C++ implementation (provided in this issue)

Today I spent a bit of time on implementing a C++ version of this library that can run the "First iteration siamese" model using TFLite/LiteRT. I'm providing it here in case someone else needs it.

main.cpp

```cpp #include #include #include #include #include #include using f32 = float; #include "embeddings.h" class HotwordDetector { std::unique_ptr lmc; std::unique_ptr bm; std::unique_ptr lmcInterpreter; int lmcInputIndex; int lmcOutputIndex; std::unique_ptr bmInterpreter; int bmInputIndex; int bmOutputIndex; std::vector> embeddings; public: HotwordDetector() { } int init( const char* logMelCalcPath, const char* baseModelPath, std::vector> newEmbeddings ) { lmc = tflite::FlatBufferModel::BuildFromFile(logMelCalcPath); if (lmc == nullptr) { return 2 + 0; } bm = tflite::FlatBufferModel::BuildFromFile(baseModelPath); if (bm == nullptr) { return 2 + 1; } tflite::ops::builtin::BuiltinOpResolver lmcResolver; tflite::InterpreterBuilder lmcInterpreterBuilder(*lmc, lmcResolver); lmcInterpreterBuilder(&lmcInterpreter); if (lmcInterpreter == nullptr) { return 4 + 0; } if (lmcInterpreter->AllocateTensors() != kTfLiteOk) { return 6 + 0; } tflite::ops::builtin::BuiltinOpResolver bmResolver; tflite::InterpreterBuilder bmInterpreterBuilder(*bm, bmResolver); bmInterpreterBuilder(&bmInterpreter); if (bmInterpreter == nullptr) { return 4 + 1; } if (bmInterpreter->AllocateTensors() != kTfLiteOk) { return 6 + 1; } if (newEmbeddings.size() <= 4) { return 8; } embeddings = newEmbeddings; return 0; } f32 process_frame(std::array audio) { // TODO implement silence check // normalize the input f32 max_val = *std::max_element(audio.begin(), audio.end()); std::transform(audio.begin(), audio.end(), audio.begin(), [max_val](f32 x) { return x / max_val; }); // set the logMelCalc model input f32* lmcInputTensor = lmcInterpreter->typed_input_tensor(0); std::copy(audio.begin(), audio.end(), lmcInputTensor); // invoke the logMelCalc model lmcInterpreter->Invoke(); // obtain the logMelCalc output and set the baseModel input f32* lmcOutputTensor = lmcInterpreter->typed_output_tensor(0); f32* bmInputTensor = bmInterpreter->typed_input_tensor(0); std::copy(lmcOutputTensor, lmcOutputTensor + 98*64, bmInputTensor); // invoke the baseModel model bmInterpreter->Invoke(); // obtain the baseModel output (i.e. the calculated embeddings) f32* bmOutputTensor = bmInterpreter->typed_output_tensor(0); std::array bmOutput; std::copy(bmOutputTensor, bmOutputTensor + bmOutput.size(), bmOutput.begin()); // calculate distances from embeddings std::vector distances; for (const auto& embedding : embeddings) { f32 sum = 0.0; for (size_t i = 0; i < 128; ++i) { f32 diff = bmOutputTensor[i] - embedding[i]; sum += diff * diff; } distances.push_back(std::sqrt(sum)); } // find the best three distances std::partial_sort(distances.begin(), distances.begin() + 3, distances.end()); // calculate the final score f32 out = 0.0f; for (int i = 0; i < 3; ++i) { f32 distance = distances[i]; distance = std::min(0.3f, distance); distance = (0.3f - distance) / 0.3f; out += (1-out) * distance; } return out; } }; int main(int argc, char* argv[]) { HotwordDetector hd; int ok = hd.init("../logmelcalc.tflite", "../baseModel.tflite", embeddings_alexa); if (ok != 0) { printf("Error %d\n", ok); } else { printf("Initialized correctly\n"); } FILE* audioFile = fopen("../test_audio_float.raw", "rb"); std::vector audioData; while (true) { float sample; if (fread(&sample, 1, 4, audioFile) == 4) { audioData.push_back(sample); } else { break; } } printf("Audio file: %d samples", audioData.size()); for (int i = 0; i < audioData.size() - 16000; i += 1000) { std::array data; std::copy(audioData.begin() + i, audioData.begin() + i + 16000, data.begin()); f32 confidence = hd.process_frame(data); printf("Confidence %.2fs %f%s\n", i / 16000.0f, confidence, confidence > 0.9f ? " DETECTED!" : ""); } } ```

embeddings.h (data is from alexa_ref.json in the commit before the ResNet50 model was added)

```cpp #ifndef EMBEDDINGS_H #define EMBEDDINGS_H #include #include std::vector> embeddings_alexa{{-0.0018443578155711293, 0.0012409311020746827, -0.0002715830923989415, 0.0004013945581391454, 8.306189556606114e-05, 0.00044053635792806745, 0.0009874621173366904, 0.0009157363674603403, -0.0012063418980687857, 0.0016062910435721278, 0.001714642159640789, 0.0010646098526194692, 0.001067142584361136, 0.0026639034040272236, 0.0010514091700315475, 0.0016106378752738237, -0.00010444383951835334, -0.43039366602897644, 0.00017202840535901487, -0.04051519185304642, -0.34928077459335327, 0.0018010134808719158, -0.0020715740974992514, 0.001733542187139392, -0.000593288685195148, -0.0008430416928604245, 0.0013320832513272762, -0.28834396600723267, -0.0011945832520723343, -0.001150083844549954, -0.0008581099100410938, 0.2882259488105774, 0.0017530254554003477, -0.00025029361131601036, 0.0017177052795886993, -0.0005449739401228726, 0.00012596861051861197, 0.0020090285688638687, -0.0017851801821962, 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The command to generate the test_audio_float.raw file is arecord -D plughw:1,0 -f FLOAT_LE -c1 -r16000 -d1 test_audio_float.raw.

Ant-Brain / EfficientWord-Net

C++ implementation (provided in this issue) #56