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/*
* Copyright (c) 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 "KwsProcessing.hpp"
#include "log_macros.h"
#include "MicroNetKwsModel.hpp"
namespace arm {
namespace app {
KwsPreProcess::KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numMfccFrames,
int mfccFrameLength, int mfccFrameStride
):
m_inputTensor{inputTensor},
m_mfccFrameLength{mfccFrameLength},
m_mfccFrameStride{mfccFrameStride},
m_numMfccFrames{numMfccFrames},
m_mfcc{audio::MicroNetKwsMFCC(numFeatures, mfccFrameLength)}
{
this->m_mfcc.Init();
/* Deduce the data length required for 1 inference from the network parameters. */
this->m_audioDataWindowSize = this->m_numMfccFrames * this->m_mfccFrameStride +
(this->m_mfccFrameLength - this->m_mfccFrameStride);
/* Creating an MFCC feature sliding window for the data required for 1 inference. */
this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(nullptr, this->m_audioDataWindowSize,
this->m_mfccFrameLength, this->m_mfccFrameStride);
/* For longer audio clips we choose to move by half the audio window size
* => for a 1 second window size there is an overlap of 0.5 seconds. */
this->m_audioDataStride = this->m_audioDataWindowSize / 2;
/* To have the previously calculated features re-usable, stride must be multiple
* of MFCC features window stride. Reduce stride through audio if needed. */
if (0 != this->m_audioDataStride % this->m_mfccFrameStride) {
this->m_audioDataStride -= this->m_audioDataStride % this->m_mfccFrameStride;
}
this->m_numMfccVectorsInAudioStride = this->m_audioDataStride / this->m_mfccFrameStride;
/* Calculate number of the feature vectors in the window overlap region.
* These feature vectors will be reused.*/
this->m_numReusedMfccVectors = this->m_mfccSlidingWindow.TotalStrides() + 1
- this->m_numMfccVectorsInAudioStride;
/* Construct feature calculation function. */
this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_inputTensor,
this->m_numReusedMfccVectors);
if (!this->m_mfccFeatureCalculator) {
printf_err("Feature calculator not initialized.");
}
}
bool KwsPreProcess::DoPreProcess(const void* data, size_t inferenceIndex)
{
if (data == nullptr) {
printf_err("Data pointer is null");
}
/* Set the features sliding window to the new address. */
auto input = static_cast<const int16_t*>(data);
this->m_mfccSlidingWindow.Reset(input);
/* Cache is only usable if we have more than 1 inference to do and it's not the first inference. */
bool useCache = inferenceIndex > 0 && this->m_numReusedMfccVectors > 0;
/* Use a sliding window to calculate MFCC features frame by frame. */
while (this->m_mfccSlidingWindow.HasNext()) {
const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next();
std::vector<int16_t> mfccFrameAudioData = std::vector<int16_t>(mfccWindow,
mfccWindow + this->m_mfccFrameLength);
/* Compute features for this window and write them to input tensor. */
this->m_mfccFeatureCalculator(mfccFrameAudioData, this->m_mfccSlidingWindow.Index(),
useCache, this->m_numMfccVectorsInAudioStride);
}
debug("Input tensor populated \n");
return true;
}
/**
* @brief Generic feature calculator factory.
*
* Returns lambda function to compute features using features cache.
* Real features math is done by a lambda function provided as a parameter.
* Features are written to input tensor memory.
*
* @tparam T Feature vector type.
* @param[in] inputTensor Model input tensor pointer.
* @param[in] cacheSize Number of feature vectors to cache. Defined by the sliding window overlap.
* @param[in] compute Features calculator function.
* @return Lambda function to compute features.
*/
template<class T>
std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
KwsPreProcess::FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
std::function<std::vector<T> (std::vector<int16_t>& )> compute)
{
/* Feature cache to be captured by lambda function. */
static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
return [=](std::vector<int16_t>& audioDataWindow,
size_t index,
bool useCache,
size_t featuresOverlapIndex)
{
T* tensorData = tflite::GetTensorData<T>(inputTensor);
std::vector<T> features;
/* Reuse features from cache if cache is ready and sliding windows overlap.
* Overlap is in the beginning of sliding window with a size of a feature cache. */
if (useCache && index < featureCache.size()) {
features = std::move(featureCache[index]);
} else {
features = std::move(compute(audioDataWindow));
}
auto size = features.size();
auto sizeBytes = sizeof(T) * size;
std::memcpy(tensorData + (index * size), features.data(), sizeBytes);
/* Start renewing cache as soon iteration goes out of the windows overlap. */
if (index >= featuresOverlapIndex) {
featureCache[index - featuresOverlapIndex] = std::move(features);
}
};
}
template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
KwsPreProcess::FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
size_t cacheSize,
std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
KwsPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor,
size_t cacheSize,
std::function<std::vector<float>(std::vector<int16_t>&)> compute);
std::function<void (std::vector<int16_t>&, int, bool, size_t)>
KwsPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
{
std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc = nullptr;
TfLiteQuantization quant = inputTensor->quantization;
if (kTfLiteAffineQuantization == quant.type) {
auto* quantParams = (TfLiteAffineQuantization*) quant.params;
const float quantScale = quantParams->scale->data[0];
const int quantOffset = quantParams->zero_point->data[0];
switch (inputTensor->type) {
case kTfLiteInt8: {
mfccFeatureCalc = this->FeatureCalc<int8_t>(inputTensor,
cacheSize,
[=, &mfcc](std::vector<int16_t>& audioDataWindow) {
return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
quantScale,
quantOffset);
}
);
break;
}
default:
printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
}
} else {
mfccFeatureCalc = this->FeatureCalc<float>(inputTensor, cacheSize,
[&mfcc](std::vector<int16_t>& audioDataWindow) {
return mfcc.MfccCompute(audioDataWindow); }
);
}
return mfccFeatureCalc;
}
KwsPostProcess::KwsPostProcess(TfLiteTensor* outputTensor, KwsClassifier& classifier,
const std::vector<std::string>& labels,
std::vector<ClassificationResult>& results, size_t averagingWindowLen)
:m_outputTensor{outputTensor},
m_kwsClassifier{classifier},
m_labels{labels},
m_results{results}
{
this->m_resultHistory = {averagingWindowLen, std::vector<float>(labels.size())};
}
bool KwsPostProcess::DoPostProcess()
{
return this->m_kwsClassifier.GetClassificationResults(
this->m_outputTensor, this->m_results,
this->m_labels, 1, true, this->m_resultHistory);
}
} /* namespace app */
} /* namespace arm */
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