Repository: ggerganov/whisper.cpp
File: whisper.cpp (C++)

Code before:
    int64_t msec = t * 10;
    int64_t hr = msec / (1000 * 60 * 60);
    msec = msec - hr * (1000 * 60 * 60);
    int64_t min = msec / (1000 * 60);
    msec = msec - min * (1000 * 60);
    int64_t sec = msec / 1000;
    msec = msec - sec * 1000;

    char buf[32];
    snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);

    return std::string(buf);
}

#define SIN_COS_N_COUNT WHISPER_N_FFT
static float sin_vals[SIN_COS_N_COUNT];
static float cos_vals[SIN_COS_N_COUNT];

// In FFT, we frequently use sine and cosine operations with the same values. 
// We can use precalculated values to speed up the process.
static void fill_sin_cos_table() {
    static bool is_filled = false;
    if (is_filled) return;
    for (int i = 0; i < SIN_COS_N_COUNT; i++) {
        double theta = (2*M_PI*i)/SIN_COS_N_COUNT;
        sin_vals[i] = sin(theta);
        cos_vals[i] = cos(theta);
    }
    is_filled = true;
}

// naive Discrete Fourier Transform
// input is real-valued
// output is complex-valued
static void dft(const std::vector<float> & in, std::vector<float> & out) {
    int N = in.size();

    out.resize(N*2);
    const int sin_cos_step = SIN_COS_N_COUNT / N;

    for (int k = 0; k < N; k++) {
        float re = 0;
        float im = 0;

        for (int n = 0; n < N; n++) {
            int idx = (k * n * sin_cos_step) % (SIN_COS_N_COUNT); // t = 2*M_PI*k*n/N
            re += in[n]*cos_vals[idx]; // cos(t)
            im -= in[n]*sin_vals[idx]; // sin(t)
        }

        out[k*2 + 0] = re;

Review comment (suggestion):
```suggestion
        sin_vals[i] = sinf(theta);
```

Code after:
    int64_t msec = t * 10;
    int64_t hr = msec / (1000 * 60 * 60);
    msec = msec - hr * (1000 * 60 * 60);
    int64_t min = msec / (1000 * 60);
    msec = msec - min * (1000 * 60);
    int64_t sec = msec / 1000;
    msec = msec - sec * 1000;

    char buf[32];
    snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);

    return std::string(buf);
}

#define SIN_COS_N_COUNT WHISPER_N_FFT
static float sin_vals[SIN_COS_N_COUNT];
static float cos_vals[SIN_COS_N_COUNT];

// In FFT, we frequently use sine and cosine operations with the same values. 
// We can use precalculated values to speed up the process.
static void fill_sin_cos_table() {
    static bool is_filled = false;
    if (is_filled) return;
    for (int i = 0; i < SIN_COS_N_COUNT; i++) {
        double theta = (2*M_PI*i)/SIN_COS_N_COUNT;
        sin_vals[i] = sinf(theta);
        cos_vals[i] = cosf(theta);
    }
    is_filled = true;
}

// naive Discrete Fourier Transform
// input is real-valued
// output is complex-valued
static void dft(const std::vector<float> & in, std::vector<float> & out) {
    int N = in.size();

    out.resize(N*2);
    const int sin_cos_step = SIN_COS_N_COUNT / N;

    for (int k = 0; k < N; k++) {
        float re = 0;
        float im = 0;

        for (int n = 0; n < N; n++) {
            int idx = (k * n * sin_cos_step) % (SIN_COS_N_COUNT); // t = 2*M_PI*k*n/N
            re += in[n]*cos_vals[idx]; // cos(t)
            im -= in[n]*sin_vals[idx]; // sin(t)
        }

        out[k*2 + 0] = re;