MLECO-3173: Add AD, KWS_ASR and Noise reduction use case API's
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I36f61ce74bf17f7b327cdae9704a22ca54144f37
diff --git a/tests/use_case/ad/PostProcessTests.cc b/tests/use_case/ad/PostProcessTests.cc
deleted file mode 100644
index 62fa9e7..0000000
--- a/tests/use_case/ad/PostProcessTests.cc
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Copyright (c) 2021 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 "AdPostProcessing.hpp"
-#include <catch.hpp>
-
-TEST_CASE("Softmax_vector") {
-
- std::vector<float> testVec = {1, 2, 3, 4, 1, 2, 3};
- arm::app::Softmax(testVec);
- CHECK((testVec[0] - 0.024) == Approx(0.0).margin(0.001));
- CHECK((testVec[1] - 0.064) == Approx(0.0).margin(0.001));
- CHECK((testVec[2] - 0.175) == Approx(0.0).margin(0.001));
- CHECK((testVec[3] - 0.475) == Approx(0.0).margin(0.001));
- CHECK((testVec[4] - 0.024) == Approx(0.0).margin(0.001));
- CHECK((testVec[5] - 0.064) == Approx(0.0).margin(0.001));
- CHECK((testVec[6] - 0.175) == Approx(0.0).margin(0.001));
-}
-
-TEST_CASE("Output machine index") {
-
- auto index = arm::app::OutputIndexFromFileName("test_id_00.wav");
- CHECK(index == 0);
-
- auto index1 = arm::app::OutputIndexFromFileName("test_id_02.wav");
- CHECK(index1 == 1);
-
- auto index2 = arm::app::OutputIndexFromFileName("test_id_4.wav");
- CHECK(index2 == 2);
-
- auto index3 = arm::app::OutputIndexFromFileName("test_id_6.wav");
- CHECK(index3 == 3);
-
- auto index4 = arm::app::OutputIndexFromFileName("test_id_id_00.wav");
- CHECK(index4 == -1);
-
- auto index5 = arm::app::OutputIndexFromFileName("test_id_7.wav");
- CHECK(index5 == -1);
-}
\ No newline at end of file
diff --git a/tests/use_case/kws_asr/MfccTests.cc b/tests/use_case/kws_asr/MfccTests.cc
index 3ebdcf4..883c215 100644
--- a/tests/use_case/kws_asr/MfccTests.cc
+++ b/tests/use_case/kws_asr/MfccTests.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");
@@ -93,13 +93,13 @@
-22.67135, -0.61615, 2.07233, 0.58137, 1.01655, 0.85816, 0.46039, 0.03393, 1.16511, 0.0072,
};
-arm::app::audio::MicroNetMFCC GetMFCCInstance() {
- const int sampFreq = arm::app::audio::MicroNetMFCC::ms_defaultSamplingFreq;
+arm::app::audio::MicroNetKwsMFCC GetMFCCInstance() {
+ const int sampFreq = arm::app::audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
const int frameLenMs = 40;
const int frameLenSamples = sampFreq * frameLenMs * 0.001;
const int numMfccFeats = 10;
- return arm::app::audio::MicroNetMFCC(numMfccFeats, frameLenSamples);
+ return arm::app::audio::MicroNetKwsMFCC(numMfccFeats, frameLenSamples);
}
template <class T>
diff --git a/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc b/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
index 6fd7df3..e343b66 100644
--- a/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
+++ b/tests/use_case/kws_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");
@@ -16,15 +16,17 @@
*/
#include "Wav2LetterPostprocess.hpp"
#include "Wav2LetterModel.hpp"
+#include "ClassificationResult.hpp"
#include <algorithm>
#include <catch.hpp>
#include <limits>
template <typename T>
-static TfLiteTensor GetTestTensor(std::vector <int>& shape,
- T initVal,
- std::vector<T>& vectorBuf)
+static TfLiteTensor GetTestTensor(
+ std::vector<int>& shape,
+ T initVal,
+ std::vector<T>& vectorBuf)
{
REQUIRE(0 != shape.size());
@@ -38,91 +40,112 @@
vectorBuf = std::vector<T>(sizeInBytes, initVal);
TfLiteIntArray* dims = tflite::testing::IntArrayFromInts(shape.data());
return tflite::testing::CreateQuantizedTensor(
- vectorBuf.data(), dims,
- 1, 0, "test-tensor");
+ vectorBuf.data(), dims,
+ 1, 0, "test-tensor");
}
TEST_CASE("Checking return value")
{
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", "$"};
