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/AudioCapture.cpp b/samples/SpeechRecognition/src/AudioCapture.cpp
new file mode 100644
index 0000000..f3b9092
--- /dev/null
+++ b/samples/SpeechRecognition/src/AudioCapture.cpp
@@ -0,0 +1,104 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "AudioCapture.hpp"
+#include <alsa/asoundlib.h>
+#include <sndfile.h>
+#include <samplerate.h>
+
+namespace asr
+{
+ std::vector<float> AudioCapture::LoadAudioFile(std::string filePath)
+ {
+ SF_INFO inputSoundFileInfo;
+ SNDFILE* infile = NULL;
+ infile = sf_open(filePath.c_str(), SFM_READ, &inputSoundFileInfo);
+
+ float audioIn[inputSoundFileInfo.channels * inputSoundFileInfo.frames];
+ sf_read_float(infile, audioIn, inputSoundFileInfo.channels * inputSoundFileInfo.frames);
+
+ float sampleRate = 16000.0f;
+ float srcRatio = sampleRate / (float)inputSoundFileInfo.samplerate;
+ int outputFrames = ceil(inputSoundFileInfo.frames * srcRatio);
+ float dataOut[outputFrames];
+
+ // Convert to mono
+ float monoData[inputSoundFileInfo.frames];
+ for(int i = 0; i < inputSoundFileInfo.frames; i++)
+ {
+ float val = 0.0f;
+ for(int j = 0; j < inputSoundFileInfo.channels; j++)
+ monoData[i] += audioIn[i * inputSoundFileInfo.channels + j];
+ monoData[i] /= inputSoundFileInfo.channels;
+ }
+
+ // Resample
+ SRC_DATA srcData;
+ srcData.data_in = monoData;
+ srcData.input_frames = inputSoundFileInfo.frames;
+ srcData.data_out = dataOut;
+ srcData.output_frames = outputFrames;
+ srcData.src_ratio = srcRatio;
+
+ src_simple(&srcData, SRC_SINC_BEST_QUALITY, 1);
+
+ // Convert to Vector
+ std::vector<float> processedInput;
+
+ for(int i = 0; i < srcData.output_frames_gen; ++i)
+ {
+ processedInput.push_back(srcData.data_out[i]);
+ }
+
+ sf_close(infile);
+
+ return processedInput;
+ }
+
+ void AudioCapture::InitSlidingWindow(float* data, size_t dataSize, int minSamples, size_t stride)
+ {
+ this->m_window = SlidingWindow<const float>(data, dataSize, minSamples, stride);
+ }
+
+ bool AudioCapture::HasNext()
+ {
+ return m_window.HasNext();
+ }
+
+ std::vector<float> AudioCapture::Next()
+ {
+ if (this->m_window.HasNext())
+ {
+ int remainingData = this->m_window.RemainingData();
+ const float* windowData = this->m_window.Next();
+
+ size_t windowSize = this->m_window.GetWindowSize();
+
+ if(remainingData < windowSize)
+ {
+ std::vector<float> mfccAudioData(windowSize, 0.0f);
+ for(int i = 0; i < remainingData; ++i)
+ {
+ mfccAudioData[i] = *windowData;
+ if(i < remainingData - 1)
+ {
+ ++windowData;
+ }
+ }
+ return mfccAudioData;
+ }
+ else
+ {
+ std::vector<float> mfccAudioData(windowData, windowData + windowSize);
+ return mfccAudioData;
+ }
+ }
+ else
+ {
+ throw std::out_of_range("Error, end of audio data reached.");
+ }
+ }
+} //namespace asr
+
diff --git a/samples/SpeechRecognition/src/Decoder.cpp b/samples/SpeechRecognition/src/Decoder.cpp
new file mode 100644
index 0000000..663d4db
--- /dev/null
+++ b/samples/SpeechRecognition/src/Decoder.cpp
@@ -0,0 +1,37 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "Decoder.hpp"
+
+namespace asr {
+
+ Decoder::Decoder(std::map<int, std::string>& labels):
+ m_labels(labels)
+ {}
+
+ std::string Decoder::FilterCharacters(std::vector<char>& unfiltered)
+ {
+ std::string filtered = "";
