diff options
Diffstat (limited to 'samples/SpeechRecognition/include')
| -rw-r--r-- | samples/SpeechRecognition/include/AudioCapture.hpp | 62 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/DataStructures.hpp | 102 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/Decoder.hpp | 63 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/MFCC.hpp | 244 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/MathUtils.hpp | 85 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/Preprocess.hpp | 175 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/SlidingWindow.hpp | 161 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/SpeechRecognitionPipeline.hpp | 139 |
8 files changed, 1031 insertions, 0 deletions
diff --git a/samples/SpeechRecognition/include/AudioCapture.hpp b/samples/SpeechRecognition/include/AudioCapture.hpp new file mode 100644 index 0000000000..90c2eccacf --- /dev/null +++ b/samples/SpeechRecognition/include/AudioCapture.hpp @@ -0,0 +1,62 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <string> +#include <iostream> + +#include <math.h> + +#include <vector> + +#include <exception> + +#include "SlidingWindow.hpp" + +namespace asr +{ + +/** +* @brief Class used to capture the audio data loaded from file, and to provide a method of + * extracting correctly positioned and appropriately sized audio windows +* +*/ + class AudioCapture + { + public: + + SlidingWindow<const float> m_window; + int lastReadIdx= 0; + + /** + * @brief Default constructor + */ + AudioCapture() + {}; + + /** + * @brief Function to load the audio data captured from the + * input file to memory. + */ + std::vector<float> LoadAudioFile(std::string filePath); + + /** + * @brief Function to initialize the sliding window. This will set its position in memory, its + * window size and its stride. + */ + void InitSlidingWindow(float* data, size_t dataSize, int minSamples, size_t stride); + + /** + * Checks whether there is another block of audio in memory to read + */ + bool HasNext(); + + /** + * Retrieves the next block of audio if its available + */ + std::vector<float> Next(); + }; +} // namespace asr
\ No newline at end of file diff --git a/samples/SpeechRecognition/include/DataStructures.hpp b/samples/SpeechRecognition/include/DataStructures.hpp new file mode 100644 index 0000000000..9922265299 --- /dev/null +++ b/samples/SpeechRecognition/include/DataStructures.hpp @@ -0,0 +1,102 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include <stdio.h> +#include <iterator> + +/** + * Class Array2d is a data structure that represents a two dimensional array. + * The data is allocated in contiguous memory, arranged row-wise + * and individual elements can be accessed with the () operator. + * For example a two dimensional array D of size (M, N) can be accessed: + * + * _|<------------- col size = N -------->| + * | D(r=0, c=0) D(r=0, c=1)... D(r=0, c=N) + * | D(r=1, c=0) D(r=1, c=1)... D(r=1, c=N) + * | ... + * row size = M ... + * | ... + * _ D(r=M, c=0) D(r=M, c=1)... D(r=M, c=N) + * + */ +template<typename T> +class Array2d +{ +private: + size_t m_rows; + size_t m_cols; + T* m_data; + +public: + /** + * Creates the array2d with the given sizes. + * + * @param rows number of rows. + * @param cols number of columns. + */ + Array2d(unsigned rows, unsigned cols) + { + if (rows == 0 || cols == 0) { + printf("Array2d constructor has 0 size.\n"); + m_data = nullptr; + return; + } + m_rows = rows; + m_cols = cols; + m_data = new T[rows * cols]; + } + + ~Array2d() + { + delete[] m_data; + } + + T& operator() (unsigned int row, unsigned int col) + { + return m_data[m_cols * row + col]; + } + + T operator() (unsigned int row, unsigned int col) const + { + return m_data[m_cols * row + col]; + } + + /** + * Gets rows number of the current array2d. + * @return number of rows. + */ + size_t size(size_t dim) + { + switch (dim) + { + case 0: + return m_rows; + case 1: + return m_cols; + default: + return 0; + } + } + + /** + * Gets the array2d total size. + */ + size_t totalSize() + { + return m_rows * m_cols; + } + + /** + * array2d iterator. + */ + using iterator=T*; + using const_iterator=T const*; + + iterator begin() { return m_data; } + iterator end() { return m_data + totalSize(); } + const_iterator begin() const { return m_data; } + const_iterator end() const { return