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Diffstat (limited to 'samples/KeywordSpotting/src/KeywordSpottingPipeline.cpp')
| -rw-r--r-- | samples/KeywordSpotting/src/KeywordSpottingPipeline.cpp | 94 |
1 files changed, 94 insertions, 0 deletions
diff --git a/samples/KeywordSpotting/src/KeywordSpottingPipeline.cpp b/samples/KeywordSpotting/src/KeywordSpottingPipeline.cpp new file mode 100644 index 0000000000..e32d9476e3 --- /dev/null +++ b/samples/KeywordSpotting/src/KeywordSpottingPipeline.cpp @@ -0,0 +1,94 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "KeywordSpottingPipeline.hpp" +#include "ArmnnNetworkExecutor.hpp" +#include "DsCNNPreprocessor.hpp" + +namespace kws +{ +KWSPipeline::KWSPipeline(std::unique_ptr<common::ArmnnNetworkExecutor<int8_t>> executor, + std::unique_ptr<Decoder> decoder, + std::unique_ptr<DsCNNPreprocessor> preProcessor + ) : + m_executor(std::move(executor)), + m_decoder(std::move(decoder)), + m_preProcessor(std::move(preProcessor)) {} + + +std::vector<int8_t> KWSPipeline::PreProcessing(std::vector<float>& audio) +{ + return m_preProcessor->Invoke(audio.data(), audio.size(), m_executor->GetQuantizationOffset(), + m_executor->GetQuantizationScale()); +} + +void KWSPipeline::Inference(const std::vector<int8_t>& preprocessedData, + common::InferenceResults<int8_t>& result) +{ + m_executor->Run(preprocessedData.data(), preprocessedData.size(), result); +} + +void KWSPipeline::PostProcessing(common::InferenceResults<int8_t>& inferenceResults, + std::map<int, std::string>& labels, + const std::function<void (int, std::string&, float)>& callback) +{ + std::pair<int,float> outputDecoder = this->m_decoder->decodeOutput(inferenceResults[0]); + int keywordIndex = std::get<0>(outputDecoder); + std::string output = labels[keywordIndex]; + callback(keywordIndex, output, std::get<1>(outputDecoder)); +} + +int KWSPipeline::getInputSamplesSize() +{ + return this->m_preProcessor->m_windowLen + + ((this->m_preProcessor->m_mfcc->m_params.m_numMfccVectors - 1) * + this->m_preProcessor->m_windowStride); +} + +IPipelinePtr CreatePipeline(common::PipelineOptions& config) +{ + if (config.m_ModelName == "DS_CNN_CLUSTERED_INT8") + { + //DS-CNN model settings + float SAMP_FREQ = 16000; + int MFCC_WINDOW_LEN = 640; + int MFCC_WINDOW_STRIDE = 320; + int NUM_MFCC_FEATS = 10; + int NUM_MFCC_VECTORS = 49; + //todo: calc in pipeline and use in main + int SAMPLES_PER_INFERENCE = NUM_MFCC_VECTORS * MFCC_WINDOW_STRIDE + + MFCC_WINDOW_LEN - MFCC_WINDOW_STRIDE; //16000 + float MEL_LO_FREQ = 20; + float MEL_HI_FREQ = 4000; + int NUM_FBANK_BIN = 40; + + MfccParams mfccParams(SAMP_FREQ, + NUM_FBANK_BIN, + MEL_LO_FREQ, + MEL_HI_FREQ, + NUM_MFCC_FEATS, + MFCC_WINDOW_LEN, false, + NUM_MFCC_VECTORS); + + std::unique_ptr<DsCnnMFCC> mfccInst = std::make_unique<DsCnnMFCC>(mfccParams); + auto preprocessor = std::make_unique<kws::DsCNNPreprocessor>( + MFCC_WINDOW_LEN, MFCC_WINDOW_STRIDE, std::move(mfccInst)); + + auto executor = std::make_unique<common::ArmnnNetworkExecutor<int8_t>>( + config.m_ModelFilePath, config.m_backends); + + auto decoder = std::make_unique<kws::Decoder>(executor->GetOutputQuantizationOffset(0), + executor->GetOutputQuantizationScale(0)); + + return std::make_unique<kws::KWSPipeline>(std::move(executor), + std::move(decoder), std::move(preprocessor)); + } + else + { + throw std::invalid_argument("Unknown Model name: " + config.m_ModelName + " ."); + } +} + +};// namespace kws
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