+
+ for(int i = 0; i < unfiltered.size(); ++i)
+ {
+ if (unfiltered.at(i) == '$')
+ {
+ continue;
+ }
+
+ else if (i + 1 < unfiltered.size() && unfiltered.at(i) == unfiltered.at(i + 1))
+ {
+ continue;
+ }
+ else
+ {
+ filtered += unfiltered.at(i);
+ }
+ }
+ return filtered;
+ }
+}// namespace
+
diff --git a/samples/SpeechRecognition/src/MFCC.cpp b/samples/SpeechRecognition/src/MFCC.cpp
new file mode 100644
index 0000000..234b14d
--- /dev/null
+++ b/samples/SpeechRecognition/src/MFCC.cpp
@@ -0,0 +1,397 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <cstdio>
+#include <float.h>
+
+#include "MFCC.hpp"
+#include "MathUtils.hpp"
+
+
+MfccParams::MfccParams(
+ const float samplingFreq,
+ const int numFbankBins,
+ const float melLoFreq,
+ const float melHiFreq,
+ const int numMfccFeats,
+ const int frameLen,
+ const bool useHtkMethod,
+ const int numMfccVectors):
+ m_samplingFreq(samplingFreq),
+ m_numFbankBins(numFbankBins),
+ m_melLoFreq(melLoFreq),
+ m_melHiFreq(melHiFreq),
+ m_numMfccFeatures(numMfccFeats),
+ m_frameLen(frameLen),
+ m_numMfccVectors(numMfccVectors),
+
+ /* Smallest power of 2 >= frame length. */
+ m_frameLenPadded(pow(2, ceil((log(frameLen)/log(2))))),
+ m_useHtkMethod(useHtkMethod)
+{}
+
+std::string MfccParams::Str()
+{
+ char strC[1024];
+ snprintf(strC, sizeof(strC) - 1, "\n \
+ \n\t Sampling frequency: %f\
+ \n\t Number of filter banks: %u\
+ \n\t Mel frequency limit (low): %f\
+ \n\t Mel frequency limit (high): %f\
+ \n\t Number of MFCC features: %u\
+ \n\t Frame length: %u\
+ \n\t Padded frame length: %u\
+ \n\t Using HTK for Mel scale: %s\n",
+ this->m_samplingFreq, this->m_numFbankBins, this->m_melLoFreq,
+ this->m_melHiFreq, this->m_numMfccFeatures, this->m_frameLen,
+ this->m_frameLenPadded, this->m_useHtkMethod ? "yes" : "no");
+ return std::string{strC};
+}
+
+MFCC::MFCC(const MfccParams& params):
+ _m_params(params),
+ _m_filterBankInitialised(false)
+{
+ this->_m_buffer = std::vector<float>(
+ this->_m_params.m_frameLenPadded, 0.0);
+ this->_m_frame = std::vector<float>(
+ this->_m_params.m_frameLenPadded, 0.0);
+ this->_m_melEnergies = std::vector<float>(
+ this->_m_params.m_numFbankBins, 0.0);
+
+ this->_m_windowFunc = std::vector<float>(this->_m_params.m_frameLen);
+ const float multiplier = 2 * M_PI / this->_m_params.m_frameLen;
+
+ /* Create window function. */
+ for (size_t i = 0; i < this->_m_params.m_frameLen; i++)
+ {
+ this->_m_windowFunc[i] = (0.5 - (0.5 * cos(static_cast<float>(i) * multiplier)));
+ }
+}
+
+void MFCC::Init()
+{
+ this->_InitMelFilterBank();
+}
+
+float MFCC::MelScale(const float freq, const bool useHTKMethod)
+{
+ if (useHTKMethod)
+ {
+ return 1127.0f * logf (1.0f + freq / 700.0f);
+ }
+ else
+ {
+ /* Slaney formula for mel scale. */
+ float mel = freq / freqStep;
+
+ if (freq >= minLogHz)
+ {
+ mel = minLogMel + logf(freq / minLogHz) / logStep;
+ }
+ return mel;
+ }
+}
+
+float MFCC::InverseMelScale(const float melFreq, const bool useHTKMethod)
+{
+ if (useHTKMethod)
+ {
+ return 700.0f * (expf (melFreq / 1127.0f) - 1.0f);
+ }
+ else
+ {
+ /* Slaney formula for mel scale. */
+ float freq = freqStep * melFreq;
+
+ if (melFreq >= minLogMel)
+ {
+ freq = minLogHz * expf(logStep * (melFreq - minLogMel));
+ }
+ return freq;
+ }
