George Gekov | 23c2627 | 2021-08-16 11:32:10 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #include "MathUtils.hpp" |
| 6 | #include <cstring> |
| 7 | #include <cmath> |
| 8 | #include <numeric> |
| 9 | #include <algorithm> |
| 10 | #include <memory> |
| 11 | #include "Wav2LetterPreprocessor.hpp" |
| 12 | #include "Wav2LetterMFCC.hpp" |
| 13 | |
| 14 | float Wav2LetterPreprocessor::GetMean(Array2d<float>& vec) |
| 15 | { |
| 16 | return MathUtils::MeanF32(vec.begin(), vec.totalSize()); |
| 17 | } |
| 18 | |
| 19 | float Wav2LetterPreprocessor::GetStdDev(Array2d<float>& vec, const float mean) |
| 20 | { |
| 21 | return MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean); |
| 22 | } |
| 23 | |
| 24 | void Wav2LetterPreprocessor::NormaliseVec(Array2d<float>& vec) |
| 25 | { |
| 26 | auto mean = Wav2LetterPreprocessor::GetMean(vec); |
| 27 | auto stddev = Wav2LetterPreprocessor::GetStdDev(vec, mean); |
| 28 | |
| 29 | if (stddev == 0) |
| 30 | { |
| 31 | std::fill(vec.begin(), vec.end(), 0); |
| 32 | } |
| 33 | else |
| 34 | { |
| 35 | const float stddevInv = 1.f/stddev; |
| 36 | const float normalisedMean = mean/stddev; |
| 37 | |
| 38 | auto NormalisingFunction = [=](float &value) { |
| 39 | value = value * stddevInv - normalisedMean; |
| 40 | }; |
| 41 | std::for_each(vec.begin(), vec.end(), NormalisingFunction); |
| 42 | } |
| 43 | } |
| 44 | |
| 45 | void Wav2LetterPreprocessor::Normalise() |
| 46 | { |
| 47 | Wav2LetterPreprocessor::NormaliseVec(this->m_mfccBuf); |
| 48 | Wav2LetterPreprocessor::NormaliseVec(this->m_delta1Buf); |
| 49 | Wav2LetterPreprocessor::NormaliseVec(this->m_delta2Buf); |
| 50 | } |
| 51 | |
| 52 | float Wav2LetterPreprocessor::GetQuantElem( |
| 53 | const float elem, |
| 54 | const float quantScale, |
| 55 | const int quantOffset, |
| 56 | const float minVal, |
| 57 | const float maxVal) |
| 58 | { |
| 59 | float val = std::round((elem/quantScale) + quantOffset); |
| 60 | float returnVal = std::min<float>(std::max<float>(val, minVal), maxVal); |
| 61 | return returnVal; |
| 62 | } |
| 63 | |
| 64 | bool Wav2LetterPreprocessor::Invoke(const float* audioData, const uint32_t audioDataLen, std::vector<int8_t>& output, |
| 65 | int quantOffset, float quantScale) |
| 66 | { |
| 67 | this->m_window = SlidingWindow<const float>( |
| 68 | audioData, audioDataLen, |
| 69 | this->m_windowLen, this->m_windowStride); |
| 70 | |
| 71 | uint32_t mfccBufIdx = 0; |
| 72 | |
| 73 | // Init buffers with 0 |
| 74 | std::fill(m_mfccBuf.begin(), m_mfccBuf.end(), 0.f); |
| 75 | std::fill(m_delta1Buf.begin(), m_delta1Buf.end(), 0.f); |
| 76 | std::fill(m_delta2Buf.begin(), m_delta2Buf.end(), 0.f); |
| 77 | |
| 78 | // While we can slide over the window |
| 79 | while (this->m_window.HasNext()) |
| 80 | { |
| 81 | const float* mfccWindow = this->m_window.Next(); |
| 82 | auto mfccAudioData = std::vector<float>( |
| 83 | mfccWindow, |
| 84 | mfccWindow + this->m_windowLen); |
| 85 | |
| 86 | auto mfcc = this->m_mfcc->MfccCompute(mfccAudioData); |
| 87 | for (size_t i = 0; i < this->m_mfccBuf.size(0); ++i) |
| 88 | { |
| 89 | this->m_mfccBuf(i, mfccBufIdx) = mfcc[i]; |
| 90 | } |
| 91 | ++mfccBufIdx; |
| 92 | } |
| 93 | |
| 94 | // Pad MFCC if needed by repeating last feature vector |
| 95 | while (mfccBufIdx != this->m_mfcc->m_params.m_numMfccVectors) |
| 96 | { |
| 97 | memcpy(&this->m_mfccBuf(0, mfccBufIdx), |
| 98 | &this->m_mfccBuf(0, mfccBufIdx - 1), sizeof(float) * this->m_mfcc->m_params.m_numMfccFeatures); |
| 99 | ++mfccBufIdx; |
