Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/source/use_case/kws/include/DsCnnMfcc.hpp b/source/use_case/kws/include/DsCnnMfcc.hpp
new file mode 100644
index 0000000..3f681af
--- /dev/null
+++ b/source/use_case/kws/include/DsCnnMfcc.hpp
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021 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_DSCNN_MFCC_HPP
+#define KWS_DSCNN_MFCC_HPP
+
+#include "Mfcc.hpp"
+
+namespace arm {
+namespace app {
+namespace audio {
+
+ /* Class to provide DS-CNN specific MFCC calculation requirements. */
+ class DsCnnMFCC : public MFCC {
+
+ public:
+ static constexpr uint32_t ms_defaultSamplingFreq = 16000;
+ static constexpr uint32_t ms_defaultNumFbankBins = 40;
+ static constexpr uint32_t ms_defaultMelLoFreq = 20;
+ static constexpr uint32_t ms_defaultMelHiFreq = 4000;
+ static constexpr bool ms_defaultUseHtkMethod = true;
+
+ explicit DsCnnMFCC(const size_t numFeats, const size_t frameLen)
+ : MFCC(MfccParams(
+ ms_defaultSamplingFreq, ms_defaultNumFbankBins,
+ ms_defaultMelLoFreq, ms_defaultMelHiFreq,
+ numFeats, frameLen, ms_defaultUseHtkMethod))
+ {}
+ DsCnnMFCC() = delete;
+ ~DsCnnMFCC() = default;
+ };
+
+} /* namespace audio */
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* KWS_DSCNN_MFCC_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws/include/DsCnnModel.hpp b/source/use_case/kws/include/DsCnnModel.hpp
new file mode 100644
index 0000000..a4e7110
--- /dev/null
+++ b/source/use_case/kws/include/DsCnnModel.hpp
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2021 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_DSCNNMODEL_HPP
+#define KWS_DSCNNMODEL_HPP
+
+#include "Model.hpp"
+
+extern const int g_FrameLength;
+extern const int g_FrameStride;
+extern const float g_ScoreThreshold;
+
+namespace arm {
+namespace app {
+
+ class DsCnnModel : public Model {
+ public:
+ /* Indices for the expected model - based on input and output tensor shapes */
+ static constexpr uint32_t ms_inputRowsIdx = 2;
+ static constexpr uint32_t ms_inputColsIdx = 3;
+ static constexpr uint32_t ms_outputRowsIdx = 2;
+ static constexpr uint32_t ms_outputColsIdx = 3;
+
+ protected:
+ /** @brief Gets the reference to op resolver interface class. */
+ const tflite::MicroOpResolver& GetOpResolver() override;
+
+ /** @brief Adds operations to the op resolver instance. */
+ bool EnlistOperations() override;
+
+ const uint8_t* ModelPointer() override;
+
+ size_t ModelSize() override;
+
+ private:
+ /* Maximum number of individual operations that can be enlisted. */
+ static constexpr int _ms_maxOpCnt = 8;
+
+ /* A mutable op resolver instance. */
+ tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+ };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* KWS_DSCNNMODEL_HPP */
diff --git a/source/use_case/kws/include/KwsResult.hpp b/source/use_case/kws/include/KwsResult.hpp
new file mode 100644
index 0000000..5a26ce1
--- /dev/null
+++ b/source/use_case/kws/include/KwsResult.hpp
@@ -0,0 +1,63 @@
+/*
+ * Copyright (c) 2021 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_RESULT_HPP
+#define KWS_RESULT_HPP
+
+#include "ClassificationResult.hpp"
+
+#include <vector>
+
+namespace arm {
+namespace app {
+namespace kws {
+
+ using ResultVec = std::vector < arm::app::ClassificationResult >;
+
+ /* Structure for holding kws result. */
+ class KwsResult {
+
+ public:
+ ResultVec m_resultVec; /* Container for "thresholded" classification results. */
+ float m_timeStamp; /* Audio timestamp for this result. */
+ uint32_t m_inferenceNumber; /* Corresponding inference number. */
+ float m_threshold; /* Threshold value for `m_resultVec`. */
+
+ KwsResult() = delete;
+ KwsResult(ResultVec& resultVec,
+ const float timestamp,
+ const uint32_t inferenceIdx,
+ const float scoreThreshold) {
+
+ this->m_threshold = scoreThreshold;
+ this->m_timeStamp = timestamp;
+ this->m_inferenceNumber = inferenceIdx;
+
+ this->m_resultVec = ResultVec();
+ for (auto & i : resultVec) {
+ if (i.m_normalisedVal >= this->m_threshold) {
+ this->m_resultVec.emplace_back(i);
+ }
+ }
+ }
+ ~KwsResult() = default;
+ };
+
+} /* namespace kws */
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* KWS_RESULT_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws/include/UseCaseHandler.hpp b/source/use_case/kws/include/UseCaseHandler.hpp
new file mode 100644
index 0000000..1eb742f
--- /dev/null
+++ b/source/use_case/kws/include/UseCaseHandler.hpp
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2021 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_EVT_HANDLER_HPP
+#define KWS_EVT_HANDLER_HPP
+
+#include "AppContext.hpp"
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief Handles the inference event.
