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FFmpeg/libavfilter/af_arnndn.c
Andreas Rheinhardt 790f793844 avutil/common: Don't auto-include mem.h 
There are lots of files that don't need it: The number of object
files that actually need it went down from 2011 to 884 here.

Keep it for external users in order to not cause breakages.

Also improve the other headers a bit while just at it.

Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
2024-03-31 00:08:43 +01:00

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/*
* Copyright (c) 2018 Gregor Richards
* Copyright (c) 2017 Mozilla
* Copyright (c) 2005-2009 Xiph.Org Foundation
* Copyright (c) 2007-2008 CSIRO
* Copyright (c) 2008-2011 Octasic Inc.
* Copyright (c) Jean-Marc Valin
* Copyright (c) 2019 Paul B Mahol
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* - Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
*
* - Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
* LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
* NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "libavutil/avassert.h"
#include "libavutil/file_open.h"
#include "libavutil/float_dsp.h"
#include "libavutil/mem.h"
#include "libavutil/mem_internal.h"
#include "libavutil/opt.h"
#include "libavutil/tx.h"
#include "avfilter.h"
#include "audio.h"
#include "filters.h"
#include "formats.h"
#define FRAME_SIZE_SHIFT 2
#define FRAME_SIZE (120<<FRAME_SIZE_SHIFT)
#define WINDOW_SIZE (2*FRAME_SIZE)
#define FREQ_SIZE (FRAME_SIZE + 1)
#define PITCH_MIN_PERIOD 60
#define PITCH_MAX_PERIOD 768
#define PITCH_FRAME_SIZE 960
#define PITCH_BUF_SIZE (PITCH_MAX_PERIOD+PITCH_FRAME_SIZE)
#define SQUARE(x) ((x)*(x))
#define NB_BANDS 22
#define CEPS_MEM 8
#define NB_DELTA_CEPS 6
#define NB_FEATURES (NB_BANDS+3*NB_DELTA_CEPS+2)
#define WEIGHTS_SCALE (1.f/256)
#define MAX_NEURONS 128
#define ACTIVATION_TANH 0
#define ACTIVATION_SIGMOID 1
#define ACTIVATION_RELU 2
#define Q15ONE 1.0f
typedef struct DenseLayer {
const float *bias;
const float *input_weights;
int nb_inputs;
int nb_neurons;
int activation;
} DenseLayer;
typedef struct GRULayer {
const float *bias;
const float *input_weights;
const float *recurrent_weights;
int nb_inputs;
int nb_neurons;
int activation;
} GRULayer;
typedef struct RNNModel {
int input_dense_size;
const DenseLayer *input_dense;
int vad_gru_size;
const GRULayer *vad_gru;
int noise_gru_size;
const GRULayer *noise_gru;
int denoise_gru_size;
const GRULayer *denoise_gru;
int denoise_output_size;
const DenseLayer *denoise_output;
int vad_output_size;
const DenseLayer *vad_output;
} RNNModel;
typedef struct RNNState {
float *vad_gru_state;
float *noise_gru_state;
float *denoise_gru_state;
RNNModel *model;
} RNNState;
typedef struct DenoiseState {
float analysis_mem[FRAME_SIZE];
float cepstral_mem[CEPS_MEM][NB_BANDS];
int memid;
DECLARE_ALIGNED(32, float, synthesis_mem)[FRAME_SIZE];
float pitch_buf[PITCH_BUF_SIZE];
float pitch_enh_buf[PITCH_BUF_SIZE];
float last_gain;
int last_period;
float mem_hp_x[2];
float lastg[NB_BANDS];
float history[FRAME_SIZE];
RNNState rnn[2];
AVTXContext *tx, *txi;
av_tx_fn tx_fn, txi_fn;
} DenoiseState;
typedef struct AudioRNNContext {
const AVClass *class;
char *model_name;
float mix;
int channels;
DenoiseState *st;
DECLARE_ALIGNED(32, float, window)[WINDOW_SIZE];
DECLARE_ALIGNED(32, float, dct_table)[FFALIGN(NB_BANDS, 4)][FFALIGN(NB_BANDS, 4)];
RNNModel *model[2];
AVFloatDSPContext *fdsp;
} AudioRNNContext;
#define F_ACTIVATION_TANH 0
#define F_ACTIVATION_SIGMOID 1
#define F_ACTIVATION_RELU 2
static void rnnoise_model_free(RNNModel *model)
{
#define FREE_MAYBE(ptr) do { if (ptr) free(ptr); } while (0)
#define FREE_DENSE(name) do { \
if (model->name) { \
av_free((void *) model->name->input_weights); \
av_free((void *) model->name->bias); \
av_free((void *) model->name); \
} \
} while (0)
#define FREE_GRU(name) do { \
if (model->name) { \
av_free((void *) model->name->input_weights); \
av_free((void *) model->name->recurrent_weights); \
av_free((void *) model->name->bias); \
av_free((void *) model->name); \
} \
} while (0)
if (!model)
return;
FREE_DENSE(input_dense);
FREE_GRU(vad_gru);
FREE_GRU(noise_gru);
FREE_GRU(denoise_gru);
FREE_DENSE(denoise_output);
