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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: cfc3e6ecc44ed7573f700065af8738a7
config: 3ca2e66d59e42ffdb5e0a5ba2153f99e
tags: 645f666f9bcd5a90fca523b33c5a78b7

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_sources/recipes/librispeech/index.rst.txt
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tdnn_lstm_ctc
conformer_ctc
lstm_pruned_stateless_transducer

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Transducer
==========
.. hint::
Please scroll down to the bottom of this page to find download links
for pretrained models if you don't want to train a model from scratch.
This tutorial shows you how to train a transducer model
with the `LibriSpeech <https://www.openslr.org/12>`_ dataset.
We use pruned RNN-T to compute the loss.
.. note::
You can find the paper about pruned RNN-T at the following address:
`<https://arxiv.org/abs/2206.13236>`_
The transducer model consists of 3 parts:
- Encoder, a.k.a, transcriber. We use an LSTM model
- Decoder, a.k.a, predictor. We use a model consisting of ``nn.Embedding``
and ``nn.Conv1d``
- Joiner, a.k.a, the joint network.
.. caution::
Contrary to the conventional RNN-T models, we use a stateless decoder.
That is, it has no recurrent connections.
.. hint::
Since the encoder model is an LSTM, not Transformer/Conformer, the
resulting model is suitable for streaming/online ASR.
Which model to use
------------------
Currently, there are two folders about LSTM stateless transducer training:
- ``(1)`` `<https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless>`_
This recipe uses only LibriSpeech during training.
- ``(2)`` `<https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless2>`_
This recipe uses GigaSpeech + LibriSpeech during training.
``(1)`` and ``(2)`` use the same model architecture. The only difference is that ``(2)`` supports
multi-dataset. Since ``(2)`` uses more data, it has a lower WER than ``(1)`` but it needs
more training time.
We use ``lstm_transducer_stateless2`` as an example below.
.. note::
You need to download the `GigaSpeech <https://github.com/SpeechColab/GigaSpeech>`_ dataset
to run ``(2)``. If you have only ``LibriSpeech`` dataset available, feel free to use ``(1)``.
Data preparation
----------------
.. code-block:: bash
$ cd egs/librispeech/ASR
$ ./prepare.sh
# If you use (1), you can **skip** the following command
$ ./prepare_giga_speech.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 <https://www.openslr.org/12>`_
dataset and the `musan <http://www.openslr.org/17/>`_ 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 <https://github.com/k2-fsa>`_, please subscribe
the following YouTube channel by `Nadira Povey <https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_:
`<https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_
.. youtube:: ofEIoJL-mGM
Training
--------
Configurable options
~~~~~~~~~~~~~~~~~~~~
.. code-block:: bash
$ cd egs/librispeech/ASR
$ ./lstm_transducer_stateless2/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,
``./lstm_transducer_stateless2/train.py --num-epochs 30`` trains for 30 epochs
and generates ``epoch-1.pt``, ``epoch-2.pt``, ..., ``epoch-30.pt``
in the folder ``./lstm_transducer_stateless2/exp``.
- ``--start-epoch``
It's used to resume training.
``./lstm_transducer_stateless2/train.py --start-epoch 10`` loads the
checkpoint ``./lstm_transducer_stateless2/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"
$ ./lstm_transducer_stateless2/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
$ ./lstm_transducer_stateless2/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"
$ ./lstm_transducer_stateless2/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.
.. 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.
- ``--giga-prob``
The probability to select a batch from the ``GigaSpeech`` dataset.
Note: It is available only for ``(2)``.
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
`lstm_transducer_stateless2/train.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/lstm_transducer_stateless2/train.py>`_
You don't need to change these pre-configured parameters. If you really need to change
them, please modify ``./lstm_transducer_stateless2/train.py`` directly.
Training logs
~~~~~~~~~~~~~
Training logs and checkpoints are saved in ``lstm_transducer_stateless2/exp``.
You will find the following files in that directory:
- ``epoch-1.pt``, ``epoch-2.pt``, ...
These are checkpoint files saved at the end of each epoch, containing model
``state_dict`` and optimizer ``state_dict``.
To resume training from some checkpoint, say ``epoch-10.pt``, you can use:
.. code-block:: bash
$ ./lstm_transducer_stateless2/train.py --start-epoch 11
- ``checkpoint-436000.pt``, ``checkpoint-438000.pt``, ...
These are checkpoint files saved every ``--save-every-n`` batches,
containing model ``state_dict`` and optimizer ``state_dict``.
To resume training from some checkpoint, say ``checkpoint-436000``, you can use:
.. code-block:: bash
$ ./lstm_transducer_stateless2/train.py --start-batch 436000
- ``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 lstm_transducer_stateless2/exp/tensorboard
$ tensorboard dev upload --logdir . --description "LSTM transducer 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/cj2vtPiwQHKN9Q1tx6PTpg/
[2022-09-20T15:50:50] Started scanning logdir.
Uploading 4468 scalars...
[2022-09-20T15:53:02] Total uploaded: 210171 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/librispeech-lstm-transducer-tensorboard-log.png
:width: 600
:alt: TensorBoard screenshot
:align: center
:target: https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/
TensorBoard screenshot.
.. hint::
If you don't have access to google, you can use the following command
to view the tensorboard log locally:
.. code-block:: bash
cd lstm_transducer_stateless2/exp/tensorboard
tensorboard --logdir . --port 6008
It will print the following message:
.. code-block::
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.8.0 at http://localhost:6008/ (Press CTRL+C to quit)
Now start your browser and go to `<http://localhost:6008>`_ to view the tensorboard
logs.
