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llama.cpp
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mtmd: mtmd_audio_streaming_istft (#18645) 

Change is decoupled from https://github.com/ggml-org/llama.cpp/pull/18641.

[LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B)
needs streaming istft for generating output audio.

* add streaming ISTFT class (`mtmd_audio_streaming_istft`) with overlap-add for audio reconstruction
* replace global audio cache with per-instance cache, the model requires
  two independent caches, for preprocessing (audio input) and for istft
  (audio output).
* unified templated FFT/IFFT implementation supporting both forward and inverse transforms
This commit is contained in:
Tarek Dakhran 2026-01-06 21:00:29 +01:00 committed by GitHub
parent 68b4d516c3
commit ccbc84a537
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2 changed files with 428 additions and 215 deletions
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570
tools/mtmd/mtmd-audio.cpp
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@ -9,207 +9,250 @@
#include <fstream>
#include <algorithm>
// most of the code here is copied from whisper.cpp
// some of the code here is copied from whisper.cpp
constexpr bool DEBUG = false;
struct mtmd_audio_mel_filters {
int32_t n_mel;
int32_t n_fft;
void mtmd_audio_cache::fill_sin_cos_table(int n) {
sin_vals.resize(n);
cos_vals.resize(n);
for (int i = 0; i < n; i++) {
double theta = (2 * M_PI * i) / n;
sin_vals[i] = sinf(theta);
cos_vals[i] = cosf(theta);
}
}
std::vector<float> data;
};
void mtmd_audio_cache::fill_hann_window(int length, bool periodic) {
hann_window.resize(length);
int offset = -1;
if (periodic) {
offset = 0;
}
for (int i = 0; i < length; i++) {
hann_window[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
}
}
// note: this global cache is shared among all preprocessors
// if we want to use multiple preprocessors at the same time,
// we will need to enclose it in the preprocessor class in the future
static struct mtmd_audio_global_cache {
// precomputed sin/cos table for FFT
std::vector<float> sin_vals;
std::vector<float> cos_vals;
// hann window
std::vector<float> hann_window;
// mel filter bank
mtmd_audio_mel_filters filters;
void fill_sin_cos_table(int n) {
sin_vals.resize(n);
cos_vals.resize(n);
for (int i = 0; i < n; i++) {
double theta = (2 * M_PI * i) / n;
sin_vals[i] = sinf(theta);
cos_vals[i] = cosf(theta);
}
void mtmd_audio_cache::fill_mel_filterbank_matrix(int n_mel,
int n_fft,
int sample_rate,
float fmin,
float fmax,
bool slaney_area_norm,
float scale) {
GGML_ASSERT(n_mel > 0 && n_fft > 1);
if (fmax <= 0.0f) {
fmax = 0.5f * sample_rate;
}
void fill_hann_window(int length, bool periodic) {
hann_window.resize(length);
int offset = -1;
if (periodic) {
offset = 0;
}
for (int i = 0; i < length; i++) {
hann_window[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
}
// Slaney scale (matches librosa default)
const double min_log_hz = 1000.0;
const double lin_slope = 3 / 200.;
const double min_log_mel = min_log_hz * lin_slope;
const double log_step = log(6.4) / 27.0;
auto hz_to_mel = [min_log_hz, lin_slope, log_step, min_log_mel](const double f_hz) -> double {
return (f_hz < min_log_hz) ? f_hz * lin_slope : min_log_mel + log(f_hz / min_log_hz) / log_step;
};
auto mel_to_hz = [min_log_hz, lin_slope, log_step, min_log_mel](const double m) -> double {
return (m < min_log_mel) ? m / lin_slope : min_log_hz * exp((m - min_log_mel) * log_step);
};
// infer N_fft from n_fft_bins
const double bin_hz_step = double(sample_rate) / double(n_fft);
// mel grid: n_mel + 2 edges
const double m_lo = hz_to_mel(fmin);
const double m_hi = hz_to_mel(fmax);
std::vector<double> mel_pts(n_mel + 2);
for (int i = 0; i < n_mel + 2; ++i) {
mel_pts[i] = m_lo + (m_hi - m_lo) * (double(i) / (n_mel + 1));
}
// Build mel filterbank matrix [n_mel × n_fft_bins] at runtime.
