| /* |
| * 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 "hal.h" |
| #include "InputFiles.hpp" |
| #include "AudioUtils.hpp" |
| #include "UseCaseCommonUtils.hpp" |
| #include "DsCnnModel.hpp" |
| #include "DsCnnMfcc.hpp" |
| #include "Classifier.hpp" |
| #include "KwsResult.hpp" |
| #include "Wav2LetterMfcc.hpp" |
| #include "Wav2LetterPreprocess.hpp" |
| #include "Wav2LetterPostprocess.hpp" |
| #include "AsrResult.hpp" |
| #include "AsrClassifier.hpp" |
| #include "OutputDecode.hpp" |
| |
| |
| using KwsClassifier = arm::app::Classifier; |
| |
| namespace arm { |
| namespace app { |
| |
| enum AsrOutputReductionAxis { |
| AxisRow = 1, |
| AxisCol = 2 |
| }; |
| |
| struct KWSOutput { |
| bool executionSuccess = false; |
| const int16_t* asrAudioStart = nullptr; |
| int32_t asrAudioSamples = 0; |
| }; |
| |
| /** |
| * @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 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 kws 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, std::vector<arm::app::kws::KwsResult>& results); |
| |
| /** |
| * @brief Presents asr 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, std::vector<arm::app::asr::AsrResult>& 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 feture vectors cache (number of feature vectors). |
| * |
| * @return function 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); |
| |
| /** |
| * @brief Performs the KWS pipeline. |
| * @param[in,out] ctx pointer to the application context object |
| * |
| * @return KWSOutput struct containing pointer to audio data where ASR should begin |
| * and how much data to process. |
| */ |
| static KWSOutput doKws(ApplicationContext& ctx) { |
| 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); |
| |
| KWSOutput output; |
| |
| auto& kwsModel = ctx.Get<Model&>("kwsmodel"); |
| if (!kwsModel.IsInited()) { |
| printf_err("KWS model has not been initialised\n"); |
| return output; |
| } |
| |
| const int kwsFrameLength = ctx.Get<int>("kwsframeLength"); |
| const int kwsFrameStride = ctx.Get<int>("kwsframeStride"); |
| const float kwsScoreThreshold = ctx.Get<float>("kwsscoreThreshold"); |
| |
| TfLiteTensor* kwsOutputTensor = kwsModel.GetOutputTensor(0); |
| TfLiteTensor* kwsInputTensor = kwsModel.GetInputTensor(0); |
| |
| if (!kwsInputTensor->dims) { |
| printf_err("Invalid input tensor dims\n"); |
| return output; |
| } else if (kwsInputTensor->dims->size < minTensorDims) { |
| printf_err("Input tensor dimension should be >= %d\n", minTensorDims); |
| return output; |
| } |
| |
| const uint32_t kwsNumMfccFeats = ctx.Get<uint32_t>("kwsNumMfcc"); |
| const uint32_t kwsNumAudioWindows = ctx.Get<uint32_t>("kwsNumAudioWins"); |
| |
| audio::DsCnnMFCC kwsMfcc = audio::DsCnnMFCC(kwsNumMfccFeats, kwsFrameLength); |
| kwsMfcc.Init(); |
| |
| /* Deduce the data length required for 1 KWS inference from the network parameters. */ |
| auto kwsAudioDataWindowSize = kwsNumAudioWindows * kwsFrameStride + |
| (kwsFrameLength - kwsFrameStride); |
| auto kwsMfccWindowSize = kwsFrameLength; |
| auto kwsMfccWindowStride = kwsFrameStride; |
| |
| /* We are choosing to move by half the window size => for a 1 second window size, |
| * this means an overlap of 0.5 seconds. */ |
| auto kwsAudioDataStride = kwsAudioDataWindowSize / 2; |
| |
| info("KWS audio data window size %u\n", kwsAudioDataWindowSize); |
| |