+ * @param[in] ctx Pointer to the application context.
+ * @param[in] clipIndex Index to the audio clip to classify.
+ * @param[in] runAll Flag to request classification of all the available audio clips.
+ * @return true or false based on execution success.
+ **/
+ bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll);
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* KWS_EVT_HANDLER_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws/src/DsCnnModel.cc b/source/use_case/kws/src/DsCnnModel.cc
new file mode 100644
index 0000000..a093eb4
--- /dev/null
+++ b/source/use_case/kws/src/DsCnnModel.cc
@@ -0,0 +1,58 @@
+/*
+ * Copyright (c) 2021 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.
+ */
+#include "DsCnnModel.hpp"
+
+#include "hal.h"
+
+const tflite::MicroOpResolver& arm::app::DsCnnModel::GetOpResolver()
+{
+ return this->_m_opResolver;
+}
+
+bool arm::app::DsCnnModel::EnlistOperations()
+{
+ this->_m_opResolver.AddReshape();
+ this->_m_opResolver.AddAveragePool2D();
+ this->_m_opResolver.AddConv2D();
+ this->_m_opResolver.AddDepthwiseConv2D();
+ this->_m_opResolver.AddFullyConnected();
+ this->_m_opResolver.AddRelu();
+ this->_m_opResolver.AddSoftmax();
+
+#if defined(ARM_NPU)
+ if (kTfLiteOk == this->_m_opResolver.AddEthosU()) {
+ info("Added %s support to op resolver\n",
+ tflite::GetString_ETHOSU());
+ } else {
+ printf_err("Failed to add Arm NPU support to op resolver.");
+ return false;
+ }
+#endif /* ARM_NPU */
+ return true;
+}
+
+extern uint8_t* GetModelPointer();
+const uint8_t* arm::app::DsCnnModel::ModelPointer()
+{
+ return GetModelPointer();
+}
+
+extern size_t GetModelLen();
+size_t arm::app::DsCnnModel::ModelSize()
+{
+ return GetModelLen();
+}
\ No newline at end of file
diff --git a/source/use_case/kws/src/MainLoop.cc b/source/use_case/kws/src/MainLoop.cc
new file mode 100644
index 0000000..24cb939
--- /dev/null
+++ b/source/use_case/kws/src/MainLoop.cc
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2021 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.
