MLECO-3173: Add AD, KWS_ASR and Noise reduction use case API's
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I36f61ce74bf17f7b327cdae9704a22ca54144f37
diff --git a/source/use_case/noise_reduction/src/RNNoiseProcessing.cc b/source/use_case/noise_reduction/src/RNNoiseProcessing.cc
new file mode 100644
index 0000000..f6a3ec4
--- /dev/null
+++ b/source/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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