- ``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 example
~~~~~~~~~~~~~
You can use the following command to start the training using 8 GPUs:
.. code-block:: bash
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
./lstm_transducer_stateless2/train.py \
--world-size 8 \
--num-epochs 35 \
--start-epoch 1 \
--full-libri 1 \
--exp-dir lstm_transducer_stateless2/exp \
--max-duration 500 \
--use-fp16 0 \
--lr-epochs 10 \
--num-workers 2 \
--giga-prob 0.9
Decoding
--------
The decoding part uses checkpoints saved by the training part, so you have
to run the training part first.
.. hint::
There are two kinds of checkpoints:
- (1) ``epoch-1.pt``, ``epoch-2.pt``, ..., which are saved at the end
of each epoch. You can pass ``--epoch`` to
``lstm_transducer_stateless2/decode.py`` to use them.
- (2) ``checkpoints-436000.pt``, ``epoch-438000.pt``, ..., which are saved
every ``--save-every-n`` batches. You can pass ``--iter`` to
``lstm_transducer_stateless2/decode.py`` to use them.
We suggest that you try both types of checkpoints and choose the one
that produces the lowest WERs.
.. code-block:: bash
$ cd egs/librispeech/ASR
$ ./lstm_transducer_stateless2/decode.py --help
shows the options for decoding.
The following shows two examples:
.. code-block:: bash
for m in greedy_search fast_beam_search modified_beam_search; do
for epoch in 17; do
for avg in 1 2; do
./lstm_transducer_stateless2/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir lstm_transducer_stateless2/exp \
--max-duration 600 \
--num-encoder-layers 12 \
--rnn-hidden-size 1024 \
--decoding-method $m \
--use-averaged-model True \
--beam 4 \
--max-contexts 4 \
--max-states 8 \
--beam-size 4
done
done
done
.. code-block:: bash
for m in greedy_search fast_beam_search modified_beam_search; do
for iter in 474000; do
for avg in 8 10 12 14 16 18; do
./lstm_transducer_stateless2/decode.py \
--iter $iter \
--avg $avg \
--exp-dir lstm_transducer_stateless2/exp \
--max-duration 600 \
--num-encoder-layers 12 \
--rnn-hidden-size 1024 \
--decoding-method $m \
--use-averaged-model True \
--beam 4 \
--max-contexts 4 \
--max-states 8 \
--beam-size 4
done
done
done
Export models
-------------
`lstm_transducer_stateless2/export.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/lstm_transducer_stateless2/export.py>`_ supports to export checkpoints from ``lstm_transducer_stateless2/exp`` in the following ways.
Export ``model.state_dict()``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Checkpoints saved by ``lstm_transducer_stateless2/train.py`` also include
``optimizer.state_dict()``. It is useful for resuming training. But after training,
we are interested only in ``model.state_dict()``. You can use the following
command to extract ``model.state_dict()``.
.. code-block:: bash
# Assume that --iter 468000 --avg 16 produces the smallest WER
# (You can get such information after running ./lstm_transducer_stateless2/decode.py)
iter=468000
avg=16
./lstm_transducer_stateless2/export.py \
--exp-dir ./lstm_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--iter $iter \
--avg $avg
It will generate a file ``./lstm_transducer_stateless2/exp/pretrained.pt``.
.. hint::
To use the generated ``pretrained.pt`` for ``lstm_transducer_stateless2/decode.py``,
you can run:
.. code-block:: bash
cd lstm_transducer_stateless2/exp
ln -s pretrained epoch-9999.pt
And then pass `--epoch 9999 --avg 1 --use-averaged-model 0` to
``./lstm_transducer_stateless2/decode.py``.
To use the exported model with ``./lstm_transducer_stateless2/pretrained.py``, you
can run:
.. code-block:: bash
./lstm_transducer_stateless2/pretrained.py \
--checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
Export model using ``torch.jit.trace()``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code-block:: bash
iter=468000
avg=16
./lstm_transducer_stateless2/export.py \
--exp-dir ./lstm_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--iter $iter \
--avg $avg \
--jit-trace 1
It will generate 3 files:
- ``./lstm_transducer_stateless2/exp/encoder_jit_trace.pt``
- ``./lstm_transducer_stateless2/exp/decoder_jit_trace.pt``
- ``./lstm_transducer_stateless2/exp/joiner_jit_trace.pt``
To use the generated files with ``./lstm_transducer_stateless2/jit_pretrained``:
.. code-block:: bash
./lstm_transducer_stateless2/jit_pretrained.py \
--bpe-model ./data/lang_bpe_500/bpe.model \
--encoder-model-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace.pt \
--decoder-model-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace.pt \
--joiner-model-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace.pt \
/path/to/foo.wav \
/path/to/bar.wav
Export model for ncnn
~~~~~~~~~~~~~~~~~~~~~
We support exporting pretrained LSTM transducer models to
`ncnn <https://github.com/tencent/ncnn>`_ using
`pnnx <https://github.com/Tencent/ncnn/tree/master/tools/pnnx>`_.
First, let us install a modified version of ``ncnn``:
.. code-block:: bash
git clone https://github.com/csukuangfj/ncnn
cd ncnn
git submodule update --recursive --init
python3 setup.py bdist_wheel
ls -lh dist/
pip install ./dist/*.whl
# now build pnnx
cd tools/pnnx
mkdir build
cd build
make -j4
export PATH=$PWD/src:$PATH
./src/pnnx
.. note::
We assume that you have added the path to the binary ``pnnx`` to the
environment variable ``PATH``.
