MLECO-3077: Add ASR use case API
* Minor adjustments to doc strings in KWS
* Remove unused score threshold in KWS
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: Ie1c5bf6f7bdbebb853b6a10cb7ba1c4a1d9a76c9
diff --git a/tests/use_case/asr/AsrFeaturesTests.cc b/tests/use_case/asr/AsrFeaturesTests.cc
index 940c25f..6c23598 100644
--- a/tests/use_case/asr/AsrFeaturesTests.cc
+++ b/tests/use_case/asr/AsrFeaturesTests.cc
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * 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");
@@ -23,29 +23,19 @@
#include <catch.hpp>
#include <random>
-class TestPreprocess : public arm::app::audio::asr::Preprocess {
+class TestPreprocess : public arm::app::ASRPreProcess {
public:
static bool ComputeDeltas(arm::app::Array2d<float>& mfcc,
arm::app::Array2d<float>& delta1,
arm::app::Array2d<float>& delta2)
{
- return Preprocess::ComputeDeltas(mfcc, delta1, delta2);
- }
-
- static float GetMean(arm::app::Array2d<float>& vec)
- {
- return Preprocess::GetMean(vec);
- }
-
- static float GetStdDev(arm::app::Array2d<float>& vec, const float mean)
- {
- return Preprocess::GetStdDev(vec, mean);
+ return ASRPreProcess::ComputeDeltas(mfcc, delta1, delta2);
}
static void NormaliseVec(arm::app::Array2d<float>& vec)
{
- return Preprocess::NormaliseVec(vec);
+ return ASRPreProcess::StandardizeVecF32(vec);
}
};
@@ -126,40 +116,6 @@
}
- SECTION("Mean")
- {
- std::vector<std::vector<float>> mean1vec{{1, 2},
- {-1, -2}};
- arm::app::Array2d<float> mean1(2,2); /* {{1, 2},{-1, -2}} */
- populateArray2dWithVectorOfVector(mean1vec, mean1);
- REQUIRE(0 == Approx(TestPreprocess::GetMean(mean1)));
-
- arm::app::Array2d<float> mean2(2, 2);
- std::fill(mean2.begin(), mean2.end(), 0.f);
- REQUIRE(0 == Approx(TestPreprocess::GetMean(mean2)));
-
- arm::app::Array2d<float> mean3(3,3);
- std::fill(mean3.begin(), mean3.end(), 1.f);
- REQUIRE(1 == Approx(TestPreprocess::GetMean(mean3)));
- }
-
- SECTION("Std")
- {
- arm::app::Array2d<float> std1(2, 2);
- std::fill(std1.begin(), std1.end(), 0.f); /* {{0, 0}, {0, 0}} */
- REQUIRE(0 == Approx(TestPreprocess::GetStdDev(std1, 0)));
-
- std::vector<std::vector<float>> std2vec{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 0}};
- arm::app::Array2d<float> std2(2,5);
- populateArray2dWithVectorOfVector(std2vec, std2);
- const float mean = TestPreprocess::GetMean(std2);
- REQUIRE(2.872281323 == Approx(TestPreprocess::GetStdDev(std2, mean)));
-
- arm::app::Array2d<float> std3(2,2);
- std::fill(std3.begin(), std3.end(), 1.f); /* std3{{1, 1}, {1, 1}}; */
- REQUIRE(0 == Approx(TestPreprocess::GetStdDev(std3, 1)));
- }
-
SECTION("Norm") {
auto checker = [&](arm::app::Array2d<float>& d, std::vector<float>& g) {
TestPreprocess::NormaliseVec(d);
diff --git a/tests/use_case/asr/Wav2LetterPostprocessingTest.cc b/tests/use_case/asr/Wav2LetterPostprocessingTest.cc
index 9ed2e1b..d0b6505 100644
--- a/tests/use_case/asr/Wav2LetterPostprocessingTest.cc
+++ b/tests/use_case/asr/Wav2LetterPostprocessingTest.cc
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * 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");
@@ -16,6 +16,7 @@
*/
#include "Wav2LetterPostprocess.hpp"
#include "Wav2LetterModel.hpp"
+#include "ClassificationResult.hpp"
#include <algorithm>
#include <catch.hpp>
@@ -47,85 +48,105 @@
{
SECTION("Mismatched post processing parameters and tensor size")
{
- const uint32_t ctxLen = 5;
- const uint32_t innerLen = 3;
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, 0};
-
+ const uint32_t outputCtxLen = 5;
+ arm::app::AsrClassifier classifier;
+ arm::app::Wav2LetterModel model;
+ model.Init();
+ std::vector<std::string> dummyLabels = {"a", "b", "$"};
+ const uint32_t blankTokenIdx = 2;
+ std::vector<arm::app::ClassificationResult> dummyResult;
std::vector <int> tensorShape = {1, 1, 1, 13};
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
- REQUIRE(false == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ tensorShape, 100, tensorVec);
+
+ arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
+
+ REQUIRE(!post.DoPostProcess());
}
SECTION("Post processing succeeds")
{
- const uint32_t ctxLen = 5;
