Install the Python Package

You can select one of the following methods to install the Python package.

Method 1 (From pre-compiled wheels, CPU only)

Hint

This method supports the following platfroms:

• Linux (x64, aarch64, armv7l),

• macOS (x64, arm64)

• Windows (x64, x86)

Note that this method installs a CPU-only version of sherpa-onnx.

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*

Hint

You can find previous releases at https://k2-fsa.github.io/sherpa/onnx/cpu.html

For Chinese users and users who have no access to huggingface, please visit https://k2-fsa.github.io/sherpa/onnx/cpu-cn.html.

You can use:

pip install sherpa-onnx -f https://k2-fsa.github.io/sherpa/onnx/cpu.html

or:

# For Chinese uers
pip install sherpa-onnx -f https://k2-fsa.github.io/sherpa/onnx/cpu-cn.html

Method 2 (From pre-compiled wheels, CPU + CUDA)

Note

This method installs a version of sherpa-onnx supporting both CUDA and CPU. You need to pass the argument provider=cuda to use NVIDIA GPU, which always uses GPU 0. Otherwise, it uses CPU by default.

Please use the environment variable CUDA_VISIBLE_DEVICES to control which GPU is mapped to GPU 0.

By default, provider is set to cpu.

Remeber to follow https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements to install CUDA 11.8.

If you have issues about installing CUDA 11.8, please have a look at https://k2-fsa.github.io/k2/installation/cuda-cudnn.html#cuda-11-8.

Note that you don’t need to have sudo permission to install CUDA 11.8

This approach supports only Linux x64 and Windows x64.

Please use the following command to install CUDA-enabled sherpa-onnx:

# We use 1.10.16 here for demonstration.
#
# Please visit https://k2-fsa.github.io/sherpa/onnx/cuda.html
# to find available versions

pip install sherpa-onnx==1.10.16+cuda -f https://k2-fsa.github.io/sherpa/onnx/cuda.html

# For Chinese users, please use
# pip install sherpa-onnx==1.10.16+cuda -f https://k2-fsa.github.io/sherpa/onnx/cuda-cn.html

The installation logs are given below:

Looking in links: https://k2-fsa.github.io/sherpa/onnx/cuda.html
Collecting sherpa-onnx==1.10.16+cuda
  Downloading https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/cuda/1.10.16/sherpa_onnx-1.10.16%2Bcuda-cp310-cp310-linux_x86_64.whl (183.3 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 183.3/183.3 MB 4.4 MB/s eta 0:00:00
Installing collected packages: sherpa-onnx
Successfully installed sherpa-onnx-1.10.16+cuda

To check that you have installed sherpa-onnx successfully, please run:

python3 -c "import sherpa_onnx; print(sherpa_onnx.__version__)"

which should print something like below:

1.10.16+cuda

Method 3 (From source)

CPUNvidia GPU (CUDA)

git clone https://github.com/k2-fsa/sherpa-onnx
cd sherpa-onnx
python3 setup.py install

Method 4 (For developers)

CPUNvidia GPU (CUDA)

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

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:

https://github.com/k2-fsa/sherpa-onnx/tree/master/python-api-examples

for usages.

Please refer to Pre-trained models for a list of pre-trained models.