Install the Python Package
You can select one of the following methods to install the Python package.
Method 1 (From pre-compiled wheels)
Hint
This method supports x86_64
, arm64
(e.g., Mac M1, 64-bit Raspberry Pi),
and arm32
(e.g., 32-bit Raspberry Pi).
pip install sherpa-onnx
To check you have installed sherpa-onnx successfully, please run
python3 -c "import sherpa_onnx; print(sherpa_onnx.__file__)"
which sherpa-onnx
sherpa-onnx --help
ls -lh $(dirname $(which sherpa-onnx))/sherpa-onnx*
Method 2 (From source)
git clone https://github.com/k2-fsa/sherpa-onnx
cd sherpa-onnx
python3 setup.py install
git clone https://github.com/k2-fsa/sherpa-onnx
export SHERPA_ONNX_CMAKE_ARGS="-DSHERPA_ONNX_ENABLE_GPU=ON"
cd sherpa-onnx
python3 setup.py install
Method 3 (For developers)
git clone https://github.com/k2-fsa/sherpa-onnx
cd sherpa-onnx
mkdir build
cd build
cmake \
-DSHERPA_ONNX_ENABLE_PYTHON=ON \
-DBUILD_SHARED_LIBS=ON \
-DSHERPA_ONNX_ENABLE_CHECK=OFF \
-DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
-DSHERPA_ONNX_ENABLE_C_API=OFF \
-DSHERPA_ONNX_ENABLE_WEBSOCKET=OFF \
..
make -j
export PYTHONPATH=$PWD/../sherpa-onnx/python/:$PWD/lib:$PYTHONPATH
git clone https://github.com/k2-fsa/sherpa-onnx
cd sherpa-onnx
mkdir build
cd build
cmake \
-DSHERPA_ONNX_ENABLE_PYTHON=ON \
-DBUILD_SHARED_LIBS=ON \
-DSHERPA_ONNX_ENABLE_CHECK=OFF \
-DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
-DSHERPA_ONNX_ENABLE_C_API=OFF \
-DSHERPA_ONNX_ENABLE_WEBSOCKET=OFF \
-DSHERPA_ONNX_ENABLE_GPU=ON \
..
make -j
export PYTHONPATH=$PWD/../sherpa-onnx/python/:$PWD/lib:$PYTHONPATH
Hint
You need to install CUDA toolkit. Otherwise, you would get errors at runtime.
You can refer to https://k2-fsa.github.io/k2/installation/cuda-cudnn.html to install CUDA toolkit.
Check your installation
To check that sherpa-onnx has been successfully installed, please use:
python3 -c "import sherpa_onnx; print(sherpa_onnx.__file__)"
It should print some output like below:
/Users/fangjun/py38/lib/python3.8/site-packages/sherpa_onnx/__init__.py
Please refer to:
for usages.
Please refer to Pre-trained models for a list of pre-trained models.