// The name of this function is important for Arduino compatibility.
void setup()
{
// tflite::InitializeTarget();
//RegisterDebugLogCallback(callback);
// Set up logging. Google style is to avoid globals or statics because of
// lifetime uncertainty, but since this has a trivial destructor it's okay.
// NOLINTNEXTLINE(runtime-global-variables)
static tflite::MicroErrorReporter micro_error_reporter;
error_reporter = µ_error_reporter;
// Map the model into a usable data structure. This doesn't involve any
// copying or parsing, it's a very lightweight operation.
model = tflite::GetModel(g_model);
if (model->version() != TFLITE_SCHEMA_VERSION)
{
TF_LITE_REPORT_ERROR(error_reporter,
"Model provided is schema version %d not equal "
"to supported version %d.",
model->version(), TFLITE_SCHEMA_VERSION);
return;
}
// Pull in only the operation implementations we need.
// This relies on a complete list of all the ops needed by this graph.
// An easier approach is to just use the AllOpsResolver, but this will
// incur some penalty in code space for op implementations that are not
// needed by this graph.
//
// tflite::AllOpsResolver resolver;
// NOLINTNEXTLINE(runtime-global-variables)
static tflite::MicroMutableOpResolver<4> micro_op_resolver(error_reporter);
if (micro_op_resolver.AddDepthwiseConv2D() != kTfLiteOk)
{
return;
}
if (micro_op_resolver.AddFullyConnected() != kTfLiteOk)
{
return;
}
if (micro_op_resolver.AddSoftmax() != kTfLiteOk)
{
return;
}
if (micro_op_resolver.AddReshape() != kTfLiteOk)
{
return;
}
// Build an interpreter to run the model with.
static tflite::MicroInterpreter static_interpreter(
model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter);
interpreter = &static_interpreter;
// Allocate memory from the tensor_arena for the model's tensors.
TfLiteStatus allocate_status = interpreter->AllocateTensors();
if (allocate_status != kTfLiteOk)
{
TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
return;
}
// Get information about the memory area to use for the model's input.
model_input = interpreter->input(0);
if ((model_input->dims->size != 2) || (model_input->dims->data[0] != 1) ||
(model_input->dims->data[1] !=
(kFeatureSliceCount * kFeatureSliceSize)) ||
(model_input->type != kTfLiteInt8))
{
TF_LITE_REPORT_ERROR(error_reporter,
"Bad input tensor parameters in model");
return;
}
model_input_buffer = model_input->data.int8;
// Prepare to access the audio spectrograms from a microphone or other source
// that will provide the inputs to the neural network.
// NOLINTNEXTLINE(runtime-global-variables)
static FeatureProvider static_feature_provider(kFeatureElementCount,
feature_buffer);
feature_provider = &static_feature_provider;
static RecognizeCommands static_recognizer(error_reporter);
recognizer = &static_recognizer;
previous_time = 0;
RespondCommandThreadInit();
}
// The name of this function is important for Arduino compatibility.
void loop()
{
// Fetch the spectrogram for the current time.
const int32_t current_time = LatestAudioTimestamp();
int how_many_new_slices = 0;
TfLiteStatus feature_status = feature_provider->PopulateFeatureData(
error_reporter, previous_time, current_time, &how_many_new_slices);
// LOG("current_time: %d, previous_time: %d, how_many_new_slices: %d\n", current_time, previous_time, how_many_new_slices);
if (feature_status != kTfLiteOk)
{
TF_LITE_REPORT_ERROR(error_reporter, "Feature generation failed");
return;
}
previous_time = current_time;
// If no new audio samples have been received since last time, don't bother
// running the network model.
if (how_many_new_slices == 0)
{
//LOG("[lk added]how_many_new_slices is 0\n");
return;
}
// Copy feature buffer to input tensor
for (int i = 0; i < kFeatureElementCount; i++)
{
model_input_buffer[i] = feature_buffer[i];
}
// Run the model on the spectrogram input and make sure it succeeds.
TfLiteStatus invoke_status = interpreter->Invoke();
if (invoke_status != kTfLiteOk)
{
TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed");
return;
}
// Obtain a pointer to the output tensor
TfLiteTensor *output = interpreter->output(0);
// Determine whether a command was recognized based on the output of inference
const char *found_command = nullptr;
uint8_t score = 0;
bool is_new_command = false;
TfLiteStatus process_status = recognizer->ProcessLatestResults(
output, current_time, &found_command, &score, &is_new_command);
if (process_status != kTfLiteOk)
{
TF_LITE_REPORT_ERROR(error_reporter,
"RecognizeCommands::ProcessLatestResults() failed");
return;
}
// Do something based on the recognized command. The default implementation
// just prints to the error console, but you should replace this with your
// own function for a real application.
RespondToCommand(error_reporter, current_time, found_command, score,
is_new_command);
}
