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[DOCS] speech sample deprecation (#19228)

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Karol Blaszczak 2023-08-25 12:26:44 +02:00 committed by GitHub
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7
docs/OV_Runtime_UG/supported_plugins/GNA.md
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@ -18,8 +18,11 @@ For more details on how to configure a system to use GNA, see the :doc:`GNA conf
.. note::
Intel's GNA is being discontinued and Intel® Core™ Ultra (formerly known as Meteor Lake) will be the last generation of hardware to include it.
Consider Intel's new Visual Processing Unit as a low-power solution for offloading neural network computation, for processors offering the technology.
Intel's GNA is being discontinued and Intel® Core™ Ultra (formerly known as Meteor Lake)
will be the last generation of hardware to include it.
For this reason, OpenVINO 2023.2 will also be the last version supporting the GNA plugin.
Consider Intel's new Visual Processing Unit as a low-power solution for offloading
neural network computation, for processors offering the technology.
Intel® GNA Generational Differences

11
samples/cpp/speech_sample/README.md
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@ -1,5 +1,6 @@
# Automatic Speech Recognition C++ Sample {#openvino_inference_engine_samples_speech_sample_README}
@sphinxdirective
.. meta::
@ -8,6 +9,14 @@
Inference Request (C++) API.
.. note::
This sample is being deprecated and will no longer be maintained after
OpenVINO 2023.2 (LTS). The main reason for it is the outdated state of
the sample and its extensive usage of GNA, which is not going to be
supported by OpenVINO beyond 2023.2.
This sample demonstrates how to execute an Asynchronous Inference of acoustic model based on Kaldi\* neural networks and speech feature vectors.
The sample works with Kaldi ARK or Numpy* uncompressed NPZ files, so it does not cover an end-to-end speech recognition scenario (speech to text), requiring additional preprocessing (feature extraction) to get a feature vector from a speech signal, as well as postprocessing (decoding) to produce text from scores.
@ -21,7 +30,7 @@ The sample works with Kaldi ARK or Numpy* uncompressed NPZ files, so it does not
+=============================================================+===============================================================================================================================================================+
| Validated Models | Acoustic model based on Kaldi\* neural networks (see :ref:`Model Preparation <model-preparation-speech>` section) |
+-------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Model Format | OpenVINO™ toolkit Intermediate Representation (\*.xml + \*.bin) |
| Model Format | OpenVINO™ toolkit Intermediate Representation (*.xml + *.bin) |
+-------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Supported devices | See :ref:`Execution Modes <execution-modes-speech>` section below and :doc:`List Supported Devices <openvino_docs_OV_UG_supported_plugins_Supported_Devices>` |
+-------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+

9
samples/python/speech_sample/README.md
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@ -1,5 +1,6 @@
# Automatic Speech Recognition Python Sample {#openvino_inference_engine_ie_bridges_python_sample_speech_sample_README}
@sphinxdirective
.. meta::
@ -8,6 +9,14 @@
Inference Request (Python) API.
.. note::
This sample is being deprecated and will no longer be maintained after
OpenVINO 2023.2 (LTS). The main reason for it is the outdated state of
the sample and its extensive usage of GNA, which is not going to be
supported by OpenVINO beyond 2023.2.
This sample demonstrates how to do a Synchronous Inference of acoustic model based on Kaldi\* neural models and speech feature vectors.
The sample works with Kaldi ARK or Numpy* uncompressed NPZ files, so it does not cover an end-to-end speech recognition scenario (speech to text), requiring additional preprocessing (feature extraction) to get a feature vector from a speech signal, as well as postprocessing (decoding) to produce text from scores.