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70 lines
1.9 KiB
ReStructuredText
70 lines
1.9 KiB
ReStructuredText
.. _export-model-with-torch-jit-trace:
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Export model with torch.jit.trace()
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===================================
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In this section, we describe how to export a model via
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``torch.jit.trace()``.
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When to use it
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--------------
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If we want to use our trained model with torchscript,
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we can use ``torch.jit.trace()``.
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.. hint::
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See :ref:`export-model-with-torch-jit-script`
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if you want to use ``torch.jit.script()``.
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How to export
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-------------
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We use
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`<https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless2>`_
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as an example in the following.
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.. code-block:: bash
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iter=468000
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avg=16
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cd egs/librispeech/ASR
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./lstm_transducer_stateless2/export.py \
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--exp-dir ./lstm_transducer_stateless2/exp \
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--tokens data/lang_bpe_500/tokens.txt \
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--iter $iter \
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--avg $avg \
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--jit-trace 1
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It will generate three files inside ``lstm_transducer_stateless2/exp``:
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- ``encoder_jit_trace.pt``
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- ``decoder_jit_trace.pt``
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- ``joiner_jit_trace.pt``
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You can use
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`<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/lstm_transducer_stateless2/jit_pretrained.py>`_
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to decode sound files with the following commands:
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.. code-block:: bash
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cd egs/librispeech/ASR
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./lstm_transducer_stateless2/jit_pretrained.py \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--encoder-model-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace.pt \
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--decoder-model-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace.pt \
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--joiner-model-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace.pt \
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/path/to/foo.wav \
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/path/to/bar.wav \
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/path/to/baz.wav
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How to use the exported models
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------------------------------
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Please refer to
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`<https://k2-fsa.github.io/sherpa/python/streaming_asr/lstm/index.html>`_
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for its usage in `sherpa <https://k2-fsa.github.io/sherpa/python/streaming_asr/lstm/index.html>`_.
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You can also find pretrained models there.
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