MLECO-3611: Formatting fixes for generated files.
Template files updated for generated files to adhere to
coding guidelines and clang format configuration. There
will still be unavoidable violations, but most of the
others have been fixed.
Change-Id: Ia03db40f8c62a369f2b07fe02eea65e41993a523
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>
diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index e733605..8a024b7 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/src/UseCaseHandler.cc
@@ -1,6 +1,6 @@
/*
- * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * 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.
@@ -16,35 +16,34 @@
*/
#include "UseCaseHandler.hpp"
-#include "hal.h"
-#include "InputFiles.hpp"
-#include "AudioUtils.hpp"
-#include "ImageUtils.hpp"
-#include "UseCaseCommonUtils.hpp"
-#include "MicroNetKwsModel.hpp"
-#include "MicroNetKwsMfcc.hpp"
-#include "Classifier.hpp"
-#include "KwsResult.hpp"
-#include "Wav2LetterModel.hpp"
-#include "Wav2LetterMfcc.hpp"
-#include "Wav2LetterPreprocess.hpp"
-#include "Wav2LetterPostprocess.hpp"
-#include "KwsProcessing.hpp"
-#include "AsrResult.hpp"
#include "AsrClassifier.hpp"
+#include "AsrResult.hpp"
+#include "AudioUtils.hpp"
+#include "Classifier.hpp"
+#include "ImageUtils.hpp"
+#include "InputFiles.hpp"
+#include "KwsProcessing.hpp"
+#include "KwsResult.hpp"
+#include "MicroNetKwsMfcc.hpp"
+#include "MicroNetKwsModel.hpp"
#include "OutputDecode.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "Wav2LetterMfcc.hpp"
+#include "Wav2LetterModel.hpp"
+#include "Wav2LetterPostprocess.hpp"
+#include "Wav2LetterPreprocess.hpp"
+#include "hal.h"
#include "log_macros.h"
-
using KwsClassifier = arm::app::Classifier;
namespace arm {
namespace app {
struct KWSOutput {
- bool executionSuccess = false;
+ bool executionSuccess = false;
const int16_t* asrAudioStart = nullptr;
- int32_t asrAudioSamples = 0;
+ int32_t asrAudioSamples = 0;
};
/**
@@ -69,23 +68,24 @@
**/
static KWSOutput doKws(ApplicationContext& ctx)
{
- auto& profiler = ctx.Get<Profiler&>("profiler");
- auto& kwsModel = ctx.Get<Model&>("kwsModel");
+ auto& profiler = ctx.Get<Profiler&>("profiler");
+ auto& kwsModel = ctx.Get<Model&>("kwsModel");
const auto kwsMfccFrameLength = ctx.Get<int>("kwsFrameLength");
const auto kwsMfccFrameStride = ctx.Get<int>("kwsFrameStride");
- const auto kwsScoreThreshold = ctx.Get<float>("kwsScoreThreshold");
+ const auto kwsScoreThreshold = ctx.Get<float>("kwsScoreThreshold");
auto currentIndex = 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);
+ constexpr int minTensorDims =
+ static_cast<int>((MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)
+ ? MicroNetKwsModel::ms_inputRowsIdx
+ : MicroNetKwsModel::ms_inputColsIdx);
/* Output struct from doing KWS. */
- KWSOutput output {};
+ KWSOutput output{};
if (!kwsModel.IsInited()) {
printf_err("KWS model has not been initialised\n");
@@ -93,7 +93,7 @@
}
/* Get Input and Output tensors for pre/post processing. */
- TfLiteTensor* kwsInputTensor = kwsModel.GetInputTensor(0);
+ TfLiteTensor* kwsInputTensor = kwsModel.GetInputTensor(0);
TfLiteTensor* kwsOutputTensor = kwsModel.GetOutputTensor(0);
if (!kwsInputTensor->dims) {
printf_err("Invalid input tensor dims\n");
@@ -104,28 +104,30 @@
}
/* Get input shape for feature extraction. */
- TfLiteIntArray* inputShape = kwsModel.GetInputShape(0);
+ TfLiteIntArray* inputShape = kwsModel.GetInputShape(0);
const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx];
- const uint32_t numMfccFrames = inputShape->data[MicroNetKwsModel::ms_inputRowsIdx];
+ const uint32_t numMfccFrames = inputShape->data[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 kwsAudioParamsSecondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
+ const float kwsAudioParamsSecondsPerSample =
+ 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
/* Set up pre and post-processing. */
- KwsPreProcess preProcess = KwsPreProcess(kwsInputTensor, numMfccFeatures, numMfccFrames,
- kwsMfccFrameLength, kwsMfccFrameStride);
+ KwsPreProcess preProcess = KwsPreProcess(
