/* * 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 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. * @return true if successful, false otherwise. **/ static bool PresentInferenceResult( hal_platform& platform, const std::vector& 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("platform"); platform.data_psn->clear(COLOR_BLACK); auto& profiler = ctx.Get("profiler"); /* If the request has a valid size, set the audio index. */ if (clipIndex < NUMBER_OF_FILES) { if (!SetAppCtxIfmIdx(ctx, clipIndex,"clipIndex")) { return false; } } /* Get model reference. */ auto& model = ctx.Get("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("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("frameLength"); auto mfccParamsWinStride = ctx.Get("frameStride"); /* Populate ASR inference context and inner lengths for input. */ auto inputCtxLen = ctx.Get("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("preprocess"); auto& postp = ctx.Get("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("clipIndex"); /* Loop to process audio clips. */ do { /* Get current audio clip index. */ auto currentIndex = ctx.Get("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 %" PRIu32 "\n", mfccParamsWinLen); return false; } /* Initialise an audio slider. */ auto audioDataSlider = audio::FractionalSlidingWindow( audioArr, audioArrSize, audioParamsWinLen, audioParamsWinStride); /* Declare a container for results. */ std::vector 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 %" PRIu32 " => %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(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. */ if (!RunInference(model, profiler)) { return false; } /* Post-process. */ postp.Invoke(outputTensor, reductionAxis, !audioDataSlider.HasNext()); /* Get results. */ std::vector classificationResult; auto& classifier = ctx.Get("classifier"); classifier.GetClassificationResults( outputTensor, classificationResult, ctx.Get&>("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>("results", results); if (!PresentInferenceResult(platform, results)) { return false; } profiler.PrintProfilingResult(); IncrementAppCtxIfmIdx(ctx,"clipIndex"); } while (runAll && ctx.Get("clipIndex") != startClipIdx); return true; } static bool PresentInferenceResult(hal_platform& platform, const std::vector& 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 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 #: %" PRIu32 "); 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 */