FREE_DENSE(vad_output);
av_free(model);
}
static int rnnoise_model_from_file(FILE *f, RNNModel **rnn)
{
RNNModel *ret = NULL;
DenseLayer *input_dense;
GRULayer *vad_gru;
GRULayer *noise_gru;
GRULayer *denoise_gru;
DenseLayer *denoise_output;
DenseLayer *vad_output;
int in;
if (fscanf(f, "rnnoise-nu model file version %d\n", &in) != 1 || in != 1)
return AVERROR_INVALIDDATA;
ret = av_calloc(1, sizeof(RNNModel));
if (!ret)
return AVERROR(ENOMEM);
#define ALLOC_LAYER(type, name) \
name = av_calloc(1, sizeof(type)); \
if (!name) { \
rnnoise_model_free(ret); \
return AVERROR(ENOMEM); \
} \
ret->name = name
ALLOC_LAYER(DenseLayer, input_dense);
ALLOC_LAYER(GRULayer, vad_gru);
ALLOC_LAYER(GRULayer, noise_gru);
ALLOC_LAYER(GRULayer, denoise_gru);
ALLOC_LAYER(DenseLayer, denoise_output);
ALLOC_LAYER(DenseLayer, vad_output);
#define INPUT_VAL(name) do { \
if (fscanf(f, "%d", &in) != 1 || in < 0 || in > 128) { \
rnnoise_model_free(ret); \
return AVERROR(EINVAL); \
} \
name = in; \
} while (0)
#define INPUT_ACTIVATION(name) do { \
int activation; \
INPUT_VAL(activation); \
switch (activation) { \
case F_ACTIVATION_SIGMOID: \
name = ACTIVATION_SIGMOID; \
break; \
case F_ACTIVATION_RELU: \
name = ACTIVATION_RELU; \
break; \
default: \
name = ACTIVATION_TANH; \
} \
} while (0)
#define INPUT_ARRAY(name, len) do { \
float *values = av_calloc((len), sizeof(float)); \
if (!values) { \
rnnoise_model_free(ret); \
return AVERROR(ENOMEM); \
} \
name = values; \
for (int i = 0; i < (len); i++) { \
if (fscanf(f, "%d", &in) != 1) { \
rnnoise_model_free(ret); \
return AVERROR(EINVAL); \
} \
values[i] = in; \
} \
} while (0)
#define INPUT_ARRAY3(name, len0, len1, len2) do { \
float *values = av_calloc(FFALIGN((len0), 4) * FFALIGN((len1), 4) * (len2), sizeof(float)); \
if (!values) { \
rnnoise_model_free(ret); \
return AVERROR(ENOMEM); \
} \
name = values; \
for (int k = 0; k < (len0); k++) { \
for (int i = 0; i < (len2); i++) { \
for (int j = 0; j < (len1); j++) { \
if (fscanf(f, "%d", &in) != 1) { \
rnnoise_model_free(ret); \
return AVERROR(EINVAL); \
} \
values[j * (len2) * FFALIGN((len0), 4) + i * FFALIGN((len0), 4) + k] = in; \
} \
} \
} \
} while (0)
#define NEW_LINE() do { \
int c; \
while ((c = fgetc(f)) != EOF) { \
if (c == '\n') \
break; \
} \
} while (0)
#define INPUT_DENSE(name) do { \
INPUT_VAL(name->nb_inputs); \
INPUT_VAL(name->nb_neurons); \
ret->name ## _size = name->nb_neurons; \
INPUT_ACTIVATION(name->activation); \
NEW_LINE(); \
INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons); \
NEW_LINE(); \
INPUT_ARRAY(name->bias, name->nb_neurons); \
NEW_LINE(); \
} while (0)
#define INPUT_GRU(name) do { \
INPUT_VAL(name->nb_inputs); \
INPUT_VAL(name->nb_neurons); \
ret->name ## _size = name->nb_neurons; \
INPUT_ACTIVATION(name->activation); \
NEW_LINE(); \
INPUT_ARRAY3(name->input_weights, name->nb_inputs, name->nb_neurons, 3); \
NEW_LINE(); \
INPUT_ARRAY3(name->recurrent_weights, name->nb_neurons, name->nb_neurons, 3); \
NEW_LINE(); \
INPUT_ARRAY(name->bias, name->nb_neurons * 3); \
NEW_LINE(); \
} while (0)
INPUT_DENSE(input_dense);
INPUT_GRU(vad_gru);
INPUT_GRU(noise_gru);
INPUT_GRU(denoise_gru);
INPUT_DENSE(denoise_output);
INPUT_DENSE(vad_output);
if (vad_output->nb_neurons != 1) {
rnnoise_model_free(ret);
return AVERROR(EINVAL);
}
*rnn = ret;
return 0;
}
static int query_formats(AVFilterContext *ctx)
{
static const enum AVSampleFormat sample_fmts[] = {
AV_SAMPLE_FMT_FLTP,
AV_SAMPLE_FMT_NONE
};
int ret, sample_rates[] = { 48000, -1 };
ret = ff_set_common_formats_from_list(ctx, sample_fmts);
if (ret < 0)
return ret;
ret = ff_set_common_all_channel_counts(ctx);
if (ret < 0)
return ret;
return ff_set_common_samplerates_from_list(ctx, sample_rates);
}
static int config_input(AVFilterLink *inlink)
{
AVFilterContext *ctx = inlink->dst;
AudioRNNContext *s = ctx->priv;
int ret = 0;
s->channels = inlink->ch_layout.nb_channels;
if (!s->st)
s->st = av_calloc(s->channels, sizeof(DenoiseState));
if (!s->st)
return AVERROR(ENOMEM);
for (int i = 0; i < s->channels; i++) {
DenoiseState *st = &s->st[i];
st->rnn[0].model = s->model[0];
st->rnn[0].vad_gru_state = av_calloc(sizeof(float), FFALIGN(s->model[0]->vad_gru_size, 16));
