OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Maxim Gordeev fad66d8442
[IE Samples] New command line parameters format for speech sample (#11051)
* New command line parameters format for speech sample

* fixed notes

* changed format for scale factor

* changed format for scale factor in tests

* added more variants, when name is directy specified for i/o/r like it is done for sf

* removed nthreads flag

* fixed notes

* changed output params

* updated tests with new format

Co-authored-by: Alexander Zhogov <alexander.zhogov@intel.com>
2022-03-31 10:09:31 +03:00
.ci [Python API] Improve configuration files (#10960) 2022-03-30 20:26:36 +03:00
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samples [IE Samples] New command line parameters format for speech sample (#11051) 2022-03-31 10:09:31 +03:00
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tests [IE Samples] New command line parameters format for speech sample (#11051) 2022-03-31 10:09:31 +03:00
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OpenVINO™ Toolkit

Stable release Apache License Version 2.0 GitHub branch checks state Azure DevOps builds (branch) PyPI Downloads

This toolkit allows developers to deploy pre-trained deep learning models through a high-level OpenVINO™ Runtime C++ and Python APIs integrated with application logic.

This open source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.

Repository components

License

OpenVINO™ Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

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