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Diffstat (limited to 'source/use_case/kws/include/KwsProcessing.hpp')
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diff --git a/source/use_case/kws/include/KwsProcessing.hpp b/source/use_case/kws/include/KwsProcessing.hpp new file mode 100644 index 0000000..abf20ab --- /dev/null +++ b/source/use_case/kws/include/KwsProcessing.hpp @@ -0,0 +1,135 @@ +/* + * 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 KWS_PROCESSING_HPP +#define KWS_PROCESSING_HPP + +#include <AudioUtils.hpp> +#include "BaseProcessing.hpp" +#include "Model.hpp" +#include "Classifier.hpp" +#include "MicroNetKwsMfcc.hpp" + +#include <functional> + +namespace arm { +namespace app { + + /** + * @brief Pre-processing class for Keyword Spotting use case. + * Implements methods declared by BasePreProcess and anything else needed + * to populate input tensors ready for inference. + */ + class KWSPreProcess : public BasePreProcess { + + public: + /** + * @brief Constructor + * @param[in] model Pointer to the the KWS Model object. + * @param[in] numFeatures How many MFCC features to use. + * @param[in] mfccFrameLength Number of audio samples used to calculate one set of MFCC values when + * sliding a window through the audio sample. + * @param[in] mfccFrameStride Number of audio samples between consecutive windows. + **/ + explicit KWSPreProcess(Model* model, size_t numFeatures, int mfccFrameLength, int mfccFrameStride); + + /** + * @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; + + size_t m_audioWindowIndex = 0; /* Index of audio slider, used when caching features in longer clips. */ + size_t m_audioDataWindowSize; /* Amount of audio needed for 1 inference. */ + size_t m_audioDataStride; /* Amount of audio to stride across if doing >1 inference in longer clips. */ + + private: + const int m_mfccFrameLength; + const int m_mfccFrameStride; + + audio::MicroNetKwsMFCC m_mfcc; + audio::SlidingWindow<const int16_t> m_mfccSlidingWindow; + size_t m_numMfccVectorsInAudioStride; + size_t m_numReusedMfccVectors; + std::function<void (std::vector<int16_t>&, int, bool, size_t)> m_mfccFeatureCalculator; + + /** + * @brief Returns a function to perform feature calculation and populates input tensor data with + * MFCC data. + * + * Input tensor data type check is performed to choose correct MFCC feature data type. + * If tensor has an integer data type then original features are quantised. + * + * Warning: MFCC calculator provided as input must have the same life scope as returned function. + * + * @param[in] mfcc MFCC feature calculator. + * @param[in,out] inputTensor Input tensor pointer to store calculated features. + * @param[in] cacheSize Size of the feature vectors cache (number of feature vectors). + * @return Function to be called providing audio sample and sliding window index. + */ + std::function<void (std::vector<int16_t>&, int, bool, size_t)> + GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, + TfLiteTensor* inputTensor, + size_t cacheSize); + + template<class T> + std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> + FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize, + std::function<std::vector<T> (std::vector<int16_t>& )> compute); + }; + + /** + * @brief Post-processing class for Keyword Spotting use case. + * Implements methods declared by BasePostProcess and anything else needed + * to populate result vector. + */ + class KWSPostProcess : public BasePostProcess { + + private: + Classifier& m_kwsClassifier; + const std::vector<std::string>& m_labels; + std::vector<ClassificationResult>& m_results; + + public: + const float m_scoreThreshold; + /** + * @brief Constructor + * @param[in] classifier Classifier object used to get top N results from classification. + * @param[in] model Pointer to the the Image classification Model object. + * @param[in] labels Vector of string labels to identify each output of the model. + * @param[in] results Vector of classification results to store decoded outputs. + * @param[in] scoreThreshold Predicted model score must be larger than this value to be accepted. + **/ + KWSPostProcess(Classifier& classifier, Model* model, + const std::vector<std::string>& labels, + std::vector<ClassificationResult>& results, + float scoreThreshold); + + /** + * @brief Should perform post-processing of the result of inference then populate + * populate KWS result data for any later use. + * @return true if successful, false otherwise. + **/ + bool DoPostProcess() override; + }; + +} /* namespace app */ +} /* namespace arm */ + +#endif /* KWS_PROCESSING_HPP */
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