alexander | 3c79893 | 2021-03-26 21:42:19 +0000 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2021 Arm Limited. All rights reserved. |
| 3 | * SPDX-License-Identifier: Apache-2.0 |
| 4 | * |
| 5 | * Licensed under the Apache License, Version 2.0 (the "License"); |
| 6 | * you may not use this file except in compliance with the License. |
| 7 | * You may obtain a copy of the License at |
| 8 | * |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | * |
| 11 | * Unless required by applicable law or agreed to in writing, software |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | * See the License for the specific language governing permissions and |
| 15 | * limitations under the License. |
| 16 | */ |
| 17 | #include "DataStructures.hpp" |
| 18 | #include "AsrGoldenFeatures.hpp" |
| 19 | #include "hal.h" |
| 20 | #include "TensorFlowLiteMicro.hpp" |
| 21 | #include "Wav2LetterPreprocess.hpp" |
| 22 | |
| 23 | #include <catch.hpp> |
| 24 | #include <random> |
| 25 | |
| 26 | class TestPreprocess : public arm::app::audio::asr::Preprocess { |
| 27 | public: |
| 28 | TestPreprocess() |
| 29 | : arm::app::audio::asr::Preprocess(0,0,0,0) |
| 30 | {} |
| 31 | |
| 32 | bool ComputeDeltas(arm::app::Array2d<float>& mfcc, |
| 33 | arm::app::Array2d<float>& delta1, |
| 34 | arm::app::Array2d<float>& delta2) |
| 35 | { |
| 36 | return this->_ComputeDeltas(mfcc, delta1, delta2); |
| 37 | } |
| 38 | |
| 39 | float GetMean(arm::app::Array2d<float>& vec) |
| 40 | { |
| 41 | return this->_GetMean(vec); |
| 42 | } |
| 43 | |
| 44 | float GetStdDev(arm::app::Array2d<float>& vec, const float mean) |
| 45 | { |
| 46 | return this->_GetStdDev(vec, mean); |
| 47 | } |
| 48 | |
| 49 | void NormaliseVec(arm::app::Array2d<float>& vec) |
| 50 | { |
| 51 | return this->_NormaliseVec(vec); |
| 52 | } |
| 53 | }; |
| 54 | |
| 55 | template<class T> |
| 56 | void CheckOutputs(const std::vector<T> goldenOutput, std::vector<T> output) |
| 57 | { |
| 58 | const size_t goldenSize = goldenOutput.size(); |
| 59 | const size_t realSize = output.size(); |
| 60 | |
| 61 | REQUIRE(realSize == goldenSize); |
| 62 | REQUIRE_THAT(output, Catch::Approx( goldenOutput ).margin(0.0001)); |
| 63 | } |
| 64 | template void CheckOutputs<float>(const std::vector<float> goldenOutput, std::vector<float> output); |
| 65 | |
| 66 | void populateBuffer(const float* input, size_t size, size_t numMfccFeats, std::vector<std::vector<float>>& buf) |
| 67 | { |
| 68 | size_t time = 0; |
| 69 | for (size_t i = 0; i < size; ++i) { |
| 70 | if (i > 0 && i % numMfccFeats == 0) { |
| 71 | ++time; |
| 72 | } |
| 73 | float featureValue = *(input + i); |
| 74 | buf[i % numMfccFeats][time] = featureValue; |
| 75 | } |
| 76 | } |
| 77 | |
| 78 | void populateArray2dWithVectorOfVector(std::vector<std::vector<float>> vec, arm::app::Array2d<float>& buf) |
| 79 | { |
| 80 | for (size_t i = 0; i < vec.size(); ++i) { |
| 81 | for (size_t j = 0; j < vec[i].size(); ++j) { |
| 82 | buf(i, j) = vec[i][j]; |
| 83 | } |
| 84 | } |
| 85 | } |
| 86 | |
| 87 | TEST_CASE("Floating point asr features calculation", "[ASR]") |
| 88 | { |
| 89 | TestPreprocess tp; |
| 90 | |
| 91 | SECTION("First and second diff") |
| 92 | { |
| 93 | constexpr uint32_t numMfccFeats = 13; |
| 94 | constexpr uint32_t numFeatVectors = 296; |
| 95 | |
| 96 | arm::app::Array2d<float> mfccBuf(numMfccFeats, numFeatVectors); |
| 97 | arm::app::Array2d<float> delta1Buf(numMfccFeats, numFeatVectors); |
| 98 | arm::app::Array2d<float> delta2Buf(numMfccFeats, numFeatVectors); |
| 99 | |
| 100 | std::vector<std::vector<float>> goldenMfccBuf(numMfccFeats, std::vector<float>(numFeatVectors)); |
| 101 | std::vector<std::vector<float>> goldenDelta1Buf(numMfccFeats, std::vector<float>(numFeatVectors)); |
| 102 | std::vector<std::vector<float>> goldenDelta2Buf(numMfccFeats, std::vector<float>(numFeatVectors)); |
| 103 | |
| 104 | populateBuffer(golden_asr_mfcc, golden_asr_mfcc_len, numMfccFeats, goldenMfccBuf); |