+}
+
+
+bool MFCC::ApplyMelFilterBank(
+ std::vector<float>& fftVec,
+ std::vector<std::vector<float>>& melFilterBank,
+ std::vector<int32_t>& filterBankFilterFirst,
+ std::vector<int32_t>& filterBankFilterLast,
+ std::vector<float>& melEnergies)
+{
+ const size_t numBanks = melEnergies.size();
+
+ if (numBanks != filterBankFilterFirst.size() ||
+ numBanks != filterBankFilterLast.size())
+ {
+ printf("unexpected filter bank lengths\n");
+ return false;
+ }
+
+ for (size_t bin = 0; bin < numBanks; ++bin)
+ {
+ auto filterBankIter = melFilterBank[bin].begin();
+ float melEnergy = 1e-10; /* Avoid log of zero at later stages */
+ const int32_t firstIndex = filterBankFilterFirst[bin];
+ const int32_t lastIndex = filterBankFilterLast[bin];
+
+ for (int32_t i = firstIndex; i <= lastIndex; ++i)
+ {
+ melEnergy += (*filterBankIter++ * fftVec[i]);
+ }
+
+ melEnergies[bin] = melEnergy;
+ }
+
+ return true;
+}
+
+void MFCC::ConvertToLogarithmicScale(std::vector<float>& melEnergies)
+{
+ float maxMelEnergy = -FLT_MAX;
+
+ /* Container for natural logarithms of mel energies */
+ std::vector <float> vecLogEnergies(melEnergies.size(), 0.f);
+
+ /* Because we are taking natural logs, we need to multiply by log10(e).
+ * Also, for wav2letter model, we scale our log10 values by 10 */
+ constexpr float multiplier = 10.0 * /* default scalar */
+ 0.4342944819032518; /* log10f(std::exp(1.0))*/
+
+ /* Take log of the whole vector */
+ MathUtils::VecLogarithmF32(melEnergies, vecLogEnergies);
+
+ /* Scale the log values and get the max */
+ for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
+ iterM != melEnergies.end(); ++iterM, ++iterL)
+ {
+ *iterM = *iterL * multiplier;
+
+ /* Save the max mel energy. */
+ if (*iterM > maxMelEnergy)
+ {
+ maxMelEnergy = *iterM;
+ }
+ }
+
+ /* Clamp the mel energies */
+ constexpr float maxDb = 80.0;
+ const float clampLevelLowdB = maxMelEnergy - maxDb;
+ for (auto iter = melEnergies.begin(); iter != melEnergies.end(); ++iter)
+ {
+ *iter = std::max(*iter, clampLevelLowdB);
+ }
+}
+
+void MFCC::_ConvertToPowerSpectrum()
+{
+ const uint32_t halfDim = this->_m_params.m_frameLenPadded / 2;
+
+ /* Handle this special case. */
+ float firstEnergy = this->_m_buffer[0] * this->_m_buffer[0];
+ float lastEnergy = this->_m_buffer[1] * this->_m_buffer[1];
+
+ MathUtils::ComplexMagnitudeSquaredF32(
+ this->_m_buffer.data(),
+ this->_m_buffer.size(),
+ this->_m_buffer.data(),
+ this->_m_buffer.size()/2);
+
+ this->_m_buffer[0] = firstEnergy;
+ this->_m_buffer[halfDim] = lastEnergy;
+}
+
+std::vector<float> MFCC::CreateDCTMatrix(
+ const int32_t inputLength,
+ const int32_t coefficientCount)
+{
+ std::vector<float> dctMatix(inputLength * coefficientCount);
+
+ /* Orthonormal normalization. */
+ const float normalizerK0 = 2 * sqrt(1.0 / static_cast<float>(4*inputLength));
+ const float normalizer = 2 * sqrt(1.0 / static_cast<float>(2*inputLength));
+
+ const float angleIncr = M_PI/inputLength;
+ float angle = angleIncr; /* we start using it at k = 1 loop */
+
+ /* First row of DCT will use normalizer K0 */
+ for (int32_t n = 0; n < inputLength; ++n)
+ {
+ dctMatix[n] = normalizerK0;
+ }
+
+ /* Second row (index = 1) onwards, we use standard normalizer */
+ for (int32_t k = 1, m = inputLength; k < coefficientCount; ++k, m += inputLength)