- const uint32_t innerLen = 3;
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, 0};
-
- std::vector <int> tensorShape = {1, 1, 13, 1};
- std::vector <int8_t> tensorVec;
+ const uint32_t outputCtxLen = 5;
+ arm::app::AsrClassifier classifier;
+ arm::app::Wav2LetterModel model;
+ model.Init();
+ std::vector<std::string> dummyLabels = {"a", "b", "$"};
+ const uint32_t blankTokenIdx = 2;
+ std::vector<arm::app::ClassificationResult> dummyResult;
+ std::vector<int> tensorShape = {1, 1, 13, 1};
+ std::vector<int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
+ tensorShape, 100, tensorVec);
+
+ arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
- std::vector <int8_t> originalVec = tensorVec;
+ std::vector<int8_t> originalVec = tensorVec;
/* This step should not erase anything. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ REQUIRE(post.DoPostProcess());
}
}
TEST_CASE("Postprocessing - erasing required elements")
{
- constexpr uint32_t ctxLen = 5;
+ constexpr uint32_t outputCtxLen = 5;
constexpr uint32_t innerLen = 3;
- constexpr uint32_t nRows = 2*ctxLen + innerLen;
+ constexpr uint32_t nRows = 2*outputCtxLen + innerLen;
constexpr uint32_t nCols = 10;
constexpr uint32_t blankTokenIdx = nCols - 1;
- std::vector <int> tensorShape = {1, 1, nRows, nCols};
+ std::vector<int> tensorShape = {1, 1, nRows, nCols};
+ arm::app::AsrClassifier classifier;
+ arm::app::Wav2LetterModel model;
+ model.Init();
+ std::vector<std::string> dummyLabels = {"a", "b", "$"};
+ std::vector<arm::app::ClassificationResult> dummyResult;
SECTION("First and last iteration")
{
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
- std::vector <int8_t> tensorVec;
- TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
+ std::vector<int8_t> tensorVec;
+ TfLiteTensor tensor = GetTestTensor<int8_t>(tensorShape, 100, tensorVec);
+ arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
- std::vector <int8_t> originalVec = tensorVec;
+ std::vector<int8_t>originalVec = tensorVec;
/* This step should not erase anything. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, true));
+ post.m_lastIteration = true;
+ REQUIRE(post.DoPostProcess());
REQUIRE(originalVec == tensorVec);
}
SECTION("Right context erase")
{
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
+ tensorShape, 100, tensorVec);
+ arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
- std::vector <int8_t> originalVec = tensorVec;
+ std::vector<int8_t> originalVec = tensorVec;
+ //auto tensorData = tflite::GetTensorData<int8_t>(tensor);
/* This step should erase the right context only. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ post.m_lastIteration = false;
+ REQUIRE(post.DoPostProcess());
REQUIRE(originalVec != tensorVec);
/* The last ctxLen * 10 elements should be gone. */
- for (size_t i = 0; i < ctxLen; ++i) {
+ for (size_t i = 0; i < outputCtxLen; ++i) {
for (size_t j = 0; j < nCols; ++j) {
- /* Check right context elements are zeroed. */
+ /* Check right context elements are zeroed. Blank token idx should be set to 1 when erasing. */
if (j == blankTokenIdx) {
- CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 1);
+ CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 1);
} else {
- CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 0);
+ CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 0);
}
/* Check left context is preserved. */
@@ -134,46 +155,47 @@
}
/* Check inner elements are preserved. */
- for (size_t i = ctxLen * nCols; i < (ctxLen + innerLen) * nCols; ++i) {
+ for (size_t i = outputCtxLen * nCols; i < (outputCtxLen + innerLen) * nCols; ++i) {
CHECK(tensorVec[i] == originalVec[i]);
}
}
SECTION("Left and right context erase")
{
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
+ tensorShape, 100, tensorVec);
+ arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
std::vector <int8_t> originalVec = tensorVec;
/* This step should erase right context. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ post.m_lastIteration = false;
+ REQUIRE(post.DoPostProcess());
/* Calling it the second time should erase the left context. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ REQUIRE(post.DoPostProcess());