m_data + totalSize(); }; +}; diff --git a/samples/SpeechRecognition/include/Decoder.hpp b/samples/SpeechRecognition/include/Decoder.hpp new file mode 100644 index 0000000000..69d97ccf64 --- /dev/null +++ b/samples/SpeechRecognition/include/Decoder.hpp @@ -0,0 +1,63 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <string> +#include <map> +#include <vector> +#include <algorithm> +#include <cmath> + +# pragma once + +namespace asr +{ +/** +* @brief Class used to Decode the output of the ASR inference +* +*/ + class Decoder + { + public: + std::map<int, std::string> m_labels; + /** + * @brief Default constructor + * @param[in] labels - map of labels to be used for decoding to text. + */ + Decoder(std::map<int, std::string>& labels); + + /** + * @brief Function to decode the output into a text string + * @param[in] output - the output vector to decode. + */ + template<typename T> + std::string DecodeOutput(std::vector<T>& contextToProcess) + { + int rowLength = 29; + + std::vector<char> unfilteredText; + + for(int row = 0; row < contextToProcess.size()/rowLength; ++row) + { + std::vector<int16_t> rowVector; + for(int j = 0; j < rowLength; ++j) + { + rowVector.emplace_back(static_cast<int16_t>(contextToProcess[row * rowLength + j])); + } + + int max_index = std::distance(rowVector.begin(),std::max_element(rowVector.begin(), rowVector.end())); + unfilteredText.emplace_back(this->m_labels.at(max_index)[0]); + } + + std::string filteredText = FilterCharacters(unfilteredText); + return filteredText; + } + + /** + * @brief Function to filter out unwanted characters + * @param[in] unfiltered - the unfiltered output to be processed. + */ + std::string FilterCharacters(std::vector<char>& unfiltered); + }; +} // namespace asr diff --git a/samples/SpeechRecognition/include/MFCC.hpp b/samples/SpeechRecognition/include/MFCC.hpp new file mode 100644 index 0000000000..14b6d9fe79 --- /dev/null +++ b/samples/SpeechRecognition/include/MFCC.hpp @@ -0,0 +1,244 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <vector> +#include <cstdint> +#include <cmath> +#include <limits> +#include <string> + +/* MFCC's consolidated parameters */ +class MfccParams +{ +public: + float m_samplingFreq; + int m_numFbankBins; + float m_melLoFreq; + float m_melHiFreq; + int m_numMfccFeatures; + int m_frameLen; + int m_frameLenPadded; + bool m_useHtkMethod; + int m_numMfccVectors; + + /** @brief Constructor */ + 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); + + /* Delete the default constructor */ + MfccParams() = delete; + + /* Default destructor */ + ~MfccParams() = default; + + /** @brief String representation of parameters */ + std::string Str(); +}; + +/** + * @brief Class for MFCC feature extraction. + * Based on https://github.com/ARM-software/ML-KWS-for-MCU/blob/master/Deployment/Source/MFCC/mfcc.cpp + * This class is designed to be generic and self-sufficient but + * certain calculation routines can be overridden to accommodate + * use-case specific requirements. + */ +class MFCC +{ + +public: + + /** + * @brief Extract MFCC features for one single small frame of + * audio data e.g. 640 samples. + * @param[in] audioData - Vector of audio samples to calculate + * features for. + * @return Vector of extracted MFCC features. + **/ + std::vector<float> MfccCompute(const std::vector<float>& audioData); + + MfccParams _m_params; + + /** + * @brief Constructor + * @param[in] params - MFCC parameters + */ + MFCC(const MfccParams& params); + + /* Delete the default constructor */ + MFCC() = delete; + + /** @brief Default destructor */ + ~MFCC() = default; + + /** @brief Initialise */ + void Init(); + + /** + * @brief Extract MFCC features and quantise for one single small + * frame of audio data e.g. 640 samples. + * @param[in] audioData - Vector of audio samples to calculate + * features for. + * @param[in] quantScale - quantisation scale. + * @param[in] quantOffset - quantisation offset + * @return Vector of extracted quantised MFCC features. + **/ + template<typename T> + std::vector<T> MfccComputeQuant(const std::vector<float>& audioData, + const float quantScale, + const int quantOffset) + { + this->_MfccComputePreFeature(audioData); + float minVal = std::numeric_limits<T>::min(); + float maxVal = std::numeric_limits<T>::max(); + + std::vector<T> mfccOut(this->_m_params.m_numMfccFeatures); + const