st->rnn[0].noise_gru_state = av_calloc(sizeof(float), FFALIGN(s->model[0]->noise_gru_size, 16));
st->rnn[0].denoise_gru_state = av_calloc(sizeof(float), FFALIGN(s->model[0]->denoise_gru_size, 16));
if (!st->rnn[0].vad_gru_state ||
!st->rnn[0].noise_gru_state ||
!st->rnn[0].denoise_gru_state)
return AVERROR(ENOMEM);
}
for (int i = 0; i < s->channels; i++) {
DenoiseState *st = &s->st[i];
float scale = 1.f;
if (!st->tx)
ret = av_tx_init(&st->tx, &st->tx_fn, AV_TX_FLOAT_FFT, 0, WINDOW_SIZE, &scale, 0);
if (ret < 0)
return ret;
if (!st->txi)
ret = av_tx_init(&st->txi, &st->txi_fn, AV_TX_FLOAT_FFT, 1, WINDOW_SIZE, &scale, 0);
if (ret < 0)
return ret;
}
return ret;
}
static void biquad(float *y, float mem[2], const float *x,
const float *b, const float *a, int N)
{
for (int i = 0; i < N; i++) {
float xi, yi;
xi = x[i];
yi = x[i] + mem[0];
mem[0] = mem[1] + (b[0]*xi - a[0]*yi);
mem[1] = (b[1]*xi - a[1]*yi);
y[i] = yi;
}
}
#define RNN_MOVE(dst, src, n) (memmove((dst), (src), (n)*sizeof(*(dst)) + 0*((dst)-(src)) ))
#define RNN_CLEAR(dst, n) (memset((dst), 0, (n)*sizeof(*(dst))))
#define RNN_COPY(dst, src, n) (memcpy((dst), (src), (n)*sizeof(*(dst)) + 0*((dst)-(src)) ))
static void forward_transform(DenoiseState *st, AVComplexFloat *out, const float *in)
{
AVComplexFloat x[WINDOW_SIZE];
AVComplexFloat y[WINDOW_SIZE];
for (int i = 0; i < WINDOW_SIZE; i++) {
x[i].re = in[i];
x[i].im = 0;
}
st->tx_fn(st->tx, y, x, sizeof(AVComplexFloat));
RNN_COPY(out, y, FREQ_SIZE);
}
static void inverse_transform(DenoiseState *st, float *out, const AVComplexFloat *in)
{
AVComplexFloat x[WINDOW_SIZE];
AVComplexFloat y[WINDOW_SIZE];
RNN_COPY(x, in, FREQ_SIZE);
for (int i = FREQ_SIZE; i < WINDOW_SIZE; i++) {
x[i].re = x[WINDOW_SIZE - i].re;
x[i].im = -x[WINDOW_SIZE - i].im;
}
st->txi_fn(st->txi, y, x, sizeof(AVComplexFloat));
for (int i = 0; i < WINDOW_SIZE; i++)
out[i] = y[i].re / WINDOW_SIZE;
}
static const uint8_t eband5ms[] = {
/*0 200 400 600 800 1k 1.2 1.4 1.6 2k 2.4 2.8 3.2 4k 4.8 5.6 6.8 8k 9.6 12k 15.6 20k*/
0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 34, 40, 48, 60, 78, 100
};
static void compute_band_energy(float *bandE, const AVComplexFloat *X)
{
float sum[NB_BANDS] = {0};
for (int i = 0; i < NB_BANDS - 1; i++) {
int band_size;
band_size = (eband5ms[i + 1] - eband5ms[i]) << FRAME_SIZE_SHIFT;
for (int j = 0; j < band_size; j++) {
float tmp, frac = (float)j / band_size;
tmp = SQUARE(X[(eband5ms[i] << FRAME_SIZE_SHIFT) + j].re);
tmp += SQUARE(X[(eband5ms[i] << FRAME_SIZE_SHIFT) + j].im);
sum[i] += (1.f - frac) * tmp;
sum[i + 1] += frac * tmp;
}
}
sum[0] *= 2;
sum[NB_BANDS - 1] *= 2;
for (int i = 0; i < NB_BANDS; i++)
bandE[i] = sum[i];
}
static void compute_band_corr(float *bandE, const AVComplexFloat *X, const AVComplexFloat *P)
{
float sum[NB_BANDS] = { 0 };
for (int i = 0; i < NB_BANDS - 1; i++) {
int band_size;
band_size = (eband5ms[i + 1] - eband5ms[i]) << FRAME_SIZE_SHIFT;
for (int j = 0; j < band_size; j++) {
float tmp, frac = (float)j / band_size;
tmp = X[(eband5ms[i]<<FRAME_SIZE_SHIFT) + j].re * P[(eband5ms[i]<<FRAME_SIZE_SHIFT) + j].re;
tmp += X[(eband5ms[i]<<FRAME_SIZE_SHIFT) + j].im * P[(eband5ms[i]<<FRAME_SIZE_SHIFT) + j].im;
sum[i] += (1 - frac) * tmp;
sum[i + 1] += frac * tmp;
}
}
sum[0] *= 2;
sum[NB_BANDS-1] *= 2;
for (int i = 0; i < NB_BANDS; i++)
bandE[i] = sum[i];
}
static void frame_analysis(AudioRNNContext *s, DenoiseState *st, AVComplexFloat *X, float *Ex, const float *in)
{
LOCAL_ALIGNED_32(float, x, [WINDOW_SIZE]);
RNN_COPY(x, st->analysis_mem, FRAME_SIZE);
RNN_COPY(x + FRAME_SIZE, in, FRAME_SIZE);
RNN_COPY(st->analysis_mem, in, FRAME_SIZE);
s->fdsp->vector_fmul(x, x, s->window, WINDOW_SIZE);
forward_transform(st, X, x);
compute_band_energy(Ex, X);
}
static void frame_synthesis(AudioRNNContext *s, DenoiseState *st, float *out, const AVComplexFloat *y)
{
LOCAL_ALIGNED_32(float, x, [WINDOW_SIZE]);
const float *src = st->history;
const float mix = s->mix;
const float imix = 1.f - FFMAX(mix, 0.f);
inverse_transform(st, x, y);
s->fdsp->vector_fmul(x, x, s->window, WINDOW_SIZE);
s->fdsp->vector_fmac_scalar(x, st->synthesis_mem, 1.f, FRAME_SIZE);
RNN_COPY(out, x, FRAME_SIZE);
RNN_COPY(st->synthesis_mem, &x[FRAME_SIZE], FRAME_SIZE);