+ */
+#include "InputFiles.hpp" /* For input audio clips. */
+#include "Classifier.hpp" /* Classifier. */
+#include "DsCnnModel.hpp" /* Model class for running inference. */
+#include "hal.h" /* Brings in platform definitions. */
+#include "Labels.hpp" /* For label strings. */
+#include "UseCaseHandler.hpp" /* Handlers for different user options. */
+#include "UseCaseCommonUtils.hpp" /* Utils functions. */
+
+using KwsClassifier = arm::app::Classifier;
+
+enum opcodes
+{
+ MENU_OPT_RUN_INF_NEXT = 1, /* Run on next vector. */
+ MENU_OPT_RUN_INF_CHOSEN, /* Run on a user provided vector index. */
+ MENU_OPT_RUN_INF_ALL, /* Run inference on all. */
+ MENU_OPT_SHOW_MODEL_INFO, /* Show model info. */
+ MENU_OPT_LIST_AUDIO_CLIPS /* List the current baked audio clips. */
+};
+
+static void DisplayMenu()
+{
+ printf("\n\nUser input required\n");
+ printf("Enter option number from:\n\n");
+ printf(" %u. Classify next audio clip\n", MENU_OPT_RUN_INF_NEXT);
+ printf(" %u. Classify audio clip at chosen index\n", MENU_OPT_RUN_INF_CHOSEN);
+ printf(" %u. Run classification on all audio clips\n", MENU_OPT_RUN_INF_ALL);
+ printf(" %u. Show NN model info\n", MENU_OPT_SHOW_MODEL_INFO);
+ printf(" %u. List audio clips\n\n", MENU_OPT_LIST_AUDIO_CLIPS);
+ printf(" Choice: ");
+}
+
+void main_loop(hal_platform& platform)
+{
+ arm::app::DsCnnModel model; /* Model wrapper object. */
+
+ /* Load the model. */
+ if (!model.Init()) {
+ printf_err("Failed to initialise model\n");
+ return;
+ }
+
+ /* Instantiate application context. */
+ arm::app::ApplicationContext caseContext;
+
+ caseContext.Set<hal_platform&>("platform", platform);
+ caseContext.Set<arm::app::Model&>("model", model);
+ caseContext.Set<uint32_t>("clipIndex", 0);
+ caseContext.Set<int>("frameLength", g_FrameLength);
+ caseContext.Set<int>("frameStride", g_FrameStride);
+ caseContext.Set<float>("scoreThreshold", g_ScoreThreshold); /* Normalised score threshold. */
+
+ KwsClassifier classifier; /* classifier wrapper object. */
+ caseContext.Set<arm::app::Classifier&>("classifier", classifier);
+
+ std::vector <std::string> labels;
+ GetLabelsVector(labels);
+
+ caseContext.Set<const std::vector <std::string>&>("labels", labels);
+
+ bool executionSuccessful = true;
+ constexpr bool bUseMenu = NUMBER_OF_FILES > 1 ? true : false;
+
+ /* Loop. */
+ do {
+ int menuOption = MENU_OPT_RUN_INF_NEXT;
+ if (bUseMenu) {
+ DisplayMenu();
+ menuOption = arm::app::ReadUserInputAsInt(platform);
+ printf("\n");
+ }
+ switch (menuOption) {
+ case MENU_OPT_RUN_INF_NEXT:
+ executionSuccessful = ClassifyAudioHandler(caseContext, caseContext.Get<uint32_t>("clipIndex"), false);
+ break;
+ case MENU_OPT_RUN_INF_CHOSEN: {
+ printf(" Enter the audio clip index [0, %d]: ", NUMBER_OF_FILES-1);
+ auto clipIndex = static_cast<uint32_t>(arm::app::ReadUserInputAsInt(platform));
+ executionSuccessful = ClassifyAudioHandler(caseContext, clipIndex, false);
+ break;
+ }
+ case MENU_OPT_RUN_INF_ALL:
+ executionSuccessful = ClassifyAudioHandler(caseContext,caseContext.Get<uint32_t>("clipIndex"), true);
+ break;
+ case MENU_OPT_SHOW_MODEL_INFO:
+ executionSuccessful = model.ShowModelInfoHandler();
+ break;
+ case MENU_OPT_LIST_AUDIO_CLIPS:
+ executionSuccessful = ListFilesHandler(caseContext);
+ break;
+ default:
+ printf("Incorrect choice, try again.");
+ break;
+ }
+ } while (executionSuccessful && bUseMenu);
+ info("Main loop terminated.\n");
+}
\ No newline at end of file
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
new file mode 100644
index 0000000..872d323
--- /dev/null
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -0,0 +1,452 @@
+/*
+ * Copyright (c) 2021 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.
+ */
+#include "UseCaseHandler.hpp"
+
+#include "InputFiles.hpp"
+#include "Classifier.hpp"
+#include "DsCnnModel.hpp"
+#include "hal.h"
+#include "DsCnnMfcc.hpp"
+#include "AudioUtils.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "KwsResult.hpp"
+
+#include <vector>
+#include <functional>
+
+using KwsClassifier = arm::app::Classifier;
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief Helper function to increment current audio clip index.
+ * @param[in,out] ctx Pointer to the application context object.
+ **/
+ static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+
+ /**
+ * @brief Helper function to set the audio clip index.
