| /* |
| * 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 "hal.h" /* Brings in platform definitions. */ |
| #include "InputFiles.hpp" /* For input images. */ |
| #include "Labels_dscnn.hpp" /* For DS-CNN label strings. */ |
| #include "Labels_wav2letter.hpp" /* For Wav2Letter label strings. */ |
| #include "Classifier.hpp" /* KWS classifier. */ |
| #include "AsrClassifier.hpp" /* ASR classifier. */ |
| #include "DsCnnModel.hpp" /* KWS model class for running inference. */ |
| #include "Wav2LetterModel.hpp" /* ASR model class for running inference. */ |
| #include "UseCaseCommonUtils.hpp" /* Utils functions. */ |
| #include "UseCaseHandler.hpp" /* Handlers for different user options. */ |
| #include "Wav2LetterPreprocess.hpp" /* ASR pre-processing class. */ |
| #include "Wav2LetterPostprocess.hpp"/* ASR post-processing class. */ |
| |
| using KwsClassifier = arm::app::Classifier; |
| |
| enum opcodes |
| { |
| MENU_OPT_RUN_INF_NEXT = 1, /* Run on next vector. */ |
| MENU_OPT_RUN_INF_CHOSEN, /* Run on a user provided vector index. */ |
| MENU_OPT_RUN_INF_ALL, /* Run inference on all. */ |
| MENU_OPT_SHOW_MODEL_INFO, /* Show model info. */ |
| MENU_OPT_LIST_AUDIO_CLIPS /* List the current baked audio clips. */ |
| }; |
| |
| static void DisplayMenu() |
| { |
| printf("\n\n"); |
| printf("User input required\n"); |
| printf("Enter option number from:\n\n"); |
| printf(" %u. Classify next audio clip\n", MENU_OPT_RUN_INF_NEXT); |
| printf(" %u. Classify audio clip at chosen index\n", MENU_OPT_RUN_INF_CHOSEN); |
| printf(" %u. Run classification on all audio clips\n", MENU_OPT_RUN_INF_ALL); |
| printf(" %u. Show NN model info\n", MENU_OPT_SHOW_MODEL_INFO); |
| printf(" %u. List audio clips\n\n", MENU_OPT_LIST_AUDIO_CLIPS); |
| printf(" Choice: "); |
| fflush(stdout); |
| } |
| |
| /** @brief Gets the number of MFCC features for a single window. */ |
| static uint32_t GetNumMfccFeatures(const arm::app::Model& model); |
| |
| /** @brief Gets the number of MFCC feature vectors to be computed. */ |
| static uint32_t GetNumMfccFeatureVectors(const arm::app::Model& model); |
| |
| /** @brief Gets the output context length (left and right) for post-processing. */ |
| static uint32_t GetOutputContextLen(const arm::app::Model& model, |
| uint32_t inputCtxLen); |
| |
| /** @brief Gets the output inner length for post-processing. */ |
| static uint32_t GetOutputInnerLen(const arm::app::Model& model, |
| uint32_t outputCtxLen); |
| |
| void main_loop(hal_platform& platform) |
| { |
| /* Model wrapper objects. */ |
| arm::app::DsCnnModel kwsModel; |
| arm::app::Wav2LetterModel asrModel; |
| |
| /* Load the models. */ |
| if (!kwsModel.Init()) { |
| printf_err("Failed to initialise KWS model\n"); |
| return; |
| } |
| |
| /* Initialise the asr model using the same allocator from KWS |
| * to re-use the tensor arena. */ |
| if (!asrModel.Init(kwsModel.GetAllocator())) { |
| printf_err("Failed to initalise ASR model\n"); |
| return; |
| } |
| |
| /* Initialise ASR pre-processing. */ |
| arm::app::audio::asr::Preprocess prep( |
| GetNumMfccFeatures(asrModel), |
| arm::app::asr::g_FrameLength, |
| arm::app::asr::g_FrameStride, |
| GetNumMfccFeatureVectors(asrModel)); |
| |
| /* Initialise ASR post-processing. */ |
| const uint32_t outputCtxLen = GetOutputContextLen(asrModel, arm::app::asr::g_ctxLen); |
| const uint32_t blankTokenIdx = 28; |
| arm::app::audio::asr::Postprocess postp( |
| outputCtxLen, |
| GetOutputInnerLen(asrModel, outputCtxLen), |
| blankTokenIdx); |
| |
| /* Instantiate application context. */ |
| arm::app::ApplicationContext caseContext; |
| |
| arm::app::Profiler profiler{&platform, "kws_asr"}; |
| caseContext.Set<arm::app::Profiler&>("profiler", profiler); |
| |
| caseContext.Set<hal_platform&>("platform", platform); |
| caseContext.Set<arm::app::Model&>("kwsmodel", kwsModel); |
| caseContext.Set<arm::app::Model&>("asrmodel", asrModel); |
| caseContext.Set<uint32_t>("clipIndex", 0); |
| caseContext.Set<uint32_t>("ctxLen", arm::app::asr::g_ctxLen); /* Left and right context length (MFCC feat vectors). */ |
| caseContext.Set<int>("kwsframeLength", arm::app::kws::g_FrameLength); |
| caseContext.Set<int>("kwsframeStride", arm::app::kws::g_FrameStride); |
| caseContext.Set<float>("kwsscoreThreshold", arm::app::kws::g_ScoreThreshold); /* Normalised score threshold. */ |
| caseContext.Set<uint32_t >("kwsNumMfcc", arm::app::kws::g_NumMfcc); |
| caseContext.Set<uint32_t >("kwsNumAudioWins", arm::app::kws::g_NumAudioWins); |
| |
