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/*
* 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 */
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