tqcq/llama.cpp
0
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-06-27 03:55:20 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
add-pidfile
llama.cpp/tools/mtmd/mtmd-audio.cpp
Xuan-Son Nguyen 10961339b2 mtmd : move helpers to dedicated library (⚠️ breaking change) (#13866) 
* mtmd : move helpers to dedicated library

* fix server build

* rm leftover cmakelist code
2025-05-28 22:35:22 +02:00

770 lines
24 KiB
C++
Raw Permalink Blame History

#include "mtmd-audio.h"
#define _USE_MATH_DEFINES // for M_PI
#include <cmath>
#include <cstdint>
#include <cstring>
#include <thread>
#include <vector>
#include <fstream>
#include <algorithm>
// most of the code here is copied from whisper.cpp
// align x to upper multiple of n
#define _ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))
namespace whisper_preprocessor {
#define SIN_COS_N_COUNT WHISPER_N_FFT
namespace {
struct whisper_global_cache {
// In FFT, we frequently use sine and cosine operations with the same values.
// We can use precalculated values to speed up the process.
float sin_vals[SIN_COS_N_COUNT];
float cos_vals[SIN_COS_N_COUNT];
// Hann window (Use cosf to eliminate difference)
// ref: https://pytorch.org/docs/stable/generated/torch.hann_window.html
// ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L147
float hann_window[WHISPER_N_FFT];
whisper_global_cache() {
fill_sin_cos_table();
fill_hann_window(sizeof(hann_window)/sizeof(hann_window[0]), true, hann_window);
}
void fill_sin_cos_table() {
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);
}
}
void fill_hann_window(int length, bool periodic, float * output) {
int offset = -1;
if (periodic) {
offset = 0;
}
for (int i = 0; i < length; i++) {
output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
}
}
} global_cache;
}
// naive Discrete Fourier Transform
// input is real-valued
// output is complex-valued
static void dft(const float* in, int N, float* out) {
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]*global_cache.cos_vals[idx]; // cos(t)
im -= in[n]*global_cache.sin_vals[idx]; // sin(t)
}
out[k*2 + 0] = re;
out[k*2 + 1] = im;
}
}
// Cooley-Tukey FFT
// poor man's implementation - use something better
// input is real-valued
// output is complex-valued
static void fft(float* in, int N, float* out) {
if (N == 1) {
out[0] = in[0];
out[1] = 0;
return;
}
const int half_N = N / 2;
if (N - half_N*2 == 1) {
dft(in, N, out);
return;
}
float* even = in + N;
for (int i = 0; i < half_N; ++i) {
even[i]= in[2*i];
}
float* even_fft = out + 2 * N;
fft(even, half_N, even_fft);
float* odd = even;
for (int i = 0; i < half_N; ++i) {
odd[i] = in[2*i + 1];
}
float* odd_fft = even_fft + N;
fft(odd, half_N, odd_fft);
const int sin_cos_step = SIN_COS_N_COUNT / N;
for (int k = 0; k < half_N; k++) {
int idx = k * sin_cos_step; // t = 2*M_PI*k/N
float re = global_cache.cos_vals[idx]; // cos(t)
float im = -global_cache.sin_vals[idx]; // sin(t)
float re_odd = odd_fft[2*k + 0];
float im_odd = odd_fft[2*k + 1];
out[2*k + 0] = even_fft[2*k + 0] + re*re_odd - im*im_odd;
out[2*k + 1] = even_fft[2*k + 1] + re*im_odd + im*re_odd;
out[2*(k + half_N) + 0] = even_fft[2*k + 0] - re*re_odd + im*im_odd;
out[2*(k + half_N) + 1] = even_fft[2*k + 1] - re*im_odd - im*re_odd;
}
}
static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const std::vector<float> & samples,
int n_samples, int frame_size, int frame_step, int n_threads,
const whisper_filters & filters, whisper_mel & mel) {
std::vector<float> fft_in(frame_size * 2, 0.0);
std::vector<float> fft_out(frame_size * 2 * 2 * 2);
int n_fft = filters.n_fft;
int i = ith;
// make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist
WHISPER_ASSERT(n_fft == 1 + (frame_size / 2));
// calculate FFT only when fft_in are not all zero
for (; i < std::min(n_samples / frame_step + 1, mel.n_len); i += n_threads) {
const int offset = i * frame_step;
// apply Hann window (~10% faster)
for (int j = 0; j < std::min(frame_size, n_samples - offset); j++) {
fft_in[j] = hann[j] * samples[offset + j];
}
// fill the rest with zeros
if (n_samples - offset < frame_size) {
std::fill(fft_in.begin() + (n_samples - offset), fft_in.end(), 0.0);
}
// FFT
fft(fft_in.data(), frame_size, fft_out.data());
// Calculate modulus^2 of complex numbers
// Use pow(fft_out[2 * j + 0], 2) + pow(fft_out[2 * j + 1], 2) causes inference quality problem? Interesting.
