diff options
Diffstat (limited to 'source/use_case/kws_asr/src/Wav2LetterPreprocess.cc')
-rw-r--r-- | source/use_case/kws_asr/src/Wav2LetterPreprocess.cc | 228 |
1 files changed, 228 insertions, 0 deletions
diff --git a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc new file mode 100644 index 0000000..613ddb0 --- /dev/null +++ b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc @@ -0,0 +1,228 @@ +/* + * Copyright (c) 2021 Arm Limited. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "Wav2LetterPreprocess.hpp" + +#include "PlatformMath.hpp" +#include "TensorFlowLiteMicro.hpp" + +#include <algorithm> +#include <math.h> + +namespace arm { +namespace app { +namespace audio { +namespace asr { + + Preprocess::Preprocess( + const uint32_t numMfccFeatures, + const uint32_t windowLen, + const uint32_t windowStride, + const uint32_t numMfccVectors): + _m_mfcc(numMfccFeatures, windowLen), + _m_mfccBuf(numMfccFeatures, numMfccVectors), + _m_delta1Buf(numMfccFeatures, numMfccVectors), + _m_delta2Buf(numMfccFeatures, numMfccVectors), + _m_windowLen(windowLen), + _m_windowStride(windowStride), + _m_numMfccFeats(numMfccFeatures), + _m_numFeatVectors(numMfccVectors), + _m_window() + { + if (numMfccFeatures > 0 && windowLen > 0) { + this->_m_mfcc.Init(); + } + } + + bool Preprocess::Invoke( + const int16_t* audioData, + const uint32_t audioDataLen, + TfLiteTensor* tensor) + { + this->_m_window = SlidingWindow<const int16_t>( + audioData, audioDataLen, + this->_m_windowLen, this->_m_windowStride); + + uint32_t mfccBufIdx = 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 int16_t* mfccWindow = this->_m_window.Next(); + auto mfccAudioData = std::vector<int16_t>( + 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 adding MFCC for zeros. */ + if (mfccBufIdx != this->_m_numFeatVectors) { + std::vector<int16_t> zerosWindow = std::vector<int16_t>(this->_m_windowLen, 0); + std::vector<float> mfccZeros = this->_m_mfcc.MfccCompute(zerosWindow); + + while (mfccBufIdx != this->_m_numFeatVectors) { + memcpy(&this->_m_mfccBuf(0, mfccBufIdx), + mfccZeros.data(), sizeof(float) * _m_numMfccFeats); + ++mfccBufIdx; + } + } + + /* Compute first and second order deltas from MFCCs. */ + this->_ComputeDeltas(this->_m_mfccBuf, + this->_m_delta1Buf, + this->_m_delta2Buf); + + /* Normalise. */ + this->_Normalise(); + + /* Quantise. */ + QuantParams quantParams = GetTensorQuantParams(tensor); + + if (0 == quantParams.scale) { + printf_err("Quantisation scale can't be 0\n"); + return false; + } + + switch(tensor->type) { + case kTfLiteUInt8: + return this->_Quantise<uint8_t>( + tflite::GetTensorData<uint8_t>(tensor), tensor->bytes, + quantParams.scale, quantParams.offset); + case kTfLiteInt8: + return this->_Quantise<int8_t>( + tflite::GetTensorData<int8_t>(tensor), tensor->bytes, + quantParams.scale, quantParams.offset); + default: + printf_err("Unsupported tensor type %s\n", + TfLiteTypeGetName(tensor->type)); + } + + return false; + } + + 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 math::MathUtils::MeanF32(vec.begin(), vec.totalSize()); + } + + float Preprocess::_GetStdDev(Array2d<float>& vec, const float mean) + { + return math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean); + } + + void Preprocess::_NormaliseVec(Array2d<float>& vec) + { + auto mean = Preprocess::_GetMean(vec); + auto stddev = Preprocess::_GetStdDev(vec, mean); + + debug("Mean: %f, Stddev: %f\n", mean, stddev); + 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); + return std::min<float>(std::max<float>(val, minVal), maxVal); + } + +} /* namespace asr */ +} /* namespace audio */ +} /* namespace app */ +} /* namespace arm */
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