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Small fixes to the transducer training doc (#575)

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Fangjun Kuang 2022-09-21 14:20:49 +08:00 committed by GitHub
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docs/source/recipes/librispeech/lstm_pruned_stateless_transducer.rst
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@ -1,5 +1,5 @@
Transducer
==========
LSTM Transducer
===============
.. hint::
@ -7,7 +7,7 @@ Transducer
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.
@ -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::
@ -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``
@ -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
@ -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
@ -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()``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@ -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
@ -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
~~~~~~~~~~~~~~~~~~~~~
@ -576,7 +585,7 @@ 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
@ -605,8 +614,8 @@ To use the above generate files, run:
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
--------------------------