Second, let us export the model using ``torch.jit.trace()`` that is suitable
for ``pnnx``:
.. code-block:: bash
iter=468000
avg=16
./lstm_transducer_stateless2/export.py \
--exp-dir ./lstm_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--iter $iter \
--avg $avg \
--pnnx 1
It will generate 3 files:
- ``./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.pt``
- ``./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.pt``
- ``./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.pt``
Third, convert torchscript model to ``ncnn`` format:
.. code-block::
pnnx ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.pt
pnnx ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.pt
pnnx ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.pt
It will generate the following files:
- ``./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param``
- ``./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin``
- ``./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param``
- ``./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.bin``
- ``./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.param``
- ``./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.bin``
To use the above generate files, run:
.. code-block:: bash
./lstm_transducer_stateless2/ncnn-decode.py \
--bpe-model-filename ./data/lang_bpe_500/bpe.model \
--encoder-param-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param \
--encoder-bin-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin \
--decoder-param-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param \
--decoder-bin-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.bin \
--joiner-param-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.param \
--joiner-bin-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.bin \
/path/to/foo.wav
.. code-block:: bash
./lstm_transducer_stateless2/streaming-ncnn-decode.py \
--bpe-model-filename ./data/lang_bpe_500/bpe.model \
--encoder-param-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param \
--encoder-bin-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin \
--decoder-param-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param \
--decoder-bin-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.bin \
--joiner-param-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.param \
--joiner-bin-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.bin \
/path/to/foo.wav
To use the above generated files in C++, please see
`<https://github.com/k2-fsa/sherpa-ncnn>`_
It is able to generate a static linked library that can be run on Linux, Windows,
macOS, Raspberry Pi, etc.
Download pretrained models
--------------------------
If you don't want to train from scratch, you can download the pretrained models
by visiting the following links:
- `<https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03>`_
- `<https://huggingface.co/Zengwei/icefall-asr-librispeech-lstm-transducer-stateless-2022-08-18>`_
See `<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md>`_
for the details of the above pretrained models
You can find more usages of the pretrained models in
`<https://k2-fsa.github.io/sherpa/python/streaming_asr/lstm/index.html>`_

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@ -108,7 +108,7 @@ $ pre-commit install
<div><figure class="align-center" id="id2">
<a class="reference internal image-reference" href="../_images/pre-commit-check.png"><img alt="../_images/pre-commit-check.png" src="../_images/pre-commit-check.png" style="width: 600px;" /></a>
<figcaption>
<p><span class="caption-number">Fig. 7 </span><span class="caption-text">pre-commit hooks invoked by <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">commit</span></code> (Failed).</span><a class="headerlink" href="#id2" title="Permalink to this image"></a></p>
<p><span class="caption-number">Fig. 8 </span><span class="caption-text">pre-commit hooks invoked by <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">commit</span></code> (Failed).</span><a class="headerlink" href="#id2" title="Permalink to this image"></a></p>
</figcaption>
</figure>
</div></blockquote>
@ -127,7 +127,7 @@ it should succeed this time:</p>
<div><figure class="align-center" id="id3">
<a class="reference internal image-reference" href="../_images/pre-commit-check-success.png"><img alt="../_images/pre-commit-check-success.png" src="../_images/pre-commit-check-success.png" style="width: 600px;" /></a>
<figcaption>
<p><span class="caption-number">Fig. 8 </span><span class="caption-text">pre-commit hooks invoked by <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">commit</span></code> (Succeeded).</span><a class="headerlink" href="#id3" title="Permalink to this image"></a></p>
<p><span class="caption-number">Fig. 9 </span><span class="caption-text">pre-commit hooks invoked by <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">commit</span></code> (Succeeded).</span><a class="headerlink" href="#id3" title="Permalink to this image"></a></p>
</figcaption>
</figure>
</div></blockquote>

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contributing/doc.html
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@ -116,7 +116,7 @@ the following:</p>
<div><figure class="align-center" id="id1">
<a class="reference internal image-reference" href="../_images/doc-contrib.png"><img alt="../_images/doc-contrib.png" src="../_images/doc-contrib.png" style="width: 600px;" /></a>
<figcaption>
<p><span class="caption-number">Fig. 6 </span><span class="caption-text">View generated documentation locally with <code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">-m</span> <span class="pre">http.server</span></code>.</span><a class="headerlink" href="#id1" title="Permalink to this image"></a></p>
<p><span class="caption-number">Fig. 7 </span><span class="caption-text">View generated documentation locally with <code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">-m</span> <span class="pre">http.server</span></code>.</span><a class="headerlink" href="#id1" title="Permalink to this image"></a></p>
</figcaption>
</figure>
</div></blockquote>

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recipes/index.html
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@ -93,6 +93,7 @@ Currently, only speech recognition recipes are provided.</p>
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<section id="transducer">
<h1>Transducer<a class="headerlink" href="#transducer" title="Permalink to this heading"></a></h1>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>Please scroll down to the bottom of this page to find download links
for pretrained models if you don’t want to train a model from scratch.</p>
</div>
<p>This tutorial shows you how to train a transducer model
with the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> dataset.</p>
<p>We use pruned RNN-T to compute the loss.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>You can find the paper about pruned RNN-T at the following address:</p>
<p><a class="reference external" href="https://arxiv.org/abs/2206.13236">https://arxiv.org/abs/2206.13236</a></p>
</div>
<p>The transducer model consists of 3 parts:</p>
<blockquote>
<div><ul class="simple">
<li><p>Encoder, a.k.a, transcriber. We use an LSTM model</p></li>
<li><p>Decoder, a.k.a, predictor. We use a model consisting of <code class="docutils literal notranslate"><span class="pre">nn.Embedding</span></code>
and <code class="docutils literal notranslate"><span class="pre">nn.Conv1d</span></code></p></li>
<li><p>Joiner, a.k.a, the joint network.</p></li>
</ul>
</div></blockquote>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p>Contrary to the conventional RNN-T models, we use a stateless decoder.
