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Update doc about exporting LSTM models to ncnn (#914)

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docs/source/model-export/code/export-lstm-transducer-for-ncnn-output.txt Normal file
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2023-02-17 11:22:42,862 INFO [export-for-ncnn.py:222] device: cpu
2023-02-17 11:22:42,865 INFO [export-for-ncnn.py:231] {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'dim_feedforward': 2048, 'decoder_dim': 512, 'joiner_dim': 512, 'is_pnnx': False, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.23.4', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '62e404dd3f3a811d73e424199b3408e309c06e1a', 'k2-git-date': 'Mon Jan 30 10:26:16 2023', 'lhotse-version': '1.12.0.dev+missing.version.file', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': False, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'master', 'icefall-git-sha1': '6d7a559-dirty', 'icefall-git-date': 'Thu Feb 16 19:47:54 2023', 'icefall-path': '/star-fj/fangjun/open-source/icefall-2', 'k2-path': '/star-fj/fangjun/open-source/k2/k2/python/k2/__init__.py', 'lhotse-path': '/star-fj/fangjun/open-source/lhotse/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-3-1220120619-7695ff496b-s9n4w', 'IP address': '10.177.6.147'}, 'epoch': 99, 'iter': 0, 'avg': 1, 'exp_dir': PosixPath('icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp'), 'bpe_model': './icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/data/lang_bpe_500/bpe.model', 'context_size': 2, 'use_averaged_model': False, 'num_encoder_layers': 12, 'encoder_dim': 512, 'rnn_hidden_size': 1024, 'aux_layer_period': 0, 'blank_id': 0, 'vocab_size': 500}
2023-02-17 11:22:42,865 INFO [export-for-ncnn.py:235] About to create model
2023-02-17 11:22:43,239 INFO [train.py:472] Disable giga
2023-02-17 11:22:43,249 INFO [checkpoint.py:112] Loading checkpoint from icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/epoch-99.pt
2023-02-17 11:22:44,595 INFO [export-for-ncnn.py:324] encoder parameters: 83137520
2023-02-17 11:22:44,596 INFO [export-for-ncnn.py:325] decoder parameters: 257024
2023-02-17 11:22:44,596 INFO [export-for-ncnn.py:326] joiner parameters: 781812
2023-02-17 11:22:44,596 INFO [export-for-ncnn.py:327] total parameters: 84176356
2023-02-17 11:22:44,596 INFO [export-for-ncnn.py:329] Using torch.jit.trace()
2023-02-17 11:22:44,596 INFO [export-for-ncnn.py:331] Exporting encoder
2023-02-17 11:22:48,182 INFO [export-for-ncnn.py:158] Saved to icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.pt
2023-02-17 11:22:48,183 INFO [export-for-ncnn.py:335] Exporting decoder
/star-fj/fangjun/open-source/icefall-2/egs/librispeech/ASR/lstm_transducer_stateless2/decoder.py:101: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
need_pad = bool(need_pad)
2023-02-17 11:22:48,259 INFO [export-for-ncnn.py:180] Saved to icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.pt
2023-02-17 11:22:48,259 INFO [export-for-ncnn.py:339] Exporting joiner
2023-02-17 11:22:48,304 INFO [export-for-ncnn.py:207] Saved to icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.pt

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docs/source/model-export/code/generate-int-8-scale-table-for-lstm.txt Normal file
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Don't Use GPU. has_gpu: 0, config.use_vulkan_compute: 1
num encoder conv layers: 28
num joiner conv layers: 3
num files: 3
Processing ../test_wavs/1089-134686-0001.wav
Processing ../test_wavs/1221-135766-0001.wav
Processing ../test_wavs/1221-135766-0002.wav
Processing ../test_wavs/1089-134686-0001.wav
Processing ../test_wavs/1221-135766-0001.wav
Processing ../test_wavs/1221-135766-0002.wav
----------encoder----------
conv_15 : max = 15.942385 threshold = 15.930708 scale = 7.972025
conv_16 : max = 44.978855 threshold = 17.031788 scale = 7.456645
conv_17 : max = 17.868437 threshold = 7.830528 scale = 16.218575
linear_18 : max = 3.107259 threshold = 1.194808 scale = 106.293236
linear_19 : max = 6.193777 threshold = 4.634748 scale = 27.401705
linear_20 : max = 9.259933 threshold = 2.606617 scale = 48.722160
linear_21 : max = 5.186600 threshold = 4.790260 scale = 26.512129
linear_22 : max = 9.759041 threshold = 2.265832 scale = 56.050053
linear_23 : max = 3.931209 threshold = 3.099090 scale = 40.979767
linear_24 : max = 10.324160 threshold = 2.215561 scale = 57.321835
linear_25 : max = 3.800708 threshold = 3.599352 scale = 35.284134
linear_26 : max = 10.492444 threshold = 3.153369 scale = 40.274391
linear_27 : max = 3.660161 threshold = 2.720994 scale = 46.674126
linear_28 : max = 9.415265 threshold = 3.174434 scale = 40.007133
linear_29 : max = 4.038418 threshold = 3.118534 scale = 40.724262
linear_30 : max = 10.072084 threshold = 3.936867 scale = 32.259155
linear_31 : max = 4.342712 threshold = 3.599489 scale = 35.282787
linear_32 : max = 11.340535 threshold = 3.120308 scale = 40.701103
linear_33 : max = 3.846987 threshold = 3.630030 scale = 34.985939
linear_34 : max = 10.686298 threshold = 2.204571 scale = 57.607586
linear_35 : max = 4.904821 threshold = 4.575518 scale = 27.756420
linear_36 : max = 11.806659 threshold = 2.585589 scale = 49.118401
linear_37 : max = 6.402340 threshold = 5.047157 scale = 25.162680
linear_38 : max = 11.174589 threshold = 1.923361 scale = 66.030258
linear_39 : max = 16.178576 threshold = 7.556058 scale = 16.807705
linear_40 : max = 12.901954 threshold = 5.301267 scale = 23.956539
linear_41 : max = 14.839805 threshold = 7.597429 scale = 16.716181
linear_42 : max = 10.178945 threshold = 2.651595 scale = 47.895699
----------joiner----------
linear_2 : max = 24.829245 threshold = 16.627592 scale = 7.637907
linear_1 : max = 10.746186 threshold = 5.255032 scale = 24.167313
linear_3 : max = 1.000000 threshold = 0.999756 scale = 127.031013
ncnn int8 calibration table create success, best wish for your int8 inference has a low accuracy loss...\(^0^)/...233...