for (int n = 0; n < FRAME_SIZE; n++)
out[n] = out[n] * mix + src[n] * imix;
}
static inline void xcorr_kernel(const float *x, const float *y, float sum[4], int len)
{
float y_0, y_1, y_2, y_3 = 0;
int j;
y_0 = *y++;
y_1 = *y++;
y_2 = *y++;
for (j = 0; j < len - 3; j += 4) {
float tmp;
tmp = *x++;
y_3 = *y++;
sum[0] += tmp * y_0;
sum[1] += tmp * y_1;
sum[2] += tmp * y_2;
sum[3] += tmp * y_3;
tmp = *x++;
y_0 = *y++;
sum[0] += tmp * y_1;
sum[1] += tmp * y_2;
sum[2] += tmp * y_3;
sum[3] += tmp * y_0;
tmp = *x++;
y_1 = *y++;
sum[0] += tmp * y_2;
sum[1] += tmp * y_3;
sum[2] += tmp * y_0;
sum[3] += tmp * y_1;
tmp = *x++;
y_2 = *y++;
sum[0] += tmp * y_3;
sum[1] += tmp * y_0;
sum[2] += tmp * y_1;
sum[3] += tmp * y_2;
}
if (j++ < len) {
float tmp = *x++;
y_3 = *y++;
sum[0] += tmp * y_0;
sum[1] += tmp * y_1;
sum[2] += tmp * y_2;
sum[3] += tmp * y_3;
}
if (j++ < len) {
float tmp=*x++;
y_0 = *y++;
sum[0] += tmp * y_1;
sum[1] += tmp * y_2;
sum[2] += tmp * y_3;
sum[3] += tmp * y_0;
}
if (j < len) {
float tmp=*x++;
y_1 = *y++;
sum[0] += tmp * y_2;
sum[1] += tmp * y_3;
sum[2] += tmp * y_0;
sum[3] += tmp * y_1;
}
}
static inline float celt_inner_prod(const float *x,
const float *y, int N)
{
float xy = 0.f;
for (int i = 0; i < N; i++)
xy += x[i] * y[i];
return xy;
}
static void celt_pitch_xcorr(const float *x, const float *y,
float *xcorr, int len, int max_pitch)
{
int i;
for (i = 0; i < max_pitch - 3; i += 4) {
float sum[4] = { 0, 0, 0, 0};
xcorr_kernel(x, y + i, sum, len);
xcorr[i] = sum[0];
xcorr[i + 1] = sum[1];
xcorr[i + 2] = sum[2];
xcorr[i + 3] = sum[3];
}
/* In case max_pitch isn't a multiple of 4, do non-unrolled version. */
for (; i < max_pitch; i++) {
xcorr[i] = celt_inner_prod(x, y + i, len);
}
}
static int celt_autocorr(const float *x, /* in: [0...n-1] samples x */
float *ac, /* out: [0...lag-1] ac values */
const float *window,
int overlap,
int lag,
int n)
{
int fastN = n - lag;
int shift;
const float *xptr;
float xx[PITCH_BUF_SIZE>>1];
if (overlap == 0) {
xptr = x;
} else {
for (int i = 0; i < n; i++)
xx[i] = x[i];
for (int i = 0; i < overlap; i++) {
xx[i] = x[i] * window[i];
xx[n-i-1] = x[n-i-1] * window[i];
}
xptr = xx;
}
shift = 0;
celt_pitch_xcorr(xptr, xptr, ac, fastN, lag+1);
for (int k = 0; k <= lag; k++) {
float d = 0.f;
for (int i = k + fastN; i < n; i++)
d += xptr[i] * xptr[i-k];
ac[k] += d;
}
return shift;
}
static void celt_lpc(float *lpc, /* out: [0...p-1] LPC coefficients */
const float *ac, /* in: [0...p] autocorrelation values */
int p)
{
float r, error = ac[0];
RNN_CLEAR(lpc, p);
if (ac[0] != 0) {
for (int i = 0; i < p; i++) {
/* Sum up this iteration's reflection coefficient */
float rr = 0;
for (int j = 0; j < i; j++)
rr += (lpc[j] * ac[i - j]);
rr += ac[i + 1];
r = -rr/error;
/* Update LPC coefficients and total error */
lpc[i] = r;
for (int j = 0; j < (i + 1) >> 1; j++) {
float tmp1, tmp2;
tmp1 = lpc[j];
tmp2 = lpc[i-1-j];
lpc[j] = tmp1 + (r*tmp2);
lpc[i-1-j] = tmp2 + (r*tmp1);
}
error = error - (r * r *error);
/* Bail out once we get 30 dB gain */
if (error < .001f * ac[0])
break;
}
}
}
static void celt_fir5(const float *x,
const float *num,
float *y,
int N,
float *mem)
{
float num0, num1, num2, num3, num4;
float mem0, mem1, mem2, mem3, mem4;
num0 = num[0];
num1 = num[1];
num2 = num[2];
num3 = num[3];
num4 = num[4];
mem0 = mem[0];
mem1 = mem[1];
mem2 = mem[2];
mem3 = mem[3];
mem4 = mem[4];
for (int i = 0; i < N; i++) {
float sum = x[i];
sum += (num0*mem0);
sum += (num1*mem1);
sum += (num2*mem2);
sum += (num3*mem3);
sum += (num4*mem4);
mem4 = mem3;
mem3 = mem2;
mem2 = mem1;
mem1 = mem0;
mem0 = x[i];
y[i] = sum;
}
mem[0] = mem0;
mem[1] = mem1;
mem[2] = mem2;
mem[3] = mem3;
mem[4] = mem4;
}
static void pitch_downsample(float *x[], float *x_lp,
int len, int C)
{
float ac[5];
float tmp=Q15ONE;
float lpc[4], mem[5]={0,0,0,0,0};
float lpc2[5];
float c1 = .8f;
for (int i = 1; i < len >> 1; i++)
x_lp[i] = .5f * (.5f * (x[0][(2*i-1)]+x[0][(2*i+1)])+x[0][2*i]);
x_lp[0] = .5f * (.5f * (x[0][1])+x[0][0]);
if (C==2) {
for (int i = 1; i < len >> 1; i++)
x_lp[i] += (.5f * (.5f * (x[1][(2*i-1)]+x[1][(2*i+1)])+x[1][2*i]));
x_lp[0] += .5f * (.5f * (x[1][1])+x[1][0]);
}
celt_autocorr(x_lp, ac, NULL, 0, 4, len>>1);
