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-rw-r--r--source/use_case/kws_asr/src/Wav2LetterPreprocess.cc208
1 files changed, 0 insertions, 208 deletions
diff --git a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
deleted file mode 100644
index 92b0631..0000000
--- a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
+++ /dev/null
@@ -1,208 +0,0 @@
-/*
- * 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 */ \ No newline at end of file
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-28 23:24:57 +0000