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.. _export_streaming_zipformer_transducer_models_to_ncnn:
Export streaming Zipformer transducer models to ncnn
----------------------------------------------------
We use the pre-trained model from the following repository as an example:
`<https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29>`_
We will show you step by step how to export it to `ncnn`_ and run it with `sherpa-ncnn`_.
.. hint::
We use ``Ubuntu 18.04``, ``torch 1.13``, and ``Python 3.8`` for testing.
.. caution::
Please use a more recent version of PyTorch. For instance, ``torch 1.8``
may ``not`` work.
1. Download the pre-trained model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. hint::
You have to install `git-lfs`_ before you continue.
.. code-block:: bash
cd egs/librispeech/ASR
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
git lfs pull --include "exp/pretrained.pt"
git lfs pull --include "data/lang_bpe_500/bpe.model"
cd ..
.. note::
We downloaded ``exp/pretrained-xxx.pt``, not ``exp/cpu-jit_xxx.pt``.
In the above code, we downloaded the pre-trained model into the directory
``egs/librispeech/ASR/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29``.
2. Install ncnn and pnnx
^^^^^^^^^^^^^^^^^^^^^^^^
Please refer to :ref:`export_for_ncnn_install_ncnn_and_pnnx` .
3. Export the model via torch.jit.trace()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
First, let us rename our pre-trained model:
.. code-block::
cd egs/librispeech/ASR
cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
ln -s pretrained.pt epoch-99.pt
cd ../..
Next, we use the following code to export our model:
.. code-block:: bash
dir=./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
./pruned_transducer_stateless7_streaming/export-for-ncnn.py \
--bpe-model $dir/data/lang_bpe_500/bpe.model \
--exp-dir $dir/exp \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
\
--decode-chunk-len 32 \
--num-left-chunks 4 \
--num-encoder-layers "2,4,3,2,4" \
--feedforward-dims "1024,1024,2048,2048,1024" \
--nhead "8,8,8,8,8" \
--encoder-dims "384,384,384,384,384" \
--attention-dims "192,192,192,192,192" \
--encoder-unmasked-dims "256,256,256,256,256" \
--zipformer-downsampling-factors "1,2,4,8,2" \
--cnn-module-kernels "31,31,31,31,31" \
--decoder-dim 512 \
--joiner-dim 512
.. caution::
If your model has different configuration parameters, please change them accordingly.
.. hint::
We have renamed our model to ``epoch-99.pt`` so that we can use ``--epoch 99``.
There is only one pre-trained model, so we use ``--avg 1 --use-averaged-model 0``.
If you have trained a model by yourself and if you have all checkpoints
available, please first use ``decode.py`` to tune ``--epoch --avg``
and select the best combination with with ``--use-averaged-model 1``.
.. note::
You will see the following log output:
.. literalinclude:: ./code/export-zipformer-transducer-for-ncnn-output.txt
The log shows the model has ``69920376`` parameters, i.e., ``~69.9 M``.
.. code-block:: bash
ls -lh icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/pretrained.pt
-rw-r--r-- 1 kuangfangjun root 269M Jan 12 12:53 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/pretrained.pt
You can see that the file size of the pre-trained model is ``269 MB``, which
is roughly equal to ``69920376*4/1024/1024 = 266.725 MB``.
After running ``pruned_transducer_stateless7_streaming/export-for-ncnn.py``,
we will get the following files:
.. code-block:: bash
ls -lh icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/*pnnx.pt
-rw-r--r-- 1 kuangfangjun root 1022K Feb 27 20:23 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/decoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 266M Feb 27 20:23 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/encoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 2.8M Feb 27 20:23 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/joiner_jit_trace-pnnx.pt
.. _zipformer-transducer-step-4-export-torchscript-model-via-pnnx:
4. Export torchscript model via pnnx
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. hint::
Make sure you have set up the ``PATH`` environment variable
in :ref:`export_for_ncnn_install_ncnn_and_pnnx`. Otherwise,
it will throw an error saying that ``pnnx`` could not be found.
Now, it's time to export our models to `ncnn`_ via ``pnnx``.
.. code-block::
cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/
pnnx ./encoder_jit_trace-pnnx.pt
pnnx ./decoder_jit_trace-pnnx.pt
pnnx ./joiner_jit_trace-pnnx.pt
It will generate the following files:
.. code-block:: bash
ls -lh icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/*ncnn*{bin,param}
-rw-r--r-- 1 kuangfangjun root 509K Feb 27 20:31 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 437 Feb 27 20:31 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 133M Feb 27 20:30 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 152K Feb 27 20:30 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 1.4M Feb 27 20:31 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 488 Feb 27 20:31 icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/joiner_jit_trace-pnnx.ncnn.param
There are two types of files:
- ``param``: It is a text file containing the model architectures. You can
use a text editor to view its content.