That is, it has no recurrent connections.</p>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>Since the encoder model is an LSTM, not Transformer/Conformer, the
resulting model is suitable for streaming/online ASR.</p>
</div>
<section id="which-model-to-use">
<h2>Which model to use<a class="headerlink" href="#which-model-to-use" title="Permalink to this heading"></a></h2>
<p>Currently, there are two folders about LSTM stateless transducer training:</p>
<blockquote>
<div><ul>
<li><p><code class="docutils literal notranslate"><span class="pre">(1)</span></code> <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless">https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless</a></p>
<p>This recipe uses only LibriSpeech during training.</p>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">(2)</span></code> <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless2">https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless2</a></p>
<p>This recipe uses GigaSpeech + LibriSpeech during training.</p>
</li>
</ul>
</div></blockquote>
<p><code class="docutils literal notranslate"><span class="pre">(1)</span></code> and <code class="docutils literal notranslate"><span class="pre">(2)</span></code> use the same model architecture. The only difference is that <code class="docutils literal notranslate"><span class="pre">(2)</span></code> supports
multi-dataset. Since <code class="docutils literal notranslate"><span class="pre">(2)</span></code> uses more data, it has a lower WER than <code class="docutils literal notranslate"><span class="pre">(1)</span></code> but it needs
more training time.</p>
<p>We use <code class="docutils literal notranslate"><span class="pre">lstm_transducer_stateless2</span></code> as an example below.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>You need to download the <a class="reference external" href="https://github.com/SpeechColab/GigaSpeech">GigaSpeech</a> dataset
to run <code class="docutils literal notranslate"><span class="pre">(2)</span></code>. If you have only <code class="docutils literal notranslate"><span class="pre">LibriSpeech</span></code> dataset available, feel free to use <code class="docutils literal notranslate"><span class="pre">(1)</span></code>.</p>
</div>
</section>
<section id="data-preparation">
<h2>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading"></a></h2>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
$ ./prepare.sh
<span class="c1"># If you use (1), you can **skip** the following command</span>
$ ./prepare_giga_speech.sh
</pre></div>
</div>
<p>The script <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> handles the data preparation for you, <strong>automagically</strong>.
All you need to do is to run it.</p>
<p>The data preparation contains several stages, you can use the following two
options:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">--stage</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">--stop-stage</span></code></p></li>
</ul>
</div></blockquote>
<p>to control which stage(s) should be run. By default, all stages are executed.</p>
<p>For example,</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
$ ./prepare.sh --stage <span class="m">0</span> --stop-stage <span class="m">0</span>
</pre></div>
</div>
<p>means to run only stage 0.</p>
<p>To run stage 2 to stage 5, use:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ ./prepare.sh --stage <span class="m">2</span> --stop-stage <span class="m">5</span>
</pre></div>
</div>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>If you have pre-downloaded the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a>
dataset and the <a class="reference external" href="http://www.openslr.org/17/">musan</a> dataset, say,
they are saved in <code class="docutils literal notranslate"><span class="pre">/tmp/LibriSpeech</span></code> and <code class="docutils literal notranslate"><span class="pre">/tmp/musan</span></code>, you can modify
the <code class="docutils literal notranslate"><span class="pre">dl_dir</span></code> variable in <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> to point to <code class="docutils literal notranslate"><span class="pre">/tmp</span></code> so that
<code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> won’t re-download them.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>All generated files by <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code>, e.g., features, lexicon, etc,
are saved in <code class="docutils literal notranslate"><span class="pre">./data</span></code> directory.</p>
</div>
<p>We provide the following YouTube video showing how to run <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To get the latest news of <a class="reference external" href="https://github.com/k2-fsa">next-gen Kaldi</a>, please subscribe
the following YouTube channel by <a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">Nadira Povey</a>:</p>
<blockquote>
<div><p><a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw</a></p>
</div></blockquote>
</div>
<div class="video_wrapper" style="">
<iframe allowfullscreen="true" src="https://www.youtube.com/embed/ofEIoJL-mGM" style="border: 0; height: 345px; width: 560px">
</iframe></div></section>
<section id="training">
<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading"></a></h2>
<section id="configurable-options">
<h3>Configurable options<a class="headerlink" href="#configurable-options" title="Permalink to this heading"></a></h3>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
$ ./lstm_transducer_stateless2/train.py --help
</pre></div>
</div>
<p>shows you the training options that can be passed from the commandline.
The following options are used quite often:</p>
<blockquote>
<div><ul>
<li><p><code class="docutils literal notranslate"><span class="pre">--full-libri</span></code></p>
<p>If it’s True, the training part uses all the training data, i.e.,
960 hours. Otherwise, the training part uses only the subset
<code class="docutils literal notranslate"><span class="pre">train-clean-100</span></code>, which has 100 hours of training data.</p>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p>The training set is perturbed by speed with two factors: 0.9 and 1.1.
If <code class="docutils literal notranslate"><span class="pre">--full-libri</span></code> is True, each epoch actually processes
<code class="docutils literal notranslate"><span class="pre">3x960</span> <span class="pre">==</span> <span class="pre">2880</span></code> hours of data.</p>
</div>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">--num-epochs</span></code></p>
<p>It is the number of epochs to train. For instance,
<code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/train.py</span> <span class="pre">--num-epochs</span> <span class="pre">30</span></code> trains for 30 epochs
and generates <code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …, <code class="docutils literal notranslate"><span class="pre">epoch-30.pt</span></code>
in the folder <code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp</span></code>.</p>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">--start-epoch</span></code></p>
<p>It’s used to resume training.