for (int j = 0; j < n_fft; j++) {
fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]);
}
// mel spectrogram
for (int j = 0; j < mel.n_mel; j++) {
double sum = 0.0;
// unroll loop (suggested by GH user @lunixbochs)
int k = 0;
for (k = 0; k < n_fft - 3; k += 4) {
sum +=
fft_out[k + 0] * filters.data[j * n_fft + k + 0] +
fft_out[k + 1] * filters.data[j * n_fft + k + 1] +
fft_out[k + 2] * filters.data[j * n_fft + k + 2] +
fft_out[k + 3] * filters.data[j * n_fft + k + 3];
}
// handle n_fft remainder
for (; k < n_fft; k++) {
sum += fft_out[k] * filters.data[j * n_fft + k];
}
sum = log10(std::max(sum, 1e-10));
mel.data[j * mel.n_len + i] = sum;
}
}
// Otherwise fft_out are all zero
double sum = log10(1e-10);
for (; i < mel.n_len; i += n_threads) {
for (int j = 0; j < mel.n_mel; j++) {
mel.data[j * mel.n_len + i] = sum;
}
}
}
// ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L110-L157
static bool log_mel_spectrogram(
const float * samples,
const int n_samples,
const int /*sample_rate*/,
const int frame_size,
const int frame_step,
const int n_mel,
const int n_threads,
const whisper_filters & filters,
const bool debug,
whisper_mel & mel) {
//const int64_t t_start_us = ggml_time_us();
// Hann window
WHISPER_ASSERT(frame_size == WHISPER_N_FFT && "Unsupported frame_size");
const float * hann = global_cache.hann_window;
// Calculate the length of padding
int64_t stage_1_pad = WHISPER_SAMPLE_RATE * 30;
int64_t stage_2_pad = frame_size / 2;
// Initialize a vector and copy data from C array to it.
std::vector<float> samples_padded;
samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2);
std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad);
// pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio
std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0);
// reflective pad 200 samples at the beginning of audio
std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin());
mel.n_mel = n_mel;
// https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/SpectralOps.cpp#L936
// Calculate number of frames + remove the last frame
mel.n_len = (samples_padded.size() - frame_size) / frame_step;
// Calculate semi-padded sample length to ensure compatibility
mel.n_len_org = 1 + (n_samples + stage_2_pad - frame_size) / frame_step;
mel.data.resize(mel.n_mel * mel.n_len);
{
std::vector<std::thread> workers(n_threads - 1);
for (int iw = 0; iw < n_threads - 1; ++iw) {
workers[iw] = std::thread(
log_mel_spectrogram_worker_thread, iw + 1, hann, std::cref(samples_padded),
n_samples + stage_2_pad, frame_size, frame_step, n_threads,
std::cref(filters), std::ref(mel));
}
// main thread
log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples + stage_2_pad, frame_size, frame_step, n_threads, filters, mel);
for (int iw = 0; iw < n_threads - 1; ++iw) {
workers[iw].join();
}
}
// clamping and normalization
double mmax = -1e20;
for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
if (mel.data[i] > mmax) {
mmax = mel.data[i];
}
}
mmax -= 8.0;
for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
if (mel.data[i] < mmax) {
mel.data[i] = mmax;
}
mel.data[i] = (mel.data[i] + 4.0)/4.0;
}
// Dump log_mel_spectrogram
if (debug) {
std::ofstream outFile("log_mel_spectrogram.json");