+ kwsInputTensor, numMfccFeatures, numMfccFrames, kwsMfccFrameLength, kwsMfccFrameStride);
std::vector<ClassificationResult> singleInfResult;
- KwsPostProcess postProcess = KwsPostProcess(kwsOutputTensor, ctx.Get<KwsClassifier &>("kwsClassifier"),
+ KwsPostProcess postProcess = KwsPostProcess(kwsOutputTensor,
+ ctx.Get<KwsClassifier&>("kwsClassifier"),
ctx.Get<std::vector<std::string>&>("kwsLabels"),
singleInfResult);
/* 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);
+ auto audioDataSlider = audio::SlidingWindow<const int16_t>(GetAudioArray(currentIndex),
+ GetAudioArraySize(currentIndex),
+ preProcess.m_audioDataWindowSize,
+ preProcess.m_audioDataStride);
/* Declare a container to hold kws results from across the whole audio clip. */
std::vector<kws::KwsResult> finalResults;
@@ -133,11 +135,11 @@
/* Display message on the LCD - inference running. */
std::string str_inf{"Running KWS inference... "};
hal_lcd_display_text(
- str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
info("Running KWS inference on audio clip %" PRIu32 " => %s\n",
- currentIndex, get_filename(currentIndex));
+ currentIndex,
+ GetFilename(currentIndex));
/* Start sliding through audio clip. */
while (audioDataSlider.HasNext()) {
@@ -159,22 +161,26 @@
return output;
}
- info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
+ info("Inference %zu/%zu\n",
+ audioDataSlider.Index() + 1,
audioDataSlider.TotalStrides() + 1);
/* Add results from this window to our final results vector. */
finalResults.emplace_back(
- kws::KwsResult(singleInfResult,
- audioDataSlider.Index() * kwsAudioParamsSecondsPerSample * preProcess.m_audioDataStride,
- audioDataSlider.Index(), kwsScoreThreshold));
+ kws::KwsResult(singleInfResult,
+ audioDataSlider.Index() * kwsAudioParamsSecondsPerSample *
+ preProcess.m_audioDataStride,
+ audioDataSlider.Index(),
+ kwsScoreThreshold));
/* Break out when trigger keyword is detected. */
- if (singleInfResult[0].m_label == ctx.Get<const std::string&>("triggerKeyword")
- && singleInfResult[0].m_normalisedVal > kwsScoreThreshold) {
+ if (singleInfResult[0].m_label == ctx.Get<const std::string&>("triggerKeyword") &&
+ singleInfResult[0].m_normalisedVal > kwsScoreThreshold) {
output.asrAudioStart = inferenceWindow + preProcess.m_audioDataWindowSize;
- output.asrAudioSamples = get_audio_array_size(currentIndex) -
- (audioDataSlider.NextWindowStartIndex() -
- preProcess.m_audioDataStride + preProcess.m_audioDataWindowSize);
+ output.asrAudioSamples =
+ GetAudioArraySize(currentIndex) -
+ (audioDataSlider.NextWindowStartIndex() - preProcess.m_audioDataStride +
+ preProcess.m_audioDataWindowSize);
break;
}
@@ -186,8 +192,8 @@
/* Erase. */
str_inf = std::string(str_inf.size(), ' ');
- hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+ hal_lcd_display_text(
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
if (!PresentInferenceResult(finalResults)) {
return output;
@@ -208,12 +214,12 @@
**/
static bool doAsr(ApplicationContext& ctx, const KWSOutput& kwsOutput)
{
- auto& asrModel = ctx.Get<Model&>("asrModel");
- auto& profiler = ctx.Get<Profiler&>("profiler");
- auto asrMfccFrameLen = ctx.Get<uint32_t>("asrFrameLength");
+ auto& asrModel = ctx.Get<Model&>("asrModel");
+ auto& profiler = ctx.Get<Profiler&>("profiler");
+ auto asrMfccFrameLen = ctx.Get<uint32_t>("asrFrameLength");
auto asrMfccFrameStride = ctx.Get<uint32_t>("asrFrameStride");
- auto asrScoreThreshold = ctx.Get<float>("asrScoreThreshold");
- auto asrInputCtxLen = ctx.Get<uint32_t>("ctxLen");
+ auto asrScoreThreshold = ctx.Get<float>("asrScoreThreshold");
+ auto asrInputCtxLen = ctx.Get<uint32_t>("ctxLen");
constexpr uint32_t dataPsnTxtInfStartX = 20;
constexpr uint32_t dataPsnTxtInfStartY = 40;
@@ -226,31 +232,32 @@
hal_lcd_clear(COLOR_BLACK);
/* Get Input and Output tensors for pre/post processing. */
- TfLiteTensor* asrInputTensor = asrModel.GetInputTensor(0);
+ TfLiteTensor* asrInputTensor = asrModel.GetInputTensor(0);
TfLiteTensor* asrOutputTensor = asrModel.GetOutputTensor(0);
/* Get input shape. Dimensions of the tensor should have been verified by
- * the callee. */