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docs/source/model-export/code/test-stremaing-ncnn-decode-conv-emformer-transducer-libri.txt → docs/source/model-export/code/test-streaming-ncnn-decode-conv-emformer-transducer-libri.txt
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2023-02-17 11:37:30,861 INFO [streaming-ncnn-decode.py:255] {'tokens': './icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/data/lang_bpe_500/tokens.txt', 'encoder_param_filename': './icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param', 'encoder_bin_filename': './icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin', 'decoder_param_filename': './icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param', 'decoder_bin_filename': './icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin', 'joiner_param_filename': './icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param', 'joiner_bin_filename': './icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin', 'sound_filename': './icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/test_wavs/1089-134686-0001.wav'}
2023-02-17 11:37:31,425 INFO [streaming-ncnn-decode.py:263] Constructing Fbank computer
2023-02-17 11:37:31,427 INFO [streaming-ncnn-decode.py:266] Reading sound files: ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/test_wavs/1089-134686-0001.wav
2023-02-17 11:37:31,431 INFO [streaming-ncnn-decode.py:271] torch.Size([106000])
2023-02-17 11:37:34,115 INFO [streaming-ncnn-decode.py:342] ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/test_wavs/1089-134686-0001.wav
2023-02-17 11:37:34,115 INFO [streaming-ncnn-decode.py:343] AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS

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.. _export_conv_emformer_transducer_models_to_ncnn:
Export ConvEmformer transducer models to ncnn
=============================================
We use the pre-trained model from the following repository as an example:
- `<https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05>`_
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 can also refer to `<https://k2-fsa.github.io/sherpa/cpp/pretrained_models/online_transducer.html#icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05>`_ to download the pre-trained model.
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-conv-emformer-transducer-stateless2-2022-07-05
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
git lfs pull --include "exp/pretrained-epoch-30-avg-10-averaged.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-conv-emformer-transducer-stateless2-2022-07-05``.
.. _export_for_ncnn_install_ncnn_and_pnnx:
2. Install ncnn and pnnx
------------------------
.. code-block:: bash
# We put ncnn into $HOME/open-source/ncnn
# You can change it to anywhere you like
cd $HOME
mkdir -p open-source
cd open-source
git clone https://github.com/csukuangfj/ncnn
cd ncnn
git submodule update --recursive --init
# Note: We don't use "python setup.py install" or "pip install ." here
mkdir -p build-wheel
cd build-wheel
cmake \
-DCMAKE_BUILD_TYPE=Release \
-DNCNN_PYTHON=ON \
-DNCNN_BUILD_BENCHMARK=OFF \
-DNCNN_BUILD_EXAMPLES=OFF \
-DNCNN_BUILD_TOOLS=ON \
..
make -j4
cd ..
# Note: $PWD here is $HOME/open-source/ncnn
export PYTHONPATH=$PWD/python:$PYTHONPATH
export PATH=$PWD/tools/pnnx/build/src:$PATH
export PATH=$PWD/build-wheel/tools/quantize:$PATH
# Now build pnnx
cd tools/pnnx
mkdir build
cd build
cmake ..
make -j4
./src/pnnx
Congratulations! You have successfully installed the following components:
- ``pnxx``, which is an executable located in
``$HOME/open-source/ncnn/tools/pnnx/build/src``. We will use
it to convert models exported by ``torch.jit.trace()``.
- ``ncnn2int8``, which is an executable located in
``$HOME/open-source/ncnn/build-wheel/tools/quantize``. We will use
it to quantize our models to ``int8``.
- ``ncnn.cpython-38-x86_64-linux-gnu.so``, which is a Python module located
in ``$HOME/open-source/ncnn/python/ncnn``.
.. note::
I am using ``Python 3.8``, so it
is ``ncnn.cpython-38-x86_64-linux-gnu.so``. If you use a different
version, say, ``Python 3.9``, the name would be
``ncnn.cpython-39-x86_64-linux-gnu.so``.
Also, if you are not using Linux, the file name would also be different.
But that does not matter. As long as you can compile it, it should work.
We have set up ``PYTHONPATH`` so that you can use ``import ncnn`` in your
Python code. We have also set up ``PATH`` so that you can use
``pnnx`` and ``ncnn2int8`` later in your terminal.
.. caution::
Please don't use `<https://github.com/tencent/ncnn>`_.
We have made some modifications to the offical `ncnn`_.
We will synchronize `<https://github.com/csukuangfj/ncnn>`_ periodically
with the official one.
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-conv-emformer-transducer-stateless2-2022-07-05/exp
ln -s pretrained-epoch-30-avg-10-averaged.pt epoch-30.pt
cd ../..
Next, we use the following code to export our model:
.. code-block:: bash
dir=./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/
./conv_emformer_transducer_stateless2/export-for-ncnn.py \
--exp-dir $dir/exp \
--bpe-model $dir/data/lang_bpe_500/bpe.model \
--epoch 30 \
--avg 1 \
--use-averaged-model 0 \
\
--num-encoder-layers 12 \
--chunk-length 32 \
--cnn-module-kernel 31 \
--left-context-length 32 \
--right-context-length 8 \
--memory-size 32 \
--encoder-dim 512
.. hint::
We have renamed our model to ``epoch-30.pt`` so that we can use ``--epoch 30``.
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-conv-emformer-transducer-for-ncnn-output.txt
The log shows the model has ``75490012`` parameters, i.e., ``~75 M``.
.. code-block::
ls -lh icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/pretrained-epoch-30-avg-10-averaged.pt
-rw-r--r-- 1 kuangfangjun root 289M Jan 11 12:05 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/pretrained-epoch-30-avg-10-averaged.pt
You can see that the file size of the pre-trained model is ``289 MB``, which
is roughly equal to ``75490012*4/1024/1024 = 287.97 MB``.
After running ``conv_emformer_transducer_stateless2/export-for-ncnn.py``,
we will get the following files:
.. code-block:: bash
ls -lh icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/*pnnx*
-rw-r--r-- 1 kuangfangjun root 1010K Jan 11 12:15 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 283M Jan 11 12:15 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 3.0M Jan 11 12:15 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.pt
.. _conv-emformer-step-4-export-torchscript-model-via-pnnx:
4. Export torchscript model via pnnx
------------------------------------
.. hint::
Make sure you have set up the ``PATH`` environment variable. 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-conv-emformer-transducer-stateless2-2022-07-05/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-conv-emformer-transducer-stateless2-2022-07-05/exp/*ncnn*{bin,param}
-rw-r--r-- 1 kuangfangjun root 503K Jan 11 12:38 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 437 Jan 11 12:38 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 142M Jan 11 12:36 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 79K Jan 11 12:36 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 1.5M Jan 11 12:38 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 488 Jan 11 12:38 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/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 | 283 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin | 142 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin | 503 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin | 1.5 MB |
+----------------------------------+------------+
You can see that the file sizes of the models after conversion are about one half
of the models before conversion:
- encoder: 283 MB vs 142 MB
- decoder: 1010 KB vs 503 KB
- joiner: 3.0 MB vs 1.5 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
./conv_emformer_transducer_stateless2/streaming-ncnn-decode.py \
--tokens ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/data/lang_bpe_500/tokens.txt \
--encoder-param-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.param \
--encoder-bin-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.bin \
--decoder-param-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.param \
--decoder-bin-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.bin \
--joiner-param-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.param \
--joiner-bin-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.bin \
./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/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-conv-emformer-transducer-libri.txt
Congratulations! You have successfully exported a model from PyTorch to `ncnn`_!
.. _conv-emformer-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
1060 1342
Input in0 0 1 in0
**Explanation** of the above three lines:
1. ``7767517``, it is a magic number and should not be changed.