/* Noise floor -40 dB */
ac[0] *= 1.0001f;
/* Lag windowing */
for (int i = 1; i <= 4; i++) {
/*ac[i] *= exp(-.5*(2*M_PI*.002*i)*(2*M_PI*.002*i));*/
ac[i] -= ac[i]*(.008f*i)*(.008f*i);
}
celt_lpc(lpc, ac, 4);
for (int i = 0; i < 4; i++) {
tmp = .9f * tmp;
lpc[i] = (lpc[i] * tmp);
}
/* Add a zero */
lpc2[0] = lpc[0] + .8f;
lpc2[1] = lpc[1] + (c1 * lpc[0]);
lpc2[2] = lpc[2] + (c1 * lpc[1]);
lpc2[3] = lpc[3] + (c1 * lpc[2]);
lpc2[4] = (c1 * lpc[3]);
celt_fir5(x_lp, lpc2, x_lp, len>>1, mem);
}
static inline void dual_inner_prod(const float *x, const float *y01, const float *y02,
int N, float *xy1, float *xy2)
{
float xy01 = 0, xy02 = 0;
for (int i = 0; i < N; i++) {
xy01 += (x[i] * y01[i]);
xy02 += (x[i] * y02[i]);
}
*xy1 = xy01;
*xy2 = xy02;
}
static float compute_pitch_gain(float xy, float xx, float yy)
{
return xy / sqrtf(1.f + xx * yy);
}
static const uint8_t second_check[16] = {0, 0, 3, 2, 3, 2, 5, 2, 3, 2, 3, 2, 5, 2, 3, 2};
static float remove_doubling(float *x, int maxperiod, int minperiod, int N,
int *T0_, int prev_period, float prev_gain)
{
int k, i, T, T0;
float g, g0;
float pg;
float xy,xx,yy,xy2;
float xcorr[3];
float best_xy, best_yy;
int offset;
int minperiod0;
float yy_lookup[PITCH_MAX_PERIOD+1];
minperiod0 = minperiod;
maxperiod /= 2;
minperiod /= 2;
*T0_ /= 2;
prev_period /= 2;
N /= 2;
x += maxperiod;
if (*T0_>=maxperiod)
*T0_=maxperiod-1;
T = T0 = *T0_;
dual_inner_prod(x, x, x-T0, N, &xx, &xy);
yy_lookup[0] = xx;
yy=xx;
for (i = 1; i <= maxperiod; i++) {
yy = yy+(x[-i] * x[-i])-(x[N-i] * x[N-i]);
yy_lookup[i] = FFMAX(0, yy);
}
yy = yy_lookup[T0];
best_xy = xy;
best_yy = yy;
g = g0 = compute_pitch_gain(xy, xx, yy);
/* Look for any pitch at T/k */
for (k = 2; k <= 15; k++) {
int T1, T1b;
float g1;
float cont=0;
float thresh;
T1 = (2*T0+k)/(2*k);
if (T1 < minperiod)
break;
/* Look for another strong correlation at T1b */
if (k==2)
{
if (T1+T0>maxperiod)
T1b = T0;
else
T1b = T0+T1;
} else
{
T1b = (2*second_check[k]*T0+k)/(2*k);
}
dual_inner_prod(x, &x[-T1], &x[-T1b], N, &xy, &xy2);
xy = .5f * (xy + xy2);
yy = .5f * (yy_lookup[T1] + yy_lookup[T1b]);
g1 = compute_pitch_gain(xy, xx, yy);
if (FFABS(T1-prev_period)<=1)
cont = prev_gain;
else if (FFABS(T1-prev_period)<=2 && 5 * k * k < T0)
cont = prev_gain * .5f;
else
cont = 0;
thresh = FFMAX(.3f, (.7f * g0) - cont);
/* Bias against very high pitch (very short period) to avoid false-positives
due to short-term correlation */
if (T1<3*minperiod)
thresh = FFMAX(.4f, (.85f * g0) - cont);
else if (T1<2*minperiod)
thresh = FFMAX(.5f, (.9f * g0) - cont);
if (g1 > thresh)
{
best_xy = xy;
best_yy = yy;
T = T1;
g = g1;
}
}
best_xy = FFMAX(0, best_xy);
if (best_yy <= best_xy)
pg = Q15ONE;
else
pg = best_xy/(best_yy + 1);
for (k = 0; k < 3; k++)
xcorr[k] = celt_inner_prod(x, x-(T+k-1), N);
if ((xcorr[2]-xcorr[0]) > .7f * (xcorr[1]-xcorr[0]))
offset = 1;
else if ((xcorr[0]-xcorr[2]) > (.7f * (xcorr[1] - xcorr[2])))
offset = -1;
else
offset = 0;
if (pg > g)
pg = g;
*T0_ = 2*T+offset;
if (*T0_<minperiod0)
*T0_=minperiod0;
return pg;
}
static void find_best_pitch(float *xcorr, float *y, int len,
int max_pitch, int *best_pitch)
{
float best_num[2];
float best_den[2];
float Syy = 1.f;
best_num[0] = -1;
best_num[1] = -1;
best_den[0] = 0;
best_den[1] = 0;
best_pitch[0] = 0;
best_pitch[1] = 1;
for (int j = 0; j < len; j++)
Syy += y[j] * y[j];
for (int i = 0; i < max_pitch; i++) {
if (xcorr[i]>0) {
float num;
float xcorr16;
xcorr16 = xcorr[i];
/* Considering the range of xcorr16, this should avoid both underflows
and overflows (inf) when squaring xcorr16 */
xcorr16 *= 1e-12f;
num = xcorr16 * xcorr16;
if ((num * best_den[1]) > (best_num[1] * Syy)) {
if ((num * best_den[0]) > (best_num[0] * Syy)) {
best_num[1] = best_num[0];
best_den[1] = best_den[0];
best_pitch[1] = best_pitch[0];
best_num[0] = num;
best_den[0] = Syy;
best_pitch[0] = i;
} else {
best_num[1] = num;
best_den[1] = Syy;
best_pitch[1] = i;
}
}
}
Syy += y[i+len]*y[i+len] - y[i] * y[i];
Syy = FFMAX(1, Syy);
}
}
static void pitch_search(const float *x_lp, float *y,
int len, int max_pitch, int *pitch)
{
int lag;
int best_pitch[2]={0,0};
int offset;
float x_lp4[WINDOW_SIZE];
float y_lp4[WINDOW_SIZE];
