Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/tests/use_case/kws_asr/InferenceTestDSCNN.cc b/tests/use_case/kws_asr/InferenceTestDSCNN.cc
new file mode 100644
index 0000000..f0e5c02
--- /dev/null
+++ b/tests/use_case/kws_asr/InferenceTestDSCNN.cc
@@ -0,0 +1,111 @@
+/*
+ * 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 "DsCnnModel.hpp"
+#include "hal.h"
+#include "TestData_kws.hpp"
+#include "TensorFlowLiteMicro.hpp"
+
+#include <catch.hpp>
+#include <random>
+
+namespace arm {
+namespace app {
+namespace kws {
+bool RunInference(arm::app::Model& model, const int8_t vec[])
+{
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ REQUIRE(inputTensor);
+
+ const size_t copySz = inputTensor->bytes < IFM_DATA_SIZE ?
+ inputTensor->bytes :
+ IFM_DATA_SIZE;
+ memcpy(inputTensor->data.data, vec, copySz);
+
+ return model.RunInference();
+}
+
+bool RunInferenceRandom(arm::app::Model& model)
+{
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ REQUIRE(inputTensor);
+
+ std::random_device rndDevice;
+ std::mt19937 mersenneGen{rndDevice()};
+ std::uniform_int_distribution<short> dist {-128, 127};
+
+ auto gen = [&dist, &mersenneGen](){
+ return dist(mersenneGen);
+ };
+
+ std::vector<int8_t> randomAudio(inputTensor->bytes);
+ std::generate(std::begin(randomAudio), std::end(randomAudio), gen);
+
+ REQUIRE(RunInference(model, randomAudio.data()));
+ return true;
+}
+
+template<typename T>
+void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model)
+{
+ REQUIRE(RunInference(model, input_goldenFV));
+
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+
+ REQUIRE(outputTensor);
+ REQUIRE(outputTensor->bytes == OFM_DATA_SIZE);
+ auto tensorData = tflite::GetTensorData<T>(outputTensor);
+ REQUIRE(tensorData);
+
+ for (size_t i = 0; i < outputTensor->bytes; i++) {
+ REQUIRE((int)tensorData[i] == (int)((T)output_goldenFV[i]));
+ }
+}
+
+TEST_CASE("Running random inference with Tflu and DsCnnModel Int8", "[DS_CNN]")
+{
+ arm::app::DsCnnModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ REQUIRE(RunInferenceRandom(model));
+}
+
+TEST_CASE("Running inference with Tflu and DsCnnModel Uint8", "[DS_CNN]")
+{
+ for (uint32_t i = 0 ; i < NUMBER_OF_FM_FILES; ++i) {
+ const int8_t* input_goldenFV = get_ifm_data_array(i);
+ const int8_t* output_goldenFV = get_ofm_data_array(i);
+
+ DYNAMIC_SECTION("Executing inference with re-init")
+ {
+ arm::app::DsCnnModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ TestInference<int8_t>(input_goldenFV, output_goldenFV, model);
+
+ }
+ }
+}
+
+} //namespace
+} //namespace
+} //namespace
diff --git a/tests/use_case/kws_asr/InferenceTestWav2Letter.cc b/tests/use_case/kws_asr/InferenceTestWav2Letter.cc
new file mode 100644
index 0000000..ee63c2f
--- /dev/null
+++ b/tests/use_case/kws_asr/InferenceTestWav2Letter.cc
@@ -0,0 +1,114 @@
+/*
+ * 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 "hal.h"
+#include "TensorFlowLiteMicro.hpp"
+#include "Wav2LetterModel.hpp"
+#include "TestData_asr.hpp"
+
+#include <catch.hpp>
+#include <random>
+
+namespace arm {
+namespace app {
+namespace asr {
+
+bool RunInference(arm::app::Model& model, const int8_t vec[], const size_t copySz)
+{
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ REQUIRE(inputTensor);
+
+ memcpy(inputTensor->data.data, vec, copySz);
+
+ return model.RunInference();
+}
+
+bool RunInferenceRandom(arm::app::Model& model)
+{
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ REQUIRE(inputTensor);
+
+ std::random_device rndDevice;
+ std::mt19937 mersenneGen{rndDevice()};
+ std::uniform_int_distribution<short> dist {-128, 127};
+
+ auto gen = [&dist, &mersenneGen](){
+ return dist(mersenneGen);
+ };
+
+ std::vector<int8_t> randomAudio(inputTensor->bytes);
+ std::generate(std::begin(randomAudio), std::end(randomAudio), gen);
+
+ REQUIRE(RunInference(model, randomAudio.data(), inputTensor->bytes));
+ return true;
+}
+
+/* Skip this test, Wav2LetterModel if not Vela optimized but only from ML-zoo will fail. */
+TEST_CASE("Running random inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter][.]")
