| /* |
| * SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com> |
| * 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 "KwsClassifier.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] inputTensor Pointer to the TFLite Micro input Tensor. |
| * @param[in] numFeatures How many MFCC features to use. |
| * @param[in] numFeatureFrames Number of MFCC vectors that need to be calculated |
| * for an inference. |
| * @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(TfLiteTensor* inputTensor, size_t numFeatures, size_t numFeatureFrames, |
| 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 inferenceIndex = 0) override; |
| |
| 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: |
| TfLiteTensor* m_inputTensor; /* Model input tensor. */ |
| const int m_mfccFrameLength; |
| const int m_mfccFrameStride; |
| const size_t m_numMfccFrames; /* How many sets of m_numMfccFeats. */ |
| |
| 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: |
| TfLiteTensor* m_outputTensor; /* Model output tensor. */ |
| KwsClassifier& m_kwsClassifier; /* KWS Classifier object. */ |
| const std::vector<std::string>& m_labels; /* KWS Labels. */ |
| std::vector<ClassificationResult>& m_results; /* Results vector for a single inference. */ |
| std::vector<std::vector<float>> m_resultHistory; /* Store previous results so they can be averaged. */ |
| public: |
| /** |
| * @brief Constructor |
| * @param[in] outputTensor Pointer to the TFLite Micro output Tensor. |
| * @param[in] classifier Classifier object used to get top N results from classification. |
| * @param[in] labels Vector of string labels to identify each output of the model. |
| * @param[in/out] results Vector of classification results to store decoded outputs. |
| **/ |
| KwsPostProcess(TfLiteTensor* outputTensor, KwsClassifier& classifier, |
| const std::vector<std::string>& labels, |
| std::vector<ClassificationResult>& results, size_t averagingWindowLen = 1); |
| |
| /** |
| * @brief Should perform post-processing of the result of inference then |
| * 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 */ |