| /* |
| * 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 inputSize) |
| { |
| UNUSED(inputSize); |
| 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 in an audio clip. */ |
| bool useCache = this->m_audioWindowIndex > 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; |
| |
| 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, Classifier& classifier, |
| const std::vector<std::string>& labels, |
| std::vector<ClassificationResult>& results) |
| :m_outputTensor{outputTensor}, |
| m_kwsClassifier{classifier}, |
| m_labels{labels}, |
| m_results{results} |
| {} |
| |
| bool KwsPostProcess::DoPostProcess() |
| { |
| return this->m_kwsClassifier.GetClassificationResults( |
| this->m_outputTensor, this->m_results, |
| this->m_labels, 1, true); |
| } |
| |
| } /* namespace app */ |
| } /* namespace arm */ |