Richard Burton | e6398cd | 2022-04-13 11:58:28 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2022 Arm Limited. All rights reserved. |
| 3 | * SPDX-License-Identifier: Apache-2.0 |
| 4 | * |
| 5 | * Licensed under the Apache License, Version 2.0 (the "License"); |
| 6 | * you may not use this file except in compliance with the License. |
| 7 | * You may obtain a copy of the License at |
| 8 | * |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | * |
| 11 | * Unless required by applicable law or agreed to in writing, software |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | * See the License for the specific language governing permissions and |
| 15 | * limitations under the License. |
| 16 | */ |
| 17 | #include "KwsProcessing.hpp" |
| 18 | #include "ImageUtils.hpp" |
| 19 | #include "log_macros.h" |
| 20 | #include "MicroNetKwsModel.hpp" |
| 21 | |
| 22 | namespace arm { |
| 23 | namespace app { |
| 24 | |
| 25 | KWSPreProcess::KWSPreProcess(Model* model, size_t numFeatures, int mfccFrameLength, int mfccFrameStride): |
| 26 | m_mfccFrameLength{mfccFrameLength}, |
| 27 | m_mfccFrameStride{mfccFrameStride}, |
| 28 | m_mfcc{audio::MicroNetKwsMFCC(numFeatures, mfccFrameLength)} |
| 29 | { |
| 30 | if (!model->IsInited()) { |
| 31 | printf_err("Model is not initialised!.\n"); |
| 32 | } |
| 33 | this->m_model = model; |
| 34 | this->m_mfcc.Init(); |
| 35 | |
| 36 | TfLiteIntArray* inputShape = model->GetInputShape(0); |
| 37 | const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx]; |
| 38 | |
| 39 | /* Deduce the data length required for 1 inference from the network parameters. */ |
| 40 | this->m_audioDataWindowSize = numMfccFrames * this->m_mfccFrameStride + |
| 41 | (this->m_mfccFrameLength - this->m_mfccFrameStride); |
| 42 | |
| 43 | /* Creating an MFCC feature sliding window for the data required for 1 inference. */ |
| 44 | this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(nullptr, this->m_audioDataWindowSize, |
| 45 | this->m_mfccFrameLength, this->m_mfccFrameStride); |
| 46 | |
| 47 | /* For longer audio clips we choose to move by half the audio window size |
| 48 | * => for a 1 second window size there is an overlap of 0.5 seconds. */ |
| 49 | this->m_audioDataStride = this->m_audioDataWindowSize / 2; |
| 50 | |
| 51 | /* To have the previously calculated features re-usable, stride must be multiple |
| 52 | * of MFCC features window stride. Reduce stride through audio if needed. */ |
| 53 | if (0 != this->m_audioDataStride % this->m_mfccFrameStride) { |
| 54 | this->m_audioDataStride -= this->m_audioDataStride % this->m_mfccFrameStride; |
| 55 | } |
| 56 | |
| 57 | this->m_numMfccVectorsInAudioStride = this->m_audioDataStride / this->m_mfccFrameStride; |
| 58 | |
| 59 | /* Calculate number of the feature vectors in the window overlap region. |
| 60 | * These feature vectors will be reused.*/ |
| 61 | this->m_numReusedMfccVectors = this->m_mfccSlidingWindow.TotalStrides() + 1 |
| 62 | - this->m_numMfccVectorsInAudioStride; |
| 63 | |
| 64 | /* Construct feature calculation function. */ |
| 65 | this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_model->GetInputTensor(0), |
| 66 | this->m_numReusedMfccVectors); |
| 67 | |
| 68 | if (!this->m_mfccFeatureCalculator) { |
| 69 | printf_err("Feature calculator not initialized."); |
| 70 | } |
| 71 | } |
| 72 | |
| 73 | bool KWSPreProcess::DoPreProcess(const void* data, size_t inputSize) |
| 74 | { |
| 75 | UNUSED(inputSize); |
| 76 | if (data == nullptr) { |
| 77 | printf_err("Data pointer is null"); |
| 78 | } |
| 79 | |
| 80 | /* Set the features sliding window to the new address. */ |
| 81 | auto input = static_cast<const int16_t*>(data); |
| 82 | this->m_mfccSlidingWindow.Reset(input); |
| 83 | |
| 84 | /* Cache is only usable if we have more than 1 inference in an audio clip. */ |
| 85 | bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedMfccVectors > 0; |
| 86 | |
| 87 | /* Use a sliding window to calculate MFCC features frame by frame. */ |
| 88 | while (this->m_mfccSlidingWindow.HasNext()) { |
| 89 | const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next(); |
| 90 | |
| 91 | std::vector<int16_t> mfccFrameAudioData = std::vector<int16_t>(mfccWindow, |
| 92 | mfccWindow + this->m_mfccFrameLength); |
| 93 | |
| 94 | /* Compute features for this window and write them to input tensor. */ |
| 95 | this->m_mfccFeatureCalculator(mfccFrameAudioData, this->m_mfccSlidingWindow.Index(), |
| 96 | useCache, this->m_numMfccVectorsInAudioStride); |
| 97 | } |
| 98 | |
| 99 | debug("Input tensor populated \n"); |
| 100 | |
| 101 | return true; |
| 102 | } |
| 103 | |
| 104 | /** |
| 105 | * @brief Generic feature calculator factory. |
| 106 | * |
| 107 | * Returns lambda function to compute features using features cache. |
| 108 | * Real features math is done by a lambda function provided as a parameter. |