+{
+ arm::app::Wav2LetterModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ REQUIRE(RunInferenceRandom(model));
+}
+
+
+template<typename T>
+void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model)
+{
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ REQUIRE(inputTensor);
+
+ REQUIRE(RunInference(model, input_goldenFV, inputTensor->bytes));
+
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+
+ REQUIRE(outputTensor);
+ REQUIRE(outputTensor->bytes == OFM_DATA_SIZE);
+ auto tensorData = tflite::GetTensorData<T>(outputTensor);
+ REQUIRE(tensorData);
+
+ for (size_t i = 0; i < outputTensor->bytes; i++) {
+ REQUIRE((int)tensorData[i] == (int)((T)output_goldenFV[i]));
+ }
+}
+
+TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter][.]")
+{
+ for (uint32_t i = 0 ; i < NUMBER_OF_FM_FILES; ++i) {
+ auto input_goldenFV = get_ifm_data_array(i);;
+ auto output_goldenFV = get_ofm_data_array(i);
+
+ DYNAMIC_SECTION("Executing inference with re-init")
+ {
+ arm::app::Wav2LetterModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ TestInference<int8_t>(input_goldenFV, output_goldenFV, model);
+
+ }
+ }
+}
+
+} //namespace
+} //namespace
+} //namespace
diff --git a/tests/use_case/kws_asr/InitModels.cc b/tests/use_case/kws_asr/InitModels.cc
new file mode 100644
index 0000000..770944d
--- /dev/null
+++ b/tests/use_case/kws_asr/InitModels.cc
@@ -0,0 +1,52 @@
+/*
+ * 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 "DsCnnModel.hpp"
+#include "Wav2LetterModel.hpp"
+
+#include <catch.hpp>
+
+/* Skip this test, Wav2LetterModel if not Vela optimized but only from ML-zoo will fail. */
+TEST_CASE("Init two Models", "[.]")
+{
+ arm::app::DsCnnModel model1;
+ arm::app::DsCnnModel model2;
+
+ /* Ideally we should load the wav2letter model here, but there is
+ * none available to run on native (ops not supported on unoptimised
+ * version). However, we can certainly create two instances of the
+ * same type of model to see if our tensor arena re-use works as
+ * intended.
+ *
+ * @TODO: uncomment this when this model can run on native pipeline. */
+ //arm::app::Wav2LetterModel model2; /* model2. */
+
+ /* Load/initialise the first model. */
+ REQUIRE(model1.Init());
+
+ /* Allocator instance should have been created. */
+ REQUIRE(nullptr != model1.GetAllocator());
+
+ /* Load the second model using the same allocator as model 1. */
+ REQUIRE(model2.Init(model1.GetAllocator()));
+
+ /* Make sure they point to the same allocator object. */
+ REQUIRE(model1.GetAllocator() == model2.GetAllocator());
+
+ /* Both models should report being initialised. */
+ REQUIRE(true == model1.IsInited());
+ REQUIRE(true == model2.IsInited());
+}
\ No newline at end of file
diff --git a/tests/use_case/kws_asr/KwsAsrTests.cc b/tests/use_case/kws_asr/KwsAsrTests.cc
new file mode 100644
index 0000000..09f82da
--- /dev/null
+++ b/tests/use_case/kws_asr/KwsAsrTests.cc
@@ -0,0 +1,18 @@
+/*
+ * 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.