2. ``1060 1342``, the first number ``1060`` specifies the number of layers
in this file, while ``1342`` 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
1061 1342
SherpaMetaData sherpa_meta_data1 0 0 0=1 1=12 2=32 3=31 4=8 5=32 6=8 7=512
Input in0 0 1 in0
**Explanation**
1. ``7767517``, it is still the same
2. ``1061 1342``, we have added an extra layer, so we need to update ``1060`` to ``1061``.
We don't need to change ``1342`` since the newly added layer has no inputs or outputs.
3. ``SherpaMetaData sherpa_meta_data1 0 0 0=1 1=12 2=32 3=31 4=8 5=32 6=8 7=512``
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=1``, 0 is the key and 1 is the value. MUST be ``0=1``
- ``1=12``, 1 is the key and 12 is the value of the
parameter ``--num-encoder-layers`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``2=32``, 2 is the key and 32 is the value of the
parameter ``--memory-size`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``3=31``, 3 is the key and 31 is the value of the
parameter ``--cnn-module-kernel`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``4=8``, 4 is the key and 8 is the value of the
parameter ``--left-context-length`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``5=32``, 5 is the key and 32 is the value of the
parameter ``--chunk-length`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``6=8``, 6 is the key and 8 is the value of the
parameter ``--right-context-length`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``7=512``, 7 is the key and 512 is the value of the
parameter ``--encoder-dim`` that you provided when running
``conv_emformer_transducer_stateless2/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 | 1 (fixed) |
+------+-----------------------------+
| 1 | ``--num-encoder-layers`` |
+------+-----------------------------+
| 2 | ``--memory-size`` |
+------+-----------------------------+
| 3 | ``--cnn-module-kernel`` |
+------+-----------------------------+
| 4 | ``--left-context-length`` |
+------+-----------------------------+
| 5 | ``--chunk-length`` |
+------+-----------------------------+
| 6 | ``--right-context-length`` |
+------+-----------------------------+
| 7 | ``--encoder-dim`` |
+------+-----------------------------+
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 ``1060`` to ``1061``. 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.
7. (Optional) int8 quantization with sherpa-ncnn
------------------------------------------------
This step is optional.
In this step, we describe how to quantize our model with ``int8``.
Change :ref:`conv-emformer-step-4-export-torchscript-model-via-pnnx` to
disable ``fp16`` when using ``pnnx``:
.. code-block::
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
pnnx ./encoder_jit_trace-pnnx.pt fp16=0
pnnx ./decoder_jit_trace-pnnx.pt
pnnx ./joiner_jit_trace-pnnx.pt fp16=0
.. note::
We add ``fp16=0`` when exporting the encoder and joiner. `ncnn`_ does not
support quantizing the decoder model yet. We will update this documentation
once `ncnn`_ supports it. (Maybe in this year, 2023).
It will generate the following files
.. code-block:: bash
ls -lh icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/*_jit_trace-pnnx.ncnn.{param,bin}
-rw-r--r-- 1 kuangfangjun root 503K Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 437 Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 283M Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 79K Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 3.0M Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 488 Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.param
Let us compare again the file sizes:
+----------------------------------------+------------+
| File name | File size |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.pt | 283 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 142 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 283 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
+----------------------------------------+------------+
You can see that the file sizes are doubled when we disable ``fp16``.
.. note::
You can again use ``streaming-ncnn-decode.py`` to test the exported models.
Next, follow :ref:`conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn`
to modify ``encoder_jit_trace-pnnx.ncnn.param``.
Change
.. code-block:: bash
7767517
1060 1342
Input in0 0 1 in0
to
.. code-block:: bash
7767517
1061 1342
SherpaMetaData sherpa_meta_data1 0 0 0=1 1=12 2=32 3=31 4=8 5=32 6=8 7=512
Input in0 0 1 in0
.. caution::
Please follow :ref:`conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn`
to change the values for ``SherpaMetaData`` if your model uses a different setting.
Next, let us compile `sherpa-ncnn`_ since we will quantize our models within
`sherpa-ncnn`_.
.. code-block:: bash
# We will download sherpa-ncnn to $HOME/open-source/
# You can change it to anywhere you like.
cd $HOME
mkdir -p open-source
cd open-source
git clone https://github.com/k2-fsa/sherpa-ncnn
cd sherpa-ncnn
mkdir build
cd build
cmake ..
make -j 4
./bin/generate-int8-scale-table
export PATH=$HOME/open-source/sherpa-ncnn/build/bin:$PATH
The output of the above commands are:
.. code-block:: bash
(py38) kuangfangjun:build$ generate-int8-scale-table
Please provide 10 arg. Currently given: 1
Usage:
generate-int8-scale-table encoder.param encoder.bin decoder.param decoder.bin joiner.param joiner.bin encoder-scale-table.txt joiner-scale-table.txt wave_filenames.txt
Each line in wave_filenames.txt is a path to some 16k Hz mono wave file.
We need to create a file ``wave_filenames.txt``, in which we need to put
some calibration wave files. For testing purpose, we put the ``test_wavs``
from the pre-trained model repository `<https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05>`_
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
cat <<EOF > wave_filenames.txt
../test_wavs/1089-134686-0001.wav
../test_wavs/1221-135766-0001.wav
../test_wavs/1221-135766-0002.wav
EOF
Now we can calculate the scales needed for quantization with the calibration data:
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
generate-int8-scale-table \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
./encoder-scale-table.txt \
./joiner-scale-table.txt \
./wave_filenames.txt
The output logs are in the following:
.. literalinclude:: ./code/generate-int-8-scale-table-for-conv-emformer.txt
It generates the following two files:
.. code-block:: bash
$ ls -lh encoder-scale-table.txt joiner-scale-table.txt
-rw-r--r-- 1 kuangfangjun root 955K Jan 11 17:28 encoder-scale-table.txt
-rw-r--r-- 1 kuangfangjun root 18K Jan 11 17:28 joiner-scale-table.txt
.. caution::
Definitely, you need more calibration data to compute the scale table.
Finally, let us use the scale table to quantize our models into ``int8``.
.. code-block:: bash
ncnn2int8
usage: ncnn2int8 [inparam] [inbin] [outparam] [outbin] [calibration table]
First, we quantize the encoder model:
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
ncnn2int8 \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./encoder_jit_trace-pnnx.ncnn.int8.param \
./encoder_jit_trace-pnnx.ncnn.int8.bin \
./encoder-scale-table.txt
Next, we quantize the joiner model:
.. code-block:: bash
ncnn2int8 \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.int8.param \
./joiner_jit_trace-pnnx.ncnn.int8.bin \
./joiner-scale-table.txt
The above two commands generate the following 4 files:
.. code-block:: bash
-rw-r--r-- 1 kuangfangjun root 99M Jan 11 17:34 encoder_jit_trace-pnnx.ncnn.int8.bin
-rw-r--r-- 1 kuangfangjun root 78K Jan 11 17:34 encoder_jit_trace-pnnx.ncnn.int8.param
-rw-r--r-- 1 kuangfangjun root 774K Jan 11 17:35 joiner_jit_trace-pnnx.ncnn.int8.bin
-rw-r--r-- 1 kuangfangjun root 496 Jan 11 17:35 joiner_jit_trace-pnnx.ncnn.int8.param
Congratulations! You have successfully quantized your model from ``float32`` to ``int8``.
.. caution::
``ncnn.int8.param`` and ``ncnn.int8.bin`` must be used in pairs.