- ``bin``: It is a binary file containing the model parameters.
We compare the file sizes of the models below before and after converting via ``pnnx``:
.. see https://tableconvert.com/restructuredtext-generator
+----------------------------------+------------+
| File name | File size |
+==================================+============+
| encoder_jit_trace-pnnx.pt | 266 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1022 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 2.8 MB |
+----------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin | 133 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin | 509 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin | 1.4 MB |
+----------------------------------+------------+
You can see that the file sizes of the models after conversion are about one half
of the models before conversion:
- encoder: 266 MB vs 133 MB
- decoder: 1022 KB vs 509 KB
- joiner: 2.8 MB vs 1.4 MB
The reason is that by default ``pnnx`` converts ``float32`` parameters
to ``float16``. A ``float32`` parameter occupies 4 bytes, while it is 2 bytes
for ``float16``. Thus, it is ``twice smaller`` after conversion.
.. hint::
If you use ``pnnx ./encoder_jit_trace-pnnx.pt fp16=0``, then ``pnnx``
won't convert ``float32`` to ``float16``.
5. Test the exported models in icefall
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. note::
We assume you have set up the environment variable ``PYTHONPATH`` when
building `ncnn`_.
Now we have successfully converted our pre-trained model to `ncnn`_ format.
The generated 6 files are what we need. You can use the following code to
test the converted models:
.. code-block:: bash
python3 ./pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py \
--tokens ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/tokens.txt \
--encoder-param-filename ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/encoder_jit_trace-pnnx.ncnn.param \
--encoder-bin-filename ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/encoder_jit_trace-pnnx.ncnn.bin \
--decoder-param-filename ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/decoder_jit_trace-pnnx.ncnn.param \
--decoder-bin-filename ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/decoder_jit_trace-pnnx.ncnn.bin \
--joiner-param-filename ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/joiner_jit_trace-pnnx.ncnn.param \
--joiner-bin-filename ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/joiner_jit_trace-pnnx.ncnn.bin \
./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/test_wavs/1089-134686-0001.wav
.. hint::
`ncnn`_ supports only ``batch size == 1``, so ``streaming-ncnn-decode.py`` accepts
only 1 wave file as input.
The output is given below:
.. literalinclude:: ./code/test-streaming-ncnn-decode-zipformer-transducer-libri.txt
Congratulations! You have successfully exported a model from PyTorch to `ncnn`_!
.. _zipformer-modify-the-exported-encoder-for-sherpa-ncnn:
6. Modify the exported encoder for sherpa-ncnn
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In order to use the exported models in `sherpa-ncnn`_, we have to modify
``encoder_jit_trace-pnnx.ncnn.param``.
Let us have a look at the first few lines of ``encoder_jit_trace-pnnx.ncnn.param``:
.. code-block::
7767517
2028 2547
Input in0 0 1 in0
**Explanation** of the above three lines:
1. ``7767517``, it is a magic number and should not be changed.
2. ``2028 2547``, the first number ``2028`` specifies the number of layers
in this file, while ``2547`` specifies the number of intermediate outputs
of this file
3. ``Input in0 0 1 in0``, ``Input`` is the layer type of this layer; ``in0``
is the layer name of this layer; ``0`` means this layer has no input;
``1`` means this layer has one output; ``in0`` is the output name of
this layer.
We need to add 1 extra line and also increment the number of layers.
The result looks like below:
.. code-block:: bash
7767517
2029 2547
SherpaMetaData sherpa_meta_data1 0 0 0=2 1=32 2=4 3=7 -23316=5,2,4,3,2,4 -23317=5,384,384,384,384,384 -23318=5,192,192,192,192,192 -23319=5,1,2,4,8,2 -23320=5,31,31,31,31,31
Input in0 0 1 in0
**Explanation**
1. ``7767517``, it is still the same
2. ``2029 2547``, we have added an extra layer, so we need to update ``2028`` to ``2029``.
We don't need to change ``2547`` since the newly added layer has no inputs or outputs.
3. ``SherpaMetaData sherpa_meta_data1 0 0 0=2 1=32 2=4 3=7 -23316=5,2,4,3,2,4 -23317=5,384,384,384,384,384 -23318=5,192,192,192,192,192 -23319=5,1,2,4,8,2 -23320=5,31,31,31,31,31``
This line is newly added. Its explanation is given below:
- ``SherpaMetaData`` is the type of this layer. Must be ``SherpaMetaData``.
- ``sherpa_meta_data1`` is the name of this layer. Must be ``sherpa_meta_data1``.