<code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/train.py</span> <span class="pre">--start-epoch</span> <span class="pre">10</span></code> loads the
checkpoint <code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/epoch-9.pt</span></code> and starts
training from epoch 10, based on the state from epoch 9.</p>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">--world-size</span></code></p>
<p>It is used for multi-GPU single-machine DDP training.</p>
<blockquote>
<div><ul class="simple">
<li><ol class="loweralpha simple">
<li><p>If it is 1, then no DDP training is used.</p></li>
</ol>
</li>
<li><ol class="loweralpha simple" start="2">
<li><p>If it is 2, then GPU 0 and GPU 1 are used for DDP training.</p></li>
</ol>
</li>
</ul>
</div></blockquote>
<p>The following shows some use cases with it.</p>
<blockquote>
<div><p><strong>Use case 1</strong>: You have 4 GPUs, but you only want to use GPU 0 and
GPU 2 for training. You can do the following:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
$ <span class="nb">export</span> <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;0,2&quot;</span>
$ ./lstm_transducer_stateless2/train.py --world-size <span class="m">2</span>
</pre></div>
</div>
</div></blockquote>
<p><strong>Use case 2</strong>: You have 4 GPUs and you want to use all of them
for training. You can do the following:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
$ ./lstm_transducer_stateless2/train.py --world-size <span class="m">4</span>
</pre></div>
</div>
</div></blockquote>
<p><strong>Use case 3</strong>: You have 4 GPUs but you only want to use GPU 3
for training. You can do the following:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
$ <span class="nb">export</span> <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;3&quot;</span>
$ ./lstm_transducer_stateless2/train.py --world-size <span class="m">1</span>
</pre></div>
</div>
</div></blockquote>
</div></blockquote>
<div class="admonition caution">
<p class="admonition-title">Caution</p>
<p>Only multi-GPU single-machine DDP training is implemented at present.
Multi-GPU multi-machine DDP training will be added later.</p>
</div>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">--max-duration</span></code></p>
<p>It specifies the number of seconds over all utterances in a
batch, before <strong>padding</strong>.
If you encounter CUDA OOM, please reduce it.</p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>Due to padding, the number of seconds of all utterances in a
batch will usually be larger than <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code>.</p>
<p>A larger value for <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code> may cause OOM during training,
while a smaller value may increase the training time. You have to
tune it.</p>
</div>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">--giga-prob</span></code></p>
<p>The probability to select a batch from the <code class="docutils literal notranslate"><span class="pre">GigaSpeech</span></code> dataset.
Note: It is available only for <code class="docutils literal notranslate"><span class="pre">(2)</span></code>.</p>
</li>
</ul>
</div></blockquote>
</section>
<section id="pre-configured-options">
<h3>Pre-configured options<a class="headerlink" href="#pre-configured-options" title="Permalink to this heading"></a></h3>
<p>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 <code class="docutils literal notranslate"><span class="pre">get_params()</span></code> in
<a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/lstm_transducer_stateless2/train.py">lstm_transducer_stateless2/train.py</a></p>
<p>You don’t need to change these pre-configured parameters. If you really need to change
them, please modify <code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/train.py</span></code> directly.</p>
</section>
<section id="training-logs">
<h3>Training logs<a class="headerlink" href="#training-logs" title="Permalink to this heading"></a></h3>
<p>Training logs and checkpoints are saved in <code class="docutils literal notranslate"><span class="pre">lstm_transducer_stateless2/exp</span></code>.
You will find the following files in that directory:</p>
<blockquote>
<div><ul>
<li><p><code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …</p>
<p>These are checkpoint files saved at the end of each epoch, containing model
<code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.
To resume training from some checkpoint, say <code class="docutils literal notranslate"><span class="pre">epoch-10.pt</span></code>, you can use:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ ./lstm_transducer_stateless2/train.py --start-epoch <span class="m">11</span>
</pre></div>
</div>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">checkpoint-436000.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">checkpoint-438000.pt</span></code>, …</p>
<p>These are checkpoint files saved every <code class="docutils literal notranslate"><span class="pre">--save-every-n</span></code> batches,
containing model <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.
To resume training from some checkpoint, say <code class="docutils literal notranslate"><span class="pre">checkpoint-436000</span></code>, you can use:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ ./lstm_transducer_stateless2/train.py --start-batch <span class="m">436000</span>
</pre></div>
</div>
</div></blockquote>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">tensorboard/</span></code></p>
<p>This folder contains TensorBoard logs. Training loss, validation loss, learning
rate, etc, are recorded in these logs. You can visualize them by:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> lstm_transducer_stateless2/exp/tensorboard
$ tensorboard dev upload --logdir . --description <span class="s2">&quot;LSTM transducer training for LibriSpeech with icefall&quot;</span>
</pre></div>
</div>
</div></blockquote>
<p>It will print something like below:</p>
<blockquote>
<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TensorFlow</span> <span class="n">installation</span> <span class="ow">not</span> <span class="n">found</span> <span class="o">-</span> <span class="n">running</span> <span class="k">with</span> <span class="n">reduced</span> <span class="n">feature</span> <span class="nb">set</span><span class="o">.</span>
<span class="n">Upload</span> <span class="n">started</span> <span class="ow">and</span> <span class="n">will</span> <span class="k">continue</span> <span class="n">reading</span> <span class="nb">any</span> <span class="n">new</span> <span class="n">data</span> <span class="k">as</span> <span class="n">it</span><span class="s1">&#39;s added to the logdir.</span>
<span class="n">To</span> <span class="n">stop</span> <span class="n">uploading</span><span class="p">,</span> <span class="n">press</span> <span class="n">Ctrl</span><span class="o">-</span><span class="n">C</span><span class="o">.</span>