+ {
+ for (int32_t n = 0; n < inputLength; ++n)
+ {
+ dctMatix[m+n] = normalizer *
+ cos((n + 0.5) * angle);
+ }
+ angle += angleIncr;
+ }
+ return dctMatix;
+}
+
+float MFCC::GetMelFilterBankNormaliser(
+ const float& leftMel,
+ const float& rightMel,
+ const bool useHTKMethod)
+{
+/* Slaney normalization for mel weights. */
+ return (2.0f / (MFCC::InverseMelScale(rightMel, useHTKMethod) -
+ MFCC::InverseMelScale(leftMel, useHTKMethod)));
+}
+
+void MFCC::_InitMelFilterBank()
+{
+ if (!this->_IsMelFilterBankInited())
+ {
+ this->_m_melFilterBank = this->_CreateMelFilterBank();
+ this->_m_dctMatrix = this->CreateDCTMatrix(
+ this->_m_params.m_numFbankBins,
+ this->_m_params.m_numMfccFeatures);
+ this->_m_filterBankInitialised = true;
+ }
+}
+
+bool MFCC::_IsMelFilterBankInited()
+{
+ return this->_m_filterBankInitialised;
+}
+
+void MFCC::_MfccComputePreFeature(const std::vector<float>& audioData)
+{
+ this->_InitMelFilterBank();
+
+ /* TensorFlow way of normalizing .wav data to (-1, 1). */
+ constexpr float normaliser = 1.0;
+ for (size_t i = 0; i < this->_m_params.m_frameLen; i++)
+ {
+ this->_m_frame[i] = static_cast<float>(audioData[i]) * normaliser;
+ }
+
+ /* Apply window function to input frame. */
+ for(size_t i = 0; i < this->_m_params.m_frameLen; i++)
+ {
+ this->_m_frame[i] *= this->_m_windowFunc[i];
+ }
+
+ /* Set remaining frame values to 0. */
+ std::fill(this->_m_frame.begin() + this->_m_params.m_frameLen,this->_m_frame.end(), 0);
+
+ /* Compute FFT. */
+ MathUtils::FftF32(this->_m_frame, this->_m_buffer);
+
+ /* Convert to power spectrum. */
+ this->_ConvertToPowerSpectrum();
+
+ /* Apply mel filterbanks. */
+ if (!this->ApplyMelFilterBank(this->_m_buffer,
+ this->_m_melFilterBank,
+ this->_m_filterBankFilterFirst,
+ this->_m_filterBankFilterLast,
+ this->_m_melEnergies))
+ {
+ printf("Failed to apply MEL filter banks\n");
+ }
+
+ /* Convert to logarithmic scale */
+ this->ConvertToLogarithmicScale(this->_m_melEnergies);
+}
+
+std::vector<float> MFCC::MfccCompute(const std::vector<float>& audioData)
+{
+ this->_MfccComputePreFeature(audioData);
+
+ std::vector<float> mfccOut(this->_m_params.m_numMfccFeatures);
+
+ float * ptrMel = this->_m_melEnergies.data();
+ float * ptrDct = this->_m_dctMatrix.data();
+ float * ptrMfcc = mfccOut.data();
+
+ /* Take DCT. Uses matrix mul. */
+ for (size_t i = 0, j = 0; i < mfccOut.size();
+ ++i, j += this->_m_params.m_numFbankBins)
+ {
+ *ptrMfcc++ = MathUtils::DotProductF32(
+ ptrDct + j,
+ ptrMel,
+ this->_m_params.m_numFbankBins);
+ }
+
+ return mfccOut;
+}
+
+std::vector<std::vector<float>> MFCC::_CreateMelFilterBank()
+{
+ size_t numFftBins = this->_m_params.m_frameLenPadded / 2;
+ float fftBinWidth = static_cast<float>(this->_m_params.m_samplingFreq) / this->_m_params.m_frameLenPadded;
+
+ float melLowFreq = MFCC::MelScale(this->_m_params.m_melLoFreq,
+ this->_m_params.m_useHtkMethod);
+ float melHighFreq = MFCC::MelScale(this->_m_params.m_melHiFreq,
+ this->_m_params.m_useHtkMethod);
+ float melFreqDelta = (melHighFreq - melLowFreq) / (this->_m_params.m_numFbankBins + 1);
+
+ std::vector<float> thisBin = std::vector<float>(numFftBins);
+ std::vector<std::vector<float>> melFilterBank(
+ this->_m_params.m_numFbankBins);
