/* * Copyright (c) 2021-2022 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 "AudioUtils.hpp" #include "ImageUtils.hpp" #include "UseCaseCommonUtils.hpp" #include "AsrResult.hpp" #include "Wav2LetterPreprocess.hpp" #include "Wav2LetterPostprocess.hpp" #include "OutputDecode.hpp" #include "log_macros.h" namespace arm { namespace app { /** * @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(const std::vector& results); /* ASR inference handler. */ bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll) { auto& model = ctx.Get("model"); auto& profiler = ctx.Get("profiler"); auto mfccFrameLen = ctx.Get("frameLength"); auto mfccFrameStride = ctx.Get("frameStride"); auto scoreThreshold = ctx.Get("scoreThreshold"); auto inputCtxLen = ctx.Get("ctxLen"); /* If the request has a valid size, set the audio index. */ if (clipIndex < NUMBER_OF_FILES) { if (!SetAppCtxIfmIdx(ctx, clipIndex,"clipIndex")) { return false; } } auto initialClipIdx = ctx.Get("clipIndex"); constexpr uint32_t dataPsnTxtInfStartX = 20; constexpr uint32_t dataPsnTxtInfStartY = 40; if (!model.IsInited()) { printf_err("Model is not initialised! Terminating processing.\n"); return false; } TfLiteTensor* inputTensor = model.GetInputTensor(0); TfLiteTensor* outputTensor = model.GetOutputTensor(0); /* Get input shape. Dimensions of the tensor should have been verified by * the callee. */ TfLiteIntArray* inputShape = model.GetInputShape(0); const uint32_t inputRowsSize = inputShape->data[Wav2LetterModel::ms_inputRowsIdx]; const uint32_t inputInnerLen = inputRowsSize - (2 * inputCtxLen); /* Audio data stride corresponds to inputInnerLen feature vectors. */ const uint32_t audioDataWindowLen = (inputRowsSize - 1) * mfccFrameStride + (mfccFrameLen); const uint32_t audioDataWindowStride = inputInnerLen * mfccFrameStride; /* NOTE: This is only used for time stamp calculation. */ const float secondsPerSample = (1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq); /* Set up pre and post-processing objects. */ AsrPreProcess preProcess = AsrPreProcess(inputTensor, Wav2LetterModel::ms_numMfccFeatures, inputShape->data[Wav2LetterModel::ms_inputRowsIdx], mfccFrameLen, mfccFrameStride); std::vector singleInfResult; const uint32_t outputCtxLen = AsrPostProcess::GetOutputContextLen(model, inputCtxLen); AsrPostProcess postProcess = AsrPostProcess( outputTensor, ctx.Get("classifier"), ctx.Get&>("labels"), singleInfResult, outputCtxLen, Wav2LetterModel::ms_blankTokenIdx, Wav2LetterModel::ms_outputRowsIdx ); /* Loop to process audio clips. */ do { hal_lcd_clear(COLOR_BLACK); /* 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 needs enough samples to produce at least 1 MFCC feature. */ if (audioArrSize < mfccFrameLen) { printf_err("Not enough audio samples, minimum needed is %" PRIu32 "\n", mfccFrameLen); return false; } /* Creating a sliding window through the whole audio clip. */ auto audioDataSlider = audio::FractionalSlidingWindow( audioArr, audioArrSize, audioDataWindowLen, audioDataWindowStride); /* Declare a container for final results. */ std::vector finalResults; /* Display message on the LCD - inference running. */ std::string str_inf{"Running inference... "}; hal_lcd_display_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 = audioDataWindowLen; /* 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 + audioDataWindowLen > audioArrSize) { inferenceWindowLen = audioArrSize - nextStartIndex; } const int16_t* inferenceWindow = audioDataSlider.Next(); info("Inference %zu/%zu\n", audioDataSlider.Index() + 1, static_cast(ceilf(audioDataSlider.FractionalTotalStrides() + 1))); /* Run the pre-processing, inference and post-processing. */ if (!preProcess.DoPreProcess(inferenceWindow, inferenceWindowLen)) { printf_err("Pre-processing failed."); return false; } if (!RunInference(model, profiler)) { printf_err("Inference failed."); return false; } /* Post processing needs to know if we are on the last audio window. */ postProcess.m_lastIteration = !audioDataSlider.HasNext(); if (!postProcess.DoPostProcess()) { printf_err("Post-processing failed."); return false; } /* Add results from this window to our final results vector. */ finalResults.emplace_back(asr::AsrResult(singleInfResult, (audioDataSlider.Index() * secondsPerSample * audioDataWindowStride), audioDataSlider.Index(), scoreThreshold)); #if VERIFY_TEST_OUTPUT armDumpTensor(outputTensor, outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]); #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, 0); ctx.Set>("results", finalResults); if (!PresentInferenceResult(finalResults)) { return false; } profiler.PrintProfilingResult(); IncrementAppCtxIfmIdx(ctx,"clipIndex"); } while (runAll && ctx.Get("clipIndex") != initialClipIdx); return true; } static bool PresentInferenceResult(const std::vector& results) { constexpr uint32_t dataPsnTxtStartX1 = 20; constexpr uint32_t dataPsnTxtStartY1 = 60; constexpr bool allow_multiple_lines = true; hal_lcd_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 (const 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); hal_lcd_display_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 */