<span class="n">New</span> <span class="n">experiment</span> <span class="n">created</span><span class="o">.</span> <span class="n">View</span> <span class="n">your</span> <span class="n">TensorBoard</span> <span class="n">at</span><span class="p">:</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">tensorboard</span><span class="o">.</span><span class="n">dev</span><span class="o">/</span><span class="n">experiment</span><span class="o">/</span><span class="n">cj2vtPiwQHKN9Q1tx6PTpg</span><span class="o">/</span>
<span class="p">[</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">20</span><span class="n">T15</span><span class="p">:</span><span class="mi">50</span><span class="p">:</span><span class="mi">50</span><span class="p">]</span> <span class="n">Started</span> <span class="n">scanning</span> <span class="n">logdir</span><span class="o">.</span>
<span class="n">Uploading</span> <span class="mi">4468</span> <span class="n">scalars</span><span class="o">...</span>
<span class="p">[</span><span class="mi">2022</span><span class="o">-</span><span class="mi">09</span><span class="o">-</span><span class="mi">20</span><span class="n">T15</span><span class="p">:</span><span class="mi">53</span><span class="p">:</span><span class="mi">02</span><span class="p">]</span> <span class="n">Total</span> <span class="n">uploaded</span><span class="p">:</span> <span class="mi">210171</span> <span class="n">scalars</span><span class="p">,</span> <span class="mi">0</span> <span class="n">tensors</span><span class="p">,</span> <span class="mi">0</span> <span class="n">binary</span> <span class="n">objects</span>
<span class="n">Listening</span> <span class="k">for</span> <span class="n">new</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">logdir</span><span class="o">...</span>
</pre></div>
</div>
</div></blockquote>
<p>Note there is a URL in the above output, click it and you will see
the following screenshot:</p>
<blockquote>
<div><figure class="align-center" id="id2">
<a class="reference external image-reference" href="https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/"><img alt="TensorBoard screenshot" src="../../_images/librispeech-lstm-transducer-tensorboard-log.png" style="width: 600px;" /></a>
<figcaption>
<p><span class="caption-number">Fig. 5 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id2" title="Permalink to this image"></a></p>
</figcaption>
</figure>
</div></blockquote>
</li>
</ul>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>If you don’t have access to google, you can use the following command
to view the tensorboard log locally:</p>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> lstm_transducer_stateless2/exp/tensorboard
tensorboard --logdir . --port <span class="m">6008</span>
</pre></div>
</div>
</div></blockquote>
<p>It will print the following message:</p>
<blockquote>
<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Serving</span> <span class="n">TensorBoard</span> <span class="n">on</span> <span class="n">localhost</span><span class="p">;</span> <span class="n">to</span> <span class="n">expose</span> <span class="n">to</span> <span class="n">the</span> <span class="n">network</span><span class="p">,</span> <span class="n">use</span> <span class="n">a</span> <span class="n">proxy</span> <span class="ow">or</span> <span class="k">pass</span> <span class="o">--</span><span class="n">bind_all</span>
<span class="n">TensorBoard</span> <span class="mf">2.8.0</span> <span class="n">at</span> <span class="n">http</span><span class="p">:</span><span class="o">//</span><span class="n">localhost</span><span class="p">:</span><span class="mi">6008</span><span class="o">/</span> <span class="p">(</span><span class="n">Press</span> <span class="n">CTRL</span><span class="o">+</span><span class="n">C</span> <span class="n">to</span> <span class="n">quit</span><span class="p">)</span>
</pre></div>
</div>
</div></blockquote>
<p>Now start your browser and go to <a class="reference external" href="http://localhost:6008">http://localhost:6008</a> to view the tensorboard
logs.</p>
</div>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">log/log-train-xxxx</span></code></p>
<p>It is the detailed training log in text format, same as the one
you saw printed to the console during training.</p>
</li>
</ul>
</div></blockquote>
</section>
<section id="usage-example">
<h3>Usage example<a class="headerlink" href="#usage-example" title="Permalink to this heading"></a></h3>
<p>You can use the following command to start the training using 8 GPUs:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;0,1,2,3,4,5,6,7&quot;</span>
./lstm_transducer_stateless2/train.py <span class="se">\</span>
--world-size <span class="m">8</span> <span class="se">\</span>
--num-epochs <span class="m">35</span> <span class="se">\</span>
--start-epoch <span class="m">1</span> <span class="se">\</span>
--full-libri <span class="m">1</span> <span class="se">\</span>
--exp-dir lstm_transducer_stateless2/exp <span class="se">\</span>
--max-duration <span class="m">500</span> <span class="se">\</span>
--use-fp16 <span class="m">0</span> <span class="se">\</span>
--lr-epochs <span class="m">10</span> <span class="se">\</span>
--num-workers <span class="m">2</span> <span class="se">\</span>
--giga-prob <span class="m">0</span>.9
</pre></div>
</div>
</section>
</section>
<section id="decoding">
<h2>Decoding<a class="headerlink" href="#decoding" title="Permalink to this heading"></a></h2>
<p>The decoding part uses checkpoints saved by the training part, so you have
to run the training part first.</p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>There are two kinds of checkpoints:</p>
<blockquote>
<div><ul class="simple">
<li><p>(1) <code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …, which are saved at the end
of each epoch. You can pass <code class="docutils literal notranslate"><span class="pre">--epoch</span></code> to
<code class="docutils literal notranslate"><span class="pre">lstm_transducer_stateless2/decode.py</span></code> to use them.</p></li>
<li><p>(2) <code class="docutils literal notranslate"><span class="pre">checkpoints-436000.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-438000.pt</span></code>, …, which are saved
every <code class="docutils literal notranslate"><span class="pre">--save-every-n</span></code> batches. You can pass <code class="docutils literal notranslate"><span class="pre">--iter</span></code> to
<code class="docutils literal notranslate"><span class="pre">lstm_transducer_stateless2/decode.py</span></code> to use them.</p></li>
</ul>
<p>We suggest that you try both types of checkpoints and choose the one
that produces the lowest WERs.</p>
</div></blockquote>
</div>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
$ ./lstm_transducer_stateless2/decode.py --help
</pre></div>
</div>
<p>shows the options for decoding.</p>
<p>The following shows two examples:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> m <span class="k">in</span> greedy_search fast_beam_search modified_beam_search<span class="p">;</span> <span class="k">do</span>
