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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 "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<asr::AsrResult>& results);
/* ASR inference handler. */
bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
{
auto& model = ctx.Get<Model&>("model");
auto& profiler = ctx.Get<Profiler&>("profiler");
auto mfccFrameLen = ctx.Get<uint32_t>("frameLength");
auto mfccFrameStride = ctx.Get<uint32_t>("frameStride");
auto scoreThreshold = ctx.Get<float>("scoreThreshold");
auto inputCtxLen = ctx.Get<uint32_t>("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<uint32_t>("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<ClassificationResult> singleInfResult;
const uint32_t outputCtxLen = AsrPostProcess::GetOutputContextLen(model, inputCtxLen);
AsrPostProcess postProcess = AsrPostProcess(
outputTensor, ctx.Get<AsrClassifier&>("classifier"),
ctx.Get<std::vector<std::string>&>("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<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 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<const int16_t>(
audioArr, audioArrSize,
audioDataWindowLen, audioDataWindowStride);
/* Declare a container for final results. */
std::vector<asr::AsrResult> 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<size_t>(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<std::vector<asr::AsrResult>>("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<asr::AsrResult>& 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<ClassificationResult> 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 */
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