MLECO-1252 ASR sample application using the public ArmNN C++ API.
Change-Id: I98cd505b8772a8c8fa88308121bc94135bb45068
Signed-off-by: Éanna Ó Catháin <eanna.ocathain@arm.com>
diff --git a/samples/SpeechRecognition/src/Preprocess.cpp b/samples/SpeechRecognition/src/Preprocess.cpp
new file mode 100644
index 0000000..8627961
--- /dev/null
+++ b/samples/SpeechRecognition/src/Preprocess.cpp
@@ -0,0 +1,192 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <algorithm>
+#include <numeric>
+#include <math.h>
+#include <string.h>
+
+#include "MathUtils.hpp"
+#include "Preprocess.hpp"
+
+Preprocess::Preprocess(
+ const uint32_t windowLen,
+ const uint32_t windowStride,
+ const MFCC mfccInst):
+ _m_mfcc(mfccInst),
+ _m_mfccBuf(mfccInst._m_params.m_numMfccFeatures, mfccInst._m_params.m_numMfccVectors),
+ _m_delta1Buf(mfccInst._m_params.m_numMfccFeatures, mfccInst._m_params.m_numMfccVectors),
+ _m_delta2Buf(mfccInst._m_params.m_numMfccFeatures, mfccInst._m_params.m_numMfccVectors),
+ _m_windowLen(windowLen),
+ _m_windowStride(windowStride)
+{
+ if (mfccInst._m_params.m_numMfccFeatures > 0 && windowLen > 0)
+ {
+ this->_m_mfcc.Init();
+ }
+}
+
+Preprocess::~Preprocess()
+{
+}
+
+bool Preprocess::Invoke( const float* audioData, const uint32_t audioDataLen, std::vector<int8_t>& output,
+ int quantOffset, float quantScale)
+{
+ this->_m_window = SlidingWindow<const float>(
+ audioData, audioDataLen,
+ this->_m_windowLen, this->_m_windowStride);
+
+ uint32_t mfccBufIdx = 0;
+
+ // Init buffers with 0
+ std::fill(_m_mfccBuf.begin(), _m_mfccBuf.end(), 0.f);
+ std::fill(_m_delta1Buf.begin(), _m_delta1Buf.end(), 0.f);
+ std::fill(_m_delta2Buf.begin(), _m_delta2Buf.end(), 0.f);
+
+ /* While we can slide over the window */
+ while (this->_m_window.HasNext())
+ {
+ const float* mfccWindow = this->_m_window.Next();
+ auto mfccAudioData = std::vector<float>(
+ mfccWindow,
+ mfccWindow + this->_m_windowLen);
+
+ auto mfcc = this->_m_mfcc.MfccCompute(mfccAudioData);
+ for (size_t i = 0; i < this->_m_mfccBuf.size(0); ++i)
+ {
+ this->_m_mfccBuf(i, mfccBufIdx) = mfcc[i];
+ }
+ ++mfccBufIdx;
+ }
+
+ /* Pad MFCC if needed by repeating last feature vector */
+ while (mfccBufIdx != this->_m_mfcc._m_params.m_numMfccVectors)
+ {
+ memcpy(&this->_m_mfccBuf(0, mfccBufIdx),
+ &this->_m_mfccBuf(0, mfccBufIdx-1), sizeof(float)*this->_m_mfcc._m_params.m_numMfccFeatures);
+ ++mfccBufIdx;
+ }
+
+ /* Compute first and second order deltas from MFCCs */
+ this->_ComputeDeltas(this->_m_mfccBuf,
+ this->_m_delta1Buf,
+ this->_m_delta2Buf);
+
+ /* Normalise */
+ this->_Normalise();
+
+ return this->_Quantise<int8_t>(output.data(), quantOffset, quantScale);
+}
+
+bool Preprocess::_ComputeDeltas(Array2d<float>& mfcc,
+ Array2d<float>& delta1,
+ Array2d<float>& delta2)
+{
+ const std::vector <float> delta1Coeffs =
+ {6.66666667e-02, 5.00000000e-02, 3.33333333e-02,
+ 1.66666667e-02, -3.46944695e-18, -1.66666667e-02,