You can replace ``ncnn.param`` and ``ncnn.bin`` with ``ncnn.int8.param``
and ``ncnn.int8.bin`` in `sherpa-ncnn`_ if you like.
For instance, to use only the ``int8`` encoder in ``sherpa-ncnn``, you can
replace the following invocation:
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
sherpa-ncnn \
../data/lang_bpe_500/tokens.txt \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
../test_wavs/1089-134686-0001.wav
with
.. code-block::
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
sherpa-ncnn \
../data/lang_bpe_500/tokens.txt \
./encoder_jit_trace-pnnx.ncnn.int8.param \
./encoder_jit_trace-pnnx.ncnn.int8.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
../test_wavs/1089-134686-0001.wav
The following table compares again the file sizes:
+----------------------------------------+------------+
| File name | File size |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.pt | 283 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 142 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 283 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.int8.bin | 99 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.int8.bin | 774 KB |
+----------------------------------------+------------+
You can see that the file sizes of the model after ``int8`` quantization
are much smaller.
.. hint::
Currently, only linear layers and convolutional layers are quantized
with ``int8``, so you don't see an exact ``4x`` reduction in file sizes.
.. note::
You need to test the recognition accuracy after ``int8`` quantization.
You can find the speed comparison at `<https://github.com/k2-fsa/sherpa-ncnn/issues/44>`_.
That's it! Have fun with `sherpa-ncnn`_!

644
docs/source/model-export/export-ncnn-lstm.rst Normal file
View File

@ -0,0 +1,644 @@
.. _export_lstm_transducer_models_to_ncnn:
Export LSTM transducer models to ncnn
-------------------------------------
We use the pre-trained model from the following repository as an example:
`<https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03>`_
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/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
git lfs pull --include "exp/pretrained-iter-468000-avg-16.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-lstm-transducer-stateless2-2022-09-03``.
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-lstm-transducer-stateless2-2022-09-03/exp
ln -s pretrained-iter-468000-avg-16.pt epoch-99.pt
cd ../..
Next, we use the following code to export our model:
.. code-block:: bash
dir=./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
./lstm_transducer_stateless2/export-for-ncnn.py \
--exp-dir $dir/exp \
--bpe-model $dir/data/lang_bpe_500/bpe.model \
--epoch 99 \
--avg 1 \
--use-averaged-model 0 \
--num-encoder-layers 12 \
--encoder-dim 512 \
--rnn-hidden-size 1024
.. 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-lstm-transducer-for-ncnn-output.txt
The log shows the model has ``84176356`` parameters, i.e., ``~84 M``.
.. code-block::
ls -lh icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/pretrained-iter-468000-avg-16.pt
-rw-r--r-- 1 kuangfangjun root 324M Feb 17 10:34 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/pretrained-iter-468000-avg-16.pt
You can see that the file size of the pre-trained model is ``324 MB``, which
is roughly equal to ``84176356*4/1024/1024 = 321.107 MB``.
After running ``lstm_transducer_stateless2/export-for-ncnn.py``,
we will get the following files:
.. code-block:: bash
ls -lh icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/*pnnx.pt
-rw-r--r-- 1 kuangfangjun root 1010K Feb 17 11:22 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 318M Feb 17 11:22 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 3.0M Feb 17 11:22 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.pt
.. _lstm-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-lstm-transducer-stateless2-2022-09-03/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-lstm-transducer-stateless2-2022-09-03/exp/*ncnn*{bin,param}
-rw-r--r-- 1 kuangfangjun root 503K Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 437 Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 159M Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 21K Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 1.5M Feb 17 11:33 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 488 Feb 17 11:33 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/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 | 318 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin | 159 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin | 503 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin | 1.5 MB |
+----------------------------------+------------+
You can see that the file sizes of the models after conversion are about one half
of the models before conversion:
- encoder: 318 MB vs 159 MB
- decoder: 1010 KB vs 503 KB
- joiner: 3.0 MB vs 1.5 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 ./lstm_transducer_stateless2/streaming-ncnn-decode.py \
--tokens ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/data/lang_bpe_500/tokens.txt \
--encoder-param-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param \
--encoder-bin-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin \
--decoder-param-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param \
--decoder-bin-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin \
--joiner-param-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param \
--joiner-bin-filename ./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin \
./icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/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-lstm-transducer-libri.txt
Congratulations! You have successfully exported a model from PyTorch to `ncnn`_!
.. _lstm-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
267 379
Input in0 0 1 in0
**Explanation** of the above three lines:
1. ``7767517``, it is a magic number and should not be changed.
2. ``267 379``, the first number ``267`` specifies the number of layers
in this file, while ``379`` 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
268 379
SherpaMetaData sherpa_meta_data1 0 0 0=3 1=12 2=512 3=1024
Input in0 0 1 in0
**Explanation**
1. ``7767517``, it is still the same
2. ``268 379``, we have added an extra layer, so we need to update ``267`` to ``268``.
We don't need to change ``379`` since the newly added layer has no inputs or outputs.
3. ``SherpaMetaData sherpa_meta_data1 0 0 0=3 1=12 2=512 3=1024``
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=3``, 0 is the key and 3 is the value. MUST be ``0=3``
- ``1=12``, 1 is the key and 12 is the value of the
parameter ``--num-encoder-layers`` that you provided when running
``./lstm_transducer_stateless2/export-for-ncnn.py``.
- ``2=512``, 2 is the key and 512 is the value of the
parameter ``--encoder-dim`` that you provided when running
``./lstm_transducer_stateless2/export-for-ncnn.py``.
- ``3=1024``, 3 is the key and 1024 is the value of the
parameter ``--rnn-hidden-size`` that you provided when running
``./lstm_transducer_stateless2/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 | 3 (fixed) |
+------+-----------------------------+
| 1 | ``--num-encoder-layers`` |
+------+-----------------------------+
| 2 | ``--encoder-dim`` |
+------+-----------------------------+
| 3 | ``--rnn-hidden-size`` |
+------+-----------------------------+
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 ``267`` to ``268``. 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.
7. (Optional) int8 quantization with sherpa-ncnn
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This step is optional.
In this step, we describe how to quantize our model with ``int8``.
Change :ref:`lstm-transducer-step-4-export-torchscript-model-via-pnnx` to
disable ``fp16`` when using ``pnnx``:
.. code-block::
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
pnnx ./encoder_jit_trace-pnnx.pt fp16=0
pnnx ./decoder_jit_trace-pnnx.pt
pnnx ./joiner_jit_trace-pnnx.pt fp16=0
.. note::
We add ``fp16=0`` when exporting the encoder and joiner. `ncnn`_ does not
support quantizing the decoder model yet. We will update this documentation
once `ncnn`_ supports it. (Maybe in this year, 2023).
.. code-block:: bash
ls -lh icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/*_jit_trace-pnnx.ncnn.{param,bin}
-rw-r--r-- 1 kuangfangjun root 503K Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 437 Feb 17 11:32 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 317M Feb 17 11:54 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 21K Feb 17 11:54 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 3.0M Feb 17 11:54 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 488 Feb 17 11:54 icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/joiner_jit_trace-pnnx.ncnn.param
Let us compare again the file sizes:
+----------------------------------------+------------+
| File name | File size |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.pt | 318 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 159 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 317 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
+----------------------------------------+------------+
You can see that the file sizes are doubled when we disable ``fp16``.
