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 | 4 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/MFCC.hpp | 244 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/MathUtils.hpp | 85 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/SlidingWindow.hpp | 161 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/SpeechRecognitionPipeline.hpp | 59 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/Wav2LetterMFCC.hpp | 78 | ||||
| -rw-r--r-- | samples/SpeechRecognition/include/Wav2LetterPreprocessor.hpp (renamed from samples/SpeechRecognition/include/Preprocess.hpp) | 123 |
9 files changed, 161 insertions, 757 deletions
diff --git a/samples/SpeechRecognition/include/AudioCapture.hpp b/samples/SpeechRecognition/include/AudioCapture.hpp deleted file mode 100644 index 90c2eccacf..0000000000 --- a/samples/SpeechRecognition/include/AudioCapture.hpp +++ /dev/null @@ -1,62 +0,0 @@ -// -// 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 deleted file mode 100644 index 9922265299..0000000000 --- a/samples/SpeechRecognition/include/DataStructures.hpp +++ /dev/null @@ -1,102 +0,0 @@ -// -// 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 index 69d97ccf64..9dd484a5d1 100644 --- a/samples/SpeechRecognition/include/Decoder.hpp +++ b/samples/SpeechRecognition/include/Decoder.hpp @@ -46,8 +46,8 @@ namespace asr 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]); + int maxIndex = std::distance(rowVector.begin(), std::max_element(rowVector.begin(), rowVector.end())); + unfilteredText.emplace_back(this->m_labels.at(maxIndex)[0]); } std::string filteredText = FilterCharacters(unfilteredText); diff --git a/samples/SpeechRecognition/include/MFCC.hpp b/samples/SpeechRecognition/include/MFCC.hpp deleted file mode 100644 index 14b6d9fe79..0000000000 --- a/samples/SpeechRecognition/include/MFCC.hpp +++ /dev/null @@ -1,244 +0,0 @@ -// -// 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 deleted file mode 100644 index 5f81fb6507..0000000000 --- a/samples/SpeechRecognition/include/MathUtils.hpp +++ /dev/null @@ -1,85 +0,0 @@ -// -// 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/SlidingWindow.hpp b/samples/SpeechRecognition/include/SlidingWindow.hpp deleted file mode 100644 index 791a0b7fc0..0000000000 --- a/samples/SpeechRecognition/include/SlidingWindow.hpp +++ /dev/null @@ -1,161 +0,0 @@ -// -// 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 index 47ce30416f..bc3fbfe151 100644 --- a/samples/SpeechRecognition/include/SpeechRecognitionPipeline.hpp +++ b/samples/SpeechRecognition/include/SpeechRecognitionPipeline.hpp @@ -8,16 +8,16 @@ #include "ArmnnNetworkExecutor.hpp" #include "Decoder.hpp" #include "MFCC.hpp" -#include "Preprocess.hpp" +#include "Wav2LetterPreprocessor.hpp" -namespace asr +namespace asr { /** * Generic Speech Recognition pipeline with 3 steps: data pre-processing, inference execution and inference * result post-processing. * */ -class ASRPipeline +class ASRPipeline { public: @@ -27,7 +27,7 @@ public: * @param decoder - unique pointer to inference results decoder */ ASRPipeline(std::unique_ptr<common::ArmnnNetworkExecutor<int8_t>> executor, - std::unique_ptr<Decoder> decoder); + std::unique_ptr<Decoder> decoder, std::unique_ptr<Wav2LetterPreprocessor> preprocessor); /** * @brief Standard audio pre-processing implementation. @@ -36,20 +36,16 @@ public: * extracting the MFCC features. * @param[in] audio - the raw audio data - * @param[out] preprocessor - the preprocessor object, which handles the data prepreration + * @param[out] preprocessor - the preprocessor object, which handles the data preparation */ - 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; - } + std::vector<int8_t> PreProcessing(std::vector<float>& audio); + + int getInputSamplesSize(); + int getSlidingWindowOffset(); + + // Exposing hardcoded constant as it can only be derived from model knowledge and not from model itself + // Will need to be refactored so that hard coded values are not defined outside of model settings + int SLIDING_WINDOW_OFFSET; /** * @brief Executes inference @@ -60,9 +56,9 @@ public: * @param[out] result - raw inference results. */ template<typename T> - void Inference(const std::vector<T>& preprocessedData, common::InferenceResults<int8_t>& result) + void Inference(const std::vector<T>& preprocessedData, common::InferenceResults<int8_t>& result) { - size_t