| 105 | populateBuffer(golden_diff1_features, golden_diff1_len, numMfccFeats, goldenDelta1Buf); |
| 106 | populateBuffer(golden_diff2_features, golden_diff2_len, numMfccFeats, goldenDelta2Buf); |
| 107 | |
| 108 | populateArray2dWithVectorOfVector(goldenMfccBuf, mfccBuf); |
| 109 | std::fill(delta1Buf.begin(), delta1Buf.end(), 0.f); |
| 110 | std::fill(delta2Buf.begin(), delta2Buf.end(), 0.f); |
| 111 | |
| 112 | tp.ComputeDeltas(mfccBuf, delta1Buf, delta2Buf); |
| 113 | |
| 114 | /* First 4 and last 4 values are different because we pad AFTER diff calculated. */ |
| 115 | for (size_t i = 0; i < numMfccFeats; ++i) { |
| 116 | const float* start_goldenDelta1Buf = goldenDelta1Buf[i].data() + 4; |
| 117 | const float* start_delta1 = delta1Buf.begin() + i * delta1Buf.size(1) + 4; |
| 118 | std::vector<float> goldenDataDelta1(start_goldenDelta1Buf, start_goldenDelta1Buf + numFeatVectors - 8); |
| 119 | std::vector<float> tensorDataDelta1(start_delta1, start_delta1 + numFeatVectors - 8); |
| 120 | |
| 121 | CheckOutputs<float>(goldenDataDelta1,tensorDataDelta1); |
| 122 | |
| 123 | const float* start_goldenDelta2Buf = goldenDelta2Buf[i].data() + 4; |
| 124 | const float* start_delta2 = delta2Buf.begin() + i * delta2Buf.size(1) + 4; |
| 125 | std::vector<float> goldenDataDelta2(start_goldenDelta2Buf, start_goldenDelta2Buf + numFeatVectors - 8); |
| 126 | std::vector<float> tensorDataDelta2(start_delta2, start_delta2 + numFeatVectors - 8); |
| 127 | |
| 128 | CheckOutputs<float>(goldenDataDelta2,tensorDataDelta2); |
| 129 | } |
| 130 | |
| 131 | } |
| 132 | |
| 133 | SECTION("Mean") |
| 134 | { |
| 135 | std::vector<std::vector<float>> mean1vec{{1, 2}, |
| 136 | {-1, -2}}; |
| 137 | arm::app::Array2d<float> mean1(2,2); /* {{1, 2},{-1, -2}} */ |
| 138 | populateArray2dWithVectorOfVector(mean1vec, mean1); |
| 139 | REQUIRE(0 == Approx(tp.GetMean(mean1))); |
| 140 | |
| 141 | arm::app::Array2d<float> mean2(2, 2); |
| 142 | std::fill(mean2.begin(), mean2.end(), 0.f); |
| 143 | REQUIRE(0 == Approx(tp.GetMean(mean2))); |
| 144 | |
| 145 | arm::app::Array2d<float> mean3(3,3); |
| 146 | std::fill(mean3.begin(), mean3.end(), 1.f); |
| 147 | REQUIRE(1 == Approx(tp.GetMean(mean3))); |
| 148 | } |
| 149 | |
| 150 | SECTION("Std") |
| 151 | { |
| 152 | arm::app::Array2d<float> std1(2, 2); |
| 153 | std::fill(std1.begin(), std1.end(), 0.f); /* {{0, 0}, {0, 0}} */ |
| 154 | REQUIRE(0 == Approx(tp.GetStdDev(std1, 0))); |
| 155 | |
| 156 | std::vector<std::vector<float>> std2vec{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 0}}; |
| 157 | arm::app::Array2d<float> std2(2,5); |
| 158 | populateArray2dWithVectorOfVector(std2vec, std2); |
| 159 | const float mean = tp.GetMean(std2); |
| 160 | REQUIRE(2.872281323 == Approx(tp.GetStdDev(std2, mean))); |
| 161 | |
| 162 | arm::app::Array2d<float> std3(2,2); |
| 163 | std::fill(std3.begin(), std3.end(), 1.f); /* std3{{1, 1}, {1, 1}}; */ |
| 164 | REQUIRE(0 == Approx(tp.GetStdDev(std3, 1))); |
| 165 | } |
| 166 | |
| 167 | SECTION("Norm") { |
| 168 | auto checker = [&](arm::app::Array2d<float>& d, std::vector<float>& g) { |
| 169 | tp.NormaliseVec(d); |
| 170 | std::vector<float> d_vec(d.begin(), d.end()); |
| 171 | REQUIRE_THAT(g, Catch::Approx(d_vec)); |
| 172 | }; |
| 173 | |
| 174 | std::vector<std::vector<float>> norm0vec{{1, 1}, {1, 1}}; |
| 175 | std::vector<float> goldenNorm0 {0, 0, 0, 0}; |
| 176 | arm::app::Array2d<float> norm0(2, 2); |
| 177 | populateArray2dWithVectorOfVector(norm0vec, norm0); |
| 178 | checker(norm0, goldenNorm0); |
| 179 | |
| 180 | std::vector<std::vector<float>> norm1vec{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 0}}; |
| 181 | std::vector<float> goldenNorm1 { |
| 182 | -1.218543592, -0.87038828, -0.522232968, -0.174077656, 0.174077656, |
| 183 | 0.522232968, 0.87038828, 1.218543592, 1.566698904, -1.566698904}; |
| 184 | arm::app::Array2d<float> norm1(2, 5); |
| 185 | populateArray2dWithVectorOfVector(norm1vec, norm1); |
| 186 | checker(norm1, goldenNorm1); |
| 187 | } |
| 188 | } |