float xcorr[WINDOW_SIZE];
lag = len+max_pitch;
/* Downsample by 2 again */
for (int j = 0; j < len >> 2; j++)
x_lp4[j] = x_lp[2*j];
for (int j = 0; j < lag >> 2; j++)
y_lp4[j] = y[2*j];
/* Coarse search with 4x decimation */
celt_pitch_xcorr(x_lp4, y_lp4, xcorr, len>>2, max_pitch>>2);
find_best_pitch(xcorr, y_lp4, len>>2, max_pitch>>2, best_pitch);
/* Finer search with 2x decimation */
for (int i = 0; i < max_pitch >> 1; i++) {
float sum;
xcorr[i] = 0;
if (FFABS(i-2*best_pitch[0])>2 && FFABS(i-2*best_pitch[1])>2)
continue;
sum = celt_inner_prod(x_lp, y+i, len>>1);
xcorr[i] = FFMAX(-1, sum);
}
find_best_pitch(xcorr, y, len>>1, max_pitch>>1, best_pitch);
/* Refine by pseudo-interpolation */
if (best_pitch[0] > 0 && best_pitch[0] < (max_pitch >> 1) - 1) {
float a, b, c;
a = xcorr[best_pitch[0] - 1];
b = xcorr[best_pitch[0]];
c = xcorr[best_pitch[0] + 1];
if (c - a > .7f * (b - a))
offset = 1;
else if (a - c > .7f * (b-c))
offset = -1;
else
offset = 0;
} else {
offset = 0;
}
*pitch = 2 * best_pitch[0] - offset;
}
static void dct(AudioRNNContext *s, float *out, const float *in)
{
for (int i = 0; i < NB_BANDS; i++) {
float sum;
sum = s->fdsp->scalarproduct_float(in, s->dct_table[i], FFALIGN(NB_BANDS, 4));
out[i] = sum * sqrtf(2.f / 22);
}
}
static int compute_frame_features(AudioRNNContext *s, DenoiseState *st, AVComplexFloat *X, AVComplexFloat *P,
float *Ex, float *Ep, float *Exp, float *features, const float *in)
{
float E = 0;
float *ceps_0, *ceps_1, *ceps_2;
float spec_variability = 0;
LOCAL_ALIGNED_32(float, Ly, [NB_BANDS]);
LOCAL_ALIGNED_32(float, p, [WINDOW_SIZE]);
float pitch_buf[PITCH_BUF_SIZE>>1];
int pitch_index;
float gain;
float *(pre[1]);
float tmp[NB_BANDS];
float follow, logMax;
frame_analysis(s, st, X, Ex, in);
RNN_MOVE(st->pitch_buf, &st->pitch_buf[FRAME_SIZE], PITCH_BUF_SIZE-FRAME_SIZE);
RNN_COPY(&st->pitch_buf[PITCH_BUF_SIZE-FRAME_SIZE], in, FRAME_SIZE);
pre[0] = &st->pitch_buf[0];
pitch_downsample(pre, pitch_buf, PITCH_BUF_SIZE, 1);
pitch_search(pitch_buf+(PITCH_MAX_PERIOD>>1), pitch_buf, PITCH_FRAME_SIZE,
PITCH_MAX_PERIOD-3*PITCH_MIN_PERIOD, &pitch_index);
pitch_index = PITCH_MAX_PERIOD-pitch_index;
gain = remove_doubling(pitch_buf, PITCH_MAX_PERIOD, PITCH_MIN_PERIOD,
PITCH_FRAME_SIZE, &pitch_index, st->last_period, st->last_gain);
st->last_period = pitch_index;
st->last_gain = gain;
for (int i = 0; i < WINDOW_SIZE; i++)
p[i] = st->pitch_buf[PITCH_BUF_SIZE-WINDOW_SIZE-pitch_index+i];
s->fdsp->vector_fmul(p, p, s->window, WINDOW_SIZE);
forward_transform(st, P, p);
compute_band_energy(Ep, P);
compute_band_corr(Exp, X, P);
for (int i = 0; i < NB_BANDS; i++)
Exp[i] = Exp[i] / sqrtf(.001f+Ex[i]*Ep[i]);
dct(s, tmp, Exp);
for (int i = 0; i < NB_DELTA_CEPS; i++)
features[NB_BANDS+2*NB_DELTA_CEPS+i] = tmp[i];
features[NB_BANDS+2*NB_DELTA_CEPS] -= 1.3;
features[NB_BANDS+2*NB_DELTA_CEPS+1] -= 0.9;
features[NB_BANDS+3*NB_DELTA_CEPS] = .01*(pitch_index-300);
logMax = -2;
follow = -2;
for (int i = 0; i < NB_BANDS; i++) {
Ly[i] = log10f(1e-2f + Ex[i]);
Ly[i] = FFMAX(logMax-7, FFMAX(follow-1.5, Ly[i]));
logMax = FFMAX(logMax, Ly[i]);
follow = FFMAX(follow-1.5, Ly[i]);
E += Ex[i];
}
if (E < 0.04f) {
/* If there's no audio, avoid messing up the state. */
RNN_CLEAR(features, NB_FEATURES);
return 1;
}
dct(s, features, Ly);
features[0] -= 12;
features[1] -= 4;
ceps_0 = st->cepstral_mem[st->memid];
ceps_1 = (st->memid < 1) ? st->cepstral_mem[CEPS_MEM+st->memid-1] : st->cepstral_mem[st->memid-1];
ceps_2 = (st->memid < 2) ? st->cepstral_mem[CEPS_MEM+st->memid-2] : st->cepstral_mem[st->memid-2];
for (int i = 0; i < NB_BANDS; i++)
ceps_0[i] = features[i];
st->memid++;
for (int i = 0; i < NB_DELTA_CEPS; i++) {
features[i] = ceps_0[i] + ceps_1[i] + ceps_2[i];
features[NB_BANDS+i] = ceps_0[i] - ceps_2[i];
features[NB_BANDS+NB_DELTA_CEPS+i] = ceps_0[i] - 2*ceps_1[i] + ceps_2[i];
}
/* Spectral variability features. */
if (st->memid == CEPS_MEM)
st->memid = 0;
for (int i = 0; i < CEPS_MEM; i++) {
float mindist = 1e15f;
for (int j = 0; j < CEPS_MEM; j++) {
float dist = 0.f;
for (int k = 0; k < NB_BANDS; k++) {
float tmp;
tmp = st->cepstral_mem[i][k] - st->cepstral_mem[j][k];
dist += tmp*tmp;
}
if (j != i)
mindist = FFMIN(mindist, dist);