data_bytes = sizeof(std::vector<T>) + (sizeof(T) * preprocessedData.size()); + size_t data_bytes = sizeof(T) * preprocessedData.size(); m_executor->Run(preprocessedData.data(), data_bytes, result); } @@ -78,9 +74,9 @@ public: */ template<typename T> void PostProcessing(common::InferenceResults<int8_t>& inferenceResult, - bool& isFirstWindow, - bool isLastWindow, - std::string currentRContext) + bool& isFirstWindow, + bool isLastWindow, + std::string currentRContext) { int rowLength = 29; int middleContextStart = 49; @@ -92,17 +88,17 @@ public: std::vector<T> contextToProcess; // If isFirstWindow we keep the left context of the output - if(isFirstWindow) + if (isFirstWindow) { std::vector<T> chunk(&inferenceResult[0][leftContextStart], - &inferenceResult[0][middleContextEnd * rowLength]); + &inferenceResult[0][middleContextEnd * rowLength]); contextToProcess = chunk; } - // Else we only keep the middle context of the output - else + else { + // Else we only keep the middle context of the output std::vector<T> chunk(&inferenceResult[0][middleContextStart * rowLength], - &inferenceResult[0][middleContextEnd * rowLength]); + &inferenceResult[0][middleContextEnd * rowLength]); contextToProcess = chunk; } std::string output = this->m_decoder->DecodeOutput<T>(contextToProcess); @@ -110,10 +106,10 @@ public: std::cout << output << std::flush; // If this is the last window, we print the right context of the output - if(isLastWindow) + if (isLastWindow) { - std::vector<T> rContext(&inferenceResult[0][rightContextStart*rowLength], - &inferenceResult[0][rightContextEnd * rowLength]); + std::vector<T> rContext(&inferenceResult[0][rightContextStart * rowLength], + &inferenceResult[0][rightContextEnd * rowLength]); currentRContext = this->m_decoder->DecodeOutput(rContext); std::cout << currentRContext << std::endl; } @@ -122,6 +118,7 @@ public: protected: std::unique_ptr<common::ArmnnNetworkExecutor<int8_t>> m_executor; std::unique_ptr<Decoder> m_decoder; + std::unique_ptr<Wav2LetterPreprocessor> m_preProcessor; }; using IPipelinePtr = std::unique_ptr<asr::ASRPipeline>; @@ -136,4 +133,4 @@ using IPipelinePtr = std::unique_ptr<asr::ASRPipeline>; */ IPipelinePtr CreatePipeline(common::PipelineOptions& config, std::map<int, std::string>& labels); -}// namespace asr
\ No newline at end of file +} // namespace asr
\ No newline at end of file diff --git a/samples/SpeechRecognition/include/Wav2LetterMFCC.hpp b/samples/SpeechRecognition/include/Wav2LetterMFCC.hpp new file mode 100644 index 0000000000..aa88aafb3b --- /dev/null +++ b/samples/SpeechRecognition/include/Wav2LetterMFCC.hpp @@ -0,0 +1,78 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "MFCC.hpp" + +/* Class to provide Wav2Letter specific MFCC calculation requirements. */ +class Wav2LetterMFCC : public MFCC +{ + +public: + explicit Wav2LetterMFCC(const MfccParams& params) + : MFCC(params) + {} + + Wav2LetterMFCC() = delete; + ~Wav2LetterMFCC() = default; + +protected: + + /** + * @brief Overrides base class implementation of this 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 + */ + bool ApplyMelFilterBank( + std::vector<float>& fftVec, + std::vector<std::vector<float>>& melFilterBank, + std::vector<uint32_t>& filterBankFilterFirst, + std::vector<uint32_t>& filterBankFilterLast, + std::vector<float>& melEnergies) override; + + /** + * @brief Override for the base class implementation convert mel + * energies to logarithmic scale. The difference from + * default behaviour is that the power is converted to dB + * and subsequently clamped. + * @param[in,out] melEnergies 1D vector of Mel energies + **/ + void ConvertToLogarithmicScale(std::vector<float>& melEnergies) override; + + /** + * @brief Create a matrix used to calculate Discrete Cosine + * Transform. Override for the base class' default + * implementation as the first and last elements + * use a different normaliser. + * @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. + */ + std::vector<float> CreateDCTMatrix(int32_t inputLength, + int32_t coefficientCount) override; + + /** + * @brief Given the low and high Mel values, get the normaliser + * for weights to be applied when populating the filter + * bank. Override for the base class implementation. + * @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. + * @return Value to use for normalising. + */ + float GetMelFilterBankNormaliser(const float& leftMel, + const float& rightMel, + bool useHTKMethod) override; +};
