mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 01:52:41 +00:00
70 lines
1.9 KiB
ReStructuredText
70 lines
1.9 KiB
ReStructuredText
Export to ONNX
|
|
==============
|
|
|
|
In this section, we describe how to export models to ONNX.
|
|
|
|
.. hint::
|
|
|
|
Only non-streaming conformer transducer models are tested.
|
|
|
|
|
|
When to use it
|
|
--------------
|
|
|
|
It you want to use an inference framework that supports ONNX
|
|
to run the pretrained model.
|
|
|
|
|
|
How to export
|
|
-------------
|
|
|
|
We use
|
|
`<https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless3>`_
|
|
as an example in the following.
|
|
|
|
.. code-block:: bash
|
|
|
|
cd egs/librispeech/ASR
|
|
epoch=14
|
|
avg=2
|
|
|
|
./pruned_transducer_stateless3/export.py \
|
|
--exp-dir ./pruned_transducer_stateless3/exp \
|
|
--bpe-model data/lang_bpe_500/bpe.model \
|
|
--epoch $epoch \
|
|
--avg $avg \
|
|
--onnx 1
|
|
|
|
It will generate the following files inside ``pruned_transducer_stateless3/exp``:
|
|
|
|
- ``encoder.onnx``
|
|
- ``decoder.onnx``
|
|
- ``joiner.onnx``
|
|
- ``joiner_encoder_proj.onnx``
|
|
- ``joiner_decoder_proj.onnx``
|
|
|
|
You can use ``./pruned_transducer_stateless3/exp/onnx_pretrained.py`` to decode
|
|
waves with the generated files:
|
|
|
|
.. code-block:: bash
|
|
|
|
./pruned_transducer_stateless3/onnx_pretrained.py \
|
|
--bpe-model ./data/lang_bpe_500/bpe.model \
|
|
--encoder-model-filename ./pruned_transducer_stateless3/exp/encoder.onnx \
|
|
--decoder-model-filename ./pruned_transducer_stateless3/exp/decoder.onnx \
|
|
--joiner-model-filename ./pruned_transducer_stateless3/exp/joiner.onnx \
|
|
--joiner-encoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_encoder_proj.onnx \
|
|
--joiner-decoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_decoder_proj.onnx \
|
|
/path/to/foo.wav \
|
|
/path/to/bar.wav \
|
|
/path/to/baz.wav
|
|
|
|
|
|
How to use the exported model
|
|
-----------------------------
|
|
|
|
We also provide `<https://github.com/k2-fsa/sherpa-onnx>`_
|
|
performing speech recognition using `onnxruntime <https://github.com/microsoft/onnxruntime>`_
|
|
with exported models.
|
|
It has been tested on Linux, macOS, and Windows.
|