size_t numFbankBins = this->_m_params.m_numFbankBins; + + /* Take DCT. Uses matrix mul. */ + for (size_t i = 0, j = 0; i < mfccOut.size(); ++i, j += numFbankBins) + { + float sum = 0; + for (size_t k = 0; k < numFbankBins; ++k) + { + sum += this->_m_dctMatrix[j + k] * this->_m_melEnergies[k]; + } + /* Quantize to T. */ + sum = std::round((sum / quantScale) + quantOffset); + mfccOut[i] = static_cast<T>(std::min<float>(std::max<float>(sum, minVal), maxVal)); + } + + return mfccOut; + } + + /* Constants */ + static constexpr float logStep = 1.8562979903656 / 27.0; + static constexpr float freqStep = 200.0 / 3; + static constexpr float minLogHz = 1000.0; + static constexpr float minLogMel = minLogHz / freqStep; + +protected: + /** + * @brief Project input frequency to Mel Scale. + * @param[in] freq - input frequency in floating point + * @param[in] useHTKmethod - bool to signal if HTK method is to be + * used for calculation + * @return Mel transformed frequency in floating point + **/ + static float MelScale(const float freq, + const bool useHTKMethod = true); + + /** + * @brief Inverse Mel transform - convert MEL warped frequency + * back to normal frequency + * @param[in] freq - Mel frequency in floating point + * @param[in] useHTKmethod - bool to signal if HTK method is to be + * used for calculation + * @return Real world frequency in floating point + **/ + static float InverseMelScale(const float melFreq, + const bool useHTKMethod = true); + + /** + * @brief Populates MEL energies after applying the MEL filter + * bank weights and adding them up to be placed into + * bins, according to the filter bank's first and last + * indices (pre-computed for each filter bank element + * by _CreateMelFilterBank function). + * @param[in] fftVec Vector populated with FFT magnitudes + * @param[in] melFilterBank 2D Vector with filter bank weights + * @param[in] filterBankFilterFirst Vector containing the first indices of filter bank + * to be used for each bin. + * @param[in] filterBankFilterLast Vector containing the last indices of filter bank + * to be used for each bin. + * @param[out] melEnergies Pre-allocated vector of MEL energies to be + * populated. + * @return true if successful, false otherwise + */ + virtual bool 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); + + /** + * @brief Converts the Mel energies for logarithmic scale + * @param[in/out] melEnergies - 1D vector of Mel energies + **/ + virtual void ConvertToLogarithmicScale(std::vector<float>& melEnergies); + + /** + * @brief Create a matrix used to calculate Discrete Cosine + * Transform. + * @param[in] inputLength - input length of the buffer on which + * DCT will be performed + * @param[in] coefficientCount - Total coefficients per input + * length + * @return 1D vector with inputLength x coefficientCount elements + * populated with DCT coefficients. + */ + virtual std::vector<float> CreateDCTMatrix( + const int32_t inputLength, + const int32_t coefficientCount); + + /** + * @brief Given the low and high Mel values, get the normaliser + * for weights to be applied when populating the filter + * bank. + * @param[in] leftMel - low Mel frequency value + * @param[in] rightMel - high Mel frequency value + * @param[in] useHTKMethod - bool to signal if HTK method is to be + * used for calculation + */ + virtual float GetMelFilterBankNormaliser( + const float& leftMel, + const float& rightMel, + const bool useHTKMethod); + +private: + + std::vector<float> _m_frame; + std::vector<float> _m_buffer; + std::vector<float> _m_melEnergies; + std::vector<float> _m_windowFunc; + std::vector<std::vector<float>> _m_melFilterBank; + std::vector<float> _m_dctMatrix; + std::vector<int32_t> _m_filterBankFilterFirst; + std::vector<int32_t> _m_filterBankFilterLast; + bool _m_filterBankInitialised; + + /** + * @brief Initialises the filter banks and the DCT matrix **/ + void _InitMelFilterBank(); + + /** + * @brief Signals whether the instance of MFCC has had its + * required buffers initialised + * @return True if initialised, false otherwise + **/ + bool _IsMelFilterBankInited(); + + /** + * @brief Create mel filter banks for MFCC calculation. + * @return 2D vector of floats + **/ + std::vector<std::vector<float>> _CreateMelFilterBank(); + + /** + * @brief Computes and populates