| /* |
| * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates <open-source-office@arm.com> |
| * 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 "ImageUtils.hpp" |
| #include "UseCaseCommonUtils.hpp" |
| #include "MicroNetKwsModel.hpp" |
| #include "MicroNetKwsMfcc.hpp" |
| #include "Classifier.hpp" |
| #include "KwsResult.hpp" |
| #include "Wav2LetterModel.hpp" |
| #include "Wav2LetterMfcc.hpp" |
| #include "Wav2LetterPreprocess.hpp" |
| #include "Wav2LetterPostprocess.hpp" |
| #include "KwsProcessing.hpp" |
| #include "AsrResult.hpp" |
| #include "AsrClassifier.hpp" |
| #include "OutputDecode.hpp" |
| #include "log_macros.h" |
| |
| |
| using KwsClassifier = arm::app::Classifier; |
| |
| namespace arm { |
| namespace app { |
| |
| struct KWSOutput { |
| bool executionSuccess = false; |
| const int16_t* asrAudioStart = nullptr; |
| int32_t asrAudioSamples = 0; |
| }; |
| |
| /** |
| * @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(std::vector<kws::KwsResult>& results); |
| |
| /** |
| * @brief Presents ASR inference results. |
| * @param[in] results Vector of ASR classification results to be displayed. |
| * @return true if successful, false otherwise. |
| **/ |
| static bool PresentInferenceResult(std::vector<asr::AsrResult>& results); |
| |
| /** |
| * @brief Performs the KWS pipeline. |
| * @param[in,out] ctx pointer to the application context object |
| * @return struct containing pointer to audio data where ASR should begin |
| * and how much data to process. |
| **/ |
| static KWSOutput doKws(ApplicationContext& ctx) |
| { |
| auto& profiler = ctx.Get<Profiler&>("profiler"); |
| auto& kwsModel = ctx.Get<Model&>("kwsModel"); |
| const auto kwsMfccFrameLength = ctx.Get<int>("kwsFrameLength"); |
| const auto kwsMfccFrameStride = ctx.Get<int>("kwsFrameStride"); |
| const auto kwsScoreThreshold = ctx.Get<float>("kwsScoreThreshold"); |
| |
| auto currentIndex = 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); |
| |
| /* Output struct from doing KWS. */ |
| KWSOutput output {}; |
| |
| if (!kwsModel.IsInited()) { |
| printf_err("KWS model has not been initialised\n"); |
| return output; |
| } |
| |
| /* Get Input and Output tensors for pre/post processing. */ |
| TfLiteTensor* kwsInputTensor = kwsModel.GetInputTensor(0); |
| TfLiteTensor* kwsOutputTensor = kwsModel.GetOutputTensor(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; |
| } |
| |
| /* Get input shape for feature extraction. */ |
| TfLiteIntArray* inputShape = kwsModel.GetInputShape(0); |
| const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx]; |
| const uint32_t numMfccFrames = inputShape->data[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 kwsAudioParamsSecondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq; |
| |
| /* Set up pre and post-processing. */ |
| KwsPreProcess preProcess = KwsPreProcess(kwsInputTensor, numMfccFeatures, numMfccFrames, |
| kwsMfccFrameLength, kwsMfccFrameStride); |
| |
| std::vector<ClassificationResult> singleInfResult; |
| KwsPostProcess postProcess = KwsPostProcess(kwsOutputTensor, ctx.Get<KwsClassifier &>("kwsClassifier"), |
| ctx.Get<std::vector<std::string>&>("kwsLabels"), |
| singleInfResult); |
| |
| /* 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 kws results from across the whole audio clip. */ |
| std::vector<kws::KwsResult> finalResults; |
| |
| /* Display message on the LCD - inference running. */ |
| std::string str_inf{"Running KWS inference... "}; |
| hal_lcd_display_text( |
| str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, false); |
| |
| info("Running KWS 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(); |
| |
| /* Run the pre-processing, inference and post-processing. */ |
| if (!preProcess.DoPreProcess(inferenceWindow, audioDataSlider.Index())) { |
| printf_err("KWS Pre-processing failed."); |
| return output; |
| } |
| |
| if (!RunInference(kwsModel, profiler)) { |
| printf_err("KWS Inference failed."); |
| return output; |
| } |
| |
| if (!postProcess.DoPostProcess()) { |
| printf_err("KWS Post-processing failed."); |
| return output; |
| } |
| |