\ No newline at end of file diff --git a/samples/SpeechRecognition/include/Preprocess.hpp b/samples/SpeechRecognition/include/Wav2LetterPreprocessor.hpp index 80c568439b..ebc9e864e3 100644 --- a/samples/SpeechRecognition/include/Preprocess.hpp +++ b/samples/SpeechRecognition/include/Wav2LetterPreprocessor.hpp @@ -1,48 +1,23 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // +#ifndef SPEECH_RECOGNITION_EXAMPLE_WAV2LETTERPREPROCESSOR_HPP +#define SPEECH_RECOGNITION_EXAMPLE_WAV2LETTERPREPROCESSOR_HPP -#pragma once - +#include <numeric> #include "DataStructures.hpp" #include "SlidingWindow.hpp" -#include <numeric> #include "MFCC.hpp" +#include "Wav2LetterMFCC.hpp" +// Class to facilitate pre-processing calculation for Wav2Letter model for ASR +using AudioWindow = SlidingWindow<const float>; -/* Class to facilitate pre-processing calculation for Wav2Letter model - * for ASR */ -using AudioWindow = SlidingWindow <const float>; - -class Preprocess +class Wav2LetterPreprocessor { 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(); + Wav2LetterPreprocessor(uint32_t windowLen, uint32_t windowStride, + std::unique_ptr<Wav2LetterMFCC> mfccInst); /** * @brief Calculates the features required from audio data. This @@ -55,12 +30,19 @@ public: * @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, + bool Invoke(const float* audioData, uint32_t audioDataLen, std::vector<int8_t>& output, int quantOffset, float quantScale); + std::unique_ptr<MFCC> m_mfcc; + + // 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 protected: /** @@ -73,16 +55,18 @@ protected: * * @return true if successful, false otherwise */ - static bool _ComputeDeltas(Array2d<float>& mfcc, - Array2d<float>& delta1, - Array2d<float>& delta2); + static bool ComputeDeltas(Array2d<float>& mfcc, + Array2d<float>& delta1, + Array2d<float>& delta2); + +protected: /** * @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); + static float GetMean(Array2d<float>& vec); /** * @brief Given a 2D vector of floats, computes the stddev @@ -90,8 +74,7 @@ protected: * @param[in] mean mean value of the vector passed in * @return stddev value */ - static float _GetStdDev(Array2d<float>& vec, - const float mean); + static float GetStdDev(Array2d<float>& vec, float mean); /** * @brief Given a 2D vector of floats, normalises it using @@ -99,13 +82,13 @@ protected: * @param[in/out] vec vector of vector of floats * @return */ - static void _NormaliseVec(Array2d<float>& vec); + static void NormaliseVec(Array2d<float>& vec); /** * @brief Normalises the MFCC and delta buffers * @return */ - void _Normalise(); + void Normalise(); /** * @brief Given the quantisation and data type limits, computes @@ -117,12 +100,12 @@ protected: * @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); + static float GetQuantElem( + float elem, + float quantScale, + int quantOffset, + float minVal, + float maxVal); /** * @brief Quantises the MFCC and delta buffers, and places them @@ -137,39 +120,39 @@ protected: * @param[in] quantScale quantisation scale * @param[in] quantOffset quantisation offset */ - template <typename T> - bool _Quantise(T* outputBuf, int quantOffset, float quantScale) + template<typename T> + bool Quantise(T*outputBuf, int quantOffset, float quantScale) { - /* Populate */ + // 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) */ + 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) + // 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, + *outputBufMfcc++ = static_cast<T>(Wav2LetterPreprocessor::GetQuantElem( + this->m_mfccBuf(i, j), quantScale, quantOffset, minVal, maxVal)); - *outputBufD1++ = static_cast<T>(this->_GetQuantElem( - this->_m_delta1Buf(i, j), quantScale, + *outputBufD1++ = static_cast<T>(Wav2LetterPreprocessor::GetQuantElem( + this->m_delta1Buf(i, j), quantScale, quantOffset, minVal, maxVal)); - *outputBufD2++ = static_cast<T>(this->_GetQuantElem( - this->_m_delta2Buf(i, j), quantScale, + *outputBufD2++ = static_cast<T>(Wav2LetterPreprocessor::GetQuantElem( + this->m_delta2Buf(i, j), quantScale, quantOffset, minVal, maxVal)); } outputBufMfcc += ptrIncr; outputBufD1 += ptrIncr; outputBufD2 += ptrIncr; } - return true; } }; +#endif //SPEECH_RECOGNITION_EXAMPLE_WAV2LETTERPREPROCESSOR_HPP |