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author | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2022-05-06 09:13:03 +0100 |
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committer | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2022-05-06 17:11:41 +0100 |
commit | aa4bcb14d0cbee910331545dd2fc086b58c37170 (patch) | |
tree | e67a43a43f61c6f8b6aad19018b0827baf7e31a6 /source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc | |
parent | fcca863bafd5f33522bc14c23dde4540e264ec94 (diff) | |
download | ml-embedded-evaluation-kit-aa4bcb14d0cbee910331545dd2fc086b58c37170.tar.gz |
MLECO-3183: Refactoring application sources
Platform agnostic application sources are moved into application
api module with their own independent CMake projects.
Changes for MLECO-3080 also included - they create CMake projects
individial API's (again, platform agnostic) that dependent on the
common logic. The API for KWS_API "joint" API has been removed and
now the use case relies on individual KWS, and ASR API libraries.
Change-Id: I1f7748dc767abb3904634a04e0991b74ac7b756d
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>
Diffstat (limited to 'source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc')
-rw-r--r-- | source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc | 100 |
1 files changed, 100 insertions, 0 deletions
diff --git a/source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc b/source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc new file mode 100644 index 0000000..f6a3ec4 --- /dev/null +++ b/source/application/api/use_case/noise_reduction/src/RNNoiseProcessing.cc @@ -0,0 +1,100 @@ +/* + * Copyright (c) 2022 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "RNNoiseProcessing.hpp" +#include "log_macros.h" + +namespace arm { +namespace app { + + RNNoisePreProcess::RNNoisePreProcess(TfLiteTensor* inputTensor, + std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor, std::shared_ptr<rnn::FrameFeatures> frameFeatures) + : m_inputTensor{inputTensor}, + m_featureProcessor{featureProcessor}, + m_frameFeatures{frameFeatures} + {} + + bool RNNoisePreProcess::DoPreProcess(const void* data, size_t inputSize) + { + if (data == nullptr) { + printf_err("Data pointer is null"); + return false; + } + + auto input = static_cast<const int16_t*>(data); + this->m_audioFrame = rnn::vec1D32F(input, input + inputSize); + m_featureProcessor->PreprocessFrame(this->m_audioFrame.data(), inputSize, *this->m_frameFeatures); + + QuantizeAndPopulateInput(this->m_frameFeatures->m_featuresVec, + this->m_inputTensor->params.scale, this->m_inputTensor->params.zero_point, + this->m_inputTensor); + + debug("Input tensor populated \n"); + + return true; + } + + void RNNoisePreProcess::QuantizeAndPopulateInput(rnn::vec1D32F& inputFeatures, + const float quantScale, const int quantOffset, + TfLiteTensor* inputTensor) + { + const float minVal = std::numeric_limits<int8_t>::min(); + const float maxVal = std::numeric_limits<int8_t>::max(); + + auto* inputTensorData = tflite::GetTensorData<int8_t>(inputTensor); + + for (size_t i=0; i < inputFeatures.size(); ++i) { + float quantValue = ((inputFeatures[i] / quantScale) + quantOffset); + inputTensorData[i] = static_cast<int8_t>(std::min<float>(std::max<float>(quantValue, minVal), maxVal)); + } + } + + RNNoisePostProcess::RNNoisePostProcess(TfLiteTensor* outputTensor, + std::vector<int16_t>& denoisedAudioFrame, + std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor, + std::shared_ptr<rnn::FrameFeatures> frameFeatures) + : m_outputTensor{outputTensor}, + m_denoisedAudioFrame{denoisedAudioFrame}, + m_featureProcessor{featureProcessor}, + m_frameFeatures{frameFeatures} + { + this->m_denoisedAudioFrameFloat.reserve(denoisedAudioFrame.size()); + this->m_modelOutputFloat.resize(outputTensor->bytes); + } + + bool RNNoisePostProcess::DoPostProcess() + { + const auto* outputData = tflite::GetTensorData<int8_t>(this->m_outputTensor); + auto outputQuantParams = GetTensorQuantParams(this->m_outputTensor); + + for (size_t i = 0; i < this->m_outputTensor->bytes; ++i) { + this->m_modelOutputFloat[i] = (static_cast<float>(outputData[i]) - outputQuantParams.offset) + * outputQuantParams.scale; + } + + this->m_featureProcessor->PostProcessFrame(this->m_modelOutputFloat, + *this->m_frameFeatures, this->m_denoisedAudioFrameFloat); + + for (size_t i = 0; i < this->m_denoisedAudioFrame.size(); ++i) { + this->m_denoisedAudioFrame[i] = static_cast<int16_t>( + std::roundf(this->m_denoisedAudioFrameFloat[i])); + } + + return true; + } + +} /* namespace app */ +} /* namespace arm */
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