| /* |
| * Copyright (c) 2021-2022 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 "UseCaseHandler.hpp" |
| |
| #include "InputFiles.hpp" |
| #include "Classifier.hpp" |
| #include "MicroNetKwsModel.hpp" |
| #include "hal.h" |
| #include "AudioUtils.hpp" |
| #include "ImageUtils.hpp" |
| #include "UseCaseCommonUtils.hpp" |
| #include "KwsResult.hpp" |
| #include "log_macros.h" |
| #include "KwsProcessing.hpp" |
| |
| #include <vector> |
| |
| using KwsClassifier = arm::app::Classifier; |
| |
| namespace arm { |
| namespace app { |
| |
| /** |
| * @brief Presents KWS inference results. |
| * @param[in] results Vector of KWS classification results to be displayed. |
| * @return true if successful, false otherwise. |
| **/ |
| static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results); |
| |
| /* KWS inference handler. */ |
| bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll) |
| { |
| auto& profiler = ctx.Get<Profiler&>("profiler"); |
| auto& model = ctx.Get<Model&>("model"); |
| const auto mfccFrameLength = ctx.Get<int>("frameLength"); |
| const auto mfccFrameStride = ctx.Get<int>("frameStride"); |
| const auto scoreThreshold = ctx.Get<float>("scoreThreshold"); |
| |
| /* If the request has a valid size, set the audio index. */ |
| if (clipIndex < NUMBER_OF_FILES) { |
| if (!SetAppCtxIfmIdx(ctx, clipIndex,"clipIndex")) { |
| return false; |
| } |
| } |
| auto initialClipIdx = ctx.Get<uint32_t>("clipIndex"); |
| |
| constexpr uint32_t dataPsnTxtInfStartX = 20; |
| constexpr uint32_t dataPsnTxtInfStartY = 40; |
| constexpr int minTensorDims = static_cast<int>( |
| (MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)? |
| MicroNetKwsModel::ms_inputRowsIdx : MicroNetKwsModel::ms_inputColsIdx); |
| |
| if (!model.IsInited()) { |
| printf_err("Model is not initialised! Terminating processing.\n"); |
| return false; |
| } |
| |
| /* Get Input and Output tensors for pre/post processing. */ |
| TfLiteTensor* inputTensor = model.GetInputTensor(0); |
| TfLiteTensor* outputTensor = model.GetOutputTensor(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; |
| } |
| |
| /* Get input shape for feature extraction. */ |
| TfLiteIntArray* inputShape = model.GetInputShape(0); |
| const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx]; |
| const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx]; |
| |
| /* 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::MicroNetKwsMFCC::ms_defaultSamplingFreq; |
| |
| /* Set up pre and post-processing. */ |
| KwsPreProcess preProcess = KwsPreProcess(inputTensor, numMfccFeatures, numMfccFrames, |
| mfccFrameLength, mfccFrameStride); |
| |
| std::vector<ClassificationResult> singleInfResult; |
| KwsPostProcess postProcess = KwsPostProcess(outputTensor, ctx.Get<KwsClassifier &>("classifier"), |
| ctx.Get<std::vector<std::string>&>("labels"), |
| singleInfResult); |
| |
| /* Loop to process audio clips. */ |
| do { |
| hal_lcd_clear(COLOR_BLACK); |
| |
| auto currentIndex = ctx.Get<uint32_t>("clipIndex"); |
| |
| /* 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), |
| preProcess.m_audioDataWindowSize, preProcess.m_audioDataStride); |
| |
| /* Declare a container to hold results from across the whole audio clip. */ |
| std::vector<kws::KwsResult> finalResults; |
| |
| /* Display message on the LCD - inference running. */ |
| std::string str_inf{"Running inference... "}; |
| hal_lcd_display_text(str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); |
| info("Running inference on audio clip %" PRIu32 " => %s\n", currentIndex, |