| 109 | * Features are written to input tensor memory. |
| 110 | * |
| 111 | * @tparam T Feature vector type. |
| 112 | * @param[in] inputTensor Model input tensor pointer. |
| 113 | * @param[in] cacheSize Number of feature vectors to cache. Defined by the sliding window overlap. |
| 114 | * @param[in] compute Features calculator function. |
| 115 | * @return Lambda function to compute features. |
| 116 | */ |
| 117 | template<class T> |
| 118 | std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> |
| 119 | KWSPreProcess::FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize, |
| 120 | std::function<std::vector<T> (std::vector<int16_t>& )> compute) |
| 121 | { |
| 122 | /* Feature cache to be captured by lambda function. */ |
| 123 | static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize); |
| 124 | |
| 125 | return [=](std::vector<int16_t>& audioDataWindow, |
| 126 | size_t index, |
| 127 | bool useCache, |
| 128 | size_t featuresOverlapIndex) |
| 129 | { |
| 130 | T* tensorData = tflite::GetTensorData<T>(inputTensor); |
| 131 | std::vector<T> features; |
| 132 | |
| 133 | /* Reuse features from cache if cache is ready and sliding windows overlap. |
| 134 | * Overlap is in the beginning of sliding window with a size of a feature cache. */ |
| 135 | if (useCache && index < featureCache.size()) { |
| 136 | features = std::move(featureCache[index]); |
| 137 | } else { |
| 138 | features = std::move(compute(audioDataWindow)); |
| 139 | } |
| 140 | auto size = features.size(); |
| 141 | auto sizeBytes = sizeof(T) * size; |
| 142 | std::memcpy(tensorData + (index * size), features.data(), sizeBytes); |
| 143 | |
| 144 | /* Start renewing cache as soon iteration goes out of the windows overlap. */ |
| 145 | if (index >= featuresOverlapIndex) { |
| 146 | featureCache[index - featuresOverlapIndex] = std::move(features); |
| 147 | } |
| 148 | }; |
| 149 | } |
| 150 | |
| 151 | template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)> |
| 152 | KWSPreProcess::FeatureCalc<int8_t>(TfLiteTensor* inputTensor, |
| 153 | size_t cacheSize, |
| 154 | std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute); |
| 155 | |
| 156 | template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)> |
| 157 | KWSPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor, |
| 158 | size_t cacheSize, |
| 159 | std::function<std::vector<float>(std::vector<int16_t>&)> compute); |
| 160 | |
| 161 | |
| 162 | std::function<void (std::vector<int16_t>&, int, bool, size_t)> |
| 163 | KWSPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize) |
| 164 | { |
| 165 | std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc; |
| 166 | |
| 167 | TfLiteQuantization quant = inputTensor->quantization; |
| 168 | |
| 169 | if (kTfLiteAffineQuantization == quant.type) { |
| 170 | auto *quantParams = (TfLiteAffineQuantization *) quant.params; |
| 171 | const float quantScale = quantParams->scale->data[0]; |
| 172 | const int quantOffset = quantParams->zero_point->data[0]; |
| 173 | |
| 174 | switch (inputTensor->type) { |
| 175 | case kTfLiteInt8: { |
| 176 | mfccFeatureCalc = this->FeatureCalc<int8_t>(inputTensor, |
| 177 | cacheSize, |
| 178 | [=, &mfcc](std::vector<int16_t>& audioDataWindow) { |
| 179 | return mfcc.MfccComputeQuant<int8_t>(audioDataWindow, |
| 180 | quantScale, |
| 181 | quantOffset); |
| 182 | } |
| 183 | ); |
| 184 | break; |
| 185 | } |
| 186 | default: |
| 187 | printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type)); |
| 188 | } |
| 189 | } else { |
| 190 | mfccFeatureCalc = this->FeatureCalc<float>(inputTensor, cacheSize, |
| 191 | [&mfcc](std::vector<int16_t>& audioDataWindow) { |
| 192 | return mfcc.MfccCompute(audioDataWindow); } |
| 193 | ); |
| 194 | } |
| 195 | return mfccFeatureCalc; |
| 196 | } |
| 197 | |
| 198 | KWSPostProcess::KWSPostProcess(Classifier& classifier, Model* model, |
| 199 | const std::vector<std::string>& labels, |
Richard Burton | c291144 | 2022-04-22 09:08:21 +0100 | [diff] [blame^] | 200 | std::vector<ClassificationResult>& results) |
Richard Burton | e6398cd | 2022-04-13 11:58:28 +0100 | [diff] [blame] | 201 | :m_kwsClassifier{classifier}, |
| 202 | m_labels{labels}, |
Richard Burton | c291144 | 2022-04-22 09:08:21 +0100 | [diff] [blame^] | 203 | m_results{results} |
Richard Burton | e6398cd | 2022-04-13 11:58:28 +0100 | [diff] [blame] | 204 | { |
| 205 | if (!model->IsInited()) { |
| 206 | printf_err("Model is not initialised!.\n"); |
| 207 | } |
| 208 | this->m_model = model; |
| 209 | } |
| 210 | |
| 211 | bool KWSPostProcess::DoPostProcess() |
| 212 | { |
| 213 | return this->m_kwsClassifier.GetClassificationResults( |
| 214 | this->m_model->GetOutputTensor(0), this->m_results, |
| 215 | this->m_labels, 1, true); |
| 216 | } |
| 217 | |
| 218 | } /* namespace app */ |
| 219 | } /* namespace arm */ |