}
spec_variability += mindist;
}
features[NB_BANDS+3*NB_DELTA_CEPS+1] = spec_variability/CEPS_MEM-2.1;
return 0;
}
static void interp_band_gain(float *g, const float *bandE)
{
memset(g, 0, sizeof(*g) * FREQ_SIZE);
for (int i = 0; i < NB_BANDS - 1; i++) {
const int band_size = (eband5ms[i + 1] - eband5ms[i]) << FRAME_SIZE_SHIFT;
for (int j = 0; j < band_size; j++) {
float frac = (float)j / band_size;
g[(eband5ms[i] << FRAME_SIZE_SHIFT) + j] = (1.f - frac) * bandE[i] + frac * bandE[i + 1];
}
}
}
static void pitch_filter(AVComplexFloat *X, const AVComplexFloat *P, const float *Ex, const float *Ep,
const float *Exp, const float *g)
{
float newE[NB_BANDS];
float r[NB_BANDS];
float norm[NB_BANDS];
float rf[FREQ_SIZE] = {0};
float normf[FREQ_SIZE]={0};
for (int i = 0; i < NB_BANDS; i++) {
if (Exp[i]>g[i]) r[i] = 1;
else r[i] = SQUARE(Exp[i])*(1-SQUARE(g[i]))/(.001 + SQUARE(g[i])*(1-SQUARE(Exp[i])));
r[i] = sqrtf(av_clipf(r[i], 0, 1));
r[i] *= sqrtf(Ex[i]/(1e-8+Ep[i]));
}
interp_band_gain(rf, r);
for (int i = 0; i < FREQ_SIZE; i++) {
X[i].re += rf[i]*P[i].re;
X[i].im += rf[i]*P[i].im;
}
compute_band_energy(newE, X);
for (int i = 0; i < NB_BANDS; i++) {
norm[i] = sqrtf(Ex[i] / (1e-8+newE[i]));
}
interp_band_gain(normf, norm);
for (int i = 0; i < FREQ_SIZE; i++) {
X[i].re *= normf[i];
X[i].im *= normf[i];
}
}
static const float tansig_table[201] = {
0.000000f, 0.039979f, 0.079830f, 0.119427f, 0.158649f,
0.197375f, 0.235496f, 0.272905f, 0.309507f, 0.345214f,
0.379949f, 0.413644f, 0.446244f, 0.477700f, 0.507977f,
0.537050f, 0.564900f, 0.591519f, 0.616909f, 0.641077f,
0.664037f, 0.685809f, 0.706419f, 0.725897f, 0.744277f,
0.761594f, 0.777888f, 0.793199f, 0.807569f, 0.821040f,
0.833655f, 0.845456f, 0.856485f, 0.866784f, 0.876393f,
0.885352f, 0.893698f, 0.901468f, 0.908698f, 0.915420f,
0.921669f, 0.927473f, 0.932862f, 0.937863f, 0.942503f,
0.946806f, 0.950795f, 0.954492f, 0.957917f, 0.961090f,
0.964028f, 0.966747f, 0.969265f, 0.971594f, 0.973749f,
0.975743f, 0.977587f, 0.979293f, 0.980869f, 0.982327f,
0.983675f, 0.984921f, 0.986072f, 0.987136f, 0.988119f,
0.989027f, 0.989867f, 0.990642f, 0.991359f, 0.992020f,
0.992631f, 0.993196f, 0.993718f, 0.994199f, 0.994644f,
0.995055f, 0.995434f, 0.995784f, 0.996108f, 0.996407f,
0.996682f, 0.996937f, 0.997172f, 0.997389f, 0.997590f,
0.997775f, 0.997946f, 0.998104f, 0.998249f, 0.998384f,
0.998508f, 0.998623f, 0.998728f, 0.998826f, 0.998916f,
0.999000f, 0.999076f, 0.999147f, 0.999213f, 0.999273f,
0.999329f, 0.999381f, 0.999428f, 0.999472f, 0.999513f,
0.999550f, 0.999585f, 0.999617f, 0.999646f, 0.999673f,
0.999699f, 0.999722f, 0.999743f, 0.999763f, 0.999781f,
0.999798f, 0.999813f, 0.999828f, 0.999841f, 0.999853f,
0.999865f, 0.999875f, 0.999885f, 0.999893f, 0.999902f,
0.999909f, 0.999916f, 0.999923f, 0.999929f, 0.999934f,
0.999939f, 0.999944f, 0.999948f, 0.999952f, 0.999956f,
0.999959f, 0.999962f, 0.999965f, 0.999968f, 0.999970f,
0.999973f, 0.999975f, 0.999977f, 0.999978f, 0.999980f,
0.999982f, 0.999983f, 0.999984f, 0.999986f, 0.999987f,
0.999988f, 0.999989f, 0.999990f, 0.999990f, 0.999991f,
0.999992f, 0.999992f, 0.999993f, 0.999994f, 0.999994f,
0.999994f, 0.999995f, 0.999995f, 0.999996f, 0.999996f,
0.999996f, 0.999997f, 0.999997f, 0.999997f, 0.999997f,
0.999997f, 0.999998f, 0.999998f, 0.999998f, 0.999998f,
0.999998f, 0.999998f, 0.999999f, 0.999999f, 0.999999f,
0.999999f, 0.999999f, 0.999999f, 0.999999f, 0.999999f,
0.999999f, 0.999999f, 0.999999f, 0.999999f, 0.999999f,
1.000000f, 1.000000f, 1.000000f, 1.000000f, 1.000000f,
1.000000f, 1.000000f, 1.000000f, 1.000000f, 1.000000f,
1.000000f,
};
static inline float tansig_approx(float x)
{
float y, dy;
float sign=1;
int i;
/* Tests are reversed to catch NaNs */
if (!(x<8))
return 1;
if (!(x>-8))
return -1;
/* Another check in case of -ffast-math */
if (isnan(x))
return 0;
if (x < 0) {
x=-x;
sign=-1;
}
i = (int)floor(.5f+25*x);
x -= .04f*i;
y = tansig_table[i];
dy = 1-y*y;
y = y + x*dy*(1 - y*x);
return sign*y;
}
static inline float sigmoid_approx(float x)
{
return .5f + .5f*tansig_approx(.5f*x);
}
static void compute_dense(const DenseLayer *layer, float *output, const float *input)
{
const int N = layer->nb_neurons, M = layer->nb_inputs, stride = N;