+ * @param[in,out] ctx Pointer to the application context object.
+ * @param[in] idx Value to be set.
+ * @return true if index is set, false otherwise.
+ **/
+ static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+
+ /**
+ * @brief Presents inference results using the data presentation
+ * object.
+ * @param[in] platform Reference to the hal platform object.
+ * @param[in] results Vector of classification results to be displayed.
+ * @param[in] infTimeMs Inference time in milliseconds, if available,
+ * otherwise, this can be passed in as 0.
+ * @return true if successful, false otherwise.
+ **/
+ static bool _PresentInferenceResult(hal_platform& platform,
+ const std::vector<arm::app::kws::KwsResult>& results);
+
+ /**
+ * @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.
+ */
+ static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
+ GetFeatureCalculator(audio::DsCnnMFCC& mfcc,
+ TfLiteTensor* inputTensor,
+ size_t cacheSize);
+
+ /* Audio inference handler. */
+ bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
+ {
+ auto& platform = ctx.Get<hal_platform&>("platform");
+
+ constexpr uint32_t dataPsnTxtInfStartX = 20;
+ constexpr uint32_t dataPsnTxtInfStartY = 40;
+ constexpr int minTensorDims = static_cast<int>(
+ (arm::app::DsCnnModel::ms_inputRowsIdx > arm::app::DsCnnModel::ms_inputColsIdx)?
+ arm::app::DsCnnModel::ms_inputRowsIdx : arm::app::DsCnnModel::ms_inputColsIdx);
+
+ platform.data_psn->clear(COLOR_BLACK);
+
+ auto& model = ctx.Get<Model&>("model");
+
+ /* If the request has a valid size, set the audio index. */
+ if (clipIndex < NUMBER_OF_FILES) {
+ if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+ return false;
+ }
+ }
+ if (!model.IsInited()) {
+ printf_err("Model is not initialised! Terminating processing.\n");
+ return false;
+ }
+
+ const auto frameLength = ctx.Get<int>("frameLength");
+ const auto frameStride = ctx.Get<int>("frameStride");
+ const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+ auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
+
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+
+ if (!inputTensor->dims) {
+ printf_err("Invalid input tensor dims\n");
+ return false;
+ } else if (inputTensor->dims->size < minTensorDims) {
+ printf_err("Input tensor dimension should be >= %d\n", minTensorDims);
+ return false;
+ }
+
+ TfLiteIntArray* inputShape = model.GetInputShape(0);
+ const uint32_t kNumCols = inputShape->data[arm::app::DsCnnModel::ms_inputColsIdx];
+ const uint32_t kNumRows = inputShape->data[arm::app::DsCnnModel::ms_inputRowsIdx];
+
+ audio::DsCnnMFCC mfcc = audio::DsCnnMFCC(kNumCols, frameLength);
+ mfcc.Init();
+
+ /* Deduce the data length required for 1 inference from the network parameters. */
+ auto audioDataWindowSize = kNumRows * frameStride + (frameLength - frameStride);
+ auto mfccWindowSize = frameLength;
+ auto mfccWindowStride = frameStride;
+
+ /* We choose to move by half the window size => for a 1 second window size
+ * there is an overlap of 0.5 seconds. */
+ auto audioDataStride = audioDataWindowSize / 2;
+
+ /* To have the previously calculated features re-usable, stride must be multiple
+ * of MFCC features window stride. */
+ if (0 != audioDataStride % mfccWindowStride) {
+
+ /* Reduce the stride. */
+ audioDataStride -= audioDataStride % mfccWindowStride;
+ }
+
+ auto nMfccVectorsInAudioStride = audioDataStride/mfccWindowStride;
+
+ /* We expect to be sampling 1 second worth of data at a time.
+ * NOTE: This is only used for time stamp calculation. */
+ const float secondsPerSample = 1.0/audio::DsCnnMFCC::ms_defaultSamplingFreq;
+
+ do {
+ auto currentIndex = ctx.Get<uint32_t>("clipIndex");
+
+ /* Creating a mfcc features sliding window for the data required for 1 inference. */
+ auto audioMFCCWindowSlider = audio::SlidingWindow<const int16_t>(
+ get_audio_array(currentIndex),
+ audioDataWindowSize, mfccWindowSize,
+ mfccWindowStride);
+
+ /* Creating a sliding window through the whole audio clip. */
+ auto audioDataSlider = audio::SlidingWindow<const int16_t>(
+ get_audio_array(currentIndex),
+ get_audio_array_size(currentIndex),
+ audioDataWindowSize, audioDataStride);
+
+ /* Calculate number of the feature vectors in the window overlap region.