<span class="k">for</span> epoch <span class="k">in</span> <span class="m">17</span><span class="p">;</span> <span class="k">do</span>
<span class="k">for</span> avg <span class="k">in</span> <span class="m">1</span> <span class="m">2</span><span class="p">;</span> <span class="k">do</span>
./lstm_transducer_stateless2/decode.py <span class="se">\</span>
--epoch <span class="nv">$epoch</span> <span class="se">\</span>
--avg <span class="nv">$avg</span> <span class="se">\</span>
--exp-dir lstm_transducer_stateless2/exp <span class="se">\</span>
--max-duration <span class="m">600</span> <span class="se">\</span>
--num-encoder-layers <span class="m">12</span> <span class="se">\</span>
--rnn-hidden-size <span class="m">1024</span> <span class="se">\</span>
--decoding-method <span class="nv">$m</span> <span class="se">\</span>
--use-averaged-model True <span class="se">\</span>
--beam <span class="m">4</span> <span class="se">\</span>
--max-contexts <span class="m">4</span> <span class="se">\</span>
--max-states <span class="m">8</span> <span class="se">\</span>
--beam-size <span class="m">4</span>
<span class="k">done</span>
<span class="k">done</span>
<span class="k">done</span>
</pre></div>
</div>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> m <span class="k">in</span> greedy_search fast_beam_search modified_beam_search<span class="p">;</span> <span class="k">do</span>
<span class="k">for</span> iter <span class="k">in</span> <span class="m">474000</span><span class="p">;</span> <span class="k">do</span>
<span class="k">for</span> avg <span class="k">in</span> <span class="m">8</span> <span class="m">10</span> <span class="m">12</span> <span class="m">14</span> <span class="m">16</span> <span class="m">18</span><span class="p">;</span> <span class="k">do</span>
./lstm_transducer_stateless2/decode.py <span class="se">\</span>
--iter <span class="nv">$iter</span> <span class="se">\</span>
--avg <span class="nv">$avg</span> <span class="se">\</span>
--exp-dir lstm_transducer_stateless2/exp <span class="se">\</span>
--max-duration <span class="m">600</span> <span class="se">\</span>
--num-encoder-layers <span class="m">12</span> <span class="se">\</span>
--rnn-hidden-size <span class="m">1024</span> <span class="se">\</span>
--decoding-method <span class="nv">$m</span> <span class="se">\</span>
--use-averaged-model True <span class="se">\</span>
--beam <span class="m">4</span> <span class="se">\</span>
--max-contexts <span class="m">4</span> <span class="se">\</span>
--max-states <span class="m">8</span> <span class="se">\</span>
--beam-size <span class="m">4</span>
<span class="k">done</span>
<span class="k">done</span>
<span class="k">done</span>
</pre></div>
</div>
</section>
<section id="export-models">
<h2>Export models<a class="headerlink" href="#export-models" title="Permalink to this heading"></a></h2>
<p><a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/lstm_transducer_stateless2/export.py">lstm_transducer_stateless2/export.py</a> supports to export checkpoints from <code class="docutils literal notranslate"><span class="pre">lstm_transducer_stateless2/exp</span></code> in the following ways.</p>
<section id="export-model-state-dict">
<h3>Export <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code><a class="headerlink" href="#export-model-state-dict" title="Permalink to this heading"></a></h3>
<p>Checkpoints saved by <code class="docutils literal notranslate"><span class="pre">lstm_transducer_stateless2/train.py</span></code> also include
<code class="docutils literal notranslate"><span class="pre">optimizer.state_dict()</span></code>. It is useful for resuming training. But after training,
we are interested only in <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code>. You can use the following
command to extract <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Assume that --iter 468000 --avg 16 produces the smallest WER</span>
<span class="c1"># (You can get such information after running ./lstm_transducer_stateless2/decode.py)</span>
<span class="nv">iter</span><span class="o">=</span><span class="m">468000</span>
<span class="nv">avg</span><span class="o">=</span><span class="m">16</span>
./lstm_transducer_stateless2/export.py <span class="se">\</span>
--exp-dir ./lstm_transducer_stateless2/exp <span class="se">\</span>
--bpe-model data/lang_bpe_500/bpe.model <span class="se">\</span>
--iter <span class="nv">$iter</span> <span class="se">\</span>
--avg <span class="nv">$avg</span>
</pre></div>
</div>
<p>It will generate a file <code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/pretrained.pt</span></code>.</p>
<div class="admonition hint">
<p class="admonition-title">Hint</p>
<p>To use the generated <code class="docutils literal notranslate"><span class="pre">pretrained.pt</span></code> for <code class="docutils literal notranslate"><span class="pre">lstm_transducer_stateless2/decode.py</span></code>,
you can run:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> lstm_transducer_stateless2/exp
ln -s pretrained epoch-9999.pt
</pre></div>
</div>
<p>And then pass <cite>–epoch 9999 –avg 1 –use-averaged-model 0</cite> to
<code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/decode.py</span></code>.</p>
</div>
<p>To use the exported model with <code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/pretrained.py</span></code>, you
can run:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./lstm_transducer_stateless2/pretrained.py <span class="se">\</span>
--checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt <span class="se">\</span>
--bpe-model ./data/lang_bpe_500/bpe.model <span class="se">\</span>
--method greedy_search <span class="se">\</span>
/path/to/foo.wav <span class="se">\</span>
/path/to/bar.wav
</pre></div>
</div>
</section>
<section id="export-model-using-torch-jit-trace">
<h3>Export model using <code class="docutils literal notranslate"><span class="pre">torch.jit.trace()</span></code><a class="headerlink" href="#export-model-using-torch-jit-trace" title="Permalink to this heading"></a></h3>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">iter</span><span class="o">=</span><span class="m">468000</span>
<span class="nv">avg</span><span class="o">=</span><span class="m">16</span>
./lstm_transducer_stateless2/export.py <span class="se">\</span>
--exp-dir ./lstm_transducer_stateless2/exp <span class="se">\</span>
--bpe-model data/lang_bpe_500/bpe.model <span class="se">\</span>
--iter <span class="nv">$iter</span> <span class="se">\</span>
--avg <span class="nv">$avg</span> <span class="se">\</span>
--jit-trace <span class="m">1</span>
</pre></div>
</div>
<p>It will generate 3 files:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/encoder_jit_trace.pt</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/decoder_jit_trace.pt</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/joiner_jit_trace.pt</span></code></p></li>
</ul>
</div></blockquote>