| info("Inference %zu/%zu\n", audioDataSlider.Index() + 1, |
| audioDataSlider.TotalStrides() + 1); |
| |
| /* Add results from this window to our final results vector. */ |
| finalResults.emplace_back( |
| kws::KwsResult(singleInfResult, |
| audioDataSlider.Index() * kwsAudioParamsSecondsPerSample * preProcess.m_audioDataStride, |
| audioDataSlider.Index(), kwsScoreThreshold)); |
| |
| /* Break out when trigger keyword is detected. */ |
| if (singleInfResult[0].m_label == ctx.Get<const std::string&>("triggerKeyword") |
| && singleInfResult[0].m_normalisedVal > kwsScoreThreshold) { |
| output.asrAudioStart = inferenceWindow + preProcess.m_audioDataWindowSize; |
| output.asrAudioSamples = get_audio_array_size(currentIndex) - |
| (audioDataSlider.NextWindowStartIndex() - |
| preProcess.m_audioDataStride + preProcess.m_audioDataWindowSize); |
| break; |
| } |
| |
| #if VERIFY_TEST_OUTPUT |
| DumpTensor(kwsOutputTensor); |
| #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); |
| |
| if (!PresentInferenceResult(finalResults)) { |
| return output; |
| } |
| |
| profiler.PrintProfilingResult(); |
| |
| output.executionSuccess = true; |
| return output; |
| } |
| |
| /** |
| * @brief Performs the ASR pipeline. |
| * @param[in,out] ctx Pointer to the application context object. |
| * @param[in] kwsOutput Struct containing pointer to audio data where ASR should begin |
| * and how much data to process. |
| * @return true if pipeline executed without failure. |
| **/ |
| static bool doAsr(ApplicationContext& ctx, const KWSOutput& kwsOutput) |
| { |
| auto& asrModel = ctx.Get<Model&>("asrModel"); |
| auto& profiler = ctx.Get<Profiler&>("profiler"); |
| auto asrMfccFrameLen = ctx.Get<uint32_t>("asrFrameLength"); |
| auto asrMfccFrameStride = ctx.Get<uint32_t>("asrFrameStride"); |
| auto asrScoreThreshold = ctx.Get<float>("asrScoreThreshold"); |
| auto asrInputCtxLen = ctx.Get<uint32_t>("ctxLen"); |
| |
| constexpr uint32_t dataPsnTxtInfStartX = 20; |
| constexpr uint32_t dataPsnTxtInfStartY = 40; |
| |
| if (!asrModel.IsInited()) { |
| printf_err("ASR model has not been initialised\n"); |
| return false; |
| } |
| |
| hal_lcd_clear(COLOR_BLACK); |
| |
| /* Get Input and Output tensors for pre/post processing. */ |
| TfLiteTensor* asrInputTensor = asrModel.GetInputTensor(0); |
| TfLiteTensor* asrOutputTensor = asrModel.GetOutputTensor(0); |
| |
| /* Get input shape. Dimensions of the tensor should have been verified by |
| * the callee. */ |
| TfLiteIntArray* inputShape = asrModel.GetInputShape(0); |
| |
| |
| const uint32_t asrInputRows = asrInputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx]; |
| 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 %" PRIu32 "\n", |
| asrInputCtxLen); |
| return false; |
| } |
| |
| /* Audio data stride corresponds to inputInnerLen feature vectors. */ |
| const uint32_t asrAudioDataWindowLen = (asrInputRows - 1) * asrMfccFrameStride + (asrMfccFrameLen); |
| const uint32_t asrAudioDataWindowStride = asrInputInnerLen * asrMfccFrameStride; |
| const float asrAudioParamsSecondsPerSample = 1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq; |
| |
| /* 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 < asrMfccFrameLen) { |
| printf_err("Not enough audio samples, minimum needed is %" PRIu32 "\n", |
| asrMfccFrameLen); |
| return false; |
| } |
| |
| /* Initialise an audio slider. */ |
| auto audioDataSlider = audio::FractionalSlidingWindow<const int16_t>( |
| audioBuffer.data(), |
| audioBuffer.size(), |
| asrAudioDataWindowLen, |
| asrAudioDataWindowStride); |
| |
| /* Declare a container for results. */ |
| std::vector<asr::AsrResult> asrResults; |
| |
| /* Display message on the LCD - inference running. */ |
| std::string str_inf{"Running ASR inference... "}; |
| hal_lcd_display_text(str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, false); |
| |
| size_t asrInferenceWindowLen = asrAudioDataWindowLen; |
| |
| /* Set up pre and post-processing objects. */ |
| AsrPreProcess asrPreProcess = AsrPreProcess(asrInputTensor, arm::app::Wav2LetterModel::ms_numMfccFeatures, |
| inputShape->data[Wav2LetterModel::ms_inputRowsIdx], |