// n_fft_bins must be (N_fft / 2 + 1). Example: if N_fft=512 -> n_fft_bins=257.
void fill_mel_filterbank_matrix(
int n_mel,
int n_fft,
int sample_rate, // e.g. 16000
float fmin = 0.0f, // e.g. 0.0
float fmax = -1.0f, // e.g. sr/2; pass -1 for auto
bool slaney_area_norm = true,
float scale = 1.0f // optional extra scaling; use 1.0f/1000.0f to mimic your code
) {
GGML_ASSERT(n_mel > 0 && n_fft > 1);
if (fmax <= 0.0f) {
fmax = 0.5f * sample_rate;
}
// convert to Hz
std::vector<double> hz_pts(n_mel + 2);
for (int i = 0; i < n_mel + 2; ++i) {
hz_pts[i] = mel_to_hz(mel_pts[i]);
}
// Slaney scale (matches librosa default)
const double min_log_hz = 1000.0;
const double lin_slope = 3 / 200.;
const double min_log_mel = min_log_hz * lin_slope;
const double log_step = log(6.4) / 27.0;
auto hz_to_mel = [min_log_hz, lin_slope, log_step, min_log_mel](const double f_hz) -> double {
return (f_hz < min_log_hz) ? f_hz * lin_slope : min_log_mel + log(f_hz / min_log_hz) / log_step;
};
auto mel_to_hz = [min_log_hz, lin_slope, log_step, min_log_mel](const double m) -> double {
return (m < min_log_mel) ? m / lin_slope : min_log_hz * exp((m - min_log_mel) * log_step);
};
const int n_fft_bins = n_fft / 2 + 1;
// infer N_fft from n_fft_bins
const double bin_hz_step = double(sample_rate) / double(n_fft);
// filterbank
std::vector<float> out(n_mel * n_fft_bins, 0);
for (int m = 0; m < n_mel; ++m) {
const double f_left = hz_pts[m];
const double f_center = hz_pts[m + 1];
const double f_right = hz_pts[m + 2];
// mel grid: n_mel + 2 edges
const double m_lo = hz_to_mel(fmin);
const double m_hi = hz_to_mel(fmax);
std::vector<double> mel_pts(n_mel + 2);
for (int i = 0; i < n_mel + 2; ++i) {
mel_pts[i] = m_lo + (m_hi - m_lo) * (double(i) / (n_mel + 1));
}
const double denom_l = std::max(1e-30, f_center - f_left);
const double denom_r = std::max(1e-30, f_right - f_center);
const double enorm = slaney_area_norm ? (2.0 / std::max(1e-30, f_right - f_left)) : 1.0;
// convert to Hz
std::vector<double> hz_pts(n_mel + 2);
for (int i = 0; i < n_mel + 2; ++i) {
hz_pts[i] = mel_to_hz(mel_pts[i]);
}
const int n_fft_bins = n_fft / 2 + 1;
// filterbank
std::vector<float> out(n_mel * n_fft_bins, 0);
for (int m = 0; m < n_mel; ++m) {
const double f_left = hz_pts[m];
const double f_center = hz_pts[m + 1];
const double f_right = hz_pts[m + 2];
const double denom_l = std::max(1e-30, f_center - f_left);
const double denom_r = std::max(1e-30, f_right - f_center);
const double enorm = slaney_area_norm ? (2.0 / std::max(1e-30, f_right - f_left)) : 1.0;
for (int k = 0; k < n_fft_bins; ++k) {
const double f = k * bin_hz_step;
double w = 0.0;
if (f >= f_left && f <= f_center) {
w = (f - f_left) / denom_l;
} else if (f > f_center && f <= f_right) {
w = (f_right - f) / denom_r;
}
out[size_t(m) * size_t(n_fft_bins) + size_t(k)] = float(w * enorm * scale);
for (int k = 0; k < n_fft_bins; ++k) {
const double f = k * bin_hz_step;
double w = 0.0;
if (f >= f_left && f <= f_center) {
w = (f - f_left) / denom_l;
} else if (f > f_center && f <= f_right) {
w = (f_right - f) / denom_r;
}
out[size_t(m) * size_t(n_fft_bins) + size_t(k)] = float(w * enorm * scale);
}
}
filters.n_mel = n_mel;
filters.n_fft = n_fft;
filters.data = std::move(out);
filters.n_mel = n_mel;
filters.n_fft = n_fft;
