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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <iostream>
#include <map>
#include <vector>
#include <algorithm>
#include <cmath>
#include "KeywordSpottingPipeline.hpp"
#include "CmdArgsParser.hpp"
#include "ArmnnNetworkExecutor.hpp"
#include "AudioCapture.hpp"
const std::string AUDIO_FILE_PATH = "--audio-file-path";
const std::string MODEL_FILE_PATH = "--model-file-path";
const std::string LABEL_PATH = "--label-path";
const std::string PREFERRED_BACKENDS = "--preferred-backends";
const std::string HELP = "--help";
/*
* The accepted options for this Speech Recognition executable
*/
static std::map<std::string, std::string> CMD_OPTIONS =
{
{AUDIO_FILE_PATH, "[REQUIRED] Path to the Audio file to run speech recognition on"},
{MODEL_FILE_PATH, "[REQUIRED] Path to the Speech Recognition model to use"},
{PREFERRED_BACKENDS, "[OPTIONAL] Takes the preferred backends in preference order, separated by comma."
" For example: CpuAcc,GpuAcc,CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]."
" Defaults to CpuAcc,CpuRef"}
};
/*
* Reads the user supplied backend preference, splits it by comma, and returns an ordered vector
*/
std::vector<armnn::BackendId> GetPreferredBackendList(const std::string& preferredBackends)
{
std::vector<armnn::BackendId> backends;
std::stringstream ss(preferredBackends);
while (ss.good())
{
std::string backend;
std::getline(ss, backend, ',');
backends.emplace_back(backend);
}
return backends;
}
//Labels for this model
std::map<int, std::string> labels =
{
{0, "silence"},
{1, "unknown"},
{2, "yes"},
{3, "no"},
{4, "up"},
{5, "down"},
{6, "left"},
{7, "right"},
{8, "on"},
{9, "off"},
{10, "stop"},
{11, "go"}
};
int main(int argc, char* argv[])
{
printf("ArmNN major version: %d\n", ARMNN_MAJOR_VERSION);
std::map<std::string, std::string> options;
//Read command line args
int result = ParseOptions(options, CMD_OPTIONS, argv, argc);
if (result != 0)
{
return result;
}
// Create the ArmNN inference runner
common::PipelineOptions pipelineOptions;
pipelineOptions.m_ModelName = "DS_CNN_CLUSTERED_INT8";
pipelineOptions.m_ModelFilePath = GetSpecifiedOption(options, MODEL_FILE_PATH);
if (CheckOptionSpecified(options, PREFERRED_BACKENDS))
{
pipelineOptions.m_backends = GetPreferredBackendList(
(GetSpecifiedOption(options, PREFERRED_BACKENDS)));
}
else
{
pipelineOptions.m_backends = {"CpuAcc", "CpuRef"};
}
kws::IPipelinePtr kwsPipeline = kws::CreatePipeline(pipelineOptions);
//Extract audio data from sound file
auto filePath = GetSpecifiedOption(options, AUDIO_FILE_PATH);
std::vector<float> audioData = audio::AudioCapture::LoadAudioFile(filePath);
audio::AudioCapture capture;
//todo: read samples and stride from pipeline
capture.InitSlidingWindow(audioData.data(),
audioData.size(),
kwsPipeline->getInputSamplesSize(),
kwsPipeline->getInputSamplesSize()/2);
//Loop through audio data buffer
while (capture.HasNext())
{
std::vector<float> audioBlock = capture.Next();
common::InferenceResults<int8_t> results;
//Prepare input tensors
std::vector<int8_t> preprocessedData = kwsPipeline->PreProcessing(audioBlock);
//Run inference
kwsPipeline->Inference(preprocessedData, results);
//Decode output
kwsPipeline->PostProcessing(results, labels,
[](int index, std::string& label, float prob) -> void {
printf("Keyword \"%s\", index %d:, probability %f\n",
label.c_str(),
index,
prob);
});
}
return 0;
}
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