+ this->_m_filterBankFilterFirst =
+ std::vector<int32_t>(this->_m_params.m_numFbankBins);
+ this->_m_filterBankFilterLast =
+ std::vector<int32_t>(this->_m_params.m_numFbankBins);
+
+ for (size_t bin = 0; bin < this->_m_params.m_numFbankBins; bin++)
+ {
+ float leftMel = melLowFreq + bin * melFreqDelta;
+ float centerMel = melLowFreq + (bin + 1) * melFreqDelta;
+ float rightMel = melLowFreq + (bin + 2) * melFreqDelta;
+
+ int32_t firstIndex = -1;
+ int32_t lastIndex = -1;
+ const float normaliser = this->GetMelFilterBankNormaliser(leftMel, rightMel, this->_m_params.m_useHtkMethod);
+
+ for (size_t i = 0; i < numFftBins; i++)
+ {
+ float freq = (fftBinWidth * i); /* Center freq of this fft bin. */
+ float mel = MFCC::MelScale(freq, this->_m_params.m_useHtkMethod);
+ thisBin[i] = 0.0;
+
+ if (mel > leftMel && mel < rightMel)
+ {
+ float weight;
+ if (mel <= centerMel)
+ {
+ weight = (mel - leftMel) / (centerMel - leftMel);
+ }
+ else
+ {
+ weight = (rightMel - mel) / (rightMel - centerMel);
+ }
+
+ thisBin[i] = weight * normaliser;
+ if (firstIndex == -1)
+ {
+ firstIndex = i;
+ }
+ lastIndex = i;
+ }
+ }
+
+ this->_m_filterBankFilterFirst[bin] = firstIndex;
+ this->_m_filterBankFilterLast[bin] = lastIndex;
+
+ /* Copy the part we care about. */
+ for (int32_t i = firstIndex; i <= lastIndex; i++)
+ {
+ melFilterBank[bin].push_back(thisBin[i]);
+ }
+ }
+
+ return melFilterBank;
+}
+
diff --git a/samples/SpeechRecognition/src/Main.cpp b/samples/SpeechRecognition/src/Main.cpp
new file mode 100644
index 0000000..de37e23
--- /dev/null
+++ b/samples/SpeechRecognition/src/Main.cpp
@@ -0,0 +1,157 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include <iostream>
+#include <map>
+#include <vector>
+#include <algorithm>
+#include <cmath>
+
+#include "CmdArgsParser.hpp"
+#include "ArmnnNetworkExecutor.hpp"
+#include "AudioCapture.hpp"
+#include "Preprocess.hpp"
+#include "Decoder.hpp"
+#include "SpeechRecognitionPipeline.hpp"
+
+
+using InferenceResult = std::vector<int8_t>;
+using InferenceResults = std::vector<InferenceResult>;
+
+const std::string AUDIO_FILE_PATH = "--audio-file-path";
+const std::string MODEL_FILE_PATH = "--model-file-path";
+const std::string LABEL_PATH = "--label-path";
+const std::string PREFERRED_BACKENDS = "--preferred-backends";
+const std::string HELP = "--help";
+
+std::map<int, std::string> labels = {
+ {0, "a" },
+ {1, "b" },
+ {2, "c" },
+ {3, "d" },
+ {4, "e" },
+ {5, "f" },
+ {6, "g" },
+ {7, "h" },
+ {8, "i" },
+ {9, "j" },
+ {10,"k" },
+ {11,"l" },
+ {12,"m" },
+ {13,"n" },
+ {14,"o" },
+ {15,"p" },
+ {16,"q" },
+ {17,"r" },
+ {18,"s" },
+ {19,"t" },
+ {20,"u" },
+ {21,"v" },
+ {22,"w" },
+ {23,"x" },
+ {24,"y" },
+ {25,"z" },
+ {26, "\'" },
+ {27, " "},
+ {28,"$" }
+};
+
+/*
+ * The accepted options for this Speech Recognition executable
+ */
+static std::map<std::string, std::string> CMD_OPTIONS = {
+ {AUDIO_FILE_PATH, "[REQUIRED] Path to the Audio file to run speech recognition on"},
+ {MODEL_FILE_PATH, "[REQUIRED] Path to the Speech Recognition model to use"},
+ {PREFERRED_BACKENDS, "[OPTIONAL] Takes the preferred backends in preference order, separated by comma."
+ " For example: CpuAcc,GpuAcc,CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]."