+ * These feature vectors will be reused.*/
+ auto numberOfReusedFeatureVectors = audioMFCCWindowSlider.TotalStrides() + 1
+ - nMfccVectorsInAudioStride;
+
+ /* Construct feature calculation function. */
+ auto mfccFeatureCalc = GetFeatureCalculator(mfcc, inputTensor,
+ numberOfReusedFeatureVectors);
+
+ if (!mfccFeatureCalc){
+ return false;
+ }
+
+ /* Declare a container for results. */
+ std::vector<arm::app::kws::KwsResult> results;
+
+ /* Display message on the LCD - inference running. */
+ std::string str_inf{"Running inference... "};
+ platform.data_psn->present_data_text(
+ str_inf.c_str(), str_inf.size(),
+ dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+ info("Running inference on audio clip %u => %s\n", currentIndex,
+ get_filename(currentIndex));
+
+ /* Start sliding through audio clip. */
+ while (audioDataSlider.HasNext()) {
+ const int16_t *inferenceWindow = audioDataSlider.Next();
+
+ /* We moved to the next window - set the features sliding to the new address. */
+ audioMFCCWindowSlider.Reset(inferenceWindow);
+
+ /* The first window does not have cache ready. */
+ bool useCache = audioDataSlider.Index() > 0 && numberOfReusedFeatureVectors > 0;
+
+ /* Start calculating features inside one audio sliding window. */
+ while (audioMFCCWindowSlider.HasNext()) {
+ const int16_t *mfccWindow = audioMFCCWindowSlider.Next();
+ std::vector<int16_t> mfccAudioData = std::vector<int16_t>(mfccWindow,
+ mfccWindow + mfccWindowSize);
+ /* Compute features for this window and write them to input tensor. */
+ mfccFeatureCalc(mfccAudioData,
+ audioMFCCWindowSlider.Index(),
+ useCache,
+ nMfccVectorsInAudioStride);
+ }
+
+ info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
+ audioDataSlider.TotalStrides() + 1);
+
+ /* Run inference over this audio clip sliding window. */
+ arm::app::RunInference(platform, model);
+
+ std::vector<ClassificationResult> classificationResult;
+ auto& classifier = ctx.Get<KwsClassifier&>("classifier");
+ classifier.GetClassificationResults(outputTensor, classificationResult,
+ ctx.Get<std::vector<std::string>&>("labels"), 1);
+
+ results.emplace_back(kws::KwsResult(classificationResult,
+ audioDataSlider.Index() * secondsPerSample * audioDataStride,
+ audioDataSlider.Index(), scoreThreshold));
+
+#if VERIFY_TEST_OUTPUT
+ arm::app::DumpTensor(outputTensor);
+#endif /* VERIFY_TEST_OUTPUT */
+ } /* while (audioDataSlider.HasNext()) */
+
+ /* Erase. */
+ str_inf = std::string(str_inf.size(), ' ');
+ platform.data_psn->present_data_text(
+ str_inf.c_str(), str_inf.size(),
+ dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+
+ ctx.Set<std::vector<arm::app::kws::KwsResult>>("results", results);
+
+ if (!_PresentInferenceResult(platform, results)) {
+ return false;
+ }
+
+ _IncrementAppCtxClipIdx(ctx);
+
+ } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
+
+ return true;
+ }
+
+ static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+ {
+ auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
+
+ if (curAudioIdx + 1 >= NUMBER_OF_FILES) {
+ ctx.Set<uint32_t>("clipIndex", 0);
+ return;
+ }
+ ++curAudioIdx;
+ ctx.Set<uint32_t>("clipIndex", curAudioIdx);
+ }
+
+ static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+ {
+ if (idx >= NUMBER_OF_FILES) {
+ printf_err("Invalid idx %u (expected less than %u)\n",
+ idx, NUMBER_OF_FILES);
+ return false;
+ }
+ ctx.Set<uint32_t>("clipIndex", idx);
+ return true;
+ }
+
+ static bool _PresentInferenceResult(hal_platform& platform,
+ const std::vector<arm::app::kws::KwsResult>& results)
+ {
+ constexpr uint32_t dataPsnTxtStartX1 = 20;
+ constexpr uint32_t dataPsnTxtStartY1 = 30;
+ constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
+
+ platform.data_psn->set_text_color(COLOR_GREEN);
+
+ /* Display each result */
+ uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
+
+ for (uint32_t i = 0; i < results.size(); ++i) {
+
+ std::string topKeyword{"<none>"};
+ float score = 0.f;
+
+ if (results[i].m_resultVec.size()) {
+ topKeyword = results[i].m_resultVec[0].m_label;
+ score = results[i].m_resultVec[0].m_normalisedVal;
+ }
+
+ std::string resultStr =
+ std::string{"@"} + std::to_string(results[i].m_timeStamp) +
+ std::string{"s: "} + topKeyword + std::string{" ("} +
+ std::to_string(static_cast<int>(score * 100)) + std::string{"%)"};
+
+ platform.data_psn->present_data_text(