outFile << "[";
for (uint64_t i = 0; i < mel.data.size() - 1; i++) {
outFile << mel.data[i] << ", ";
}
outFile << mel.data[mel.data.size() - 1] << "]";
outFile.close();
}
return true;
}
bool preprocess_audio(
const float * samples,
size_t n_samples,
const whisper_filters & filters,
std::vector<whisper_mel> & output) {
if (n_samples == 0) {
// empty audio
return false;
}
whisper_mel out_full;
bool ok = log_mel_spectrogram(
samples,
n_samples,
COMMON_SAMPLE_RATE,
WHISPER_N_FFT,
WHISPER_HOP_LENGTH,
filters.n_mel,
4, // n_threads
filters,
false, // debug
out_full);
if (!ok) {
return false;
}
// because the cgraph in clip.cpp only accepts 3000 frames each, we need to split the mel
// we always expect the mel to have 3000 silent frames at the end
// printf("n_len %d\n", out_full.n_len);
const size_t frames_per_chunk = 3000;
GGML_ASSERT((size_t)out_full.n_len > frames_per_chunk);
for (size_t off = 0; off < (size_t)out_full.n_len; off += frames_per_chunk) {
int n_len = std::min(frames_per_chunk, (size_t)out_full.n_len - off);
if ((size_t)n_len < frames_per_chunk) {
break; // last uncomplete chunk will always be a padded chunk, safe to ignore
}
whisper_mel out_chunk;
out_chunk.n_len = n_len;
out_chunk.n_mel = out_full.n_mel;
out_chunk.n_len_org = out_full.n_mel; // unused
out_chunk.data.reserve(out_chunk.n_mel * out_chunk.n_len);
for (int i = 0; i < out_full.n_mel; i++) {
auto src = out_full.data.begin() + i*out_full.n_len + off;
out_chunk.data.insert(out_chunk.data.end(), src, src + frames_per_chunk);
}
output.push_back(std::move(out_chunk));
}
return true;
}
} // namespace whisper_preprocessor
// precalculated mel filter banks
// values are multiplied by 1000.0 to save space, and will be divided by 1000.0 in the end of the function
//
// generated from python code:
//
// from numpy import load
// data = load('mel_filters.npz')
// lst = data.files
// for item in lst:
// print(item)
// print(data[item].shape)
// n_mel = data[item].shape[0]
// n_fft = data[item].shape[1]
// for i, row in enumerate(data[item]):
// for j, val in enumerate(row):
// val = val * 1000.0
// if val != 0:
// print(f"data[{i*n_fft + j}] = {val:.6f};")
namespace whisper_precalc_filters {
whisper_preprocessor::whisper_filters get_128_bins() {
whisper_preprocessor::whisper_filters filters;
filters.n_mel = 128;
filters.n_fft = 201;
std::vector data(filters.n_mel * filters.n_fft, 0.0f);
data[1] = 12.37398665;
data[202] = 30.39256483;
data[404] = 24.74797331;
data[605] = 18.01857911;
data[807] = 37.12195903;
data[1008] = 5.64459199;
data[1009] = 6.72939420;
data[1210] = 36.03715822;
data[1412] = 19.10337992;
data[1613] = 23.66316877;
data[1815] = 31.47736564;
data[2016] = 11.28918398;
data[2017] = 1.08480197;
data[2218] = 41.68175161;
data[2420] = 13.45878839;
data[2621] = 29.30776216;
data[2823] = 25.83277412;
data[3024] = 16.93377644;
data[3226] = 38.20675984;
data[3427] = 4.55979025;
data[3428] = 7.81419594;
data[3629] = 34.95235741;
data[3831] = 20.18818259;
data[4032] = 22.57836796;
data[4234] = 32.56217018;
data[4435] = 10.20438317;
data[4436] = 2.16960395;
data[4637] = 40.59694707;
data[4839] = 14.54358920;
data[5040] = 28.22295949;
data[5242] = 26.91757679;
data[5443] = 15.84897563;