| asrMfccFrameLen, asrMfccFrameStride); |
| |
| std::vector<ClassificationResult> singleInfResult; |
| const uint32_t outputCtxLen = AsrPostProcess::GetOutputContextLen(asrModel, asrInputCtxLen); |
| AsrPostProcess asrPostProcess = AsrPostProcess( |
| asrOutputTensor, ctx.Get<AsrClassifier&>("asrClassifier"), |
| ctx.Get<std::vector<std::string>&>("asrLabels"), |
| singleInfResult, outputCtxLen, |
| Wav2LetterModel::ms_blankTokenIdx, Wav2LetterModel::ms_outputRowsIdx |
| ); |
| /* 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 + asrAudioDataWindowLen > 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))); |
| |
| /* Run the pre-processing, inference and post-processing. */ |
| if (!asrPreProcess.DoPreProcess(asrInferenceWindow, asrInferenceWindowLen)) { |
| printf_err("ASR pre-processing failed."); |
| return false; |
| } |
| |
| /* Run inference over this audio clip sliding window. */ |
| if (!RunInference(asrModel, profiler)) { |
| printf_err("ASR inference failed\n"); |
| return false; |
| } |
| |
| /* Post processing needs to know if we are on the last audio window. */ |
| asrPostProcess.m_lastIteration = !audioDataSlider.HasNext(); |
| if (!asrPostProcess.DoPostProcess()) { |
| printf_err("ASR post-processing failed."); |
| return false; |
| } |
| |
| /* 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 * |
| asrAudioDataWindowStride), |
| audioDataSlider.Index(), asrScoreThreshold)); |
| |
| #if VERIFY_TEST_OUTPUT |
| armDumpTensor(asrOutputTensor, asrOutputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]); |
| #endif /* VERIFY_TEST_OUTPUT */ |
| |
| /* Erase */ |
| str_inf = std::string(str_inf.size(), ' '); |
| hal_lcd_display_text( |
| str_inf.c_str(), str_inf.size(), |
| dataPsnTxtInfStartX, dataPsnTxtInfStartY, false); |
| } |
| if (!PresentInferenceResult(asrResults)) { |
| return false; |
| } |
| |
| profiler.PrintProfilingResult(); |
| |
| return true; |
| } |
| |
| /* KWS and ASR inference handler. */ |
| bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll) |
| { |
| hal_lcd_clear(COLOR_BLACK); |
| |
| /* If the request has a valid size, set the audio index. */ |
| if (clipIndex < NUMBER_OF_FILES) { |
| if (!SetAppCtxIfmIdx(ctx, clipIndex,"kws_asr")) { |
| return false; |
| } |
| } |
| |
| auto startClipIdx = ctx.Get<uint32_t>("clipIndex"); |
| |
| do { |
| KWSOutput kwsOutput = doKws(ctx); |
| if (!kwsOutput.executionSuccess) { |
| printf_err("KWS failed\n"); |
| return false; |
| } |
| |
| if (kwsOutput.asrAudioStart != nullptr && kwsOutput.asrAudioSamples > 0) { |
| info("Trigger keyword spotted\n"); |
| if(!doAsr(ctx, kwsOutput)) { |
| printf_err("ASR failed\n"); |
| return false; |
| } |
| } |
| |
| IncrementAppCtxIfmIdx(ctx,"kws_asr"); |
| |
| } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx); |
| |
| return true; |
| } |
| |
| static bool PresentInferenceResult(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. */ |
| |
| hal_lcd_set_text_color(COLOR_GREEN); |
| |
| /* Display each result. */ |
| uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr; |
| |
| for (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, 0); |
| rowIdx1 += dataPsnTxtYIncr; |
| |
| info("For timestamp: %f (inference #: %" PRIu32 "); threshold: %f\n", |
| result.m_timeStamp, result.m_inferenceNumber, |
| result.m_threshold); |
| for (uint32_t j = 0; j < result.m_resultVec.size(); ++j) { |
| info("\t\tlabel @ %" PRIu32 ": %s, score: %f\n", j, |
| result.m_resultVec[j].m_label.c_str(), |
| result.m_resultVec[j].m_normalisedVal); |
| } |
| } |
| |
| return true; |
| } |
| |
| static bool PresentInferenceResult(std::vector<arm::app::asr::AsrResult>& results) |
| { |
| constexpr uint32_t dataPsnTxtStartX1 = 20; |
| constexpr uint32_t dataPsnTxtStartY1 = 80; |
| constexpr bool allow_multiple_lines = true; |
| |
| hal_lcd_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 %" PRIu32 ": %s\n", result.m_inferenceNumber, |
| infResultStr.c_str()); |
| } |
| |
| std::string finalResultStr = audio::asr::DecodeOutput(combinedResults); |
| |
| hal_lcd_display_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 */ |