filters.data = std::move(out);
if (DEBUG) { // debug
for (size_t i = 0; i < filters.data.size(); ++i) {
if (filters.data[i] != 0.0f) {
printf("filters[%zu] = %f\n", i, filters.data[i] * 1000.0f);
}
if (DEBUG) { // debug
for (size_t i = 0; i < filters.data.size(); ++i) {
if (filters.data[i] != 0.0f) {
printf("filters[%zu] = %f\n", i, filters.data[i] * 1000.0f);
}
}
}
} g_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 n_sin_cos_vals = g_cache.sin_vals.size();
const int sin_cos_step = n_sin_cos_vals / N;
// Unified DFT implementation for both forward and inverse transforms
// Template parameters:
// Inverse: false = DFT with exp(-2πi·k·n/N), no scaling
// true = IDFT with exp(+2πi·k·n/N), scales by 1/N
// RealInput: true = input is real-valued (stride 1), avoids imaginary computations
// false = input is complex-valued (interleaved real/imag, stride 2)
template <bool Inverse, bool RealInput>
static void dft_impl(const mtmd_audio_cache & cache, const float * in, int N, float * out) {
const int n_sin_cos_vals = cache.sin_vals.size();
const int sin_cos_step = n_sin_cos_vals / N;
constexpr float sign = Inverse ? 1.0f : -1.0f;
const float scale = Inverse ? (1.0f / N) : 1.0f;
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) % (n_sin_cos_vals); // t = 2*M_PI*k*n/N
re += in[n] * g_cache.cos_vals[idx]; // cos(t)
im -= in[n] * g_cache.sin_vals[idx]; // sin(t)
int idx = (k * n * sin_cos_step) % n_sin_cos_vals;
float cos_val = cache.cos_vals[idx];
float sin_val = cache.sin_vals[idx];
if constexpr (RealInput) {
// Real input: in_im = 0, simplifies to:
// re += in_re * cos_val
// im += sign * in_re * sin_val
float in_re = in[n];
re += in_re * cos_val;
im += sign * in_re * sin_val;
} else {
float in_re = in[n * 2 + 0];
float in_im = in[n * 2 + 1];
// (a + bi) * (cos + sign*i*sin) = (a*cos - sign*b*sin) + (sign*a*sin + b*cos)i
re += in_re * cos_val - sign * in_im * sin_val;
im += sign * in_re * sin_val + in_im * cos_val;
}
}
out[k*2 + 0] = re;
out[k*2 + 1] = im;
out[k * 2 + 0] = re * scale;
out[k * 2 + 1] = im * scale;
}
}
// 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) {
const int n_sin_cos_vals = g_cache.sin_vals.size();
// Cooley-Tukey FFT/IFFT unified implementation
// Template parameters:
// Inverse: false = FFT with exp(-2πi·k/N), no scaling
// true = IFFT with exp(+2πi·k/N), scales by 0.5 at each level
// RealInput: true = input is real-valued (stride 1)
// false = input is complex-valued (interleaved real/imag, stride 2)
template <bool Inverse, bool RealInput>
static void fft_impl(const mtmd_audio_cache & cache, float * in, int N, float * out) {
const int n_sin_cos_vals = cache.sin_vals.size();
if (N == 1) {
out[0] = in[0];
out[1] = 0;
if constexpr (RealInput) {
out[1] = 0.0f;
} else {
out[1] = in[1];
}
return;
}
const int half_N = N / 2;
if (N - half_N*2 == 1) {
dft(in, N, out);
if (N - half_N * 2 == 1) {
// Odd N: fall back to DFT
dft_impl<Inverse, RealInput>(cache, 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);
// Split into even and odd
if constexpr (RealInput) {
// Real input: stride is 1, copy only real values
float * even = in + N;
for (int i = 0; i < half_N; ++i) {
even[i] = in[2 * i];
}
float * even_fft = out + 2 * N;
fft_impl<Inverse, true>(cache, even, half_N, even_fft);
float* odd = even;
