore.cu:246:int main(int, char**) Sample 100 paths 2021-11-10 14:02:56.215 [I] k2/torch/bin/attention_rescore.cu:293:int main(int, char**) Run attention decoder 2021-11-10 14:02:57.35 [I] k2/torch/bin/attention_rescore.cu:303:int main(int, char**) Rescoring 2021-11-10 14:02:57.179 [I] k2/torch/bin/attention_rescore.cu:369:int main(int, char**) Decoding result: ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav 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 ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION There is a Colab notebook showing you how to run a torch scripted model in C++. Please see |librispeech asr conformer ctc torch script colab notebook| .. |librispeech asr conformer ctc torch script colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/drive/1BIGLWzS36isskMXHKcqC9ysN6pspYXs_?usp=sharing