REQUIRE(originalVec != tensorVec);
/* The first and last ctxLen * 10 elements should be gone. */
- for (size_t i = 0; i < ctxLen; ++i) {
+ for (size_t i = 0; i < outputCtxLen; ++i) {
for (size_t j = 0; j < nCols; ++j) {
/* Check left and right context elements are zeroed. */
if (j == blankTokenIdx) {
- CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 1);
+ CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 1);
CHECK(tensorVec[i*nCols + j] == 1);
} else {
- CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 0);
+ CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 0);
CHECK(tensorVec[i*nCols + j] == 0);
}
}
}
/* Check inner elements are preserved. */
- for (size_t i = ctxLen * nCols; i < (ctxLen + innerLen) * nCols; ++i) {
+ for (size_t i = outputCtxLen * nCols; i < (outputCtxLen + innerLen) * nCols; ++i) {
/* Check left context is preserved. */
CHECK(tensorVec[i] == originalVec[i]);
}
@@ -181,18 +203,20 @@
SECTION("Try left context erase")
{
- /* Should not be able to erase the left context if it is the first iteration. */
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
+ tensorShape, 100, tensorVec);
+
+ /* Should not be able to erase the left context if it is the first iteration. */
+ arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
std::vector <int8_t> originalVec = tensorVec;
/* Calling it the second time should erase the left context. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, true));
+ post.m_lastIteration = true;
+ REQUIRE(post.DoPostProcess());
REQUIRE(originalVec == tensorVec);
}
diff --git a/tests/use_case/asr/Wav2LetterPreprocessingTest.cc b/tests/use_case/asr/Wav2LetterPreprocessingTest.cc
index 457257f..0280af6 100644
--- a/tests/use_case/asr/Wav2LetterPreprocessingTest.cc
+++ b/tests/use_case/asr/Wav2LetterPreprocessingTest.cc
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * 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");
@@ -24,55 +24,46 @@
/* Test vector output: generated using test-asr-preprocessing.py. */
int8_t expectedResult[numMfccVectors][numMfccFeatures * 3] = {
- /* Feature vec 0. */
- -32, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11, /* MFCCs. */
- -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, /* Delta 1. */
- -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, /* Delta 2. */
-
- /* Feature vec 1. */
- -31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
- -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
- -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
- /* Feature vec 2. */
- -31, 4, -9, -9, -10, -10, -11, -11, -11, -11, -12, -12, -12,
- -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
- -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
- /* Feature vec 3. */
- -31, 4, -9, -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
- -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
- -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
- /* Feature vec 4 : this should have valid delta 1 and delta 2. */
- -31, 4, -9, -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
- -38, -29, -9, 1, -2, -7, -8, -8, -12, -16, -14, -5, 5,
- -68, -50, -13, 5, 0, -9, -9, -8, -13, -20, -19, -3, 15,
-
- /* Feature vec 5 : this should have valid delta 1 and delta 2. */
- -31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -11, -12, -12,
- -62, -45, -11, 5, 0, -8, -9, -8, -12, -19, -17, -3, 13,
- -27, -22, -13, -9, -11, -12, -12, -11, -11, -13, -13, -10, -6,
-
- /* Feature vec 6. */
- -31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
- -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
- -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
- /* Feature vec 7. */
- -32, 4, -9, -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
- -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
- -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
- /* Feature vec 8. */
- -32, 4, -9, -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
- -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
- -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
-
- /* Feature vec 9. */
