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Small fixes to the transducer training doc #575

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Sep 21, 2022
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Small fixes to the transducer training doc #575

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73 changes: 41 additions & 32 deletions docs/source/recipes/librispeech/lstm_pruned_stateless_transducer.rst
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Original file line number Diff line number Diff line change
@@ -1,13 +1,13 @@
Transducer
==========
LSTM 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
This tutorial shows you how to train an LSTM transducer model
with the `LibriSpeech <https://www.openslr.org/12>`_ dataset.

We use pruned RNN-T to compute the loss.
Expand All @@ -20,9 +20,9 @@ We use pruned RNN-T to compute the loss.

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``
- Encoder, a.k.a, the transcription network. We use an LSTM model
- Decoder, a.k.a, the prediction network. We use a stateless model consisting of
``nn.Embedding`` and ``nn.Conv1d``
- Joiner, a.k.a, the joint network.

.. caution::
Expand Down Expand Up @@ -74,7 +74,11 @@ Data preparation
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
.. note::

We encourage you to read ``./prepare.sh``.

The data preparation contains several stages. You can use the following two
options:

- ``--stage``
Expand Down Expand Up @@ -263,7 +267,7 @@ You will find the following files in that directory:

- ``tensorboard/``

This folder contains TensorBoard logs. Training loss, validation loss, learning
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
Expand All @@ -287,7 +291,7 @@ You will find the following files in that directory:
[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
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
Expand Down Expand Up @@ -422,7 +426,7 @@ The following shows two examples:
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.
`lstm_transducer_stateless2/export.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/lstm_transducer_stateless2/export.py>`_ supports exporting checkpoints from ``lstm_transducer_stateless2/exp`` in the following ways.

Export ``model.state_dict()``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Expand Down Expand Up @@ -458,7 +462,7 @@ It will generate a file ``./lstm_transducer_stateless2/exp/pretrained.pt``.
cd lstm_transducer_stateless2/exp
ln -s pretrained epoch-9999.pt

And then pass `--epoch 9999 --avg 1 --use-averaged-model 0` to
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
Expand Down Expand Up @@ -506,6 +510,11 @@ To use the generated files with ``./lstm_transducer_stateless2/jit_pretrained``:
/path/to/foo.wav \
/path/to/bar.wav

.. hint::

Please see `<https://k2-fsa.github.io/sherpa/python/streaming_asr/lstm/english/server.html>`_
for how to use the exported models in ``sherpa``.

Export model for ncnn
~~~~~~~~~~~~~~~~~~~~~

Expand Down Expand Up @@ -576,37 +585,37 @@ It will generate the following files:
- ``./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:
To use the above generated 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
./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
./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.
It is able to generate a static linked executable that can be run on Linux, Windows,
macOS, Raspberry Pi, etc, without external dependencies.

Download pretrained models
--------------------------
Expand Down