+ */
+#define CATCH_CONFIG_MAIN
+#include <catch.hpp>
diff --git a/tests/use_case/kws_asr/MfccTests.cc b/tests/use_case/kws_asr/MfccTests.cc
new file mode 100644
index 0000000..9509519
--- /dev/null
+++ b/tests/use_case/kws_asr/MfccTests.cc
@@ -0,0 +1,156 @@
+/*
+ * 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 "DsCnnMfcc.hpp"
+
+#include <algorithm>
+#include <catch.hpp>
+#include <limits>
+
+/* First 640 samples from yes.wav. */
+const std::vector<int16_t> testWav = std::vector<int16_t>{
+ 139, 143, 164, 163, 157, 156, 151, 148, 172, 171,
+ 165, 169, 149, 142, 145, 147, 166, 146, 112, 132,
+ 132, 136, 165, 176, 176, 152, 138, 158, 179, 185,
+ 183, 148, 121, 130, 167, 204, 163, 132, 165, 184,
+ 193, 205, 210, 204, 195, 178, 168, 197, 207, 201,
+ 197, 177, 185, 196, 191, 198, 196, 183, 193, 181,
+ 157, 170, 167, 159, 164, 152, 146, 167, 180, 171,
+ 194, 232, 204, 173, 171, 172, 184, 169, 175, 199,
+ 200, 195, 185, 214, 214, 193, 196, 191, 204, 191,
+ 172, 187, 183, 192, 203, 172, 182, 228, 232, 205,
+ 177, 174, 191, 210, 210, 211, 197, 177, 198, 217,
+ 233, 236, 203, 191, 169, 145, 149, 161, 198, 206,
+ 176, 137, 142, 181, 200, 215, 201, 188, 166, 162,
+ 184, 155, 135, 132, 126, 142, 169, 184, 172, 156,
+ 132, 119, 150, 147, 154, 160, 125, 130, 137, 154,
+ 161, 168, 195, 182, 160, 134, 138, 146, 130, 120,
+ 101, 122, 137, 118, 117, 131, 145, 140, 146, 148,
+ 148, 168, 159, 134, 114, 114, 130, 147, 147, 134,
+ 125, 98, 107, 127, 99, 79, 84, 107, 117, 114,
+ 93, 92, 127, 112, 109, 110, 96, 118, 97, 87,
+ 110, 95, 128, 153, 147, 165, 146, 106, 101, 137,
+ 139, 96, 73, 90, 91, 51, 69, 102, 100, 103,
+ 96, 101, 123, 107, 82, 89, 118, 127, 99, 100,
+ 111, 97, 111, 123, 106, 121, 133, 103, 100, 88,
+ 85, 111, 114, 125, 102, 91, 97, 84, 139, 157,
+ 109, 66, 72, 129, 111, 90, 127, 126, 101, 109,
+ 142, 138, 129, 159, 140, 80, 74, 78, 76, 98,
+ 68, 42, 106, 143, 112, 102, 115, 114, 82, 75,
+ 92, 80, 110, 114, 66, 86, 119, 101, 101, 103,
+ 118, 145, 85, 40, 62, 88, 95, 87, 73, 64,
+ 86, 71, 71, 105, 80, 73, 96, 92, 85, 90,
+ 81, 86, 105, 100, 89, 78, 102, 114, 95, 98,
+ 69, 70, 108, 112, 111, 90, 104, 137, 143, 160,
+ 145, 121, 98, 86, 91, 87, 115, 123, 109, 99,
+ 85, 120, 131, 116, 125, 144, 153, 111, 98, 110,
+ 93, 89, 101, 137, 155, 142, 108, 94, 136, 145,
+ 129, 129, 122, 109, 90, 76, 81, 110, 119, 96,
+ 95, 102, 105, 111, 90, 89, 111, 115, 86, 51,
+ 107, 140, 105, 105, 110, 142, 125, 76, 75, 69,
+ 65, 52, 61, 69, 55, 42, 47, 58, 37, 35,
+ 24, 20, 44, 22, 16, 26, 6, 3, 4, 23,
+ 60, 51, 30, 12, 24, 31, -9, -16, -13, 13,
+ 19, 9, 37, 55, 70, 36, 23, 57, 45, 33,
+ 50, 59, 18, 11, 62, 74, 52, 8, -3, 26,
+ 51, 48, -5, -9, 12, -7, -12, -5, 28, 41,
+ -2, -30, -13, 31, 33, -12, -22, -8, -15, -17,
+ 2, -6, -25, -27, -24, -8, 4, -9, -52, -47,
+ -9, -32, -45, -5, 41, 15, -32, -14, 2, -1,
+ -10, -30, -32, -25, -21, -17, -14, 8, -4, -13,
+ 34, 18, -36, -38, -18, -19, -28, -17, -14, -16,