+ * the callee. */
TfLiteIntArray* inputShape = asrModel.GetInputShape(0);
-
const uint32_t asrInputRows = asrInputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx];
const uint32_t asrInputInnerLen = asrInputRows - (2 * asrInputCtxLen);
/* Make sure the input tensor supports the above context and inner lengths. */
if (asrInputRows <= 2 * asrInputCtxLen || asrInputRows <= asrInputInnerLen) {
printf_err("ASR input rows not compatible with ctx length %" PRIu32 "\n",
- asrInputCtxLen);
+ asrInputCtxLen);
return false;
}
/* Audio data stride corresponds to inputInnerLen feature vectors. */
- const uint32_t asrAudioDataWindowLen = (asrInputRows - 1) * asrMfccFrameStride + (asrMfccFrameLen);
+ const uint32_t asrAudioDataWindowLen =
+ (asrInputRows - 1) * asrMfccFrameStride + (asrMfccFrameLen);
const uint32_t asrAudioDataWindowStride = asrInputInnerLen * asrMfccFrameStride;
- const float asrAudioParamsSecondsPerSample = 1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq;
+ const float asrAudioParamsSecondsPerSample =
+ 1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq;
/* Get the remaining audio buffer and respective size from KWS results. */
- const int16_t* audioArr = kwsOutput.asrAudioStart;
+ const int16_t* audioArr = kwsOutput.asrAudioStart;
const uint32_t audioArrSize = kwsOutput.asrAudioSamples;
/* Audio clip must have enough samples to produce 1 MFCC feature. */
@@ -262,35 +269,40 @@
}
/* Initialise an audio slider. */
- auto audioDataSlider = audio::FractionalSlidingWindow<const int16_t>(
- audioBuffer.data(),
- audioBuffer.size(),
- asrAudioDataWindowLen,
- asrAudioDataWindowStride);
+ auto audioDataSlider =
+ audio::FractionalSlidingWindow<const int16_t>(audioBuffer.data(),
+ audioBuffer.size(),
+ asrAudioDataWindowLen,
+ asrAudioDataWindowStride);
/* Declare a container for results. */
std::vector<asr::AsrResult> asrResults;
/* Display message on the LCD - inference running. */
std::string str_inf{"Running ASR inference... "};
- hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+ hal_lcd_display_text(
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
size_t asrInferenceWindowLen = asrAudioDataWindowLen;
/* Set up pre and post-processing objects. */
- AsrPreProcess asrPreProcess = AsrPreProcess(asrInputTensor, arm::app::Wav2LetterModel::ms_numMfccFeatures,
- inputShape->data[Wav2LetterModel::ms_inputRowsIdx],
- asrMfccFrameLen, asrMfccFrameStride);
+ AsrPreProcess asrPreProcess =
+ AsrPreProcess(asrInputTensor,
+ arm::app::Wav2LetterModel::ms_numMfccFeatures,
+ inputShape->data[Wav2LetterModel::ms_inputRowsIdx],
+ asrMfccFrameLen,
+ asrMfccFrameStride);
std::vector<ClassificationResult> singleInfResult;
const uint32_t outputCtxLen = AsrPostProcess::GetOutputContextLen(asrModel, asrInputCtxLen);
- AsrPostProcess asrPostProcess = AsrPostProcess(
- asrOutputTensor, ctx.Get<AsrClassifier&>("asrClassifier"),
- ctx.Get<std::vector<std::string>&>("asrLabels"),
- singleInfResult, outputCtxLen,
- Wav2LetterModel::ms_blankTokenIdx, Wav2LetterModel::ms_outputRowsIdx
- );
+ AsrPostProcess asrPostProcess =
+ AsrPostProcess(asrOutputTensor,
+ ctx.Get<AsrClassifier&>("asrClassifier"),
+ ctx.Get<std::vector<std::string>&>("asrLabels"),
+ singleInfResult,
+ outputCtxLen,
+ Wav2LetterModel::ms_blankTokenIdx,
+ Wav2LetterModel::ms_outputRowsIdx);
/* Start sliding through audio clip. */
while (audioDataSlider.HasNext()) {
@@ -302,8 +314,9 @@
const int16_t* asrInferenceWindow = audioDataSlider.Next();
- info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
- static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
+ 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 (!asrPreProcess.DoPreProcess(asrInferenceWindow, asrInferenceWindowLen)) {
@@ -327,25 +340,27 @@
/* Get results. */
std::vector<ClassificationResult> asrClassificationResult;
auto& asrClassifier = ctx.Get<AsrClassifier&>("asrClassifier");
- asrClassifier.GetClassificationResults(
- asrOutputTensor, asrClassificationResult,
- ctx.Get<std::vector<std::string>&>("asrLabels"), 1);
+ asrClassifier.GetClassificationResults(asrOutputTensor,