| 100 | } |
| 101 | |
| 102 | // Compute first and second order deltas from MFCCs |
| 103 | Wav2LetterPreprocessor::ComputeDeltas(this->m_mfccBuf, |
| 104 | this->m_delta1Buf, |
| 105 | this->m_delta2Buf); |
| 106 | |
| 107 | // Normalise |
| 108 | this->Normalise(); |
| 109 | |
| 110 | return this->Quantise<int8_t>(output.data(), quantOffset, quantScale); |
| 111 | } |
| 112 | |
| 113 | bool Wav2LetterPreprocessor::ComputeDeltas(Array2d<float>& mfcc, |
| 114 | Array2d<float>& delta1, |
| 115 | Array2d<float>& delta2) |
| 116 | { |
| 117 | const std::vector <float> delta1Coeffs = |
| 118 | {6.66666667e-02, 5.00000000e-02, 3.33333333e-02, |
| 119 | 1.66666667e-02, -3.46944695e-18, -1.66666667e-02, |
| 120 | -3.33333333e-02, -5.00000000e-02, -6.66666667e-02}; |
| 121 | |
| 122 | const std::vector <float> delta2Coeffs = |
| 123 | {0.06060606, 0.01515152, -0.01731602, |
| 124 | -0.03679654, -0.04329004, -0.03679654, |
| 125 | -0.01731602, 0.01515152, 0.06060606}; |
| 126 | |
| 127 | if (delta1.size(0) == 0 || delta2.size(0) != delta1.size(0) || |
| 128 | mfcc.size(0) == 0 || mfcc.size(1) == 0) |
| 129 | { |
| 130 | return false; |
| 131 | } |
| 132 | |
| 133 | // Get the middle index; coeff vec len should always be odd |
| 134 | const size_t coeffLen = delta1Coeffs.size(); |
| 135 | const size_t fMidIdx = (coeffLen - 1)/2; |
| 136 | const size_t numFeatures = mfcc.size(0); |
| 137 | const size_t numFeatVectors = mfcc.size(1); |
| 138 | |
| 139 | // iterate through features in MFCC vector |
| 140 | for (size_t i = 0; i < numFeatures; ++i) |
| 141 | { |
| 142 | /* for each feature, iterate through time (t) samples representing feature evolution and |
| 143 | * calculate d/dt and d^2/dt^2, using 1d convolution with differential kernels. |
| 144 | * Convolution padding = valid, result size is `time length - kernel length + 1`. |
| 145 | * The result is padded with 0 from both sides to match the size of initial time samples data. |
| 146 | * |
| 147 | * For the small filter, conv1d implementation as a simple loop is efficient enough. |
| 148 | * Filters of a greater size would need CMSIS-DSP functions to be used, like arm_fir_f32. |
| 149 | */ |
| 150 | |
| 151 | for (size_t j = fMidIdx; j < numFeatVectors - fMidIdx; ++j) |
| 152 | { |
| 153 | float d1 = 0; |
| 154 | float d2 = 0; |
| 155 | const size_t mfccStIdx = j - fMidIdx; |
| 156 | |
| 157 | for (size_t k = 0, m = coeffLen - 1; k < coeffLen; ++k, --m) |
| 158 | { |
| 159 | |
| 160 | d1 += mfcc(i,mfccStIdx + k) * delta1Coeffs[m]; |
| 161 | d2 += mfcc(i,mfccStIdx + k) * delta2Coeffs[m]; |
| 162 | } |
| 163 | |
| 164 | delta1(i,j) = d1; |
| 165 | delta2(i,j) = d2; |
| 166 | } |
| 167 | } |
| 168 | |
| 169 | return true; |
| 170 | } |
| 171 | |
| 172 | Wav2LetterPreprocessor::Wav2LetterPreprocessor(const uint32_t windowLen, |
| 173 | const uint32_t windowStride, |
| 174 | std::unique_ptr<Wav2LetterMFCC> mfccInst): |
| 175 | m_mfcc(std::move(mfccInst)), |
| 176 | m_mfccBuf(m_mfcc->m_params.m_numMfccFeatures, m_mfcc->m_params.m_numMfccVectors), |
| 177 | m_delta1Buf(m_mfcc->m_params.m_numMfccFeatures, m_mfcc->m_params.m_numMfccVectors), |
| 178 | m_delta2Buf(m_mfcc->m_params.m_numMfccFeatures, m_mfcc->m_params.m_numMfccVectors), |
| 179 | m_windowLen(windowLen), |
| 180 | m_windowStride(windowStride) |
| 181 | { |
| 182 | if (m_mfcc->m_params.m_numMfccFeatures > 0 && windowLen > 0) |
| 183 | { |
| 184 | this->m_mfcc->Init(); |
| 185 | } |
| 186 | std::fill(m_mfccBuf.begin(), m_mfccBuf.end(), 0.f); |
| 187 | } |