C++ implementation (provided in this issue)

[Stypox]

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

#include <cstdio>
#include <vector>

#include <tensorflow/lite/interpreter.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/model.h>
#include <tensorflow/lite/optional_debug_tools.h>

using f32 = float;
#include "embeddings.h"


class HotwordDetector {

    std::unique_ptr<tflite::FlatBufferModel> lmc;
    std::unique_ptr<tflite::FlatBufferModel> bm;

    std::unique_ptr<tflite::Interpreter> lmcInterpreter;
    int lmcInputIndex;
    int lmcOutputIndex;

    std::unique_ptr<tflite::Interpreter> bmInterpreter;
    int bmInputIndex;
    int bmOutputIndex;

    std::vector<std::array<f32, 128>> embeddings;


public:

    HotwordDetector()
    {
    }

    int init(
        const char* logMelCalcPath,
        const char* baseModelPath,
        std::vector<std::array<f32, 128>> 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<f32, 16000> 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<f32>(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<f32>(0);
        f32* bmInputTensor = bmInterpreter->typed_input_tensor<f32>(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<f32>(0);
        std::array<f32, 128> bmOutput;
        std::copy(bmOutputTensor, bmOutputTensor + bmOutput.size(), bmOutput.begin());

        // calculate distances from embeddings
        std::vector<f32> 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<float> 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<f32, 16000> 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)

#ifndef EMBEDDINGS_H
#define EMBEDDINGS_H

#include <vector>
#include <array>

std::vector<std::array<f32, 128>> 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, 0.00013088944251649082, -0.00031638433574698865, -0.0017561435233801603, 0.0018899600254371762, -0.4391557276248932, 0.001379559631459415, -0.001019650255329907, -0.0011724227806553245, 0.36617204546928406, 0.023888058960437775, 0.001750345341861248, -0.001019726158119738, 0.0012767650187015533, -0.002323133172467351, -0.0013836531434208155, 0.0018793671624734998, 0.0012834904482588172, 0.001410864875651896, -0.0038892882876098156, 0.000821766210719943, 4.7008310502860695e-05, 0.0005266448715701699, 0.19474084675312042, 0.00013599770318251103, -0.000907463429030031, -0.0012136111035943031, 0.0006211597938090563, 0.0007279961719177663, -0.0019102797377854586, -0.0021515635307878256, 0.0010963628301396966, -0.0732651874423027, 0.0019330763025209308, 0.002107506850734353, 0.0012016203254461288, -2.8716011001961306e-05, -0.0019159488147124648, -0.0015521880704909563, -0.0024353181943297386, -0.0020603861194103956, 0.0017349357949569821, -0.0016230623004958034, -0.0014153642114251852, 0.001310766558162868, 0.001488858019001782, 0.0016663441201671958, 0.0018254319438710809, 0.0024388344027101994, -0.0009016821859404445, 0.07546431571245193, -0.0012602577917277813, 0.0017456549685448408, 0.0012205323437228799, 0.00033814305788837373, -0.0025566036347299814, 0.00130564346909523, 0.00119997828733176, 0.00132357282564044, 0.000850820739287883, 0.00023926336143631488, 0.0008439748780801892, -0.0014458457008004189, 0.14081107079982758, 0.000839119020383805, -0.00046436034608632326, -0.0011383205419406295, -0.0024229867849498987, -0.0011970826890319586, -0.0007554360199719667, 0.0016834212001413107, 0.0001794159907149151, 0.0003878429706674069, -0.004022049717605114, 0.30528026819229126, -0.0016906275413930416, -0.0010010339319705963, 0.0016334225656464696, -0.0009830307681113482, 0.0015193283325061202, 0.0018329325830563903, 0.0008684337954036891, 0.0007554229232482612, -0.0013552817981690168, -0.15109840035438538, 0.002244429662823677, 0.11094605922698975, 0.00016276723181363195, -0.0017068249871954322, 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{-0.0029506904538720846, 0.0007545671542175114, 0.00025700521655380726, 0.001587261795066297, -0.004217327572405338, 0.00034811231307685375, -0.0021977871656417847, 0.00023275577405001968, -0.00024322803074028343, 0.0016569038853049278, 0.0018964203773066401, -0.0006294742925092578, 6.31033763056621e-05, 0.00013130740262567997, -0.0018934633117169142, 0.0021087739150971174, -4.7066489059943706e-05, -0.35626471042633057, -0.0037763414438813925, 0.0015456597320735455, -0.3770938813686371, 0.0021937047131359577, -0.0013909584376960993, 0.002051087561994791, -4.386096043162979e-05, 0.0025120647624135017, -0.0002062350104097277, -0.27969682216644287, 0.0012652123114094138, 0.0001547522988403216, 0.0005131364450789988, 0.30294036865234375, 0.0006402382859960198, -0.0004010084376204759, 0.0021061149891465902, -0.00013685732847079635, 0.0019085099920630455, 0.0023171729408204556, -0.0019417403964325786, -0.0005446713184937835, 0.0027780551463365555, -0.0023972894996404648, 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{-0.003331949235871434, -4.0892882680054754e-05, -0.0004109063884243369, -2.1086707420181483e-06, -0.002926705637946725, 3.585101876524277e-05, -0.00015815399819985032, 0.0011701962212100625, -0.0011255416320636868, 0.002179102273657918, 0.002486106939613819, 0.0008896252838894725, -0.0002387767017353326, -0.0011096902890130877, -0.00015551221440546215, 0.0027023539878427982, 2.7639940526569262e-05, -0.40583112835884094, -0.0017331629060208797, -0.029486749321222305, -0.45447495579719543, 0.002751121763139963, -0.0025494361761957407, 0.0028328809421509504, -0.0009335164213553071, 0.0013004833599552512, 0.0011456080246716738, -0.26588016748428345, -0.000602804240770638, -0.0008550725760869682, -0.0009532556869089603, 0.2807413935661316, 0.0013647619634866714, -2.7464890081319027e-05, 0.0027129834052175283, -0.00018683182133827358, -7.748845382593572e-05, 0.0003490635135676712, -0.0025576057378202677, -2.1125486455275677e-05, 0.0010451632551848888, -0.002996979746967554, 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#endif

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.

[aman-17]

@Stypox Thank you for providing C++ version of this library.

[TheSeriousProgrammer]

If you could make the same as a github gist we will happily include the link of the same in our readme

[Stypox]

@TheSeriousProgrammer here it is: https://gist.github.com/Stypox/2bf47017ea5361776d9c096e886e0540