+ " Defaults to CpuAcc,CpuRef"}
+};
+
+/*
+ * Reads the user supplied backend preference, splits it by comma, and returns an ordered vector
+ */
+std::vector<armnn::BackendId> GetPreferredBackendList(const std::string& preferredBackends)
+{
+ std::vector<armnn::BackendId> backends;
+ std::stringstream ss(preferredBackends);
+
+ while(ss.good())
+ {
+ std::string backend;
+ std::getline( ss, backend, ',' );
+ backends.emplace_back(backend);
+ }
+ return backends;
+}
+
+int main(int argc, char *argv[])
+{
+ // Wav2Letter ASR SETTINGS
+ int SAMP_FREQ = 16000;
+ int FRAME_LEN_MS = 32;
+ int FRAME_LEN_SAMPLES = SAMP_FREQ * FRAME_LEN_MS * 0.001;
+ int NUM_MFCC_FEATS = 13;
+ int MFCC_WINDOW_LEN = 512;
+ int MFCC_WINDOW_STRIDE = 160;
+ const int NUM_MFCC_VECTORS = 296;
+ int SAMPLES_PER_INFERENCE = MFCC_WINDOW_LEN + ((NUM_MFCC_VECTORS -1) * MFCC_WINDOW_STRIDE);
+ int MEL_LO_FREQ = 0;
+ int MEL_HI_FREQ = 8000;
+ int NUM_FBANK_BIN = 128;
+ int INPUT_WINDOW_LEFT_CONTEXT = 98;
+ int INPUT_WINDOW_RIGHT_CONTEXT = 98;
+ int INPUT_WINDOW_INNER_CONTEXT = NUM_MFCC_VECTORS -
+ (INPUT_WINDOW_LEFT_CONTEXT + INPUT_WINDOW_RIGHT_CONTEXT);
+ int SLIDING_WINDOW_OFFSET = INPUT_WINDOW_INNER_CONTEXT * MFCC_WINDOW_STRIDE;
+
+
+ MfccParams mfccParams(SAMP_FREQ, NUM_FBANK_BIN,
+ MEL_LO_FREQ, MEL_HI_FREQ, NUM_MFCC_FEATS, FRAME_LEN_SAMPLES, false, NUM_MFCC_VECTORS);
+
+ MFCC mfccInst = MFCC(mfccParams);
+
+ Preprocess preprocessor(MFCC_WINDOW_LEN, MFCC_WINDOW_STRIDE, mfccInst);
+
+ bool isFirstWindow = true;
+ std::string currentRContext = "";
+
+ std::map <std::string, std::string> options;
+
+ int result = ParseOptions(options, CMD_OPTIONS, argv, argc);
+ if (result != 0)
+ {
+ return result;
+ }
+
+ // Create the network options
+ common::PipelineOptions pipelineOptions;
+ pipelineOptions.m_ModelFilePath = GetSpecifiedOption(options, MODEL_FILE_PATH);
+
+ if (CheckOptionSpecified(options, PREFERRED_BACKENDS))
+ {
+ pipelineOptions.m_backends = GetPreferredBackendList((GetSpecifiedOption(options, PREFERRED_BACKENDS)));
+ }
+ else
+ {
+ pipelineOptions.m_backends = {"CpuAcc", "CpuRef"};
+ }
+
+ asr::IPipelinePtr asrPipeline = asr::CreatePipeline(pipelineOptions, labels);
+
+ asr::AudioCapture capture;
+ std::vector<float> audioData = capture.LoadAudioFile(GetSpecifiedOption(options, AUDIO_FILE_PATH));
+ capture.InitSlidingWindow(audioData.data(), audioData.size(), SAMPLES_PER_INFERENCE, SLIDING_WINDOW_OFFSET);
+
+ while (capture.HasNext())
+ {
+ std::vector<float> audioBlock = capture.Next();
+ InferenceResults results;
+
+ std::vector<int8_t> preprocessedData = asrPipeline->PreProcessing<float, int8_t>(audioBlock, preprocessor);
+ asrPipeline->Inference<int8_t>(preprocessedData, results);
+ asrPipeline->PostProcessing<int8_t>(results, isFirstWindow, !capture.HasNext(), currentRContext);
+ }
+
+ return 0;
+}
\ No newline at end of file
diff --git a/samples/SpeechRecognition/src/MathUtils.cpp b/samples/SpeechRecognition/src/MathUtils.cpp
new file mode 100644
index 0000000..bf99083
--- /dev/null
+++ b/samples/SpeechRecognition/src/MathUtils.cpp
@@ -0,0 +1,112 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "MathUtils.hpp"
+#include <vector>