+ resultStr.c_str(), resultStr.size(),
+ dataPsnTxtStartX1, rowIdx1, false);
+ rowIdx1 += dataPsnTxtYIncr;
+
+ info("For timestamp: %f (inference #: %u); threshold: %f\n",
+ results[i].m_timeStamp, results[i].m_inferenceNumber,
+ results[i].m_threshold);
+ for (uint32_t j = 0; j < results[i].m_resultVec.size(); ++j) {
+ info("\t\tlabel @ %u: %s, score: %f\n", j,
+ results[i].m_resultVec[j].m_label.c_str(),
+ results[i].m_resultVec[j].m_normalisedVal);
+ }
+ }
+
+ return true;
+ }
+
+ /**
+ * @brief Generic feature calculator factory.
+ *
+ * Returns lambda function to compute features using features cache.
+ * Real features math is done by a lambda function provided as a parameter.
+ * Features are written to input tensor memory.
+ *
+ * @tparam T Feature vector type.
+ * @param inputTensor Model input tensor pointer.
+ * @param cacheSize Number of feature vectors to cache. Defined by the sliding window overlap.
+ * @param compute Features calculator function.
+ * @return Lambda function to compute features.
+ */
+ 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)
+ {
+ /* Feature cache to be captured by lambda function. */
+ static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
+
+ return [=](std::vector<int16_t>& audioDataWindow,
+ size_t index,
+ bool useCache,
+ size_t featuresOverlapIndex)
+ {
+ T *tensorData = tflite::GetTensorData<T>(inputTensor);
+ std::vector<T> features;
+
+ /* Reuse features from cache if cache is ready and sliding windows overlap.
+ * Overlap is in the beginning of sliding window with a size of a feature cache. */
+ if (useCache && index < featureCache.size()) {
+ features = std::move(featureCache[index]);
+ } else {
+ features = std::move(compute(audioDataWindow));
+ }
+ auto size = features.size();
+ auto sizeBytes = sizeof(T) * size;
+ std::memcpy(tensorData + (index * size), features.data(), sizeBytes);
+
+ /* Start renewing cache as soon iteration goes out of the windows overlap. */
+ if (index >= featuresOverlapIndex) {
+ featureCache[index - featuresOverlapIndex] = std::move(features);
+ }
+ };
+ }
+
+ template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
+ _FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int8_t> (std::vector<int16_t>& )> compute);
+
+ template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
+ _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
+
+ template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
+ _FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
+
+ template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
+ _FeatureCalc<float>(TfLiteTensor *inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+
+
+ static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
+ GetFeatureCalculator(audio::DsCnnMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+ {
+ std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
+
+ TfLiteQuantization quant = inputTensor->quantization;
+
+ if (kTfLiteAffineQuantization == quant.type) {
+
+ auto *quantParams = (TfLiteAffineQuantization *) quant.params;
+ const float quantScale = quantParams->scale->data[0];
+ const int quantOffset = quantParams->zero_point->data[0];
+
+ switch (inputTensor->type) {
+ case kTfLiteInt8: {
+ mfccFeatureCalc = _FeatureCalc<int8_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
+ );
+ break;
+ }
+ case kTfLiteUInt8: {
+ mfccFeatureCalc = _FeatureCalc<uint8_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
+ );
+ break;
+ }
+ case kTfLiteInt16: {
+ mfccFeatureCalc = _FeatureCalc<int16_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
+ );
+ break;
+ }
+ default:
+ printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
+ }
+
+
+ } else {
+ mfccFeatureCalc = mfccFeatureCalc = _FeatureCalc<float>(inputTensor,
+ cacheSize,
+ [&mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccCompute(audioDataWindow);
+ });
+ }
+ return mfccFeatureCalc;
+ }
+
+} /* namespace app */
+} /* namespace arm */
\ No newline at end of file
diff --git a/source/use_case/kws/usecase.cmake b/source/use_case/kws/usecase.cmake
new file mode 100644
index 0000000..b5ac09e
--- /dev/null
+++ b/source/use_case/kws/usecase.cmake
@@ -0,0 +1,159 @@
+#----------------------------------------------------------------------------
+# Copyright (c) 2021 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.