<p>To use the generated files with <code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/jit_pretrained</span></code>:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./lstm_transducer_stateless2/jit_pretrained.py <span class="se">\</span>
--bpe-model ./data/lang_bpe_500/bpe.model <span class="se">\</span>
--encoder-model-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace.pt <span class="se">\</span>
--decoder-model-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace.pt <span class="se">\</span>
--joiner-model-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace.pt <span class="se">\</span>
/path/to/foo.wav <span class="se">\</span>
/path/to/bar.wav
</pre></div>
</div>
</section>
<section id="export-model-for-ncnn">
<h3>Export model for ncnn<a class="headerlink" href="#export-model-for-ncnn" title="Permalink to this heading"></a></h3>
<p>We support exporting pretrained LSTM transducer models to
<a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a> using
<a class="reference external" href="https://github.com/Tencent/ncnn/tree/master/tools/pnnx">pnnx</a>.</p>
<p>First, let us install a modified version of <code class="docutils literal notranslate"><span class="pre">ncnn</span></code>:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git clone https://github.com/csukuangfj/ncnn
<span class="nb">cd</span> ncnn
git submodule update --recursive --init
python3 setup.py bdist_wheel
ls -lh dist/
pip install ./dist/*.whl
<span class="c1"># now build pnnx</span>
<span class="nb">cd</span> tools/pnnx
mkdir build
<span class="nb">cd</span> build
make -j4
<span class="nb">export</span> <span class="nv">PATH</span><span class="o">=</span><span class="nv">$PWD</span>/src:<span class="nv">$PATH</span>
./src/pnnx
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We assume that you have added the path to the binary <code class="docutils literal notranslate"><span class="pre">pnnx</span></code> to the
environment variable <code class="docutils literal notranslate"><span class="pre">PATH</span></code>.</p>
</div>
<p>Second, let us export the model using <code class="docutils literal notranslate"><span class="pre">torch.jit.trace()</span></code> that is suitable
for <code class="docutils literal notranslate"><span class="pre">pnnx</span></code>:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">iter</span><span class="o">=</span><span class="m">468000</span>
<span class="nv">avg</span><span class="o">=</span><span class="m">16</span>
./lstm_transducer_stateless2/export.py <span class="se">\</span>
--exp-dir ./lstm_transducer_stateless2/exp <span class="se">\</span>
--bpe-model data/lang_bpe_500/bpe.model <span class="se">\</span>
--iter <span class="nv">$iter</span> <span class="se">\</span>
--avg <span class="nv">$avg</span> <span class="se">\</span>
--pnnx <span class="m">1</span>
</pre></div>
</div>
<p>It will generate 3 files:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.pt</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.pt</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.pt</span></code></p></li>
</ul>
</div></blockquote>
<p>Third, convert torchscript model to <code class="docutils literal notranslate"><span class="pre">ncnn</span></code> format:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pnnx</span> <span class="o">./</span><span class="n">lstm_transducer_stateless2</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">encoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
<span class="n">pnnx</span> <span class="o">./</span><span class="n">lstm_transducer_stateless2</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">decoder_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
<span class="n">pnnx</span> <span class="o">./</span><span class="n">lstm_transducer_stateless2</span><span class="o">/</span><span class="n">exp</span><span class="o">/</span><span class="n">joiner_jit_trace</span><span class="o">-</span><span class="n">pnnx</span><span class="o">.</span><span class="n">pt</span>
</pre></div>
</div>
<p>It will generate the following files:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.bin</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.param</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.bin</span></code></p></li>
</ul>
</div></blockquote>
<p>To use the above generate files, run:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<dl class="simple">
<dt>./lstm_transducer_stateless2/ncnn-decode.py </dt><dd><p>–bpe-model-filename ./data/lang_bpe_500/bpe.model –encoder-param-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param –encoder-bin-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin –decoder-param-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param –decoder-bin-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.bin –joiner-param-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.param –joiner-bin-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.bin /path/to/foo.wav</p>
</dd>
</dl>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<dl class="simple">
<dt>./lstm_transducer_stateless2/streaming-ncnn-decode.py </dt><dd><p>–bpe-model-filename ./data/lang_bpe_500/bpe.model –encoder-param-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param –encoder-bin-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin –decoder-param-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param –decoder-bin-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.bin –joiner-param-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.param –joiner-bin-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.bin /path/to/foo.wav</p>
</dd>
</dl>
<p>To use the above generated files in C++, please see
<a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">https://github.com/k2-fsa/sherpa-ncnn</a></p>
<p>It is able to generate a static linked library that can be run on Linux, Windows,
macOS, Raspberry Pi, etc.</p>
</section>
</section>
<section id="download-pretrained-models">
<h2>Download pretrained models<a class="headerlink" href="#download-pretrained-models" title="Permalink to this heading"></a></h2>
<p>If you don’t want to train from scratch, you can download the pretrained models
by visiting the following links:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03">https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03</a></p></li>
<li><p><a class="reference external" href="https://huggingface.co/Zengwei/icefall-asr-librispeech-lstm-transducer-stateless-2022-08-18">https://huggingface.co/Zengwei/icefall-asr-librispeech-lstm-transducer-stateless-2022-08-18</a></p></li>
</ul>
<p>See <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md">https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md</a>
for the details of the above pretrained models</p>
</div></blockquote>
<p>You can find more usages of the pretrained models in
<a class="reference external" href="https://k2-fsa.github.io/sherpa/python/streaming_asr/lstm/index.html">https://k2-fsa.github.io/sherpa/python/streaming_asr/lstm/index.html</a></p>
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