+ -3.33333333e-02, -5.00000000e-02, -6.66666667e-02};
+
+ const std::vector <float> delta2Coeffs =
+ {0.06060606, 0.01515152, -0.01731602,
+ -0.03679654, -0.04329004, -0.03679654,
+ -0.01731602, 0.01515152, 0.06060606};
+
+ if (delta1.size(0) == 0 || delta2.size(0) != delta1.size(0) ||
+ mfcc.size(0) == 0 || mfcc.size(1) == 0)
+ {
+ return false;
+ }
+
+ /* Get the middle index; coeff vec len should always be odd */
+ const size_t coeffLen = delta1Coeffs.size();
+ const size_t fMidIdx = (coeffLen - 1)/2;
+ const size_t numFeatures = mfcc.size(0);
+ const size_t numFeatVectors = mfcc.size(1);
+
+ /* iterate through features in MFCC vector*/
+ for (size_t i = 0; i < numFeatures; ++i)
+ {
+ /* for each feature, iterate through time (t) samples representing feature evolution and
+ * calculate d/dt and d^2/dt^2, using 1d convolution with differential kernels.
+ * Convolution padding = valid, result size is `time length - kernel length + 1`.
+ * The result is padded with 0 from both sides to match the size of initial time samples data.
+ *
+ * For the small filter, conv1d implementation as a simple loop is efficient enough.
+ * Filters of a greater size would need CMSIS-DSP functions to be used, like arm_fir_f32.
+ */
+
+ for (size_t j = fMidIdx; j < numFeatVectors - fMidIdx; ++j)
+ {
+ float d1 = 0;
+ float d2 = 0;
+ const size_t mfccStIdx = j - fMidIdx;
+
+ for (size_t k = 0, m = coeffLen - 1; k < coeffLen; ++k, --m)
+ {
+
+ d1 += mfcc(i,mfccStIdx + k) * delta1Coeffs[m];
+ d2 += mfcc(i,mfccStIdx + k) * delta2Coeffs[m];
+ }
+
+ delta1(i,j) = d1;
+ delta2(i,j) = d2;
+ }
+ }
+
+ return true;
+}
+
+float Preprocess::_GetMean(Array2d<float>& vec)
+{
+ return MathUtils::MeanF32(vec.begin(), vec.totalSize());
+}
+
+float Preprocess::_GetStdDev(Array2d<float>& vec, const float mean)
+{
+ return MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
+}
+
+void Preprocess::_NormaliseVec(Array2d<float>& vec)
+{
+ auto mean = Preprocess::_GetMean(vec);
+ auto stddev = Preprocess::_GetStdDev(vec, mean);
+
+ if (stddev == 0)
+ {
+ std::fill(vec.begin(), vec.end(), 0);
+ }
+ else
+ {
+ const float stddevInv = 1.f/stddev;
+ const float normalisedMean = mean/stddev;
+
+ auto NormalisingFunction = [=](float &value) {
+ value = value * stddevInv - normalisedMean;
+ };
+ std::for_each(vec.begin(), vec.end(), NormalisingFunction);
+ }
+}
+
+void Preprocess::_Normalise()
+{
+ Preprocess::_NormaliseVec(this->_m_mfccBuf);
+ Preprocess::_NormaliseVec(this->_m_delta1Buf);
+ Preprocess::_NormaliseVec(this->_m_delta2Buf);
+}
+
+float Preprocess::_GetQuantElem(
+ const float elem,
+ const float quantScale,
+ const int quantOffset,
+ const float minVal,
+ const float maxVal)
+{
+ float val = std::round((elem/quantScale) + quantOffset);
+ float maxim = std::max<float>(val, minVal);
+ float returnVal = std::min<float>(std::max<float>(val, minVal), maxVal);
+ return returnVal;
+}
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