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/*
* Copyright (c) 2021-2022 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 <cmath>
namespace arm {
namespace app {
AsrPreProcess::AsrPreProcess(TfLiteTensor* inputTensor, const uint32_t numMfccFeatures,
const uint32_t numFeatureFrames, const uint32_t mfccWindowLen,
const uint32_t mfccWindowStride
):
m_mfcc(numMfccFeatures, mfccWindowLen),
m_inputTensor(inputTensor),
m_mfccBuf(numMfccFeatures, numFeatureFrames),
m_delta1Buf(numMfccFeatures, numFeatureFrames),
m_delta2Buf(numMfccFeatures, numFeatureFrames),
m_mfccWindowLen(mfccWindowLen),
m_mfccWindowStride(mfccWindowStride),
m_numMfccFeats(numMfccFeatures),
m_numFeatureFrames(numFeatureFrames)
{
if (numMfccFeatures > 0 && mfccWindowLen > 0) {
this->m_mfcc.Init();
}
}
bool AsrPreProcess::DoPreProcess(const void* audioData, const size_t audioDataLen)
{
this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(
static_cast<const int16_t*>(audioData), audioDataLen,
this->m_mfccWindowLen, this->m_mfccWindowStride);
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 audio. */
while (this->m_mfccSlidingWindow.HasNext()) {
const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next();
auto mfccAudioData = std::vector<int16_t>(
mfccWindow,
mfccWindow + this->m_mfccWindowLen);
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_numFeatureFrames) {
std::vector<int16_t> zerosWindow = std::vector<int16_t>(this->m_mfccWindowLen, 0);
std::vector<float> mfccZeros = this->m_mfcc.MfccCompute(zerosWindow);
while (mfccBufIdx != this->m_numFeatureFrames) {
memcpy(&this->m_mfccBuf(0, mfccBufIdx),
mfccZeros.data(), sizeof(float) * m_numMfccFeats);
++mfccBufIdx;
}
}
/* Compute first and second order deltas from MFCCs. */
AsrPreProcess::ComputeDeltas(this->m_mfccBuf, this->m_delta1Buf, this->m_delta2Buf);
/* Standardize calculated features. */
this->Standarize();
/* Quantise. */
QuantParams quantParams = GetTensorQuantParams(this->m_inputTensor);
if (0 == quantParams.scale) {
printf_err("Quantisation scale can't be 0\n");
return false;
}
switch(this->m_inputTensor->type) {
case kTfLiteUInt8:
return this->Quantise<uint8_t>(
tflite::GetTensorData<uint8_t>(this->m_inputTensor), this->m_inputTensor->bytes,
quantParams.scale, quantParams.offset);
case kTfLiteInt8:
return this->Quantise<int8_t>(
tflite::GetTensorData<int8_t>(this->m_inputTensor), this->m_inputTensor->bytes,
quantParams.scale, quantParams.offset);
default:
printf_err("Unsupported tensor type %s\n",
TfLiteTypeGetName(this->m_inputTensor->type));
}
return false;
}
bool AsrPreProcess::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;
}
void AsrPreProcess::StandardizeVecF32(Array2d<float>& vec)
{
auto mean = math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
auto stddev = math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), 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 AsrPreProcess::Standarize()
{
AsrPreProcess::StandardizeVecF32(this->m_mfccBuf);
AsrPreProcess::StandardizeVecF32(this->m_delta1Buf);
AsrPreProcess::StandardizeVecF32(this->m_delta2Buf);
}
float AsrPreProcess::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 app */
} /* namespace arm */
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