for (int i = 0; i < half_N; ++i) {
odd[i] = in[2*i + 1];
float * odd = even;
for (int i = 0; i < half_N; ++i) {
odd[i] = in[2 * i + 1];
}
float * odd_fft = even_fft + N;
fft_impl<Inverse, true>(cache, odd, half_N, odd_fft);
} else {
// Complex input: stride is 2, copy complex pairs
float * even = in + N * 2;
for (int i = 0; i < half_N; ++i) {
even[i * 2 + 0] = in[2 * i * 2 + 0];
even[i * 2 + 1] = in[2 * i * 2 + 1];
}
float * even_fft = out + 2 * N;
fft_impl<Inverse, false>(cache, even, half_N, even_fft);
float * odd = even;
for (int i = 0; i < half_N; ++i) {
odd[i * 2 + 0] = in[(2 * i + 1) * 2 + 0];
odd[i * 2 + 1] = in[(2 * i + 1) * 2 + 1];
}
float * odd_fft = even_fft + N;
fft_impl<Inverse, false>(cache, odd, half_N, odd_fft);
}
float* odd_fft = even_fft + N;
fft(odd, half_N, odd_fft);
float * even_fft = out + 2 * N;
float * odd_fft = even_fft + N;
const int sin_cos_step = n_sin_cos_vals / N;
constexpr float sign = Inverse ? 1.0f : -1.0f;
constexpr float scale = Inverse ? 0.5f : 1.0f;
for (int k = 0; k < half_N; k++) {
int idx = k * sin_cos_step; // t = 2*M_PI*k/N
float re = g_cache.cos_vals[idx]; // cos(t)
float im = -g_cache.sin_vals[idx]; // sin(t)
int idx = k * sin_cos_step; // t = 2*M_PI*k/N
float re = cache.cos_vals[idx];
float im = sign * cache.sin_vals[idx];
float re_odd = odd_fft[2*k + 0];
float im_odd = odd_fft[2*k + 1];
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 + 0] = scale * (even_fft[2 * k + 0] + re * re_odd - im * im_odd);
out[2 * k + 1] = scale * (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;
out[2 * (k + half_N) + 0] = scale * (even_fft[2 * k + 0] - re * re_odd + im * im_odd);
out[2 * (k + half_N) + 1] = scale * (even_fft[2 * k + 1] - re * im_odd - im * re_odd);
}
}
// Forward FFT for real input (used by mel spectrogram)
static void fft(const mtmd_audio_cache & cache, float * in, int N, float * out) {
fft_impl<false, true>(cache, in, N, out);
}
// Inverse FFT for complex input
static void ifft(const mtmd_audio_cache & cache, float * in, int N, float * out) {
fft_impl<true, false>(cache, in, N, out);
}
struct filter_params {
int32_t n_mel;
int32_t n_fft_bins;
@ -222,20 +265,27 @@ struct filter_params {
bool norm_per_feature = false;
};
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 filter_params & params, mtmd_audio_mel & out) {
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 filter_params & params,
const mtmd_audio_cache & cache,
mtmd_audio_mel & out) {
std::vector<float> fft_in(frame_size * 2, 0.0);
std::vector<float> fft_out(frame_size * 2 * 2 * 2);
int n_fft_bins = params.n_fft_bins;
int i = ith;
const auto & filters = g_cache.filters;
const auto & filters = cache.filters;
// make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist
GGML_ASSERT(n_fft_bins == 1 + (frame_size / 2));
GGML_ASSERT(g_cache.sin_vals.size() == g_cache.cos_vals.size());
GGML_ASSERT(cache.sin_vals.size() == cache.cos_vals.size());
// calculate FFT only when fft_in are not all zero
for (; i < std::min(n_samples / frame_step + 1, out.n_len); i += n_threads) {
const int offset = i * frame_step;
@ -251,7 +301,7 @@ static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const
}
// FFT
fft(fft_in.data(), frame_size, fft_out.data());
fft(cache, 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.
