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/*
* 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 "InputFiles.hpp"
#include "KwsClassifier.hpp"
#include "MicroNetKwsModel.hpp"
#include "hal.h"
#include "AudioUtils.hpp"
#include "ImageUtils.hpp"
#include "UseCaseCommonUtils.hpp"
#include "KwsResult.hpp"
#include "log_macros.h"
#include "KwsProcessing.hpp"
#include <vector>
namespace arm {
namespace app {
/**
* @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(const std::vector<kws::KwsResult>& results);
/* KWS inference handler. */
bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
{
auto& profiler = ctx.Get<Profiler&>("profiler");
auto& model = ctx.Get<Model&>("model");
const auto mfccFrameLength = ctx.Get<int>("frameLength");
const auto mfccFrameStride = ctx.Get<int>("frameStride");
const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
/* 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<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);
if (!model.IsInited()) {
printf_err("Model is not initialised! Terminating processing.\n");
return false;
}
/* Get Input and Output tensors for pre/post processing. */
TfLiteTensor* inputTensor = model.GetInputTensor(0);
TfLiteTensor* outputTensor = model.GetOutputTensor(0);
if (!inputTensor->dims) {
printf_err("Invalid input tensor dims\n");
return false;
} else if (inputTensor->dims->size < minTensorDims) {
printf_err("Input tensor dimension should be >= %d\n", minTensorDims);
return false;
}
/* Get input shape for feature extraction. */
TfLiteIntArray* inputShape = model.GetInputShape(0);
const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx];
const uint32_t numMfccFrames = inputShape->data[arm::app::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 secondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
/* Set up pre and post-processing. */
KwsPreProcess preProcess = KwsPreProcess(inputTensor, numMfccFeatures, numMfccFrames,
mfccFrameLength, mfccFrameStride);
std::vector<ClassificationResult> singleInfResult;
KwsPostProcess postProcess = KwsPostProcess(outputTensor, ctx.Get<KwsClassifier &>("classifier"),
ctx.Get<std::vector<std::string>&>("labels"),
singleInfResult);
/* Loop to process audio clips. */
do {
hal_lcd_clear(COLOR_BLACK);
auto currentIndex = ctx.Get<uint32_t>("clipIndex");
/* 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 results from across the whole audio clip. */
std::vector<kws::KwsResult> 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));
/* Start sliding through audio clip. */
while (audioDataSlider.HasNext()) {
const int16_t* inferenceWindow = audioDataSlider.Next();
info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
audioDataSlider.TotalStrides() + 1);
/* Run the pre-processing, inference and post-processing. */
if (!preProcess.DoPreProcess(inferenceWindow, audioDataSlider.Index())) {
printf_err("Pre-processing failed.");
return false;
}
if (!RunInference(model, profiler)) {
printf_err("Inference failed.");
return false;
}
if (!postProcess.DoPostProcess()) {
printf_err("Post-processing failed.");
return false;
}
/* Add results from this window to our final results vector. */
finalResults.emplace_back(kws::KwsResult(singleInfResult,
audioDataSlider.Index() * secondsPerSample * preProcess.m_audioDataStride,
audioDataSlider.Index(), scoreThreshold));
#if VERIFY_TEST_OUTPUT
DumpTensor(outputTensor);
#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);
ctx.Set<std::vector<kws::KwsResult>>("results", finalResults);
if (!PresentInferenceResult(finalResults)) {
return false;
}
profiler.PrintProfilingResult();
IncrementAppCtxIfmIdx(ctx,"clipIndex");
} while (runAll && ctx.Get<uint32_t>("clipIndex") != initialClipIdx);
return true;
}
static bool PresentInferenceResult(const std::vector<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);
info("Final results:\n");
info("Total number of inferences: %zu\n", results.size());
/* Display each result */
uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
for (const 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, false);
rowIdx1 += dataPsnTxtYIncr;
if (result.m_resultVec.empty()) {
info("For timestamp: %f (inference #: %" PRIu32
"); label: %s; threshold: %f\n",
result.m_timeStamp, result.m_inferenceNumber,
topKeyword.c_str(),
result.m_threshold);
} else {
for (uint32_t j = 0; j < result.m_resultVec.size(); ++j) {
info("For timestamp: %f (inference #: %" PRIu32
"); label: %s, score: %f; threshold: %f\n",
result.m_timeStamp,
result.m_inferenceNumber,
result.m_resultVec[j].m_label.c_str(),
result.m_resultVec[j].m_normalisedVal,
result.m_threshold);
}
}
}
return true;
}
} /* namespace app */
} /* namespace arm */
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