+#----------------------------------------------------------------------------
+
+# If the path to a directory or source file has been defined,
+# get the type here (FILEPATH or PATH):
+if (DEFINED ${use_case}_FILE_PATH)
+ get_path_type(${${use_case}_FILE_PATH} PATH_TYPE)
+
+ # Set the default type if path is not a dir or file path (or undefined)
+ if (NOT ${PATH_TYPE} STREQUAL PATH AND NOT ${PATH_TYPE} STREQUAL FILEPATH)
+ message(FATAL_ERROR "Invalid ${use_case}_FILE_PATH. It should be a dir or file path.")
+ endif()
+else()
+ # Default is a directory path
+ set(PATH_TYPE PATH)
+endif()
+
+message(STATUS "${use_case}_FILE_PATH is of type: ${PATH_TYPE}")
+USER_OPTION(${use_case}_FILE_PATH "Directory with custom WAV input files, or path to a single WAV file, to use in the evaluation application."
+ ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/
+ ${PATH_TYPE})
+
+USER_OPTION(${use_case}_LABELS_TXT_FILE "Labels' txt file for the chosen model."
+ ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/labels/ds_cnn_labels.txt
+ FILEPATH)
+
+USER_OPTION(${use_case}_AUDIO_RATE "Specify the target sampling rate. Default is 16000."
+ 16000
+ STRING)
+
+USER_OPTION(${use_case}_AUDIO_MONO "Specify if the audio needs to be converted to mono. Default is ON."
+ ON
+ BOOL)
+
+USER_OPTION(${use_case}_AUDIO_OFFSET "Specify the offset to start reading after this time (in seconds). Default is 0."
+ 0
+ STRING)
+
+USER_OPTION(${use_case}_AUDIO_DURATION "Specify the audio duration to load (in seconds). If set to 0 the entire audio will be processed."
+ 0
+ STRING)
+
+USER_OPTION(${use_case}_AUDIO_RES_TYPE "Specify re-sampling algorithm to use. By default is 'kaiser_best'."
+ kaiser_best
+ STRING)
+
+USER_OPTION(${use_case}_AUDIO_MIN_SAMPLES "Specify the minimum number of samples to use. By default is 16000, if the audio is shorter will be automatically padded."
+ 16000
+ STRING)
+
+USER_OPTION(${use_case}_MODEL_SCORE_THRESHOLD "Specify the score threshold [0.0, 1.0) that must be applied to the inference results for a label to be deemed valid."