@ -298,6 +348,7 @@ static bool log_mel_spectrogram(
const int n_samples_in,
const int n_threads,
const filter_params & params,
const mtmd_audio_cache & cache,
mtmd_audio_mel & out) {
//const int64_t t_start_us = ggml_time_us();
@ -305,9 +356,9 @@ static bool log_mel_spectrogram(
int n_samples = n_samples_in;
// Hann window
const float * hann = g_cache.hann_window.data();
const int frame_size = (params.n_fft_bins - 1) * 2;
const int frame_step = params.hop_length;
const float * hann = cache.hann_window.data();
const int frame_size = (params.n_fft_bins - 1) * 2;
const int frame_step = params.hop_length;
// Padding
std::vector<float> samples_padded;
@ -335,9 +386,9 @@ static bool log_mel_spectrogram(
// preemphasis
if (params.preemph) {
const int pad_amount = frame_size / 2;
const int pad_amount = frame_size / 2;
const float preemph = 0.97f;
float prev = samples_padded[pad_amount];
float prev = samples_padded[pad_amount];
for (int i = pad_amount + 1; i + pad_amount < n_samples; ++i) {
float cur = samples_padded[i];
samples_padded[i] = cur - preemph * prev;
@ -372,14 +423,14 @@ static bool log_mel_spectrogram(
{
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, frame_size, frame_step, n_threads,
std::cref(params), std::ref(out));
workers[iw] =
std::thread(log_mel_spectrogram_worker_thread, iw + 1, hann, std::cref(samples_padded), n_samples,
frame_size, frame_step, n_threads, std::cref(params), std::cref(cache), std::ref(out));
}
// main thread
log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples, frame_size, frame_step, n_threads, params, out);
log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples, frame_size, frame_step, n_threads, params,
cache, out);
for (int iw = 0; iw < n_threads - 1; ++iw) {
workers[iw].join();
}
@ -404,7 +455,7 @@ static bool log_mel_spectrogram(
for (int j = 0; j < effective_n_len; ++j) {
auto &value = out.data[i * out.n_len + j];
value = (value - mean) / mstd;
value = (value - mean) / mstd;
}
// pad the rest with zeros
@ -450,18 +501,14 @@ static bool log_mel_spectrogram(
//
void mtmd_audio_preprocessor_whisper::initialize() {
g_cache.fill_sin_cos_table(hparams.audio_n_fft);
g_cache.fill_hann_window(hparams.audio_window_len, true);
g_cache.fill_mel_filterbank_matrix(
hparams.n_mel_bins,
hparams.audio_n_fft,
hparams.audio_sample_rate);
cache.fill_sin_cos_table(hparams.audio_n_fft);
cache.fill_hann_window(hparams.audio_window_len, true);
cache.fill_mel_filterbank_matrix(hparams.n_mel_bins, hparams.audio_n_fft, hparams.audio_sample_rate);
}
bool mtmd_audio_preprocessor_whisper::preprocess(
const float * samples,
size_t n_samples,
std::vector<mtmd_audio_mel> & output) {
bool mtmd_audio_preprocessor_whisper::preprocess(const float * samples,
size_t n_samples,
std::vector<mtmd_audio_mel> & output) {
if (n_samples == 0) {
// empty audio
return false;
@ -471,7 +518,7 @@ bool mtmd_audio_preprocessor_whisper::preprocess(
// if input is too short, pad with zeros
// this is to avoid potential issues with stage1/2 padding in log_mel_spectrogram
// TODO: maybe handle this better
size_t min_samples = (size_t)hparams.audio_sample_rate * (hparams.audio_chunk_len + 1); // +1 second margin
size_t min_samples = (size_t) hparams.audio_sample_rate * (hparams.audio_chunk_len + 1); // +1 second margin
if (n_samples < min_samples) {
smpl.resize(min_samples, 0.0f);
std::memcpy(smpl.data(), samples, n_samples * sizeof(float));
@ -486,22 +533,19 @@ bool mtmd_audio_preprocessor_whisper::preprocess(
params.hop_length = hparams.audio_hop_len;
params.sample_rate = hparams.audio_sample_rate;
params.center_padding = false;
params.preemph = 0.0f; // disabled
params.preemph = 0.0f; // disabled
params.use_natural_log = false;
params.norm_per_feature = false;
// make sure the global cache is initialized
GGML_ASSERT(!g_cache.sin_vals.empty());
GGML_ASSERT(!g_cache.cos_vals.empty());
GGML_ASSERT(!g_cache.filters.data.empty());
// make sure the cache is initialized
GGML_ASSERT(!cache.sin_vals.empty());