data[5645] = 39.29156065;
data[5846] = 3.47498828;
data[5847] = 8.89899861;
data[6048] = 33.86755288;
data[6250] = 21.27298526;
data[6451] = 21.49356715;
data[6653] = 33.64697099;
data[6854] = 9.11958050;
data[6855] = 3.25440569;
data[7056] = 39.51214626;
data[7258] = 15.62839188;
data[7459] = 27.13815868;
data[7661] = 28.00237760;
data[7862] = 14.76417296;
data[8064] = 40.37636518;
data[8265] = 2.38068704;
data[8266] = 10.20263787;
data[8467] = 31.61146119;
data[8669] = 24.54700135;
data[8870] = 15.32919332;
data[8871] = 1.66583748;
data[9072] = 36.72905266;
data[9274] = 20.09709924;
data[9475] = 16.93102531;
data[9476] = 2.90265540;
data[9677] = 32.84499049;
data[9879] = 23.52004871;
data[10080] = 11.03894413;
data[10081] = 10.72582975;
data[10282] = 22.71829173;
data[10484] = 32.27872774;
data[10685] = 0.11626833;
data[10686] = 22.85348251;
data[10887] = 8.56344029;
data[10888] = 14.97978810;
data[11089] = 15.51398356;
data[11090] = 8.51490628;
data[11291] = 21.10680379;
data[11292] = 3.32652032;
data[11493] = 25.47064796;
data[11695] = 27.35907957;
data[11896] = 0.65853616;
data[11897] = 23.83812517;
data[12098] = 3.44359246;
data[12099] = 21.22455277;
data[12300] = 5.35842171;
data[12301] = 19.42555793;
data[12502] = 6.49324711;
data[12503] = 18.35542172;
data[12704] = 6.93138083;
data[12705] = 17.93504693;
data[12906] = 6.74968259;
data[12907] = 18.09151843;
data[13108] = 6.01899112;
data[13109] = 18.75767298;
data[13310] = 4.80452832;
data[13311] = 19.87172849;
data[13512] = 3.16627859;
data[13513] = 21.37690969;
data[13514] = 1.25317345;
data[13714] = 1.15934468;
data[13715] = 20.80361731;
data[13716] = 4.04486805;
data[13917] = 17.55363122;
data[13918] = 7.08320038;
data[14119] = 14.07538634;
data[14120] = 10.32655034;
data[14321] = 10.40921453;
data[14322] = 13.73696327;
data[14523] = 6.59187697;
data[14524] = 17.27988198;
data[14525] = 1.46804214;
data[14725] = 2.65681883;
data[14726] = 18.09193194;
data[14727] = 5.85655728;
data[14928] = 13.34277913;
data[14929] = 10.28267574;
data[15130] = 8.56800377;
data[15131] = 14.72230814;
data[15132] = 1.04039861;
data[15332] = 3.79085587;
data[15333] = 17.14678481;
data[15334] = 6.11609267;
data[15535] = 11.75929047;
data[15536] = 11.13393717;
data[15737] = 6.43857848;
data[15738] = 16.07806236;
data[15739] = 4.23917221;
data[15939] = 1.19989377;
data[15940] = 12.75671553;
data[15941] = 9.65298992;
data[16142] = 7.06935255;
data[16143] = 14.94054683;
data[16144] = 4.19024844;
data[16344] = 1.51483389;
data[16345] = 12.00899947;
data[16346] = 9.84823331;
data[16547] = 6.10224018;
data[16548] = 15.33857174;
data[16549] = 5.57676842;
data[16749] = 0.36827257;
data[16750] = 9.89749376;
data[16751] = 11.35340426;
data[16752] = 2.05122307;
data[16952] = 3.89297144;
data[16953] = 12.97352277;
data[16954] = 8.06631614;
data[17155] = 6.74493238;
data[17156] = 13.85874674;
data[17157] = 5.41190524;
data[17357] = 0.74220158;
data[17358] = 8.98779090;
data[17359] = 11.37871388;
data[17360] = 3.32958088;
data[17560] = 2.82313535;
data[17561] = 10.68049297;
data[17562] = 9.43340641;
data[17563] = 1.76325557;
data[17763] = 4.39018616;
data[17764] = 11.87758986;
data[17765] = 7.97005836;
data[17766] = 0.66104700;