- ``0 0`` means this layer has no inputs or output. Must be ``0 0``
- ``0=2``, 0 is the key and 2 is the value. MUST be ``0=2``
- ``1=32``, 1 is the key and 32 is the value of the
parameter ``--decode-chunk-len`` that you provided when running
``./pruned_transducer_stateless7_streaming/export-for-ncnn.py``.
- ``2=4``, 2 is the key and 4 is the value of the
parameter ``--num-left-chunks`` that you provided when running
``./pruned_transducer_stateless7_streaming/export-for-ncnn.py``.
- ``3=7``, 3 is the key and 7 is the value of for the amount of padding
used in the Conv2DSubsampling layer. It should be 7 for zipformer
if you don't change zipformer.py.
- ``-23316=5,2,4,3,2,4``, attribute 16, this is an array attribute.
It is attribute 16 since -23300 - (-23316) = 16.
The first element of the array is the length of the array, which is 5 in our case.
``2,4,3,2,4`` is the value of ``--num-encoder-layers``that you provided
when running ``./pruned_transducer_stateless7_streaming/export-for-ncnn.py``.
- ``-23317=5,384,384,384,384,384``, attribute 17.
The first element of the array is the length of the array, which is 5 in our case.
``384,384,384,384,384`` is the value of ``--encoder-dims``that you provided
when running ``./pruned_transducer_stateless7_streaming/export-for-ncnn.py``.
- ``-23318=5,192,192,192,192,192``, attribute 18.
The first element of the array is the length of the array, which is 5 in our case.
``192,192,192,192,192`` is the value of ``--attention-dims`` that you provided
when running ``./pruned_transducer_stateless7_streaming/export-for-ncnn.py``.
- ``-23319=5,1,2,4,8,2``, attribute 19.
The first element of the array is the length of the array, which is 5 in our case.
``1,2,4,8,2`` is the value of ``--zipformer-downsampling-factors`` that you provided
when running ``./pruned_transducer_stateless7_streaming/export-for-ncnn.py``.
- ``-23320=5,31,31,31,31,31``, attribute 20.
The first element of the array is the length of the array, which is 5 in our case.
``31,31,31,31,31`` is the value of ``--cnn-module-kernels`` that you provided
when running ``./pruned_transducer_stateless7_streaming/export-for-ncnn.py``.
For ease of reference, we list the key-value pairs that you need to add
in the following table. If your model has a different setting, please
change the values for ``SherpaMetaData`` accordingly. Otherwise, you
will be ``SAD``.
+----------+--------------------------------------------+
| key | value |
+==========+============================================+
| 0 | 2 (fixed) |
+----------+--------------------------------------------+
| 1 | ``-decode-chunk-len`` |
+----------+--------------------------------------------+
| 2 | ``--num-left-chunks`` |
+----------+--------------------------------------------+
| 3 | 7 (if you don't change code) |
+----------+--------------------------------------------+
|-23316 | ``--num-encoder-layer`` |
+----------+--------------------------------------------+
|-23317 | ``--encoder-dims`` |
+----------+--------------------------------------------+
|-23318 | ``--attention-dims`` |
+----------+--------------------------------------------+
|-23319 | ``--zipformer-downsampling-factors`` |
+----------+--------------------------------------------+
|-23320 | ``--cnn-module-kernels`` |
+----------+--------------------------------------------+
4. ``Input in0 0 1 in0``. No need to change it.
.. caution::
When you add a new layer ``SherpaMetaData``, please remember to update the
number of layers. In our case, update ``2028`` to ``2029``. Otherwise,
you will be SAD later.
.. hint::
After adding the new layer ``SherpaMetaData``, you cannot use this model
with ``streaming-ncnn-decode.py`` anymore since ``SherpaMetaData`` is
supported only in `sherpa-ncnn`_.
.. hint::
`ncnn`_ is very flexible. You can add new layers to it just by text-editing
the ``param`` file! You don't need to change the ``bin`` file.
Now you can use this model in `sherpa-ncnn`_.
Please refer to the following documentation:
- Linux/macOS/Windows/arm/aarch64: `<https://k2-fsa.github.io/sherpa/ncnn/install/index.html>`_
- ``Android``: `<https://k2-fsa.github.io/sherpa/ncnn/android/index.html>`_
- ``iOS``: `<https://k2-fsa.github.io/sherpa/ncnn/ios/index.html>`_
- Python: `<https://k2-fsa.github.io/sherpa/ncnn/python/index.html>`_
We have a list of pre-trained models that have been exported for `sherpa-ncnn`_:
- `<https://k2-fsa.github.io/sherpa/ncnn/pretrained_models/index.html>`_
You can find more usages there.
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