+ -2, -20, -27, 12, 11, -17, -33, -12, -22, -64,
+ -42, -26, -23, -22, -37, -51, -53, -30, -18, -48,
+ -69, -38, -54, -96, -72, -49, -50, -57, -41, -22,
+ -43, -64, -54, -23, -49, -69, -41, -44, -42, -49,
+ -40, -26, -54, -50, -38, -49, -70, -94, -89, -69,
+ -56, -65, -71, -47, -39, -49, -79, -91, -56, -46,
+ -62, -86, -64, -32, -47, -50, -71, -77, -65, -68,
+ -52, -51, -61, -67, -61, -81, -93, -52, -59, -62,
+ -51, -75, -76, -50, -32, -54, -68, -70, -43, 1,
+ -42, -92, -80, -41, -38, -79, -69, -49, -82, -122,
+ -93, -21, -24, -61, -70, -73, -62, -74, -69, -43,
+ -25, -15, -43, -23, -26, -69, -44, -12, 1, -51,
+ -78, -13, 3, -53, -105, -72, -24, -62, -66, -31,
+ -40, -65, -86, -64, -44, -55, -63, -61, -37, -41,
+};
+
+/* Golden audio ops mfcc output for the above wav. */
+const std::vector<float> testWavMfcc {
+ -22.67135, -0.61615, 2.07233, 0.58137, 1.01655, 0.85816, 0.46039, 0.03393, 1.16511, 0.0072,
+};
+
+arm::app::audio::DsCnnMFCC GetMFCCInstance() {
+ const int sampFreq = arm::app::audio::DsCnnMFCC::ms_defaultSamplingFreq;
+ const int frameLenMs = 40;
+ const int frameLenSamples = sampFreq * frameLenMs * 0.001;
+ const int numMfccFeats = 10;
+
+ return arm::app::audio::DsCnnMFCC(numMfccFeats, frameLenSamples);
+}
+
+template <class T>
+void TestQuntisedMFCC() {
+ const float quantScale = 1.1088106632232666;
+ const int quantOffset = 95;
+ std::vector<T> mfccOutput = GetMFCCInstance().MfccComputeQuant<T>(testWav, quantScale, quantOffset);
+
+ const long min_val = std::numeric_limits<T>::min();
+ const long max_val = std::numeric_limits<T>::max();
+
+ for (size_t i = 0; i < testWavMfcc.size(); ++i){
+ long TestWavMfcc = (std::lround((testWavMfcc[i] / quantScale) + quantOffset));
+ T quantizedTestWavMfcc = static_cast<T>(std::max(min_val, std::min(TestWavMfcc, max_val)));
+
+ REQUIRE(quantizedTestWavMfcc == Approx(mfccOutput[i]).margin(0));
+ }
+}
+template void TestQuntisedMFCC<int8_t>();
+template void TestQuntisedMFCC<uint8_t>();
+template void TestQuntisedMFCC<int16_t>();
+
+TEST_CASE("MFCC calculation test")
+{
+ hal_platform platform;
+ data_acq_module dataAcq;
+ data_psn_module dataPsn;
+ platform_timer timer;
+
+ /* Initialise the HAL and platform. */
+ hal_init(&platform, &dataAcq, &dataPsn, &timer);
+ hal_platform_init(&platform);
+
+ SECTION("FP32")
+ {
+ auto mfccOutput = GetMFCCInstance().MfccCompute(testWav);
+ REQUIRE_THAT( mfccOutput, Catch::Approx( testWavMfcc ).margin(0.0001) );
+ }
+
+ SECTION("int8_t")
+ {
+ TestQuntisedMFCC<int8_t>();
+ }
+
+ SECTION("uint8_t")
+ {
+ TestQuntisedMFCC<uint8_t>();
+ }
+
+ SECTION("MFCC quant calculation test - int16_t")
+ {
+ TestQuntisedMFCC<int16_t>();
+ }
+}
\ No newline at end of file
diff --git a/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc b/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
new file mode 100644
index 0000000..6fd7df3
--- /dev/null
+++ b/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
@@ -0,0 +1,194 @@
+/*