data[17966] = 5.49466675;
data[17967] = 12.62953598;
data[17968] = 6.93987962;
data[18169] = 6.18401915;
data[18170] = 12.93473132;
data[18171] = 6.29778765;
data[18371] = 0.02325210;
data[18372] = 6.50206627;
data[18373] = 12.32661773;
data[18374] = 6.00216538;
data[18574] = 0.31548753;
data[18575] = 6.48925547;
data[18576] = 12.04130240;
data[18577] = 6.01462880;
data[18777] = 0.29979556;
data[18778] = 6.18288014;
data[18779] = 12.04272825;
data[18780] = 6.29981188;
data[18781] = 0.55689598;
data[18980] = 0.01120471;
data[18981] = 5.61729167;
data[18982] = 11.22337859;
data[18983] = 6.82516303;
data[18984] = 1.35264499;
data[19184] = 4.82410006;
data[19185] = 10.16623247;
data[19186] = 7.56075513;
data[19187] = 2.34590308;
data[19387] = 3.83235747;
data[19388] = 8.92296247;
data[19389] = 8.47910438;
data[19390] = 3.50978645;
data[19590] = 2.66873185;
data[19591] = 7.51965167;
data[19592] = 9.55500547;
data[19593] = 4.81966138;
data[19594] = 0.08431751;
data[19793] = 1.35767367;
data[19794] = 5.98019501;
data[19795] = 10.60271543;
data[19796] = 6.25298498;
data[19797] = 1.74059917;
data[19997] = 4.32644226;
data[19998] = 8.73131864;
data[19999] = 7.78916525;
data[20000] = 3.48923868;
data[20200] = 2.57835095;
data[20201] = 6.77582854;
data[20202] = 9.40941647;
data[20203] = 5.31194592;
data[20204] = 1.21447595;
data[20403] = 0.75411191;
data[20404] = 4.75395704;
data[20405] = 8.75380263;
data[20406] = 7.19209015;
data[20407] = 3.28754401;
data[20607] = 2.68179690;
data[20608] = 6.49331464;
data[20609] = 9.11457930;
data[20610] = 5.39387390;
data[20611] = 1.67316827;
data[20810] = 0.57394296;
data[20811] = 4.20600036;
data[20812] = 7.83805829;
data[20813] = 7.52023002;
data[20814] = 3.97470826;
data[20815] = 0.42918732;
data[21014] = 1.90464477;
data[21015] = 5.36569161;
data[21016] = 8.82673822;
data[21017] = 6.27609482;
data[21018] = 2.89750961;
data[21218] = 2.89885257;
data[21219] = 6.19694078;
data[21220] = 8.56699049;
data[21221] = 5.34748193;
data[21222] = 2.12797290;
data[21421] = 0.44750227;
data[21422] = 3.59030394;
data[21423] = 6.73310598;
data[21424] = 7.77023612;
data[21425] = 4.70231380;
data[21426] = 1.63439126;
data[21625] = 1.01536023;
data[21626] = 4.01018746;
data[21627] = 7.00501446;
data[21628] = 7.23442994;
data[21629] = 4.31095669;
data[21630] = 1.38748321;
data[21829] = 1.33348850;
data[21830] = 4.18730825;
data[21831] = 7.04112789;
data[21832] = 6.93188375;
data[21833] = 4.14605811;
data[21834] = 1.36023236;
data[22033] = 1.42879714;
data[22034] = 4.14824858;
data[22035] = 6.86769979;
data[22036] = 6.83705276;
data[22037] = 4.18239459;
data[22038] = 1.52773573;
data[22237] = 1.32610439;
data[22238] = 3.91751388;
data[22239] = 6.50892360;
data[22240] = 6.92639686;
data[22241] = 4.39672917;
data[22242] = 1.86706171;
data[22441] = 1.04827771;
data[22442] = 3.51767405;
data[22443] = 5.98707050;
data[22444] = 7.17824046;
data[22445] = 4.76767914;
data[22446] = 2.35711760;
data[22645] = 0.61636406;
data[22646] = 2.96949223;
data[22647] = 5.32262027;
data[22648] = 7.57265091;
data[22649] = 5.27558755;
data[22650] = 2.97852419;
data[22651] = 0.68146095;
data[22849] = 0.04971400;
data[22850] = 2.29204819;
data[22851] = 4.53438237;