| get_filename(currentIndex)); |
| |
| /* Start sliding through audio clip. */ |
| while (audioDataSlider.HasNext()) { |
| const int16_t* inferenceWindow = audioDataSlider.Next(); |
| |
| /* The first window does not have cache ready. */ |
| preProcess.m_audioWindowIndex = audioDataSlider.Index(); |
| |
| info("Inference %zu/%zu\n", audioDataSlider.Index() + 1, |
| audioDataSlider.TotalStrides() + 1); |
| |
| /* Run the pre-processing, inference and post-processing. */ |
| if (!preProcess.DoPreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) { |
| printf_err("Pre-processing failed."); |
| return false; |
| } |
| |
| if (!RunInference(model, profiler)) { |
| printf_err("Inference failed."); |
| return false; |
| } |
| |
| if (!postProcess.DoPostProcess()) { |
| printf_err("Post-processing failed."); |
| return false; |
| } |
| |
| /* Add results from this window to our final results vector. */ |
| finalResults.emplace_back(kws::KwsResult(singleInfResult, |
| audioDataSlider.Index() * secondsPerSample * preProcess.m_audioDataStride, |
| audioDataSlider.Index(), scoreThreshold)); |
| |
| #if VERIFY_TEST_OUTPUT |
| DumpTensor(outputTensor); |
| #endif /* VERIFY_TEST_OUTPUT */ |
| } /* while (audioDataSlider.HasNext()) */ |
| |
| /* Erase. */ |
| str_inf = std::string(str_inf.size(), ' '); |
| hal_lcd_display_text(str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, false); |
| |
| ctx.Set<std::vector<kws::KwsResult>>("results", finalResults); |
| |
| if (!PresentInferenceResult(finalResults)) { |
| return false; |
| } |
| |
| profiler.PrintProfilingResult(); |
| |
| IncrementAppCtxIfmIdx(ctx,"clipIndex"); |
| |
| } while (runAll && ctx.Get<uint32_t>("clipIndex") != initialClipIdx); |
| |
| return true; |
| } |
| |
| static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results) |
| { |
| constexpr uint32_t dataPsnTxtStartX1 = 20; |
| constexpr uint32_t dataPsnTxtStartY1 = 30; |
| constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */ |
| |
| hal_lcd_set_text_color(COLOR_GREEN); |
| info("Final results:\n"); |
| info("Total number of inferences: %zu\n", results.size()); |
| |
| /* Display each result */ |
| uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr; |
| |
| for (const auto& result : results) { |
| |
| std::string topKeyword{"<none>"}; |
| float score = 0.f; |
| if (!result.m_resultVec.empty()) { |
| topKeyword = result.m_resultVec[0].m_label; |
| score = result.m_resultVec[0].m_normalisedVal; |
| } |
| |
| std::string resultStr = |
| std::string{"@"} + std::to_string(result.m_timeStamp) + |
| std::string{"s: "} + topKeyword + std::string{" ("} + |
| std::to_string(static_cast<int>(score * 100)) + std::string{"%)"}; |
| |
| hal_lcd_display_text(resultStr.c_str(), resultStr.size(), |
| dataPsnTxtStartX1, rowIdx1, false); |
| rowIdx1 += dataPsnTxtYIncr; |
| |
| if (result.m_resultVec.empty()) { |
| info("For timestamp: %f (inference #: %" PRIu32 |
| "); label: %s; threshold: %f\n", |
| result.m_timeStamp, result.m_inferenceNumber, |
| topKeyword.c_str(), |
| result.m_threshold); |
| } else { |
| for (uint32_t j = 0; j < result.m_resultVec.size(); ++j) { |
| info("For timestamp: %f (inference #: %" PRIu32 |
| "); label: %s, score: %f; threshold: %f\n", |
| result.m_timeStamp, |
| result.m_inferenceNumber, |
| result.m_resultVec[j].m_label.c_str(), |
| result.m_resultVec[j].m_normalisedVal, |
| result.m_threshold); |
| } |
| } |
| } |
| |
| return true; |
| } |
| |
| } /* namespace app */ |
| } /* namespace arm */ |