+ * 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 "Wav2LetterPostprocess.hpp"
+#include "Wav2LetterModel.hpp"
+
+#include <algorithm>
+#include <catch.hpp>
+#include <limits>
+
+template <typename T>
+static TfLiteTensor GetTestTensor(std::vector <int>& shape,
+ T initVal,
+ std::vector<T>& vectorBuf)
+{
+ REQUIRE(0 != shape.size());
+
+ shape.insert(shape.begin(), shape.size());
+ uint32_t sizeInBytes = sizeof(T);
+ for (size_t i = 1; i < shape.size(); ++i) {
+ sizeInBytes *= shape[i];
+ }
+
+ /* Allocate mem. */
+ vectorBuf = std::vector<T>(sizeInBytes, initVal);
+ TfLiteIntArray* dims = tflite::testing::IntArrayFromInts(shape.data());
+ return tflite::testing::CreateQuantizedTensor(
+ 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};
+
+ 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));
+ }
+
+ 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;
+ TfLiteTensor tensor = GetTestTensor<int8_t>(
+ tensorShape, 100, tensorVec);
+
+ /* Copy elements to compare later. */
+ std::vector <int8_t> originalVec = tensorVec;
+
+ /* This step should not erase anything. */
+ REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ }
+}
+
+TEST_CASE("Postprocessing - erasing required elements")
+{
+ constexpr uint32_t ctxLen = 5;
+ constexpr uint32_t innerLen = 3;
+ constexpr uint32_t nRows = 2*ctxLen + innerLen;
+ constexpr uint32_t nCols = 10;
+ constexpr uint32_t blankTokenIdx = nCols - 1;
+ std::vector <int> tensorShape = {1, 1, nRows, nCols};
+
+ 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);
+
+ /* Copy elements to compare later. */
+ std::vector <int8_t> originalVec = tensorVec;
+
+ /* This step should not erase anything. */
+ REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, true));
+ 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);
+
+ /* Copy elements to compare later. */
+ 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));
+ REQUIRE(originalVec != tensorVec);
+
+ /* The last ctxLen * 10 elements should be gone. */
+ for (size_t i = 0; i < ctxLen; ++i) {
+ for (size_t j = 0; j < nCols; ++j) {
+ /* Check right context elements are zeroed. */
+ if (j == blankTokenIdx) {
+ CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 1);
+ } else {
+ CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 0);
+ }
+
+ /* Check left context is preserved. */
+ CHECK(tensorVec[i*nCols + j] == originalVec[i*nCols + j]);
+ }
+ }
+
+ /* Check inner elements are preserved. */
+ for (size_t i = ctxLen * nCols; i < (ctxLen + 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);
+
+ /* 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));
+
+ /* Calling it the second time should erase the left context. */
+ REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+
+ REQUIRE(originalVec != tensorVec);
+
+ /* The first and last ctxLen * 10 elements should be gone. */
+ for (size_t i = 0; i < ctxLen; ++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);
+ } else {