.. note::
You can again use ``streaming-ncnn-decode.py`` to test the exported models.
Next, follow :ref:`lstm-modify-the-exported-encoder-for-sherpa-ncnn`
to modify ``encoder_jit_trace-pnnx.ncnn.param``.
Change
.. code-block:: bash
7767517
267 379
Input in0 0 1 in0
to
.. code-block:: bash
7767517
268 379
SherpaMetaData sherpa_meta_data1 0 0 0=3 1=12 2=512 3=1024
Input in0 0 1 in0
.. caution::
Please follow :ref:`lstm-modify-the-exported-encoder-for-sherpa-ncnn`
to change the values for ``SherpaMetaData`` if your model uses a different setting.
Next, let us compile `sherpa-ncnn`_ since we will quantize our models within
`sherpa-ncnn`_.
.. code-block:: bash
# We will download sherpa-ncnn to $HOME/open-source/
# You can change it to anywhere you like.
cd $HOME
mkdir -p open-source
cd open-source
git clone https://github.com/k2-fsa/sherpa-ncnn
cd sherpa-ncnn
mkdir build
cd build
cmake ..
make -j 4
./bin/generate-int8-scale-table
export PATH=$HOME/open-source/sherpa-ncnn/build/bin:$PATH
The output of the above commands are:
.. code-block:: bash
(py38) kuangfangjun:build$ generate-int8-scale-table
Please provide 10 arg. Currently given: 1
Usage:
generate-int8-scale-table encoder.param encoder.bin decoder.param decoder.bin joiner.param joiner.bin encoder-scale-table.txt joiner-scale-table.txt wave_filenames.txt
Each line in wave_filenames.txt is a path to some 16k Hz mono wave file.
We need to create a file ``wave_filenames.txt``, in which we need to put
some calibration wave files. For testing purpose, we put the ``test_wavs``
from the pre-trained model repository
`<https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03>`_
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
cat <<EOF > wave_filenames.txt
../test_wavs/1089-134686-0001.wav
../test_wavs/1221-135766-0001.wav
../test_wavs/1221-135766-0002.wav
EOF
Now we can calculate the scales needed for quantization with the calibration data:
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
generate-int8-scale-table \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
./encoder-scale-table.txt \
./joiner-scale-table.txt \
./wave_filenames.txt
The output logs are in the following:
.. literalinclude:: ./code/generate-int-8-scale-table-for-lstm.txt
It generates the following two files:
.. code-block:: bash
ls -lh encoder-scale-table.txt joiner-scale-table.txt
-rw-r--r-- 1 kuangfangjun root 345K Feb 17 12:13 encoder-scale-table.txt
-rw-r--r-- 1 kuangfangjun root 17K Feb 17 12:13 joiner-scale-table.txt
.. caution::
Definitely, you need more calibration data to compute the scale table.
Finally, let us use the scale table to quantize our models into ``int8``.
.. code-block:: bash
ncnn2int8
usage: ncnn2int8 [inparam] [inbin] [outparam] [outbin] [calibration table]
First, we quantize the encoder model:
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
ncnn2int8 \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./encoder_jit_trace-pnnx.ncnn.int8.param \
./encoder_jit_trace-pnnx.ncnn.int8.bin \
./encoder-scale-table.txt
Next, we quantize the joiner model:
.. code-block:: bash
ncnn2int8 \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.int8.param \
./joiner_jit_trace-pnnx.ncnn.int8.bin \
./joiner-scale-table.txt
The above two commands generate the following 4 files:
.. code-block::
-rw-r--r-- 1 kuangfangjun root 218M Feb 17 12:19 encoder_jit_trace-pnnx.ncnn.int8.bin
-rw-r--r-- 1 kuangfangjun root 21K Feb 17 12:19 encoder_jit_trace-pnnx.ncnn.int8.param
-rw-r--r-- 1 kuangfangjun root 774K Feb 17 12:19 joiner_jit_trace-pnnx.ncnn.int8.bin
-rw-r--r-- 1 kuangfangjun root 496 Feb 17 12:19 joiner_jit_trace-pnnx.ncnn.int8.param
Congratulations! You have successfully quantized your model from ``float32`` to ``int8``.
.. caution::
``ncnn.int8.param`` and ``ncnn.int8.bin`` must be used in pairs.
You can replace ``ncnn.param`` and ``ncnn.bin`` with ``ncnn.int8.param``
and ``ncnn.int8.bin`` in `sherpa-ncnn`_ if you like.
For instance, to use only the ``int8`` encoder in ``sherpa-ncnn``, you can
replace the following invocation:
.. code-block::
cd egs/librispeech/ASR
cd icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp/
sherpa-ncnn \
../data/lang_bpe_500/tokens.txt \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
../test_wavs/1089-134686-0001.wav
with
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
sherpa-ncnn \
../data/lang_bpe_500/tokens.txt \
./encoder_jit_trace-pnnx.ncnn.int8.param \
./encoder_jit_trace-pnnx.ncnn.int8.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
../test_wavs/1089-134686-0001.wav
The following table compares again the file sizes:
+----------------------------------------+------------+
| File name | File size |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.pt | 318 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 159 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 317 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.int8.bin | 218 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.int8.bin | 774 KB |
+----------------------------------------+------------+
You can see that the file size of the joiner model after ``int8`` quantization
is much smaller. However, the size of the encoder model is even larger than
the ``fp16`` counterpart. The reason is that `ncnn`_ currently does not support
quantizing ``LSTM`` layers into ``8-bit``. Please see
`<https://github.com/Tencent/ncnn/issues/4532>`_
.. hint::
Currently, only linear layers and convolutional layers are quantized
with ``int8``, so you don't see an exact ``4x`` reduction in file sizes.
.. note::
You need to test the recognition accuracy after ``int8`` quantization.
That's it! Have fun with `sherpa-ncnn`_!

780
docs/source/model-export/export-ncnn.rst
View File

@ -1,15 +1,26 @@
Export to ncnn Export to ncnn
============== ==============
We support exporting both We support exporting the following models
`LSTM transducer models <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless2>`_ to `ncnn <https://github.com/tencent/ncnn>`_:
and
`ConvEmformer transducer models <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conv_emformer_transducer_stateless2>`_
to `ncnn <https://github.com/tencent/ncnn>`_.
We also provide `<https://github.com/k2-fsa/sherpa-ncnn>`_ - `Zipformer transducer models <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless7_streaming>`_
performing speech recognition using ``ncnn`` with exported models.
It has been tested on Linux, macOS, Windows, ``Android``, and ``Raspberry Pi``. - `LSTM transducer models <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/lstm_transducer_stateless2>`_
- `ConvEmformer transducer models <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conv_emformer_transducer_stateless2>`_
We also provide `sherpa-ncnn`_
for performing speech recognition using `ncnn`_ with exported models.
It has been tested on the following platforms:
- Linux
- macOS
- Windows
- ``Android``
- ``iOS``
- ``Raspberry Pi``
- `爱芯派 <https://wiki.sipeed.com/hardware/zh/>`_ (`MAIX-III AXera-Pi <https://wiki.sipeed.com/hardware/en/maixIII/ax-pi/axpi.html>`_).