| /* Stride must be multiple of mfcc features window stride to re-use features. */ |
| if (0 != kwsAudioDataStride % kwsMfccWindowStride) { |
| kwsAudioDataStride -= kwsAudioDataStride % kwsMfccWindowStride; |
| } |
| |
| auto kwsMfccVectorsInAudioStride = kwsAudioDataStride/kwsMfccWindowStride; |
| |
| /* We expect to be sampling 1 second worth of data at a time |
| * NOTE: This is only used for time stamp calculation. */ |
| const float kwsAudioParamsSecondsPerSample = 1.0/audio::DsCnnMFCC::ms_defaultSamplingFreq; |
| |
| auto currentIndex = ctx.Get<uint32_t>("clipIndex"); |
| |
| /* Creating a mfcc features sliding window for the data required for 1 inference. */ |
| auto kwsAudioMFCCWindowSlider = audio::SlidingWindow<const int16_t>( |
| get_audio_array(currentIndex), |
| kwsAudioDataWindowSize, kwsMfccWindowSize, |
| kwsMfccWindowStride); |
| |
| /* 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), |
| kwsAudioDataWindowSize, kwsAudioDataStride); |
| |
| /* Calculate number of the feature vectors in the window overlap region. |
| * These feature vectors will be reused.*/ |
| size_t numberOfReusedFeatureVectors = kwsAudioMFCCWindowSlider.TotalStrides() + 1 |
| - kwsMfccVectorsInAudioStride; |
| |
| auto kwsMfccFeatureCalc = GetFeatureCalculator(kwsMfcc, kwsInputTensor, |
| numberOfReusedFeatureVectors); |
| |
| if (!kwsMfccFeatureCalc){ |
| return output; |
| } |
| |
| /* Container for KWS results. */ |
| std::vector<arm::app::kws::KwsResult> kwsResults; |
| |
| /* Display message on the LCD - inference running. */ |
| auto& platform = ctx.Get<hal_platform&>("platform"); |
| std::string str_inf{"Running KWS inference... "}; |
| platform.data_psn->present_data_text( |
| str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); |
| |
| info("Running KWS 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. */ |
| kwsAudioMFCCWindowSlider.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 (kwsAudioMFCCWindowSlider.HasNext()) { |
| const int16_t* kwsMfccWindow = kwsAudioMFCCWindowSlider.Next(); |
| std::vector<int16_t> kwsMfccAudioData = |
| std::vector<int16_t>(kwsMfccWindow, kwsMfccWindow + kwsMfccWindowSize); |
| |
| /* Compute features for this window and write them to input tensor. */ |
| kwsMfccFeatureCalc(kwsMfccAudioData, |
| kwsAudioMFCCWindowSlider.Index(), |
| useCache, |
| kwsMfccVectorsInAudioStride); |
| } |
| |
| info("Inference %zu/%zu\n", audioDataSlider.Index() + 1, |
| audioDataSlider.TotalStrides() + 1); |
| |
| /* Run inference over this audio clip sliding window. */ |
| arm::app::RunInference(platform, kwsModel); |
| |
| std::vector<ClassificationResult> kwsClassificationResult; |
| auto& kwsClassifier = ctx.Get<KwsClassifier&>("kwsclassifier"); |
| |
| kwsClassifier.GetClassificationResults( |
| kwsOutputTensor, kwsClassificationResult, |
| ctx.Get<std::vector<std::string>&>("kwslabels"), 1); |
| |
| kwsResults.emplace_back( |
| kws::KwsResult( |
| kwsClassificationResult, |
| audioDataSlider.Index() * kwsAudioParamsSecondsPerSample * kwsAudioDataStride, |
| audioDataSlider.Index(), kwsScoreThreshold) |
| ); |
| |
| /* Keyword detected. */ |
| if (kwsClassificationResult[0].m_labelIdx == ctx.Get<uint32_t>("keywordindex")) { |
| output.asrAudioStart = inferenceWindow + kwsAudioDataWindowSize; |
| output.asrAudioSamples = get_audio_array_size(currentIndex) - |
| (audioDataSlider.NextWindowStartIndex() - |
| kwsAudioDataStride + kwsAudioDataWindowSize); |
