MLECO-2079 Adding the C++ KWS example
Signed-off-by: Eanna O Cathain <eanna.ocathain@arm.com>
Change-Id: I81899bbfaada32f478c2e2fc6441eabb94d8d0fc
diff --git a/samples/KeywordSpotting/src/KeywordSpottingPipeline.cpp b/samples/KeywordSpotting/src/KeywordSpottingPipeline.cpp
new file mode 100644
index 0000000..e32d947
--- /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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