`sherpa-ncnn`_ is self-contained and can be statically linked to produce `sherpa-ncnn`_ is self-contained and can be statically linked to produce
a binary containing everything needed. Please refer a binary containing everything needed. Please refer
@ -18,754 +29,7 @@ to its documentation for details:
- `<https://k2-fsa.github.io/sherpa/ncnn/index.html>`_ - `<https://k2-fsa.github.io/sherpa/ncnn/index.html>`_
Export LSTM transducer models .. toctree::
-----------------------------
Please refer to :ref:`export-lstm-transducer-model-for-ncnn` for details. export-ncnn-conv-emformer
export-ncnn-lstm
Export ConvEmformer transducer models
-------------------------------------
We use the pre-trained model from the following repository as an example:
- `<https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05>`_
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 1.10``, 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 can also refer to `<https://k2-fsa.github.io/sherpa/cpp/pretrained_models/online_transducer.html#icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05>`_ to download the pre-trained model.
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-conv-emformer-transducer-stateless2-2022-07-05
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
git lfs pull --include "exp/pretrained-epoch-30-avg-10-averaged.pt"
git lfs pull --include "data/lang_bpe_500/bpe.model"
cd ..
.. note::
We download ``exp/pretrained-xxx.pt``, not ``exp/cpu-jit_xxx.pt``.
In the above code, we download the pre-trained model into the directory
``egs/librispeech/ASR/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05``.
2. Install ncnn and pnnx
^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: bash
# We put ncnn into $HOME/open-source/ncnn
# You can change it to anywhere you like
cd $HOME
mkdir -p open-source
cd open-source
git clone https://github.com/csukuangfj/ncnn
cd ncnn
git submodule update --recursive --init
# Note: We don't use "python setup.py install" or "pip install ." here
mkdir -p build-wheel
cd build-wheel
cmake \
-DCMAKE_BUILD_TYPE=Release \
-DNCNN_PYTHON=ON \
-DNCNN_BUILD_BENCHMARK=OFF \
-DNCNN_BUILD_EXAMPLES=OFF \
-DNCNN_BUILD_TOOLS=ON \
..
make -j4
cd ..
# Note: $PWD here is $HOME/open-source/ncnn
export PYTHONPATH=$PWD/python:$PYTHONPATH
export PATH=$PWD/tools/pnnx/build/src:$PATH
export PATH=$PWD/build-wheel/tools/quantize:$PATH
# Now build pnnx
cd tools/pnnx
mkdir build
cd build
cmake ..
make -j4
./src/pnnx
Congratulations! You have successfully installed the following components:
- ``pnxx``, which is an executable located in
``$HOME/open-source/ncnn/tools/pnnx/build/src``. We will use
it to convert models exported by ``torch.jit.trace()``.
- ``ncnn2int8``, which is an executable located in
``$HOME/open-source/ncnn/build-wheel/tools/quantize``. We will use
it to quantize our models to ``int8``.
- ``ncnn.cpython-38-x86_64-linux-gnu.so``, which is a Python module located
in ``$HOME/open-source/ncnn/python/ncnn``.
.. note::
I am using ``Python 3.8``, so it
is ``ncnn.cpython-38-x86_64-linux-gnu.so``. If you use a different
version, say, ``Python 3.9``, the name would be
``ncnn.cpython-39-x86_64-linux-gnu.so``.
Also, if you are not using Linux, the file name would also be different.
But that does not matter. As long as you can compile it, it should work.
We have set up ``PYTHONPATH`` so that you can use ``import ncnn`` in your
Python code. We have also set up ``PATH`` so that you can use
``pnnx`` and ``ncnn2int8`` later in your terminal.
.. caution::
Please don't use `<https://github.com/tencent/ncnn>`_.
We have made some modifications to the offical `ncnn`_.
We will synchronize `<https://github.com/csukuangfj/ncnn>`_ periodically
with the official one.
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-conv-emformer-transducer-stateless2-2022-07-05/exp
ln -s pretrained-epoch-30-avg-10-averaged.pt epoch-30.pt
cd ../..
Next, we use the following code to export our model:
.. code-block:: bash
dir=./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/
./conv_emformer_transducer_stateless2/export-for-ncnn.py \
--exp-dir $dir/exp \
--bpe-model $dir/data/lang_bpe_500/bpe.model \
--epoch 30 \
--avg 1 \
--use-averaged-model 0 \
\
--num-encoder-layers 12 \
--chunk-length 32 \
--cnn-module-kernel 31 \
--left-context-length 32 \
--right-context-length 8 \
--memory-size 32 \
--encoder-dim 512
.. hint::
We have renamed our model to ``epoch-30.pt`` so that we can use ``--epoch 30``.
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-conv-emformer-transducer-for-ncnn-output.txt
The log shows the model has ``75490012`` parameters, i.e., ``~75 M``.
.. code-block::
ls -lh icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/pretrained-epoch-30-avg-10-averaged.pt
-rw-r--r-- 1 kuangfangjun root 289M Jan 11 12:05 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/pretrained-epoch-30-avg-10-averaged.pt
You can see that the file size of the pre-trained model is ``289 MB``, which
is roughly ``75490012*4/1024/1024 = 287.97 MB``.
After running ``conv_emformer_transducer_stateless2/export-for-ncnn.py``,
we will get the following files:
.. code-block:: bash
ls -lh icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/*pnnx*
-rw-r--r-- 1 kuangfangjun root 1010K Jan 11 12:15 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 283M Jan 11 12:15 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.pt
-rw-r--r-- 1 kuangfangjun root 3.0M Jan 11 12:15 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.pt
.. _conv-emformer-step-3-export-torchscript-model-via-pnnx:
3. Export torchscript model via pnnx
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. hint::
Make sure you have set up the ``PATH`` environment variable. 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-conv-emformer-transducer-stateless2-2022-07-05/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-conv-emformer-transducer-stateless2-2022-07-05/exp/*ncnn*{bin,param}
-rw-r--r-- 1 kuangfangjun root 503K Jan 11 12:38 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 437 Jan 11 12:38 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 142M Jan 11 12:36 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 79K Jan 11 12:36 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 1.5M Jan 11 12:38 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 488 Jan 11 12:38 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/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 | 283 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin | 142 MB |
+----------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin | 503 KB |
+----------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin | 1.5 MB |
+----------------------------------+------------+
You can see that the file sizes of the models after conversion are about one half
of the models before conversion:
- encoder: 283 MB vs 142 MB
- decoder: 1010 KB vs 503 KB
- joiner: 3.0 MB vs 1.5 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``.
4. 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
./conv_emformer_transducer_stateless2/streaming-ncnn-decode.py \
--tokens ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/data/lang_bpe_500/tokens.txt \
--encoder-param-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.param \
--encoder-bin-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.bin \
--decoder-param-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.param \
--decoder-bin-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.bin \
--joiner-param-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.param \
--joiner-bin-filename ./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.bin \
./icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/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-stremaing-ncnn-decode-conv-emformer-transducer-libri.txt
Congratulations! You have successfully exported a model from PyTorch to `ncnn`_!
