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/kws_asr/include/KwsProcessing.hpp b/source/use_case/kws_asr/include/KwsProcessing.hpp
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+++ b/source/use_case/kws_asr/include/KwsProcessing.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 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] 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 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:
+ 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. */
+ Classifier& 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. */
+
+ 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, Classifier& classifier,
+ const std::vector<std::string>& labels,
+ std::vector<ClassificationResult>& results);
+
+ /**
+ * @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 */
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