| /* |
| * SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com> |
| * 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 */ |