data[22852] = 6.77671656;
data[22853] = 5.90240723;
data[22854] = 3.71349836;
data[22855] = 1.52458926;
data[23054] = 1.50285335;
data[23055] = 3.63961048;
data[23056] = 5.77636715;
data[23057] = 6.63159089;
data[23058] = 4.54574358;
data[23059] = 2.45989650;
data[23060] = 0.37404924;
data[23258] = 0.61795861;
data[23259] = 2.65410915;
data[23260] = 4.69025923;
data[23261] = 6.72641024;
data[23262] = 5.46034705;
data[23263] = 3.47270933;
data[23264] = 1.48507138;
data[23463] = 1.59233576;
data[23464] = 3.53261665;
data[23465] = 5.47289755;
data[23466] = 6.44368259;
data[23467] = 4.54962999;
data[23468] = 2.65557761;
data[23469] = 0.76152512;
data[23667] = 0.46749352;
data[23668] = 2.31641904;
data[23669] = 4.16534441;
data[23670] = 6.01426978;
data[23671] = 5.67844696;
data[23672] = 3.87357362;
data[23673] = 2.06870004;
data[23674] = 0.26382666;
data[23872] = 1.05349103;
data[23873] = 2.81536230;
data[23874] = 4.57723346;
data[23875] = 6.33910485;
data[23876] = 5.12815686;
data[23877] = 3.40826320;
data[23878] = 1.68837002;
data[24077] = 1.43350090;
data[24078] = 3.11241671;
data[24079] = 4.79133241;
data[24080] = 6.40943693;
data[24081] = 4.77052201;
data[24082] = 3.13160778;
data[24083] = 1.49269309;
data[24281] = 0.02932359;
data[24282] = 1.62918994;
data[24283] = 3.22905602;
data[24284] = 4.82892245;
data[24285] = 6.14671456;
data[24286] = 4.58496623;
data[24287] = 3.02321767;
data[24288] = 1.46146910;
data[24486] = 0.13601698;
data[24487] = 1.66055572;
data[24488] = 3.18509457;
data[24489] = 4.70963307;
data[24490] = 6.04072399;
data[24491] = 4.55250870;
data[24492] = 3.06429295;
data[24493] = 1.57607743;
data[24494] = 0.08786193;
data[24691] = 0.09328097;
data[24692] = 1.54603878;
data[24693] = 2.99879676;
data[24694] = 4.45155473;
data[24695] = 5.90431225;
data[24696] = 4.65566106;
data[24697] = 3.23751615;
data[24698] = 1.81937125;
data[24699] = 0.40122634;
data[24897] = 1.30262633;
data[24898] = 2.68698297;
data[24899] = 4.07133950;
data[24900] = 5.45569602;
data[24901] = 4.87832492;
data[24902] = 3.52695142;
data[24903] = 2.17557792;
data[24904] = 0.82420459;
data[25102] = 0.94595028;
data[25103] = 2.26512621;
data[25104] = 3.58430226;
data[25105] = 4.90347855;
data[25106] = 5.20569785;
data[25107] = 3.91795207;
data[25108] = 2.63020652;
data[25109] = 1.34246063;
data[25110] = 0.05471494;
data[25307] = 0.49037894;
data[25308] = 1.74744334;
data[25309] = 3.00450763;
data[25310] = 4.26157191;
data[25311] = 5.51863620;
data[25312] = 4.39707236;
data[25313] = 3.16995848;
data[25314] = 1.94284460;
data[25315] = 0.71573065;
data[25513] = 1.14698056;
data[25514] = 2.34485767;
data[25515] = 3.54273478;
data[25516] = 4.74061165;
data[25517] = 4.95198462;
data[25518] = 3.78264743;
data[25519] = 2.61331047;
data[25520] = 1.44397374;
data[25521] = 0.27463681;
data[25718] = 0.47569509;
data[25719] = 1.61717169;
data[25720] = 2.75864848;
data[25721] = 3.90012516;
data[25722] = 5.04160160;
data[25723] = 4.45712078;
data[25724] = 3.34284059;
data[25725] = 2.22856039;
data[25726] = 1.11428020;
for (auto & val : data) {
val /= 1000.0f;
}
filters.data = std::move(data);
return filters;
}
} // namespace whisper_precalc_filters
Reference in New Issue View Git Blame Copy Permalink