GGML_ASSERT(!cache.cos_vals.empty());
GGML_ASSERT(!cache.filters.data.empty());
mtmd_audio_mel out_full;
bool ok = log_mel_spectrogram(
samples,
n_samples,
4, // n_threads
params,
out_full);
bool ok = log_mel_spectrogram(samples, n_samples,
4, // n_threads
params, cache, out_full);
if (!ok) {
return false;
}
@ -512,21 +556,21 @@ bool mtmd_audio_preprocessor_whisper::preprocess(
printf("output: n_mel = %d, n_len = %d\n", out_full.n_mel, 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
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
}
mtmd_audio_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.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;
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);
}
@ -541,18 +585,14 @@ bool mtmd_audio_preprocessor_whisper::preprocess(
//
void mtmd_audio_preprocessor_conformer::initialize() {
g_cache.fill_sin_cos_table(hparams.audio_n_fft);
g_cache.fill_hann_window(hparams.audio_window_len, true);
g_cache.fill_mel_filterbank_matrix(
hparams.n_mel_bins,
hparams.audio_n_fft,
hparams.audio_sample_rate);
cache.fill_sin_cos_table(hparams.audio_n_fft);
cache.fill_hann_window(hparams.audio_window_len, true);
cache.fill_mel_filterbank_matrix(hparams.n_mel_bins, hparams.audio_n_fft, hparams.audio_sample_rate);
}
bool mtmd_audio_preprocessor_conformer::preprocess(
const float * samples,
size_t n_samples,
std::vector<mtmd_audio_mel> & output) {
bool mtmd_audio_preprocessor_conformer::preprocess(const float * samples,
size_t n_samples,
std::vector<mtmd_audio_mel> & output) {
// empty audio
if (n_samples == 0) {
return false;
@ -569,18 +609,15 @@ bool mtmd_audio_preprocessor_conformer::preprocess(
params.use_natural_log = true;
params.norm_per_feature = true;
// make sure the global cache is initialized
GGML_ASSERT(!g_cache.sin_vals.empty());
GGML_ASSERT(!g_cache.cos_vals.empty());
GGML_ASSERT(!g_cache.filters.data.empty());
// make sure the cache is initialized
GGML_ASSERT(!cache.sin_vals.empty());
GGML_ASSERT(!cache.cos_vals.empty());
GGML_ASSERT(!cache.filters.data.empty());
mtmd_audio_mel out_full;
bool ok = log_mel_spectrogram(
samples,
n_samples,
4, // n_threads
params,
out_full);
bool ok = log_mel_spectrogram(samples, n_samples,
4, // n_threads
params, cache, out_full);
if (!ok) {
return false;
}
@ -588,3 +625,106 @@ bool mtmd_audio_preprocessor_conformer::preprocess(
output.push_back(std::move(out_full));
return true;
}
//
// mtmd_audio_streaming_istft implementation
//
mtmd_audio_streaming_istft::mtmd_audio_streaming_istft(int n_fft, int hop_length) :
n_fft(n_fft),
hop_length(hop_length),
n_fft_bins(n_fft / 2 + 1),
overlap_buffer(n_fft, 0.0f),
window_sum_buffer(n_fft, 0.0f),
padding_to_remove((n_fft - hop_length) / 2),
ifft_in(n_fft * 2 * 4, 0.0f), // extra space for recursive IFFT
ifft_out(n_fft * 2 * 4, 0.0f) {
cache.fill_sin_cos_table(n_fft);
cache.fill_hann_window(n_fft, true);
}
void mtmd_audio_streaming_istft::reset() {
std::fill(overlap_buffer.begin(), overlap_buffer.end(), 0.0f);
std::fill(window_sum_buffer.begin(), window_sum_buffer.end(), 0.0f);
padding_to_remove = (n_fft - hop_length) / 2;
}
std::vector<float> mtmd_audio_streaming_istft::process_frame(const float * frame_spectrum) {
std::vector<float> output(hop_length);
// copy frequencies
for (int j = 0; j < n_fft_bins; j++) {
ifft_in[j * 2 + 0] = frame_spectrum[j * 2 + 0];
ifft_in[j * 2 + 1] = frame_spectrum[j * 2 + 1];
}
// mirror negative frequencies
for (int j = 1; j < n_fft_bins - 1; j++) {
int mirror_idx = n_fft - j;
ifft_in[mirror_idx * 2 + 0] = ifft_in[j * 2 + 0];
ifft_in[mirror_idx * 2 + 1] = -ifft_in[j * 2 + 1]; // conjugate
}
ifft(cache, ifft_in.data(), n_fft, ifft_out.data());
// update window sum and overlap buffer
for (int j = 0; j < n_fft; j++) {
window_sum_buffer[j] += cache.hann_window[j] * cache.hann_window[j];
overlap_buffer[j] += ifft_out[j * 2] * cache.hann_window[j];
}
// extract hop_length samples with normalization
for (int i = 0; i < hop_length; i++) {