+ CHECK(tensorVec[(ctxLen + 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) {
+ /* Check left context is preserved. */
+ CHECK(tensorVec[i] == originalVec[i]);
+ }
+ }
+
+ 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);
+
+ /* 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));
+ 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
new file mode 100644
index 0000000..e71366a
--- /dev/null
+++ b/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc
@@ -0,0 +1,152 @@
+/*
+ * 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 "Wav2LetterPreprocess.hpp"
+
+#include <algorithm>
+#include <catch.hpp>
+#include <limits>
+
+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
+};
+
+void PopulateTestWavVector(std::vector<int16_t>& vec)
+{
+ constexpr int int16max = std::numeric_limits<int16_t>::max();
+ int val = 0;
+ for (size_t i = 0; i < vec.size(); ++i, ++val) {
+
+ /* We want a differential filter response from both - order 1
+ * and 2 => Don't have a linear signal here - we use a signal
+ * using squares for example. Alternate sign flips might work
+ * just as well and will be computationally less work! */
+ int valsq = val * val;
+ if (valsq > int16max) {
+ val = 0;
+ valsq = 0;
+ }
+ vec[i] = valsq;
+ }
+}
+
+TEST_CASE("Preprocessing calculation INT8")
+{
+ /* Initialise the HAL and platform. */
+ hal_platform platform;
+ data_acq_module data_acq;
+ data_psn_module data_psn;
+ platform_timer timer;
+ hal_init(&platform, &data_acq, &data_psn, &timer);
+ hal_platform_init(&platform);
+
+ /* Constants. */
+ const uint32_t windowLen = 512;
+ const uint32_t windowStride = 160;
+ const 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));
+
+ /* Populate with dummy input. */
+ PopulateTestWavVector(testWav);
+
+ /* Allocate mem for tensor. */
+ std::vector<int8_t> tensorVec(dimArray[1]*dimArray[2]*dimArray[3]);
+
+ /* Initialise dimensions and the test tensor. */
+ TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
+ TfLiteTensor tensor = tflite::testing::CreateQuantizedTensor(
+ tensorVec.data(), dims, quantScale, quantOffset, "preprocessedInput");
+
+ /* Initialise pre-processing module. */
+ arm::app::audio::asr::Preprocess prep{
+ numMfccFeatures, windowLen, windowStride, numMfccVectors};
+
+ /* Invoke pre-processing. */
+ REQUIRE(prep.Invoke(testWav.data(), testWav.size(), &tensor));
+
+ /* Wrap the tensor with a std::vector for ease. */
+ int8_t * tensorData = tflite::GetTensorData<int8_t>(&tensor);
+ std::vector <int8_t> vecResults =
+ std::vector<int8_t>(tensorData, tensorData + tensor.bytes);
+
+ /* Check sizes. */
+ REQUIRE(vecResults.size() == sizeof(expectedResult));
+
+ /* Check that the elements have been calculated correctly. */
+ for (uint32_t j = 0; j < numMfccVectors; ++j) {
+ for (uint32_t i = 0; i < numMfccFeatures * 3; ++i) {
+ size_t tensorIdx = (j * numMfccFeatures * 3) + i;
+ CHECK(vecResults[tensorIdx] == expectedResult[j][i]);
+ }
+ }
+}