| break; |
| } |
| |
| #if VERIFY_TEST_OUTPUT |
| arm::app::DumpTensor(kwsOutputTensor); |
| #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, 0); |
| |
| if (!_PresentInferenceResult(platform, kwsResults)) { |
| return output; |
| } |
| |
| output.executionSuccess = true; |
| return output; |
| } |
| |
| /** |
| * @brief Performs the ASR pipeline. |
| * |
| * @param ctx[in/out] pointer to the application context object |
| * @param kwsOutput[in] struct containing pointer to audio data where ASR should begin |
| * and how much data to process |
| * @return bool true if pipeline executed without failure |
| */ |
| static bool doAsr(ApplicationContext& ctx, const KWSOutput& kwsOutput) { |
| constexpr uint32_t dataPsnTxtInfStartX = 20; |
| constexpr uint32_t dataPsnTxtInfStartY = 40; |
| |
| auto& platform = ctx.Get<hal_platform&>("platform"); |
| platform.data_psn->clear(COLOR_BLACK); |
| |
| /* Get model reference. */ |
| auto& asrModel = ctx.Get<Model&>("asrmodel"); |
| if (!asrModel.IsInited()) { |
| printf_err("ASR model has not been initialised\n"); |
| return false; |
| } |
| |
| /* Get score threshold to be applied for the classifier (post-inference). */ |
| auto asrScoreThreshold = ctx.Get<float>("asrscoreThreshold"); |
| |
| /* Dimensions of the tensor should have been verified by the callee. */ |
| TfLiteTensor* asrInputTensor = asrModel.GetInputTensor(0); |
| TfLiteTensor* asrOutputTensor = asrModel.GetOutputTensor(0); |
| const uint32_t asrInputRows = asrInputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputRowsIdx]; |
| |
| /* Populate ASR MFCC related parameters. */ |
| auto asrMfccParamsWinLen = ctx.Get<uint32_t>("asrframeLength"); |
| auto asrMfccParamsWinStride = ctx.Get<uint32_t>("asrframeStride"); |
| |
| /* Populate ASR inference context and inner lengths for input. */ |
| auto asrInputCtxLen = ctx.Get<uint32_t>("ctxLen"); |
| const uint32_t asrInputInnerLen = asrInputRows - (2 * asrInputCtxLen); |
| |
| /* Make sure the input tensor supports the above context and inner lengths. */ |
| if (asrInputRows <= 2 * asrInputCtxLen || asrInputRows <= asrInputInnerLen) { |
| printf_err("ASR input rows not compatible with ctx length %u\n", asrInputCtxLen); |
| return false; |
| } |
| |
| /* Audio data stride corresponds to inputInnerLen feature vectors. */ |
| const uint32_t asrAudioParamsWinLen = (asrInputRows - 1) * |
| asrMfccParamsWinStride + (asrMfccParamsWinLen); |
| const uint32_t asrAudioParamsWinStride = asrInputInnerLen * asrMfccParamsWinStride; |
| const float asrAudioParamsSecondsPerSample = |
| (1.0/audio::Wav2LetterMFCC::ms_defaultSamplingFreq); |
| |
| /* Get pre/post-processing objects */ |
| auto& asrPrep = ctx.Get<audio::asr::Preprocess&>("preprocess"); |
| auto& asrPostp = ctx.Get<audio::asr::Postprocess&>("postprocess"); |
| |
| /* Set default reduction axis for post-processing. */ |
| const uint32_t reductionAxis = arm::app::Wav2LetterModel::ms_outputRowsIdx; |
| |
| /* Get the remaining audio buffer and respective size from KWS results. */ |
| const int16_t* audioArr = kwsOutput.asrAudioStart; |
| const uint32_t audioArrSize = kwsOutput.asrAudioSamples; |
| |
| /* Audio clip must have enough samples to produce 1 MFCC feature. */ |
| std::vector<int16_t> audioBuffer = std::vector<int16_t>(audioArr, audioArr + audioArrSize); |
| if (audioArrSize < asrMfccParamsWinLen) { |