+ const uint32_t blankTokenIdx = 2;
+ std::vector<arm::app::ClassificationResult> dummyResult;
std::vector <int> tensorShape = {1, 1, 1, 13};
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
- REQUIRE(false == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ tensorShape, 100, tensorVec);
+
+ arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
+
+ REQUIRE(!post.DoPostProcess());
}
SECTION("Post processing succeeds")
{
- const uint32_t ctxLen = 5;
- const uint32_t innerLen = 3;
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, 0};
-
- std::vector <int> tensorShape = {1, 1, 13, 1};
- std::vector <int8_t> tensorVec;
+ const uint32_t outputCtxLen = 5;
+ arm::app::AsrClassifier classifier;
+ arm::app::Wav2LetterModel model;
+ model.Init();
+ std::vector<std::string> dummyLabels = {"a", "b", "$"};
+ 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{&tensor, classifier, 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{&tensor, classifier, 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{&tensor, classifier, 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 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. */
@@ -131,45 +154,47 @@
}
/* 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);
+ TfLiteTensor tensor = GetTestTensor<int8_t>(
+ tensorShape, 100, tensorVec);
+ arm::app::AsrPostProcess post{&tensor, classifier, 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[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[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]);
}
@@ -177,18 +202,21 @@
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{&tensor, classifier, 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);
}
-}
\ No newline at end of file
+}
diff --git a/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc b/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc
index 26ddb24..372152d 100644
--- a/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc
+++ b/tests/use_case/kws_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");
@@ -16,64 +16,54 @@
*/
#include "Wav2LetterPreprocess.hpp"
-#include <algorithm>
-#include <catch.hpp>
#include <limits>
+#include <catch.hpp>
constexpr uint32_t numMfccFeatures = 13;
constexpr uint32_t numMfccVectors = 10;
/* 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
+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}
};
void PopulateTestWavVector(std::vector<int16_t>& vec)
@@ -97,17 +87,17 @@
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);
@@ -117,20 +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. */
- int8_t * 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));
diff --git a/tests/use_case/noise_reduction/RNNoiseProcessingTests.cpp b/tests/use_case/noise_reduction/RNNoiseProcessingTests.cpp
index e28a6da..ca5aab1 100644
--- a/tests/use_case/noise_reduction/RNNoiseProcessingTests.cpp
+++ b/tests/use_case/noise_reduction/RNNoiseProcessingTests.cpp
@@ -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");
@@ -14,7 +14,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-#include "RNNoiseProcess.hpp"
+#include "RNNoiseFeatureProcessor.hpp"
#include <catch.hpp>
#include <limits>
@@ -208,7 +208,7 @@
{
SECTION("FP32")
{
- arm::app::rnn::RNNoiseProcess rnnoiseProcessor;
+ arm::app::rnn::RNNoiseFeatureProcessor rnnoiseProcessor;
arm::app::rnn::FrameFeatures features;
rnnoiseProcessor.PreprocessFrame(testWav0.data(), testWav0.size(), features);
@@ -223,7 +223,7 @@
TEST_CASE("RNNoise postprocessing test", "[RNNoise]")
{
- arm::app::rnn::RNNoiseProcess rnnoiseProcessor;
+ arm::app::rnn::RNNoiseFeatureProcessor rnnoiseProcessor;
arm::app::rnn::FrameFeatures p;
rnnoiseProcessor.PreprocessFrame(testWav0.data(), testWav0.size(), p);
std::vector<float> denoised(testWav0.size());