internal memeber buffers used + * in MFCC feature calculation + * @param[in] audioData - 1D vector of 16-bit audio data + */ + void _MfccComputePreFeature(const std::vector<float>& audioData); + + /** @brief Computes the magnitude from an interleaved complex array */ + void _ConvertToPowerSpectrum(); + +}; + diff --git a/samples/SpeechRecognition/include/MathUtils.hpp b/samples/SpeechRecognition/include/MathUtils.hpp new file mode 100644 index 0000000000..5f81fb6507 --- /dev/null +++ b/samples/SpeechRecognition/include/MathUtils.hpp @@ -0,0 +1,85 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <vector> +#include <cmath> +#include <cstdint> +#include <numeric> + +class MathUtils +{ + +public: + + /** + * @brief Computes the FFT for the input vector + * @param[in] input Floating point vector of input elements + * @param[out] fftOutput Output buffer to be populated by computed + * FFTs + * @return none + */ + static void FftF32(std::vector<float>& input, + std::vector<float>& fftOutput); + + + /** + * @brief Computes the dot product of two 1D floating point + * vectors. + * result = sum(srcA[0]*srcB[0] + srcA[1]*srcB[1] + ..) + * @param[in] srcPtrA pointer to the first element of first + * array + * @param[in] srcPtrB pointer to the first element of second + * array + * @param[in] srcLen Number of elements in the array/vector + * @return dot product + */ + static float DotProductF32(float* srcPtrA, float* srcPtrB, + const int srcLen); + + /** + * @brief Computes the squared magnitude of floating point + * complex number array. + * @param[in] ptrSrc pointer to the first element of input + * array + * @param[in] srcLen Number of elements in the array/vector + * @param[out] ptrDst Output buffer to be populated + * @param[in] dstLen output buffer len (for sanity check only) + * @return true if successful, false otherwise + */ + static bool ComplexMagnitudeSquaredF32(float* ptrSrc, + const int srcLen, + float* ptrDst, + const int dstLen); + + /** + * @brief Computes the natural logarithms of input floating point + * vector + * @param[in] input Floating point input vector + * @param[out] output Pre-allocated buffer to be populated with + * natural log values of each input element + * @return none + */ + static void VecLogarithmF32(std::vector <float>& input, + std::vector <float>& output); + + /** + * @brief Gets the mean of a floating point array of elements + * @param[in] ptrSrc pointer to the first element + * @param[in] srcLen Number of elements in the array/vector + * @return average value + */ + static float MeanF32(float* ptrSrc, const uint32_t srcLen); + + /** + * @brief Gets the standard deviation of a floating point array + * of elements + * @param[in] ptrSrc pointer to the first element + * @param[in] srcLen Number of elements in the array/vector + * @param[in] mean pre-computed mean value + * @return standard deviation value + */ + static float StdDevF32(float* ptrSrc, const uint32_t srcLen, + const float mean); +}; diff --git a/samples/SpeechRecognition/include/Preprocess.hpp b/samples/SpeechRecognition/include/Preprocess.hpp new file mode 100644 index 0000000000..80c568439b --- /dev/null +++ b/samples/SpeechRecognition/include/Preprocess.hpp @@ -0,0 +1,175 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "DataStructures.hpp" +#include "SlidingWindow.hpp" +#include <numeric> +#include "MFCC.hpp" + +/* Class to facilitate pre-processing calculation for Wav2Letter model + * for ASR */ +using AudioWindow = SlidingWindow <const float>; + +class Preprocess +{ +public: + + MFCC _m_mfcc; /* MFCC instance */ + + /* Actual buffers to be populated */ + Array2d<float> _m_mfccBuf; /* Contiguous buffer 1D: MFCC */ + Array2d<float> _m_delta1Buf; /* Contiguous buffer 1D: Delta 1 */ + Array2d<float> _m_delta2Buf; /* Contiguous buffer 1D: Delta 2 */ + + uint32_t _m_windowLen; /* Window length for MFCC */ + uint32_t _m_windowStride; /* Window stride len for MFCC */ + AudioWindow _m_window; /* Sliding window */ + + /** + * @brief Constructor + * @param[in] numMfccFeatures number of MFCC features per window + * @param[in] windowLen number of elements in a window + * @param[in] windowStride stride (in number of elements) for + * moving the window + * @param[in] numMfccVectors number of MFCC vectors per window + */ + Preprocess( + const uint32_t windowLen, + const