.. _conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn:
5. 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
1060 1342
Input in0 0 1 in0
**Explanation** of the above three lines:
1. ``7767517``, it is a magic number and should not be changed.
2. ``1060 1342``, the first number ``1060`` specifies the number of layers
in this file, while ``1342`` 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
1061 1342
SherpaMetaData sherpa_meta_data1 0 0 0=1 1=12 2=32 3=31 4=8 5=32 6=8 7=512
Input in0 0 1 in0
**Explanation**
1. ``7767517``, it is still the same
2. ``1061 1342``, we have added an extra layer, so we need to update ``1060`` to ``1061``.
We don't need to change ``1342`` since the newly added layer has no inputs or outputs.
3. ``SherpaMetaData sherpa_meta_data1 0 0 0=1 1=12 2=32 3=31 4=8 5=32 6=8 7=512``
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=1``, 0 is the key and 1 is the value. MUST be ``0=1``
- ``1=12``, 1 is the key and 12 is the value of the
parameter ``--num-encoder-layers`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``2=32``, 2 is the key and 32 is the value of the
parameter ``--memory-size`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``3=31``, 3 is the key and 31 is the value of the
parameter ``--cnn-module-kernel`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``4=8``, 4 is the key and 8 is the value of the
parameter ``--left-context-length`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``5=32``, 5 is the key and 32 is the value of the
parameter ``--chunk-length`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``6=8``, 6 is the key and 8 is the value of the
parameter ``--right-context-length`` that you provided when running
``conv_emformer_transducer_stateless2/export-for-ncnn.py``.
- ``7=512``, 7 is the key and 512 is the value of the
parameter ``--encoder-dim`` that you provided when running
``conv_emformer_transducer_stateless2/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 | 1 (fixed) |
+------+-----------------------------+
| 1 | ``--num-encoder-layers`` |
+------+-----------------------------+
| 2 | ``--memory-size`` |
+------+-----------------------------+
| 3 | ``--cnn-module-kernel`` |
+------+-----------------------------+
| 4 | ``--left-context-length`` |
+------+-----------------------------+
| 5 | ``--chunk-length`` |
+------+-----------------------------+
| 6 | ``--right-context-length`` |
+------+-----------------------------+
| 7 | ``--encoder-dim`` |
+------+-----------------------------+
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 ``1060`` to ``1061``. 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>`_
- 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.
6. (Optional) int8 quantization with sherpa-ncnn
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This step is optional.
In this step, we describe how to quantize our model with ``int8``.
Change :ref:`conv-emformer-step-3-export-torchscript-model-via-pnnx` to
disable ``fp16`` when using ``pnnx``:
.. code-block::
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
pnnx ./encoder_jit_trace-pnnx.pt fp16=0
pnnx ./decoder_jit_trace-pnnx.pt
pnnx ./joiner_jit_trace-pnnx.pt fp16=0
.. note::
We add ``fp16=0`` when exporting the encoder and joiner. `ncnn`_ does not
support quantizing the decoder model yet. We will update this documentation
once `ncnn`_ supports it. (Maybe in this year, 2023).
It will generate the following files
.. code-block:: bash
ls -lh icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/*_jit_trace-pnnx.ncnn.{param,bin}
-rw-r--r-- 1 kuangfangjun root 503K Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 437 Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/decoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 283M Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 79K Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/encoder_jit_trace-pnnx.ncnn.param
-rw-r--r-- 1 kuangfangjun root 3.0M Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.bin
-rw-r--r-- 1 kuangfangjun root 488 Jan 11 15:56 icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/joiner_jit_trace-pnnx.ncnn.param
Let us compare again the file sizes:
+----------------------------------------+------------+
| File name | File size |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.pt | 283 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 142 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 283 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
+----------------------------------------+------------+
You can see that the file sizes are doubled when we disable ``fp16``.
.. note::
You can again use ``streaming-ncnn-decode.py`` to test the exported models.
Next, follow :ref:`conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn`
to modify ``encoder_jit_trace-pnnx.ncnn.param``.
Change
.. code-block:: bash
7767517
1060 1342
Input in0 0 1 in0
to
.. code-block:: bash
7767517
1061 1342
SherpaMetaData sherpa_meta_data1 0 0 0=1 1=12 2=32 3=31 4=8 5=32 6=8 7=512
Input in0 0 1 in0
.. caution::
Please follow :ref:`conv-emformer-modify-the-exported-encoder-for-sherpa-ncnn`
to change the values for ``SherpaMetaData`` if your model uses a different setting.
Next, let us compile `sherpa-ncnn`_ since we will quantize our models within
`sherpa-ncnn`_.
.. code-block:: bash
# We will download sherpa-ncnn to $HOME/open-source/
# You can change it to anywhere you like.
cd $HOME
mkdir -p open-source
cd open-source
git clone https://github.com/k2-fsa/sherpa-ncnn
cd sherpa-ncnn
mkdir build
cd build
cmake ..
make -j 4
./bin/generate-int8-scale-table
export PATH=$HOME/open-source/sherpa-ncnn/build/bin:$PATH
The output of the above commands are:
.. code-block:: bash
(py38) kuangfangjun:build$ generate-int8-scale-table
Please provide 10 arg. Currently given: 1
Usage:
generate-int8-scale-table encoder.param encoder.bin decoder.param decoder.bin joiner.param joiner.bin encoder-scale-table.txt joiner-scale-table.txt wave_filenames.txt
Each line in wave_filenames.txt is a path to some 16k Hz mono wave file.
We need to create a file ``wave_filenames.txt``, in which we need to put
some calibration wave files. For testing purpose, we put the ``test_wavs``
from the pre-trained model repository `<https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05>`_
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
cat <<EOF > wave_filenames.txt
../test_wavs/1089-134686-0001.wav
../test_wavs/1221-135766-0001.wav
../test_wavs/1221-135766-0002.wav
EOF
Now we can calculate the scales needed for quantization with the calibration data:
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
generate-int8-scale-table \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
./encoder-scale-table.txt \
./joiner-scale-table.txt \
./wave_filenames.txt
The output logs are in the following:
.. literalinclude:: ./code/generate-int-8-scale-table-for-conv-emformer.txt
It generates the following two files:
.. code-block:: bash
$ ls -lh encoder-scale-table.txt joiner-scale-table.txt
-rw-r--r-- 1 kuangfangjun root 955K Jan 11 17:28 encoder-scale-table.txt
-rw-r--r-- 1 kuangfangjun root 18K Jan 11 17:28 joiner-scale-table.txt
.. caution::
Definitely, you need more calibration data to compute the scale table.
Finally, let us use the scale table to quantize our models into ``int8``.
