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Add docs for TDNN-LSTM-CTC

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TDNN-LSTM-CTC
=====
This tutorial shows you how to run a TDNN-LSTM-CTC model with the `LibriSpeech <https://www.openslr.org/12>`_ 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/yesno/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
Training
--------
Now describing the training of TDNN-LSTM-CTC model, contained in
the `tdnn_lstm_ctc <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/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"
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/yKUbhb5wRmOSXYkId1z9eg/
[2021-08-23T23:49:41] Started scanning logdir.
[2021-08-23T23:49:42] Total uploaded: 135 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 tensorboard page:
- ``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/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 <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/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/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.
.. HINT::
There are several decoding method provided in `tdnn_lstm_ctc/decode.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/tdnn_lstm_ctc/train.py>`_, you can change the decoding method by modifying ``method`` parameter in function ``get_params()``.
.. _tdnn_lstm_ctc use a pre-trained model:
Pre-trained Model
-----------------
We have uploaded the pre-trained model to
`<https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc>`_.
The following shows you how to use the pre-trained model.
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.
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
Download kaldifeat
~~~~~~~~~~~~~~~~~~
`kaldifeat <https://github.com/csukuangfj/kaldifeat>`_ is used for extracting
features from a single or multiple sound files. Please refer to
`<https://github.com/csukuangfj/kaldifeat>`_ to install ``kaldifeat`` first.
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
./conformer_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 do 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/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd
**Congratulations!** You have finished the TDNN-LSTM-CTC recipe on librispeech in ``icefall``.

18
egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
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@ -54,7 +54,7 @@ def get_parser():
parser.add_argument(
"--epoch",
type=int,
default=9,
default=19,
help="It specifies the checkpoint to use for decoding."
"Note: Epoch counts from 0.",
)
@ -66,6 +66,16 @@ def get_parser():
"consecutive checkpoints before the checkpoint specified by "
"'--epoch'. ",
)
parser.add_argument(
"--export",
type=str2bool,
default=False,
help="""When enabled, the averaged model is saved to
tdnn/exp/pretrained.pt. Note: only model.state_dict() is saved.
pretrained.pt contains a dict {"model": model.state_dict()},
which can be loaded by `icefall.checkpoint.load_checkpoint()`.
""",
)
return parser
@ -408,6 +418,12 @@ def main():
logging.info(f"averaging {filenames}")
model.load_state_dict(average_checkpoints(filenames))
if params.export:
logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
torch.save(
{"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
)
model.to(device)
model.eval()

2
egs/librispeech/ASR/tdnn_lstm_ctc/train.py
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@ -144,7 +144,7 @@ def get_params() -> AttributeDict:
"weight_decay": 5e-4,
"subsampling_factor": 3,
"start_epoch": 0,
"num_epochs": 10,
"num_epochs": 20,
"best_train_loss": float("inf"),
"best_valid_loss": float("inf"),
"best_train_epoch": -1,