+ asrClassificationResult,
+ ctx.Get<std::vector<std::string>&>("asrLabels"),
+ 1);
- asrResults.emplace_back(asr::AsrResult(asrClassificationResult,
- (audioDataSlider.Index() *
- asrAudioParamsSecondsPerSample *
- asrAudioDataWindowStride),
- audioDataSlider.Index(), asrScoreThreshold));
+ asrResults.emplace_back(
+ asr::AsrResult(asrClassificationResult,
+ (audioDataSlider.Index() * asrAudioParamsSecondsPerSample *
+ asrAudioDataWindowStride),
+ audioDataSlider.Index(),
+ asrScoreThreshold));
#if VERIFY_TEST_OUTPUT
- armDumpTensor(asrOutputTensor, asrOutputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
+ armDumpTensor(asrOutputTensor,
+ asrOutputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
#endif /* VERIFY_TEST_OUTPUT */
/* Erase */
str_inf = std::string(str_inf.size(), ' ');
hal_lcd_display_text(
- str_inf.c_str(), str_inf.size(),
- dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+ str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
}
if (!PresentInferenceResult(asrResults)) {
return false;
@@ -363,7 +378,7 @@
/* If the request has a valid size, set the audio index. */
if (clipIndex < NUMBER_OF_FILES) {
- if (!SetAppCtxIfmIdx(ctx, clipIndex,"kws_asr")) {
+ if (!SetAppCtxIfmIdx(ctx, clipIndex, "kws_asr")) {
return false;
}
}
@@ -379,13 +394,13 @@
if (kwsOutput.asrAudioStart != nullptr && kwsOutput.asrAudioSamples > 0) {
info("Trigger keyword spotted\n");
- if(!doAsr(ctx, kwsOutput)) {
+ if (!doAsr(ctx, kwsOutput)) {
printf_err("ASR failed\n");
return false;
}
}
- IncrementAppCtxIfmIdx(ctx,"kws_asr");
+ IncrementAppCtxIfmIdx(ctx, "kws_asr");
} while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
@@ -396,36 +411,38 @@
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 30;
- constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
+ constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
hal_lcd_set_text_color(COLOR_GREEN);
/* Display each result. */
uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
- for (auto & result : results) {
+ for (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;
+ 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{"%)"};
+ 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, 0);
+ hal_lcd_display_text(
+ resultStr.c_str(), resultStr.size(), dataPsnTxtStartX1, rowIdx1, 0);
rowIdx1 += dataPsnTxtYIncr;
info("For timestamp: %f (inference #: %" PRIu32 "); threshold: %f\n",
- result.m_timeStamp, result.m_inferenceNumber,
+ result.m_timeStamp,
+ result.m_inferenceNumber,
result.m_threshold);
for (uint32_t j = 0; j < result.m_resultVec.size(); ++j) {
- info("\t\tlabel @ %" PRIu32 ": %s, score: %f\n", j,
+ info("\t\tlabel @ %" PRIu32 ": %s, score: %f\n",
+ j,
result.m_resultVec[j].m_label.c_str(),
result.m_resultVec[j].m_normalisedVal);
}
@@ -438,30 +455,32 @@
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 80;
- constexpr bool allow_multiple_lines = true;
+ constexpr bool allow_multiple_lines = true;
hal_lcd_set_text_color(COLOR_GREEN);
/* Results from multiple inferences should be combined before processing. */
std::vector<arm::app::ClassificationResult> combinedResults;
for (auto& result : results) {
- combinedResults.insert(combinedResults.end(),
- result.m_resultVec.begin(),
- result.m_resultVec.end());
+ combinedResults.insert(
+ combinedResults.end(), result.m_resultVec.begin(), result.m_resultVec.end());
}
for (auto& result : results) {
/* Get the final result string using the decoder. */
std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec);
- info("Result for inf %" PRIu32 ": %s\n", result.m_inferenceNumber,
- infResultStr.c_str());
+ info(
+ "Result for inf %" PRIu32 ": %s\n", result.m_inferenceNumber, infResultStr.c_str());
}
std::string finalResultStr = audio::asr::DecodeOutput(combinedResults);
- hal_lcd_display_text(finalResultStr.c_str(), finalResultStr.size(),
- dataPsnTxtStartX1, dataPsnTxtStartY1, allow_multiple_lines);
+ hal_lcd_display_text(finalResultStr.c_str(),
+ finalResultStr.size(),
+ dataPsnTxtStartX1,
+ dataPsnTxtStartY1,
+ allow_multiple_lines);
info("Final result: %s\n", finalResultStr.c_str());
return true;