MLECO-3075: Add KWS use case API
Removed some of the templates for feature calculation that we are unlikely to ever use.
We might be able to refactor the feature caching and feature calculator code in the future
to better integrate it with with PreProcess API.
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: Ic0c0c581c71e2553d41ff72cd1ed3b3efa64fa92
diff --git a/source/use_case/kws/src/KwsProcessing.cc b/source/use_case/kws/src/KwsProcessing.cc
new file mode 100644
index 0000000..b6b230c
--- /dev/null
+++ b/source/use_case/kws/src/KwsProcessing.cc
@@ -0,0 +1,220 @@
+/*
+ * 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.
+ */
+#include "KwsProcessing.hpp"
+#include "ImageUtils.hpp"
+#include "log_macros.h"
+#include "MicroNetKwsModel.hpp"
+
+namespace arm {
+namespace app {
+
+ KWSPreProcess::KWSPreProcess(Model* model, size_t numFeatures, int mfccFrameLength, int mfccFrameStride):
+ m_mfccFrameLength{mfccFrameLength},
+ m_mfccFrameStride{mfccFrameStride},
+ m_mfcc{audio::MicroNetKwsMFCC(numFeatures, mfccFrameLength)}
+ {
+ if (!model->IsInited()) {
+ printf_err("Model is not initialised!.\n");
+ }
+ this->m_model = model;
+ this->m_mfcc.Init();
+
+ TfLiteIntArray* inputShape = model->GetInputShape(0);
+ const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
+
+ /* Deduce the data length required for 1 inference from the network parameters. */
+ this->m_audioDataWindowSize = numMfccFrames * this->m_mfccFrameStride +
+ (this->m_mfccFrameLength - this->m_mfccFrameStride);
+
+ /* Creating an MFCC feature sliding window for the data required for 1 inference. */
+ this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(nullptr, this->m_audioDataWindowSize,
+ this->m_mfccFrameLength, this->m_mfccFrameStride);
+
+ /* For longer audio clips we choose to move by half the audio window size
+ * => for a 1 second window size there is an overlap of 0.5 seconds. */
+ this->m_audioDataStride = this->m_audioDataWindowSize / 2;
+
+ /* To have the previously calculated features re-usable, stride must be multiple
+ * of MFCC features window stride. Reduce stride through audio if needed. */
+ if (0 != this->m_audioDataStride % this->m_mfccFrameStride) {
+ this->m_audioDataStride -= this->m_audioDataStride % this->m_mfccFrameStride;
+ }
+
+ this->m_numMfccVectorsInAudioStride = this->m_audioDataStride / this->m_mfccFrameStride;
+
+ /* Calculate number of the feature vectors in the window overlap region.
+ * These feature vectors will be reused.*/
+ this->m_numReusedMfccVectors = this->m_mfccSlidingWindow.TotalStrides() + 1
+ - this->m_numMfccVectorsInAudioStride;
+
+ /* Construct feature calculation function. */
+ this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_model->GetInputTensor(0),
+ this->m_numReusedMfccVectors);
+
+ if (!this->m_mfccFeatureCalculator) {
+ printf_err("Feature calculator not initialized.");
+ }
+ }
+
+ bool KWSPreProcess::DoPreProcess(const void* data, size_t inputSize)
+ {
+ UNUSED(inputSize);
+ if (data == nullptr) {
+ printf_err("Data pointer is null");
+ }
+
+ /* Set the features sliding window to the new address. */
+ auto input = static_cast<const int16_t*>(data);
+ this->m_mfccSlidingWindow.Reset(input);
+
+ /* Cache is only usable if we have more than 1 inference in an audio clip. */
+ bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedMfccVectors > 0;
+
+ /* Use a sliding window to calculate MFCC features frame by frame. */
+ while (this->m_mfccSlidingWindow.HasNext()) {
+ const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next();
+
+ std::vector<int16_t> mfccFrameAudioData = std::vector<int16_t>(mfccWindow,
+ mfccWindow + this->m_mfccFrameLength);
+
+ /* Compute features for this window and write them to input tensor. */
+ this->m_mfccFeatureCalculator(mfccFrameAudioData, this->m_mfccSlidingWindow.Index(),
+ useCache, this->m_numMfccVectorsInAudioStride);
+ }
+
+ debug("Input tensor populated \n");
+
+ 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[in] inputTensor Model input tensor pointer.
+ * @param[in] cacheSize Number of feature vectors to cache. Defined by the sliding window overlap.
+ * @param[in] 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)>
+ KWSPreProcess::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)>
+ KWSPreProcess::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)>
+ KWSPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+
+
+ std::function<void (std::vector<int16_t>&, int, bool, size_t)>
+ KWSPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& 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 = this->FeatureCalc<int8_t>(inputTensor,
+ cacheSize,
+ [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
+ quantScale,
+ quantOffset);
+ }
+ );
+ break;
+ }
+ default:
+ printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
+ }
+ } else {
+ mfccFeatureCalc = this->FeatureCalc<float>(inputTensor, cacheSize,
+ [&mfcc](std::vector<int16_t>& audioDataWindow) {
+ return mfcc.MfccCompute(audioDataWindow); }
+ );
+ }
+ return mfccFeatureCalc;
+ }
+
+ KWSPostProcess::KWSPostProcess(Classifier& classifier, Model* model,
+ const std::vector<std::string>& labels,
+ std::vector<ClassificationResult>& results, float scoreThreshold)
+ :m_kwsClassifier{classifier},
+ m_labels{labels},
+ m_results{results},
+ m_scoreThreshold{scoreThreshold}
+ {
+ if (!model->IsInited()) {
+ printf_err("Model is not initialised!.\n");
+ }
+ this->m_model = model;
+ }
+
+ bool KWSPostProcess::DoPostProcess()
+ {
+ return this->m_kwsClassifier.GetClassificationResults(
+ this->m_model->GetOutputTensor(0), this->m_results,
+ this->m_labels, 1, true);
+ }
+
+} /* namespace app */
+} /* namespace arm */
\ No newline at end of file