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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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