- -31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
- -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
- -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10
+ /* Feature vec 0. */
+ {-32, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11, /* MFCCs. */
+ -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, /* Delta 1. */
+ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10}, /* Delta 2. */
+ /* Feature vec 1. */
+ {-31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
+ -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+ /* Feature vec 2. */
+ {-31, 4, -9, -9, -10, -10, -11, -11, -11, -11, -12, -12, -12,
+ -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+ /* Feature vec 3. */
+ {-31, 4, -9, -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
+ -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+ /* Feature vec 4 : this should have valid delta 1 and delta 2. */
+ {-31, 4, -9, -9, -10, -10, -11, -11, -11, -11, -11, -12, -12,
+ -38, -29, -9, 1, -2, -7, -8, -8, -12, -16, -14, -5, 5,
+ -68, -50, -13, 5, 0, -9, -9, -8, -13, -20, -19, -3, 15},
+ /* Feature vec 5 : this should have valid delta 1 and delta 2. */
+ {-31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -11, -12, -12,
+ -62, -45, -11, 5, 0, -8, -9, -8, -12, -19, -17, -3, 13,
+ -27, -22, -13, -9, -11, -12, -12, -11, -11, -13, -13, -10, -6},
+ /* Feature vec 6. */
+ {-31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
+ -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+ /* Feature vec 7. */
+ {-32, 4, -9, -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
+ -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+ /* Feature vec 8. */
+ {-32, 4, -9, -8, -10, -10, -11, -11, -11, -12, -12, -11, -11,
+ -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10},
+ /* Feature vec 9. */
+ {-31, 4, -9, -8, -10, -10, -11, -11, -11, -11, -12, -11, -11,
+ -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11, -11,
+ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10}
};
void PopulateTestWavVector(std::vector<int16_t>& vec)
@@ -97,15 +88,16 @@
TEST_CASE("Preprocessing calculation INT8")
{
/* Constants. */
- const uint32_t windowLen = 512;
- const uint32_t windowStride = 160;
- int dimArray[] = {3, 1, numMfccFeatures * 3, numMfccVectors};
- const float quantScale = 0.1410219967365265;
- const int quantOffset = -11;
+ const uint32_t mfccWindowLen = 512;
+ const uint32_t mfccWindowStride = 160;
+ int dimArray[] = {3, 1, numMfccFeatures * 3, numMfccVectors};
+ const float quantScale = 0.1410219967365265;
+ const int quantOffset = -11;
/* Test wav memory. */
- std::vector <int16_t> testWav((windowStride * numMfccVectors) +
- (windowLen - windowStride));
+ std::vector<int16_t> testWav((mfccWindowStride * numMfccVectors) +
+ (mfccWindowLen - mfccWindowStride)
+ );
/* Populate with dummy input. */
PopulateTestWavVector(testWav);
@@ -115,20 +107,20 @@
/* Initialise dimensions and the test tensor. */
TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
- TfLiteTensor tensor = tflite::testing::CreateQuantizedTensor(
- tensorVec.data(), dims, quantScale, quantOffset, "preprocessedInput");
+ TfLiteTensor inputTensor = tflite::testing::CreateQuantizedTensor(
+ tensorVec.data(), dims, quantScale, quantOffset, "preprocessedInput");
/* Initialise pre-processing module. */
- arm::app::audio::asr::Preprocess prep{
- numMfccFeatures, windowLen, windowStride, numMfccVectors};
+ arm::app::ASRPreProcess prep{&inputTensor,
+ numMfccFeatures, numMfccVectors, mfccWindowLen, mfccWindowStride};
/* Invoke pre-processing. */
- REQUIRE(prep.Invoke(testWav.data(), testWav.size(), &tensor));
+ REQUIRE(prep.DoPreProcess(testWav.data(), testWav.size()));
/* Wrap the tensor with a std::vector for ease. */
- auto* tensorData = tflite::GetTensorData<int8_t>(&tensor);
+ auto* tensorData = tflite::GetTensorData<int8_t>(&inputTensor);
std::vector <int8_t> vecResults =
- std::vector<int8_t>(tensorData, tensorData + tensor.bytes);
+ std::vector<int8_t>(tensorData, tensorData + inputTensor.bytes);
/* Check sizes. */
REQUIRE(vecResults.size() == sizeof(expectedResult));