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/include/RNNoiseProcessing.hpp b/source/use_case/noise_reduction/include/RNNoiseProcessing.hpp
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+++ b/source/use_case/noise_reduction/include/RNNoiseProcessing.hpp
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+/*
+ * 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.
+ */
+#ifndef RNNOISE_PROCESSING_HPP
+#define RNNOISE_PROCESSING_HPP
+
+#include "BaseProcessing.hpp"
+#include "Model.hpp"
+#include "RNNoiseFeatureProcessor.hpp"
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief Pre-processing class for Noise Reduction use case.
+ * Implements methods declared by BasePreProcess and anything else needed
+ * to populate input tensors ready for inference.
+ */
+ class RNNoisePreProcess : public BasePreProcess {
+
+ public:
+ /**
+ * @brief Constructor
+ * @param[in] inputTensor Pointer to the TFLite Micro input Tensor.
+ * @param[in/out] featureProcessor RNNoise specific feature extractor object.
+ * @param[in/out] frameFeatures RNNoise specific features shared between pre & post-processing.
+ *
+ **/
+ explicit RNNoisePreProcess(TfLiteTensor* inputTensor,
+ std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor,
+ std::shared_ptr<rnn::FrameFeatures> frameFeatures);
+
+ /**
+ * @brief Should perform pre-processing of 'raw' input audio data and load it into
+ * TFLite Micro input tensors ready for inference
+ * @param[in] input Pointer to the data that pre-processing will work on.
+ * @param[in] inputSize Size of the input data.
+ * @return true if successful, false otherwise.
+ **/
+ bool DoPreProcess(const void* input, size_t inputSize) override;
+
+ private:
+ TfLiteTensor* m_inputTensor; /* Model input tensor. */
+ std::shared_ptr<rnn::RNNoiseFeatureProcessor> m_featureProcessor; /* RNNoise feature processor shared between pre & post-processing. */
+ std::shared_ptr<rnn::FrameFeatures> m_frameFeatures; /* RNNoise features shared between pre & post-processing. */
+ rnn::vec1D32F m_audioFrame; /* Audio frame cast to FP32 */
+
+ /**
+ * @brief Quantize the given features and populate the input Tensor.
+ * @param[in] inputFeatures Vector of floating point features to quantize.
+ * @param[in] quantScale Quantization scale for the inputTensor.
+ * @param[in] quantOffset Quantization offset for the inputTensor.
+ * @param[in,out] inputTensor TFLite micro tensor to populate.
+ **/
+ static void QuantizeAndPopulateInput(rnn::vec1D32F& inputFeatures,
+ float quantScale, int quantOffset,
+ TfLiteTensor* inputTensor);
+ };
+
+ /**
+ * @brief Post-processing class for Noise Reduction use case.
+ * Implements methods declared by BasePostProcess and anything else needed
+ * to populate result vector.
+ */
+ class RNNoisePostProcess : public BasePostProcess {
+
+ public:
+ /**
+ * @brief Constructor
+ * @param[in] outputTensor Pointer to the TFLite Micro output Tensor.
+ * @param[out] denoisedAudioFrame Vector to store the final denoised audio frame.
+ * @param[in/out] featureProcessor RNNoise specific feature extractor object.
+ * @param[in/out] frameFeatures RNNoise specific features shared between pre & post-processing.
+ **/
+ RNNoisePostProcess(TfLiteTensor* outputTensor,
+ std::vector<int16_t>& denoisedAudioFrame,
+ std::shared_ptr<rnn::RNNoiseFeatureProcessor> featureProcessor,
+ std::shared_ptr<rnn::FrameFeatures> frameFeatures);
+
+ /**
+ * @brief Should perform post-processing of the result of inference then
+ * populate result data for any later use.
+ * @return true if successful, false otherwise.
+ **/
+ bool DoPostProcess() override;
+
+ private:
+ TfLiteTensor* m_outputTensor; /* Model output tensor. */
+ std::vector<int16_t>& m_denoisedAudioFrame; /* Vector to store the final denoised frame. */
+ rnn::vec1D32F m_denoisedAudioFrameFloat; /* Internal vector to store the final denoised frame (FP32). */
+ std::shared_ptr<rnn::RNNoiseFeatureProcessor> m_featureProcessor; /* RNNoise feature processor shared between pre & post-processing. */
+ std::shared_ptr<rnn::FrameFeatures> m_frameFeatures; /* RNNoise features shared between pre & post-processing. */
+ std::vector<float> m_modelOutputFloat; /* Internal vector to store de-quantized model output. */
+
+ };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* RNNOISE_PROCESSING_HPP */
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