+#include <cmath>
+#include <cstdio>
+
+void MathUtils::FftF32(std::vector<float>& input,
+ std::vector<float>& fftOutput)
+{
+ const int inputLength = input.size();
+
+ for (int k = 0; k <= inputLength / 2; k++)
+ {
+ float sumReal = 0, sumImag = 0;
+
+ for (int t = 0; t < inputLength; t++)
+ {
+ float angle = 2 * M_PI * t * k / inputLength;
+ sumReal += input[t] * cosf(angle);
+ sumImag += -input[t] * sinf(angle);
+ }
+
+ /* Arrange output to [real0, realN/2, real1, im1, real2, im2, ...] */
+ if (k == 0)
+ {
+ fftOutput[0] = sumReal;
+ }
+ else if (k == inputLength / 2)
+ {
+ fftOutput[1] = sumReal;
+ }
+ else
+ {
+ fftOutput[k*2] = sumReal;
+ fftOutput[k*2 + 1] = sumImag;
+ };
+ }
+}
+
+float MathUtils::DotProductF32(float* srcPtrA, float* srcPtrB,
+ const int srcLen)
+{
+ float output = 0.f;
+
+ for (int i = 0; i < srcLen; ++i)
+ {
+ output += *srcPtrA++ * *srcPtrB++;
+ }
+ return output;
+}
+
+bool MathUtils::ComplexMagnitudeSquaredF32(float* ptrSrc,
+ const int srcLen,
+ float* ptrDst,
+ const int dstLen)
+{
+ if (dstLen < srcLen/2)
+ {
+ printf("dstLen must be greater than srcLen/2");
+ return false;
+ }
+
+ for (int j = 0; j < srcLen; ++j)
+ {
+ const float real = *ptrSrc++;
+ const float im = *ptrSrc++;
+ *ptrDst++ = real*real + im*im;
+ }
+ return true;
+}
+
+void MathUtils::VecLogarithmF32(std::vector <float>& input,
+ std::vector <float>& output)
+{
+ for (auto in = input.begin(), out = output.begin();
+ in != input.end(); ++in, ++out)
+ {
+ *out = logf(*in);
+ }
+}
+
+float MathUtils::MeanF32(float* ptrSrc, const uint32_t srcLen)
+{
+ if (!srcLen)
+ {
+ return 0.f;
+ }
+
+ float acc = std::accumulate(ptrSrc, ptrSrc + srcLen, 0.0);
+ return acc/srcLen;
+}
+
+float MathUtils::StdDevF32(float* ptrSrc, const uint32_t srcLen,
+ const float mean)
+{
+ if (!srcLen)
+ {
+ return 0.f;
+ }
+ auto VarianceFunction = [=](float acc, const float value) {
+ return acc + (((value - mean) * (value - mean))/ srcLen);
+ };
+
+ float acc = std::accumulate(ptrSrc, ptrSrc + srcLen, 0.0,
+ VarianceFunction);
+ return sqrtf(acc);
+}
+
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;
+}
\ No newline at end of file
diff --git a/samples/SpeechRecognition/src/SpeechRecognitionPipeline.cpp b/samples/SpeechRecognition/src/SpeechRecognitionPipeline.cpp
new file mode 100644
index 0000000..1b822d6
--- /dev/null
+++ b/samples/SpeechRecognition/src/SpeechRecognitionPipeline.cpp
@@ -0,0 +1,26 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "SpeechRecognitionPipeline.hpp"
+#include "ArmnnNetworkExecutor.hpp"
+
+namespace asr
+{
+ASRPipeline::ASRPipeline(std::unique_ptr<common::ArmnnNetworkExecutor<int8_t>> executor,
+ std::unique_ptr<Decoder> decoder
+ ) :
+ m_executor(std::move(executor)),
+ m_decoder(std::move(decoder)){}
+
+IPipelinePtr CreatePipeline(common::PipelineOptions& config, std::map<int, std::string>& labels)
+{
+ auto executor = std::make_unique<common::ArmnnNetworkExecutor<int8_t>>(config.m_ModelFilePath, config.m_backends);
+
+ auto decoder = std::make_unique<asr::Decoder>(labels);
+
+ return std::make_unique<asr::ASRPipeline>(std::move(executor), std::move(decoder));
+}
+
+}// namespace asr
\ No newline at end of file