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//
// 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
#include <numeric>
#include "DataStructures.hpp"
#include "SlidingWindow.hpp"
#include "MFCC.hpp"
#include "Wav2LetterMFCC.hpp"
// Class to facilitate pre-processing calculation for Wav2Letter model for ASR
using AudioWindow = SlidingWindow<const float>;
class Wav2LetterPreprocessor
{
public:
Wav2LetterPreprocessor(uint32_t windowLen, uint32_t windowStride,
std::unique_ptr<Wav2LetterMFCC> mfccInst);
/**
* @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, 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:
/**
* @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);
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);
/**
* @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, 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(
float elem,
float quantScale,
int quantOffset,
float minVal,
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>(Wav2LetterPreprocessor::GetQuantElem(
this->m_mfccBuf(i, j), quantScale,
quantOffset, minVal, maxVal));
*outputBufD1++ = static_cast<T>(Wav2LetterPreprocessor::GetQuantElem(
this->m_delta1Buf(i, j), quantScale,
quantOffset, minVal, maxVal));
*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
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