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* Add zipformer_ctc_blankskip.rst * typo fix for zipformer_mmi.rst * fix warning * Update docs/source/recipes/Non-streaming-ASR/librispeech/zipformer_ctc_blankskip.rst Co-authored-by: yifanyang <yifanyeung@yifanyangs-MacBook-Pro.local> Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
59 lines
1.7 KiB
ReStructuredText
59 lines
1.7 KiB
ReStructuredText
.. _export-model-with-torch-jit-script:
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Export model with torch.jit.script()
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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.script()``.
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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.script()``.
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.. hint::
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See :ref:`export-model-with-torch-jit-trace`
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if you want to use ``torch.jit.trace()``.
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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/pruned_transducer_stateless3>`_
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as an example in the following.
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.. code-block:: bash
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cd egs/librispeech/ASR
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epoch=14
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avg=1
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./pruned_transducer_stateless3/export.py \
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--exp-dir ./pruned_transducer_stateless3/exp \
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--bpe-model data/lang_bpe_500/bpe.model \
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--epoch $epoch \
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--avg $avg \
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--jit 1
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It will generate a file ``cpu_jit.pt`` in ``pruned_transducer_stateless3/exp``.
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.. caution::
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Don't be confused by ``cpu`` in ``cpu_jit.pt``. We move all parameters
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to CPU before saving it into a ``pt`` file; that's why we use ``cpu``
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in the filename.
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How to use the exported model
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-----------------------------
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Please refer to the following pages for usage:
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- `<https://k2-fsa.github.io/sherpa/python/streaming_asr/emformer/index.html>`_
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- `<https://k2-fsa.github.io/sherpa/python/streaming_asr/conv_emformer/index.html>`_
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- `<https://k2-fsa.github.io/sherpa/python/streaming_asr/conformer/index.html>`_
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- `<https://k2-fsa.github.io/sherpa/python/offline_asr/conformer/index.html>`_
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- `<https://k2-fsa.github.io/sherpa/cpp/offline_asr/gigaspeech.html>`_
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- `<https://k2-fsa.github.io/sherpa/cpp/offline_asr/wenetspeech.html>`_
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