| /* |
| * 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); |
| |
| auto& profiler = ctx.Get<Profiler&>("profiler"); |
| |
| /* 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))); |
| |
| /* Calculate MFCCs, deltas and populate the input tensor. */ |
| prep.Invoke(inferenceWindow, inferenceWindowLen, inputTensor); |
| |
| /* Run inference over this audio clip sliding window. */ |
| arm::app::RunInference(model, profiler); |
| |
| /* 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; |
| } |
| |
| profiler.PrintProfilingResult(); |
| |
| 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, 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); |
| |
| info("Final results:\n"); |
| info("Total number of inferences: %zu\n", results.size()); |
| /* 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("For timestamp: %f (inference #: %u); label: %s\n", |
| result.m_timeStamp, 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("Complete recognition: %s\n", finalResultStr.c_str()); |
| return true; |
| } |
| |
| } /* namespace app */ |
| } /* namespace arm */ |