if (window_sum_buffer[i] > 1e-8f) {
output[i] = overlap_buffer[i] / window_sum_buffer[i];
} else {
output[i] = overlap_buffer[i];
}
}
// shift buffers left by hop_length
std::copy(overlap_buffer.begin() + hop_length, overlap_buffer.end(), overlap_buffer.begin());
std::fill(overlap_buffer.end() - hop_length, overlap_buffer.end(), 0.0f);
std::copy(window_sum_buffer.begin() + hop_length, window_sum_buffer.end(), window_sum_buffer.begin());
std::fill(window_sum_buffer.end() - hop_length, window_sum_buffer.end(), 0.0f);
// Remove padding if needed
int to_remove = std::min(padding_to_remove, (int) output.size());
padding_to_remove -= to_remove;
output.erase(output.begin(), output.begin() + to_remove);
return output;
}
std::vector<float> mtmd_audio_streaming_istft::flush() {
std::vector<float> output;
// Extract remaining samples from overlap buffer
// Continue until we've extracted all meaningful samples
int remaining = n_fft - hop_length;
while (remaining > 0) {
int chunk_size = std::min(remaining, hop_length);
for (int i = 0; i < chunk_size; i++) {
float sample;
if (window_sum_buffer[i] > 1e-8f) {
sample = overlap_buffer[i] / window_sum_buffer[i];
} else {
sample = overlap_buffer[i];
}
output.push_back(sample);
}
// Shift buffers
std::copy(overlap_buffer.begin() + chunk_size, overlap_buffer.end(), overlap_buffer.begin());
std::fill(overlap_buffer.end() - chunk_size, overlap_buffer.end(), 0.0f);
std::copy(window_sum_buffer.begin() + chunk_size, window_sum_buffer.end(), window_sum_buffer.begin());
std::fill(window_sum_buffer.end() - chunk_size, window_sum_buffer.end(), 0.0f);
remaining -= chunk_size;
}
return output;
}

73
tools/mtmd/mtmd-audio.h
View File

@ -17,6 +17,38 @@ struct mtmd_audio_mel {
std::vector<float> data;
};
struct mtmd_audio_mel_filters {
int32_t n_mel;
int32_t n_fft;
std::vector<float> data;
};
// cache for audio processing, each processor instance owns its own cache
struct mtmd_audio_cache {
std::vector<float> sin_vals;
std::vector<float> cos_vals;
std::vector<float> hann_window;
mtmd_audio_mel_filters filters;
void fill_sin_cos_table(int n);
void fill_hann_window(int length, bool periodic);
// Build mel filterbank matrix [n_mel × n_fft_bins] at runtime.
// n_fft_bins must be (N_fft / 2 + 1). Example: if N_fft=512 -> n_fft_bins=257.
void fill_mel_filterbank_matrix(int n_mel,
int n_fft,
int sample_rate, // e.g. 16000
float fmin = 0.0f, // e.g. 0.0
float fmax = -1.0f, // e.g. sr/2; pass -1 for auto
bool slaney_area_norm = true,
float scale = 1.0f // optional extra scaling
);
};
struct mtmd_audio_preprocessor {
const clip_hparams & hparams;
@ -31,10 +63,51 @@ struct mtmd_audio_preprocessor_whisper : mtmd_audio_preprocessor {
mtmd_audio_preprocessor_whisper(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {}
void initialize() override;
bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override;
private:
mtmd_audio_cache cache;
};
struct mtmd_audio_preprocessor_conformer : mtmd_audio_preprocessor {
mtmd_audio_preprocessor_conformer(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {}
void initialize() override;
bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override;
private:
mtmd_audio_cache cache;
};
//
// streaming ISTFT - converts spectrogram frames back to audio one frame at a time
//
struct mtmd_audio_streaming_istft {
mtmd_audio_streaming_istft(int n_fft, int hop_length);
// reset streaming state
void reset();
// process a single STFT frame (streaming)
// frame_spectrum: [n_fft_bins x 2] interleaved real/imag
// returns: up to hop_length samples
std::vector<float> process_frame(const float * frame_spectrum);
// flush remaining samples at end of stream
std::vector<float> flush();
private:
int n_fft;
int hop_length;
int n_fft_bins;
// Own cache for output processing
mtmd_audio_cache cache;
// Streaming state
std::vector<float> overlap_buffer;
std::vector<float> window_sum_buffer;
int padding_to_remove;
// Working buffers for IFFT
std::vector<float> ifft_in;
std::vector<float> ifft_out;
};