Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/source/use_case/asr/src/UseCaseHandler.cc b/source/use_case/asr/src/UseCaseHandler.cc
new file mode 100644
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+++ b/source/use_case/asr/src/UseCaseHandler.cc
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+/*
+ * 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 "AsrClassifier.hpp"
+#include "Wav2LetterModel.hpp"
+#include "hal.h"
+#include "Wav2LetterMfcc.hpp"
+#include "AudioUtils.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "AsrResult.hpp"
+#include "Wav2LetterPreprocess.hpp"
+#include "Wav2LetterPostprocess.hpp"
+#include "OutputDecode.hpp"
+
+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::asr::AsrResult>& results);
+
+ /* Audio inference classification handler. */
+ bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
+ {
+ constexpr uint32_t dataPsnTxtInfStartX = 20;
+ constexpr uint32_t dataPsnTxtInfStartY = 40;
+
+ auto& platform = ctx.Get<hal_platform&>("platform");
+ platform.data_psn->clear(COLOR_BLACK);
+
+ /* If the request has a valid size, set the audio index. */
+ if (clipIndex < NUMBER_OF_FILES) {
+ if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+ return false;
+ }
+ }
+
+ /* Get model reference. */
+ auto& model = ctx.Get<Model&>("model");
+ if (!model.IsInited()) {
+ printf_err("Model is not initialised! Terminating processing.\n");
+ return false;
+ }
+
+ /* Get score threshold to be applied for the classifier (post-inference). */
+ auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+
+ /* Get tensors. Dimensions of the tensor should have been verified by
+ * the callee. */
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+ const uint32_t inputRows = inputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputRowsIdx];
+
+ /* Populate MFCC related parameters. */
+ auto mfccParamsWinLen = ctx.Get<uint32_t>("frameLength");
+ auto mfccParamsWinStride = ctx.Get<uint32_t>("frameStride");
+
+ /* Populate ASR inference context and inner lengths for input. */
+ auto inputCtxLen = ctx.Get<uint32_t>("ctxLen");
+ const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen);
+
+ /* Audio data stride corresponds to inputInnerLen feature vectors. */
+ const uint32_t audioParamsWinLen = (inputRows - 1) * mfccParamsWinStride + (mfccParamsWinLen);
+ const uint32_t audioParamsWinStride = inputInnerLen * mfccParamsWinStride;
+ const float audioParamsSecondsPerSample = (1.0/audio::Wav2LetterMFCC::ms_defaultSamplingFreq);
+
+ /* Get pre/post-processing objects. */
+ auto& prep = ctx.Get<audio::asr::Preprocess&>("preprocess");
+ auto& postp = ctx.Get<audio::asr::Postprocess&>("postprocess");
+
+ /* Set default reduction axis for post-processing. */
+ const uint32_t reductionAxis = arm::app::Wav2LetterModel::ms_outputRowsIdx;
+
+ /* Audio clip start index. */
+ auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
+
+ /* Loop to process audio clips. */
+ do {
+ /* Get current audio clip index. */
+ auto currentIndex = ctx.Get<uint32_t>("clipIndex");
+
+ /* Get the current audio buffer and respective size. */
+ const int16_t* audioArr = get_audio_array(currentIndex);
+ const uint32_t audioArrSize = get_audio_array_size(currentIndex);
+
+ if (!audioArr) {
+ printf_err("Invalid audio array pointer\n");
+ return false;
+ }
+
+ /* Audio clip must have enough samples to produce 1 MFCC feature. */
+ if (audioArrSize < mfccParamsWinLen) {
+ printf_err("Not enough audio samples, minimum needed is %u\n", mfccParamsWinLen);
+ return false;
+ }
+
+ /* Initialise an audio slider. */
+ auto audioDataSlider = audio::ASRSlidingWindow<const int16_t>(
+ audioArr,
+ audioArrSize,
+ audioParamsWinLen,
+ audioParamsWinStride);
+
+ /* Declare a container for results. */
+ std::vector<arm::app::asr::AsrResult> 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));
+
+ size_t inferenceWindowLen = audioParamsWinLen;
+
+ /* Start sliding through audio clip. */
+ while (audioDataSlider.HasNext()) {
+
+ /* If not enough audio see how much can be sent for processing. */
+ size_t nextStartIndex = audioDataSlider.NextWindowStartIndex();
+ if (nextStartIndex + audioParamsWinLen > audioArrSize) {
+ inferenceWindowLen = audioArrSize - nextStartIndex;
+ }
+
+ const int16_t* inferenceWindow = audioDataSlider.Next();
+
+ info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
+ static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
+
+ Profiler prepProfiler{&platform, "pre-processing"};
+ prepProfiler.StartProfiling();
+
+ /* Calculate MFCCs, deltas and populate the input tensor. */
+ prep.Invoke(inferenceWindow, inferenceWindowLen, inputTensor);
+
+ prepProfiler.StopProfiling();
+ std::string prepProfileResults = prepProfiler.GetResultsAndReset();
+ info("%s\n", prepProfileResults.c_str());
+
+ /* Run inference over this audio clip sliding window. */
+ arm::app::RunInference(platform, model);
+
+ /* Post-process. */
+ postp.Invoke(outputTensor, reductionAxis, !audioDataSlider.HasNext());
+
+ /* Get results. */
+ std::vector<ClassificationResult> classificationResult;
+ auto& classifier = ctx.Get<AsrClassifier&>("classifier");
+ classifier.GetClassificationResults(
+ outputTensor, classificationResult,
+ ctx.Get<std::vector<std::string>&>("labels"), 1);
+
+ results.emplace_back(asr::AsrResult(classificationResult,
+ (audioDataSlider.Index() *
+ audioParamsSecondsPerSample *
+ audioParamsWinStride),
+ audioDataSlider.Index(), scoreThreshold));
+
+#if VERIFY_TEST_OUTPUT
+ arm::app::DumpTensor(outputTensor,
+ outputTensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx]);
+#endif /* VERIFY_TEST_OUTPUT */
+
+ }
+
+ /* Erase. */
+ str_inf = std::string(str_inf.size(), ' ');
+ platform.data_psn->present_data_text(
+ str_inf.c_str(), str_inf.size(),
+ dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+
+ ctx.Set<std::vector<arm::app::asr::AsrResult>>("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::asr::AsrResult>& results)
+ {
+ constexpr uint32_t dataPsnTxtStartX1 = 20;
+ constexpr uint32_t dataPsnTxtStartY1 = 60;
+ constexpr bool allow_multiple_lines = true;
+
+ platform.data_psn->set_text_color(COLOR_GREEN);
+
+ /* Results from multiple inferences should be combined before processing. */
+ std::vector<arm::app::ClassificationResult> combinedResults;
+ for (auto& result : results) {
+ combinedResults.insert(combinedResults.end(),
+ result.m_resultVec.begin(),
+ result.m_resultVec.end());
+ }
+
+ /* Get each inference result string using the decoder. */
+ for (const auto & result : results) {
+ std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec);
+
+ info("Result for inf %u: %s\n", result.m_inferenceNumber,
+ infResultStr.c_str());
+ }
+
+ /* Get the decoded result for the combined result. */
+ std::string finalResultStr = audio::asr::DecodeOutput(combinedResults);
+
+ platform.data_psn->present_data_text(
+ finalResultStr.c_str(), finalResultStr.size(),
+ dataPsnTxtStartX1, dataPsnTxtStartY1,
+ allow_multiple_lines);
+
+ info("Final result: %s\n", finalResultStr.c_str());
+ return true;
+ }
+
+} /* namespace app */
+} /* namespace arm */
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