uint32_t windowStride, + const MFCC mfccInst); + Preprocess() = delete; + ~Preprocess(); + + /** + * @brief Calculates the features required from audio data. This + * includes MFCC, first and second order deltas, + * normalisation and finally, quantisation. The tensor is + * populated with feature from a given window placed along + * in a single row. + * @param[in] audioData pointer to the first element of audio data + * @param[in] audioDataLen number of elements in the audio data + * @param[in] tensor tensor to be populated + * @return true if successful, false in case of error. + */ + bool Invoke(const float* audioData, + const uint32_t audioDataLen, + std::vector<int8_t>& output, + int quantOffset, + float quantScale); + + +protected: + /** + * @brief Computes the first and second order deltas for the + * MFCC buffers - they are assumed to be populated. + * + * @param[in] mfcc MFCC buffers + * @param[out] delta1 result of the first diff computation + * @param[out] delta2 result of the second diff computation + * + * @return true if successful, false otherwise + */ + static bool _ComputeDeltas(Array2d<float>& mfcc, + Array2d<float>& delta1, + Array2d<float>& delta2); + + /** + * @brief Given a 2D vector of floats, computes the mean + * @param[in] vec vector of vector of floats + * @return mean value + */ + static float _GetMean(Array2d<float>& vec); + + /** + * @brief Given a 2D vector of floats, computes the stddev + * @param[in] vec vector of vector of floats + * @param[in] mean mean value of the vector passed in + * @return stddev value + */ + static float _GetStdDev(Array2d<float>& vec, + const float mean); + + /** + * @brief Given a 2D vector of floats, normalises it using + * the mean and the stddev + * @param[in/out] vec vector of vector of floats + * @return + */ + static void _NormaliseVec(Array2d<float>& vec); + + /** + * @brief Normalises the MFCC and delta buffers + * @return + */ + void _Normalise(); + + /** + * @brief Given the quantisation and data type limits, computes + * the quantised values of a floating point input data. + * @param[in] elem Element to be quantised + * @param[in] quantScale Scale + * @param[in] quantOffset Offset + * @param[in] minVal Numerical limit - minimum + * @param[in] maxVal Numerical limit - maximum + * @return floating point quantised value + */ + static float _GetQuantElem( + const float elem, + const float quantScale, + const int quantOffset, + const float minVal, + const float maxVal); + + /** + * @brief Quantises the MFCC and delta buffers, and places them + * in the output buffer. While doing so, it transposes + * the data. Reason: Buffers in this class are arranged + * for "time" axis to be row major. Primary reason for + * this being the convolution speed up (as we can use + * contiguous memory). The output, however, requires the + * time axis to be in column major arrangement. + * @param[in] outputBuf pointer to the output buffer + * @param[in] outputBufSz output buffer's size + * @param[in] quantScale quantisation scale + * @param[in] quantOffset quantisation offset + */ + template <typename T> + bool _Quantise(T* outputBuf, int quantOffset, float quantScale) + { + /* Populate */ + T* outputBufMfcc = outputBuf; + T* outputBufD1 = outputBuf + this->_m_mfcc._m_params.m_numMfccFeatures; + T* outputBufD2 = outputBufD1 + this->_m_mfcc._m_params.m_numMfccFeatures; + const uint32_t ptrIncr = this->_m_mfcc._m_params.m_numMfccFeatures * 2; /* (3 vectors - 1 vector) */ + + const float minVal = std::numeric_limits<T>::min(); + const float maxVal = std::numeric_limits<T>::max(); + + /* We need to do a transpose while copying and concatenating + * the tensor*/ + for (uint32_t j = 0; j < this->_m_mfcc._m_params.m_numMfccVectors; ++j) { + for (uint32_t i = 0; i < this->_m_mfcc._m_params.m_numMfccFeatures; ++i) + { + *outputBufMfcc++ = static_cast<T>(this->_GetQuantElem( + this->_m_mfccBuf(i, j), quantScale, + quantOffset, minVal, maxVal)); + *outputBufD1++ = static_cast<T>(this->_GetQuantElem( + this->_m_delta1Buf(i, j), quantScale, + quantOffset, minVal, maxVal)); + *outputBufD2++ = static_cast<T>(this->_GetQuantElem( + this->_m_delta2Buf(i, j), quantScale, + quantOffset, minVal, maxVal)); + } + outputBufMfcc += ptrIncr; + outputBufD1 += ptrIncr; + outputBufD2 += ptrIncr; + } + + return true; + } +}; + diff --git a/samples/SpeechRecognition/include/SlidingWindow.hpp b/samples/SpeechRecognition/include/SlidingWindow.hpp new file mode 100644 index 0000000000..791a0b7fc0 --- /dev/null +++ b/samples/SpeechRecognition/include/SlidingWindow.hpp @@ -0,0 +1,161 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +template<class T> +class SlidingWindow +{ +protected: + T* m_start = nullptr; + size_t m_dataSize = 0; + size_t m_size = 0; + size_t m_stride = 0; + size_t m_count = 0; +public: + + /** + * Creates the window slider through the given data. + * + * @param data pointer to the data to slide through. + * @param dataSize size in T type elements wise. + * @param windowSize sliding window size in T type wise elements. + * @param stride stride size in T type wise elements. + */ + SlidingWindow(T* data, size_t dataSize, + size_t windowSize, size_t stride) + { + m_start = data; + m_dataSize = dataSize; + m_size = windowSize; + m_stride = stride; + } + + SlidingWindow() = default; + + ~SlidingWindow() = default; + + /** + * Get the next data window. + * @return pointer to the next window, if next window is not available nullptr is returned. + */ + virtual T* Next() + { + if (HasNext()) + { + m_count++; + return m_start + Index() * m_stride; + } + else + { + return nullptr; + } + } + + /** + * Checks if the next data portion is available. + * @return true if next data portion is available + */ + bool HasNext() + { + return this->m_count < 1 + this->FractionalTotalStrides() && (this->NextWindowStartIndex() < this->m_dataSize); + } + + /** + * Resest the slider to the initial position. + */ + virtual void Reset() + { + m_count = 0; + } + + /** + * Resest the slider to the initial position. + */ + virtual size_t GetWindowSize() + { + return m_size; + } + + /** + * Resets the slider to the start of the new data. + * New data size MUST be the same as the old one. + * @param newStart pointer to the new data to slide through. + */ + virtual void Reset(T* newStart) + { + m_start = newStart; + Reset(); + } + + /** + * Gets current index of the sliding window. + * @return current position of the sliding window in number of strides + */ + size_t Index() + { + return m_count == 0? 0: m_count - 1; + } + + /** + * Gets the index from the start of the data where the next window will begin. + * While Index() returns the index of sliding window itself this function returns the index of the data + * element itself. + * @return Index from the start of the data where the next sliding window will begin. + */ + virtual size_t NextWindowStartIndex() + { + return m_count == 0? 0: ((m_count) * m_stride); + } + + /** + * Go to given sliding window index. + * @param index new position of the sliding window. if index is invalid (greater than possible range of strides) + * then next call to Next() will return nullptr. + */ + void FastForward(size_t index) + { + m_count = index; + } + + /** + * Calculates whole number of times the window can stride through the given data. + * @return maximum number of strides. + */ + size_t TotalStrides() + { + if (m_size > m_dataSize) + { + return 0; + } + return ((m_dataSize - m_size)/m_stride); + } + + /** + * Calculates number of times the window can stride through the given data. May not be a whole number. + * @return Number of strides to cover all data. + */ + float FractionalTotalStrides() + { + if(this->m_size > this->m_dataSize) + { + return this->m_dataSize / this->m_size; + } + else + { + return ((this->m_dataSize - this->m_size)/ static_cast<float>(this->m_stride)); + } + + } + + /** + * Calculates the remaining data left to be processed + * @return The remaining unprocessed data + */ + int RemainingData() + { + return this->m_dataSize - this->NextWindowStartIndex(); + } +};