| caseContext.Set<int>("asrframeLength", arm::app::asr::g_FrameLength); |
| caseContext.Set<int>("asrframeStride", arm::app::asr::g_FrameStride); |
| caseContext.Set<float>("asrscoreThreshold", arm::app::asr::g_ScoreThreshold); /* Normalised score threshold. */ |
| |
| KwsClassifier kwsClassifier; /* Classifier wrapper object. */ |
| arm::app::AsrClassifier asrClassifier; /* Classifier wrapper object. */ |
| caseContext.Set<arm::app::Classifier&>("kwsclassifier", kwsClassifier); |
| caseContext.Set<arm::app::AsrClassifier&>("asrclassifier", asrClassifier); |
| |
| caseContext.Set<arm::app::audio::asr::Preprocess&>("preprocess", prep); |
| caseContext.Set<arm::app::audio::asr::Postprocess&>("postprocess", postp); |
| |
| std::vector<std::string> asrLabels; |
| arm::app::asr::GetLabelsVector(asrLabels); |
| std::vector<std::string> kwsLabels; |
| arm::app::kws::GetLabelsVector(kwsLabels); |
| caseContext.Set<const std::vector <std::string>&>("asrlabels", asrLabels); |
| caseContext.Set<const std::vector <std::string>&>("kwslabels", kwsLabels); |
| |
| /* Index of the kws outputs we trigger ASR on. */ |
| caseContext.Set<uint32_t>("keywordindex", 2); |
| |
| /* Loop. */ |
| bool executionSuccessful = true; |
| constexpr bool bUseMenu = NUMBER_OF_FILES > 1 ? true : false; |
| |
| /* Loop. */ |
| do { |
| int menuOption = MENU_OPT_RUN_INF_NEXT; |
| if (bUseMenu) { |
| DisplayMenu(); |
| menuOption = arm::app::ReadUserInputAsInt(platform); |
| printf("\n"); |
| } |
| switch (menuOption) { |
| case MENU_OPT_RUN_INF_NEXT: |
| executionSuccessful = ClassifyAudioHandler( |
| caseContext, |
| caseContext.Get<uint32_t>("clipIndex"), |
| false); |
| break; |
| case MENU_OPT_RUN_INF_CHOSEN: { |
| printf(" Enter the audio clip index [0, %d]: ", |
| NUMBER_OF_FILES-1); |
| auto clipIndex = static_cast<uint32_t>( |
| arm::app::ReadUserInputAsInt(platform)); |
| executionSuccessful = ClassifyAudioHandler(caseContext, |
| clipIndex, |
| false); |
| break; |
| } |
| case MENU_OPT_RUN_INF_ALL: |
| executionSuccessful = ClassifyAudioHandler( |
| caseContext, |
| caseContext.Get<uint32_t>("clipIndex"), |
| true); |
| break; |
| case MENU_OPT_SHOW_MODEL_INFO: |
| executionSuccessful = kwsModel.ShowModelInfoHandler(); |
| executionSuccessful = asrModel.ShowModelInfoHandler(); |
| break; |
| case MENU_OPT_LIST_AUDIO_CLIPS: |
| executionSuccessful = ListFilesHandler(caseContext); |
| break; |
| default: |
| printf("Incorrect choice, try again."); |
| break; |
| } |
| } while (executionSuccessful && bUseMenu); |
| info("Main loop terminated.\n"); |
| } |
| |
| static uint32_t GetNumMfccFeatures(const arm::app::Model& model) |
| { |
| TfLiteTensor* inputTensor = model.GetInputTensor(0); |
| const int inputCols = inputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputColsIdx]; |
| if (0 != inputCols % 3) { |
| printf_err("Number of input columns is not a multiple of 3\n"); |
| } |
| return std::max(inputCols/3, 0); |
| } |
| |
| static uint32_t GetNumMfccFeatureVectors(const arm::app::Model& model) |
| { |
| TfLiteTensor* inputTensor = model.GetInputTensor(0); |
| const int inputRows = inputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputRowsIdx]; |
| return std::max(inputRows, 0); |
| } |
| |
| static uint32_t GetOutputContextLen(const arm::app::Model& model, const uint32_t inputCtxLen) |
| { |
| const uint32_t inputRows = GetNumMfccFeatureVectors(model); |
| const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen); |
| constexpr uint32_t ms_outputRowsIdx = arm::app::Wav2LetterModel::ms_outputRowsIdx; |
| |
| /* Check to make sure that the input tensor supports the above context and inner lengths. */ |
| if (inputRows <= 2 * inputCtxLen || inputRows <= inputInnerLen) { |
| printf_err("Input rows not compatible with ctx of %" PRIu32 "\n", |
| inputCtxLen); |
| return 0; |
| } |
| |
| TfLiteTensor* outputTensor = model.GetOutputTensor(0); |
| const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0); |
| |
| const float tensorColRatio = static_cast<float>(inputRows)/ |
| static_cast<float>(outputRows); |
| |
| return std::round(static_cast<float>(inputCtxLen)/tensorColRatio); |
| } |
| |
| static uint32_t GetOutputInnerLen(const arm::app::Model& model, |
| const uint32_t outputCtxLen) |
| { |
| constexpr uint32_t ms_outputRowsIdx = arm::app::Wav2LetterModel::ms_outputRowsIdx; |
| TfLiteTensor* outputTensor = model.GetOutputTensor(0); |
| const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0); |
| return (outputRows - (2 * outputCtxLen)); |
| } |