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Diffstat (limited to 'source/use_case/noise_reduction/include/RNNoiseProcessing.hpp')
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diff --git a/source/use_case/noise_reduction/include/RNNoiseProcessing.hpp b/source/use_case/noise_reduction/include/RNNoiseProcessing.hpp new file mode 100644 index 0000000..15e62d9 --- /dev/null +++ b/source/use_case/noise_reduction/include/RNNoiseProcessing.hpp @@ -0,0 +1,113 @@ +/* + * 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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