+ 0.9
+ STRING)
+
+# Generate input files
+generate_audio_code(${${use_case}_FILE_PATH} ${SRC_GEN_DIR} ${INC_GEN_DIR}
+ ${${use_case}_AUDIO_RATE}
+ ${${use_case}_AUDIO_MONO}
+ ${${use_case}_AUDIO_OFFSET}
+ ${${use_case}_AUDIO_DURATION}
+ ${${use_case}_AUDIO_RES_TYPE}
+ ${${use_case}_AUDIO_MIN_SAMPLES})
+
+# Generate labels file
+set(${use_case}_LABELS_CPP_FILE Labels)
+generate_labels_code(
+ INPUT "${${use_case}_LABELS_TXT_FILE}"
+ DESTINATION_SRC ${SRC_GEN_DIR}
+ DESTINATION_HDR ${INC_GEN_DIR}
+ OUTPUT_FILENAME "${${use_case}_LABELS_CPP_FILE}"
+)
+
+USER_OPTION(${use_case}_ACTIVATION_BUF_SZ "Activation buffer size for the chosen model"
+ 0x00100000
+ STRING)
+
+# If there is no tflite file pointed to
+if (NOT DEFINED ${use_case}_MODEL_TFLITE_PATH)
+
+ set(MODEL_FILENAME ds_cnn_clustered_int8.tflite)
+ set(MODEL_RESOURCES_DIR ${DOWNLOAD_DEP_DIR}/${use_case})
+ file(MAKE_DIRECTORY ${MODEL_RESOURCES_DIR})
+ set(DEFAULT_MODEL_PATH ${MODEL_RESOURCES_DIR}/${MODEL_FILENAME})
+
+ # Download the default model
+ set(ZOO_COMMON_SUBPATH "models/keyword_spotting/ds_cnn_large/tflite_clustered_int8")
+ set(ZOO_MODEL_SUBPATH "${ZOO_COMMON_SUBPATH}/${MODEL_FILENAME}")
+
+ download_file_from_modelzoo(${ZOO_MODEL_SUBPATH} ${DEFAULT_MODEL_PATH})
+
+ if (ETHOS_U55_ENABLED)
+ message(STATUS
+ "Ethos-U55 is enabled, but the model downloaded is not optimized by vela. "
+ "To use Ethos-U55 acceleration, optimise the downloaded model and pass it "
+ "as ${use_case}_MODEL_TFLITE_PATH to the CMake configuration.")
+ endif()
+
+ # If the target platform is native
+ if (${TARGET_PLATFORM} STREQUAL native)
+
+ # Download test vectors
+ set(ZOO_TEST_IFM_SUBPATH "${ZOO_COMMON_SUBPATH}/testing_input/input_2/0.npy")
+ set(ZOO_TEST_OFM_SUBPATH "${ZOO_COMMON_SUBPATH}/testing_output/Identity/0.npy")
+
+ set(${use_case}_TEST_IFM ${MODEL_RESOURCES_DIR}/ifm0.npy CACHE FILEPATH
+ "Input test vector for ${use_case}")
+ set(${use_case}_TEST_OFM ${MODEL_RESOURCES_DIR}/ofm0.npy CACHE FILEPATH
+ "Input test vector for ${use_case}")
+
+ download_file_from_modelzoo(${ZOO_TEST_IFM_SUBPATH} ${${use_case}_TEST_IFM})
+ download_file_from_modelzoo(${ZOO_TEST_OFM_SUBPATH} ${${use_case}_TEST_OFM})
+
+ set(TEST_SRC_GEN_DIR ${CMAKE_BINARY_DIR}/generated/${use_case}/tests/src)
+ set(TEST_INC_GEN_DIR ${CMAKE_BINARY_DIR}/generated/${use_case}/tests/include)
+ file(MAKE_DIRECTORY ${TEST_SRC_GEN_DIR} ${TEST_INC_GEN_DIR})
+
+ # Generate test data files to be included in x86 tests
+ generate_test_data_code(
+ INPUT_DIR "${DOWNLOAD_DEP_DIR}/${use_case}"
+ DESTINATION_SRC ${TEST_SRC_GEN_DIR}
+ DESTINATION_HDR ${TEST_INC_GEN_DIR}
+ USECASE "${use_case}")
+ endif()
+
+else()
+ set(DEFAULT_MODEL_PATH "N/A")
+endif()
+
+set(EXTRA_MODEL_CODE
+ "/* Model parameters for ${use_case} */"
+ "extern const int g_FrameLength = 640"
+ "extern const int g_FrameStride = 320"
+ "extern const float g_ScoreThreshold = ${${use_case}_MODEL_SCORE_THRESHOLD}"
+ )
+
+USER_OPTION(${use_case}_MODEL_TFLITE_PATH "NN models file to be used in the evaluation application. Model files must be in tflite format."
+ ${DEFAULT_MODEL_PATH}
+ FILEPATH)
+
+# Generate model file
+generate_tflite_code(
+ MODEL_PATH ${${use_case}_MODEL_TFLITE_PATH}
+ DESTINATION ${SRC_GEN_DIR}
+ EXPRESSIONS ${EXTRA_MODEL_CODE}
+)