.. code-block:: bash
ncnn2int8
usage: ncnn2int8 [inparam] [inbin] [outparam] [outbin] [calibration table]
First, we quantize the encoder model:
.. code-block:: bash
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
ncnn2int8 \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./encoder_jit_trace-pnnx.ncnn.int8.param \
./encoder_jit_trace-pnnx.ncnn.int8.bin \
./encoder-scale-table.txt
Next, we quantize the joiner model:
.. code-block:: bash
ncnn2int8 \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.int8.param \
./joiner_jit_trace-pnnx.ncnn.int8.bin \
./joiner-scale-table.txt
The above two commands generate the following 4 files:
.. code-block:: bash
-rw-r--r-- 1 kuangfangjun root 99M Jan 11 17:34 encoder_jit_trace-pnnx.ncnn.int8.bin
-rw-r--r-- 1 kuangfangjun root 78K Jan 11 17:34 encoder_jit_trace-pnnx.ncnn.int8.param
-rw-r--r-- 1 kuangfangjun root 774K Jan 11 17:35 joiner_jit_trace-pnnx.ncnn.int8.bin
-rw-r--r-- 1 kuangfangjun root 496 Jan 11 17:35 joiner_jit_trace-pnnx.ncnn.int8.param
Congratulations! You have successfully quantized your model from ``float32`` to ``int8``.
.. caution::
``ncnn.int8.param`` and ``ncnn.int8.bin`` must be used in pairs.
You can replace ``ncnn.param`` and ``ncnn.bin`` with ``ncnn.int8.param``
and ``ncnn.int8.bin`` in `sherpa-ncnn`_ if you like.
For instance, to use only the ``int8`` encoder in ``sherpa-ncnn``, you can
replace the following invocation:
.. code-block::
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
sherpa-ncnn \
../data/lang_bpe_500/tokens.txt \
./encoder_jit_trace-pnnx.ncnn.param \
./encoder_jit_trace-pnnx.ncnn.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
../test_wavs/1089-134686-0001.wav
with
.. code-block::
cd egs/librispeech/ASR
cd icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05/exp/
sherpa-ncnn \
../data/lang_bpe_500/tokens.txt \
./encoder_jit_trace-pnnx.ncnn.int8.param \
./encoder_jit_trace-pnnx.ncnn.int8.bin \
./decoder_jit_trace-pnnx.ncnn.param \
./decoder_jit_trace-pnnx.ncnn.bin \
./joiner_jit_trace-pnnx.ncnn.param \
./joiner_jit_trace-pnnx.ncnn.bin \
../test_wavs/1089-134686-0001.wav
The following table compares again the file sizes:
+----------------------------------------+------------+
| File name | File size |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.pt | 283 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.pt | 1010 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.pt | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp16) | 142 MB |
+----------------------------------------+------------+
| decoder_jit_trace-pnnx.ncnn.bin (fp16) | 503 KB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp16) | 1.5 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.bin (fp32) | 283 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.bin (fp32) | 3.0 MB |
+----------------------------------------+------------+
| encoder_jit_trace-pnnx.ncnn.int8.bin | 99 MB |
+----------------------------------------+------------+
| joiner_jit_trace-pnnx.ncnn.int8.bin | 774 KB |
+----------------------------------------+------------+
You can see that the file sizes of the model after ``int8`` quantization
are much smaller.
.. hint::
Currently, only linear layers and convolutional layers are quantized
with ``int8``, so you don't see an exact ``4x`` reduction in file sizes.
.. note::
You need to test the recognition accuracy after ``int8`` quantization.
You can find the speed comparison at `<https://github.com/k2-fsa/sherpa-ncnn/issues/44>`_.
That's it! Have fun with `sherpa-ncnn`_!

2
docs/source/model-export/export-onnx.rst
View File

@ -10,7 +10,7 @@ There is also a file named ``onnx_pretrained.py``, which you can use
the exported `ONNX`_ model in Python with `onnxruntime`_ to decode sound files. the exported `ONNX`_ model in Python with `onnxruntime`_ to decode sound files.
Example Example
======= -------
In the following, we demonstrate how to export a streaming Zipformer pre-trained In the following, we demonstrate how to export a streaming Zipformer pre-trained
model from model from

126
docs/source/recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.rst
View File

@ -515,132 +515,6 @@ To use the generated files with ``./lstm_transducer_stateless2/jit_pretrained``:
Please see `<https://k2-fsa.github.io/sherpa/python/streaming_asr/lstm/english/server.html>`_ Please see `<https://k2-fsa.github.io/sherpa/python/streaming_asr/lstm/english/server.html>`_
for how to use the exported models in ``sherpa``. for how to use the exported models in ``sherpa``.
.. _export-lstm-transducer-model-for-ncnn:
Export LSTM transducer models for ncnn
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
We support exporting pretrained LSTM transducer models to
`ncnn <https://github.com/tencent/ncnn>`_ using
`pnnx <https://github.com/Tencent/ncnn/tree/master/tools/pnnx>`_.
First, let us install a modified version of ``ncnn``:
.. code-block:: bash
git clone https://github.com/csukuangfj/ncnn
cd ncnn
git submodule update --recursive --init
# Note: We don't use "python setup.py install" or "pip install ." here
mkdir -p build-wheel
cd build-wheel
cmake \
-DCMAKE_BUILD_TYPE=Release \
-DNCNN_PYTHON=ON \
-DNCNN_BUILD_BENCHMARK=OFF \
-DNCNN_BUILD_EXAMPLES=OFF \
-DNCNN_BUILD_TOOLS=ON \
..
make -j4
cd ..
# Note: $PWD here is /path/to/ncnn
export PYTHONPATH=$PWD/python:$PYTHONPATH
export PATH=$PWD/tools/pnnx/build/src:$PATH
export PATH=$PWD/build-wheel/tools/quantize:$PATH
# now build pnnx
cd tools/pnnx
mkdir build
cd build
cmake ..
make -j4
./src/pnnx
.. note::
We assume that you have added the path to the binary ``pnnx`` to the
environment variable ``PATH``.
We also assume that you have added ``build/tools/quantize`` to the environment
variable ``PATH`` so that you are able to use ``ncnn2int8`` later.
Second, let us export the model using ``torch.jit.trace()`` that is suitable
for ``pnnx``:
.. code-block:: bash
iter=468000
avg=16
./lstm_transducer_stateless2/export-for-ncnn.py \
--exp-dir ./lstm_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--iter $iter \
--avg $avg
It will generate 3 files:
- ``./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.pt``
- ``./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.pt``
- ``./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.pt``
Third, convert torchscript model to ``ncnn`` format:
.. code-block::
pnnx ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.pt
pnnx ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.pt
pnnx ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.pt
It will generate the following files:
- ``./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param``
- ``./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin``
- ``./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param``
- ``./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.bin``
- ``./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.param``
- ``./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.bin``
To use the above generated files, run:
.. code-block:: bash
./lstm_transducer_stateless2/ncnn-decode.py \
--tokens ./data/lang_bpe_500/tokens.txt \
--encoder-param-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param \
--encoder-bin-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin \
--decoder-param-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param \
--decoder-bin-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.bin \
--joiner-param-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.param \
--joiner-bin-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.bin \
/path/to/foo.wav
.. code-block:: bash
./lstm_transducer_stateless2/streaming-ncnn-decode.py \
--tokens ./data/lang_bpe_500/tokens.txt \
--encoder-param-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param \
--encoder-bin-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin \
--decoder-param-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param \
--decoder-bin-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.bin \
--joiner-param-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.param \
--joiner-bin-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-pnnx.ncnn.bin \
/path/to/foo.wav
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 executable that can be run on Linux, Windows,
macOS, Raspberry Pi, etc, without external dependencies.
Download pretrained models Download pretrained models
-------------------------- --------------------------