| printf_err("Not enough audio samples, minimum needed is %u\n", asrMfccParamsWinLen); |
| return false; |
| } |
| |
| /* Initialise an audio slider. */ |
| auto audioDataSlider = audio::ASRSlidingWindow<const int16_t>( |
| audioBuffer.data(), |
| audioBuffer.size(), |
| asrAudioParamsWinLen, |
| asrAudioParamsWinStride); |
| |
| /* Declare a container for results. */ |
| std::vector<arm::app::asr::AsrResult> asrResults; |
| |
| /* Display message on the LCD - inference running. */ |
| std::string str_inf{"Running ASR inference... "}; |
| platform.data_psn->present_data_text( |
| str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); |
| |
| size_t asrInferenceWindowLen = asrAudioParamsWinLen; |
| |
| /* 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 + asrAudioParamsWinLen > audioBuffer.size()) { |
| asrInferenceWindowLen = audioBuffer.size() - nextStartIndex; |
| } |
| |
| const int16_t* asrInferenceWindow = 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. */ |
| asrPrep.Invoke(asrInferenceWindow, asrInferenceWindowLen, asrInputTensor); |
| |
| 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, asrModel); |
| |
| /* Post-process. */ |
| asrPostp.Invoke(asrOutputTensor, reductionAxis, !audioDataSlider.HasNext()); |
| |
| /* Get results. */ |
| std::vector<ClassificationResult> asrClassificationResult; |
| auto& asrClassifier = ctx.Get<AsrClassifier&>("asrclassifier"); |
| asrClassifier.GetClassificationResults( |
| asrOutputTensor, asrClassificationResult, |
| ctx.Get<std::vector<std::string>&>("asrlabels"), 1); |
| |
| asrResults.emplace_back(asr::AsrResult(asrClassificationResult, |
| (audioDataSlider.Index() * |
| asrAudioParamsSecondsPerSample * |
| asrAudioParamsWinStride), |
| audioDataSlider.Index(), asrScoreThreshold)); |
| |
| #if VERIFY_TEST_OUTPUT |
| arm::app::DumpTensor(asrOutputTensor, asrOutputTensor->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, false); |
| } |
| if (!_PresentInferenceResult(platform, asrResults)) { |
| return false; |
| } |
| |
| return true; |
| } |
| |
| /* Audio inference classification handler. */ |
| bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll) |
| { |
| 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; |
| } |
| } |
| |
| auto startClipIdx = ctx.Get<uint32_t>("clipIndex"); |
| |
| do { |
| KWSOutput kwsOutput = doKws(ctx); |
| if (!kwsOutput.executionSuccess) { |
| return false; |
| } |
| |
| if (kwsOutput.asrAudioStart != nullptr && kwsOutput.asrAudioSamples > 0) { |
| info("Keyword spotted\n"); |
| if(!doAsr(ctx, kwsOutput)) { |
| printf_err("ASR failed"); |
| 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, |
| 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, 0); |
| 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; |
| } |
| |
| static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results) |
| { |
| constexpr uint32_t dataPsnTxtStartX1 = 20; |
| constexpr uint32_t dataPsnTxtStartY1 = 80; |
| 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()); |
| } |
| |
| for (auto& result : results) { |
| /* Get the final result string using the decoder. */ |
| std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec); |
| |
| info("Result for inf %u: %s\n", result.m_inferenceNumber, |
| infResultStr.c_str()); |
| } |
| |
| 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; |
| } |
| |
| /** |
| * @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 */ |