\ No newline at end of file diff --git a/samples/SpeechRecognition/include/SpeechRecognitionPipeline.hpp b/samples/SpeechRecognition/include/SpeechRecognitionPipeline.hpp new file mode 100644 index 0000000000..47ce30416f --- /dev/null +++ b/samples/SpeechRecognition/include/SpeechRecognitionPipeline.hpp @@ -0,0 +1,139 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "ArmnnNetworkExecutor.hpp" +#include "Decoder.hpp" +#include "MFCC.hpp" +#include "Preprocess.hpp" + +namespace asr +{ +/** + * Generic Speech Recognition pipeline with 3 steps: data pre-processing, inference execution and inference + * result post-processing. + * + */ +class ASRPipeline +{ +public: + + /** + * Creates speech recognition pipeline with given network executor and decoder. + * @param executor - unique pointer to inference runner + * @param decoder - unique pointer to inference results decoder + */ + ASRPipeline(std::unique_ptr<common::ArmnnNetworkExecutor<int8_t>> executor, + std::unique_ptr<Decoder> decoder); + + /** + * @brief Standard audio pre-processing implementation. + * + * Preprocesses and prepares the data for inference by + * extracting the MFCC features. + + * @param[in] audio - the raw audio data + * @param[out] preprocessor - the preprocessor object, which handles the data prepreration + */ + template<typename Tin,typename Tout> + std::vector<Tout> PreProcessing(std::vector<Tin>& audio, Preprocess& preprocessor) + { + int audioDataToPreProcess = preprocessor._m_windowLen + + ((preprocessor._m_mfcc._m_params.m_numMfccVectors -1) *preprocessor._m_windowStride); + int outputBufferSize = preprocessor._m_mfcc._m_params.m_numMfccVectors + * preprocessor._m_mfcc._m_params.m_numMfccFeatures * 3; + std::vector<Tout> outputBuffer(outputBufferSize); + preprocessor.Invoke(audio.data(), audioDataToPreProcess, outputBuffer, m_executor->GetQuantizationOffset(), + m_executor->GetQuantizationScale()); + return outputBuffer; + } + + /** + * @brief Executes inference + * + * Calls inference runner provided during instance construction. + * + * @param[in] preprocessedData - input inference data. Data type should be aligned with input tensor. + * @param[out] result - raw inference results. + */ + template<typename T> + void Inference(const std::vector<T>& preprocessedData, common::InferenceResults<int8_t>& result) + { + size_t data_bytes = sizeof(std::vector<T>) + (sizeof(T) * preprocessedData.size()); + m_executor->Run(preprocessedData.data(), data_bytes, result); + } + + /** + * @brief Standard inference results post-processing implementation. + * + * Decodes inference results using decoder provided during construction. + * + * @param[in] inferenceResult - inference results to be decoded. + * @param[in] isFirstWindow - for checking if this is the first window of the sliding window. + * @param[in] isLastWindow - for checking if this is the last window of the sliding window. + * @param[in] currentRContext - the right context of the output text. To be output if it is the last window. + */ + template<typename T> + void PostProcessing(common::InferenceResults<int8_t>& inferenceResult, + bool& isFirstWindow, + bool isLastWindow, + std::string currentRContext) + { + int rowLength = 29; + int middleContextStart = 49; + int middleContextEnd = 99; + int leftContextStart = 0; + int rightContextStart = 100; + int rightContextEnd = 148; + + std::vector<T> contextToProcess; + + // If isFirstWindow we keep the left context of the output + if(isFirstWindow) + { + std::vector<T> chunk(&inferenceResult[0][leftContextStart], + &inferenceResult[0][middleContextEnd * rowLength]); + contextToProcess = chunk; + } + // Else we only keep the middle context of the output + else + { + std::vector<T> chunk(&inferenceResult[0][middleContextStart * rowLength], + &inferenceResult[0][middleContextEnd * rowLength]); + contextToProcess = chunk; + } + std::string output = this->m_decoder->DecodeOutput<T>(contextToProcess); + isFirstWindow = false; + std::cout << output << std::flush; + + // If this is the last window, we print the right context of the output + if(isLastWindow) + { + std::vector<T> rContext(&inferenceResult[0][rightContextStart*rowLength], + &inferenceResult[0][rightContextEnd * rowLength]); + currentRContext = this->m_decoder->DecodeOutput(rContext); + std::cout << currentRContext << std::endl; + } + } + +protected: + std::unique_ptr<common::ArmnnNetworkExecutor<int8_t>> m_executor; + std::unique_ptr<Decoder> m_decoder; +}; + +using IPipelinePtr = std::unique_ptr<asr::ASRPipeline>; + +/** + * Constructs speech recognition pipeline based on configuration provided. + * + * @param[in] config - speech recognition pipeline configuration. + * @param[in] labels - asr labels + * + * @return unique pointer to asr pipeline. + */ +IPipelinePtr CreatePipeline(common::PipelineOptions& config, std::map<int, std::string>& labels); + +}// namespace asr
\ No newline at end of file |