for (int i = 0; i < N; i++) {
/* Compute update gate. */
float sum = layer->bias[i];
for (int j = 0; j < M; j++)
sum += layer->input_weights[j * stride + i] * input[j];
output[i] = WEIGHTS_SCALE * sum;
}
if (layer->activation == ACTIVATION_SIGMOID) {
for (int i = 0; i < N; i++)
output[i] = sigmoid_approx(output[i]);
} else if (layer->activation == ACTIVATION_TANH) {
for (int i = 0; i < N; i++)
output[i] = tansig_approx(output[i]);
} else if (layer->activation == ACTIVATION_RELU) {
for (int i = 0; i < N; i++)
output[i] = FFMAX(0, output[i]);
} else {
av_assert0(0);
}
}
static void compute_gru(AudioRNNContext *s, const GRULayer *gru, float *state, const float *input)
{
LOCAL_ALIGNED_32(float, z, [MAX_NEURONS]);
LOCAL_ALIGNED_32(float, r, [MAX_NEURONS]);
LOCAL_ALIGNED_32(float, h, [MAX_NEURONS]);
const int M = gru->nb_inputs;
const int N = gru->nb_neurons;
const int AN = FFALIGN(N, 4);
const int AM = FFALIGN(M, 4);
const int stride = 3 * AN, istride = 3 * AM;
for (int i = 0; i < N; i++) {
/* Compute update gate. */
float sum = gru->bias[i];
sum += s->fdsp->scalarproduct_float(gru->input_weights + i * istride, input, AM);
sum += s->fdsp->scalarproduct_float(gru->recurrent_weights + i * stride, state, AN);
z[i] = sigmoid_approx(WEIGHTS_SCALE * sum);
}
for (int i = 0; i < N; i++) {
/* Compute reset gate. */
float sum = gru->bias[N + i];
sum += s->fdsp->scalarproduct_float(gru->input_weights + AM + i * istride, input, AM);
sum += s->fdsp->scalarproduct_float(gru->recurrent_weights + AN + i * stride, state, AN);
r[i] = sigmoid_approx(WEIGHTS_SCALE * sum);
}
for (int i = 0; i < N; i++) {
/* Compute output. */
float sum = gru->bias[2 * N + i];
sum += s->fdsp->scalarproduct_float(gru->input_weights + 2 * AM + i * istride, input, AM);
for (int j = 0; j < N; j++)
sum += gru->recurrent_weights[2 * AN + i * stride + j] * state[j] * r[j];
if (gru->activation == ACTIVATION_SIGMOID)
sum = sigmoid_approx(WEIGHTS_SCALE * sum);
else if (gru->activation == ACTIVATION_TANH)
sum = tansig_approx(WEIGHTS_SCALE * sum);
else if (gru->activation == ACTIVATION_RELU)
sum = FFMAX(0, WEIGHTS_SCALE * sum);
else
av_assert0(0);
h[i] = z[i] * state[i] + (1.f - z[i]) * sum;
}
RNN_COPY(state, h, N);
}
#define INPUT_SIZE 42
static void compute_rnn(AudioRNNContext *s, RNNState *rnn, float *gains, float *vad, const float *input)
{
LOCAL_ALIGNED_32(float, dense_out, [MAX_NEURONS]);
LOCAL_ALIGNED_32(float, noise_input, [MAX_NEURONS * 3]);
LOCAL_ALIGNED_32(float, denoise_input, [MAX_NEURONS * 3]);
compute_dense(rnn->model->input_dense, dense_out, input);
compute_gru(s, rnn->model->vad_gru, rnn->vad_gru_state, dense_out);
compute_dense(rnn->model->vad_output, vad, rnn->vad_gru_state);
memcpy(noise_input, dense_out, rnn->model->input_dense_size * sizeof(float));
memcpy(noise_input + rnn->model->input_dense_size,
rnn->vad_gru_state, rnn->model->vad_gru_size * sizeof(float));
memcpy(noise_input + rnn->model->input_dense_size + rnn->model->vad_gru_size,
input, INPUT_SIZE * sizeof(float));
compute_gru(s, rnn->model->noise_gru, rnn->noise_gru_state, noise_input);
memcpy(denoise_input, rnn->vad_gru_state, rnn->model->vad_gru_size * sizeof(float));
memcpy(denoise_input + rnn->model->vad_gru_size,
rnn->noise_gru_state, rnn->model->noise_gru_size * sizeof(float));
memcpy(denoise_input + rnn->model->vad_gru_size + rnn->model->noise_gru_size,
input, INPUT_SIZE * sizeof(float));
compute_gru(s, rnn->model->denoise_gru, rnn->denoise_gru_state, denoise_input);
compute_dense(rnn->model->denoise_output, gains, rnn->denoise_gru_state);
}
static float rnnoise_channel(AudioRNNContext *s, DenoiseState *st, float *out, const float *in,
int disabled)
{
AVComplexFloat X[FREQ_SIZE];
AVComplexFloat P[WINDOW_SIZE];
float x[FRAME_SIZE];
float Ex[NB_BANDS], Ep[NB_BANDS];
LOCAL_ALIGNED_32(float, Exp, [NB_BANDS]);
float features[NB_FEATURES];
float g[NB_BANDS];
float gf[FREQ_SIZE];
float vad_prob = 0;
float *history = st->history;
static const float a_hp[2] = {-1.99599, 0.99600};
static const float b_hp[2] = {-2, 1};
int silence;
biquad(x, st->mem_hp_x, in, b_hp, a_hp, FRAME_SIZE);
silence = compute_frame_features(s, st, X, P, Ex, Ep, Exp, features, x);
if (!silence && !disabled) {
compute_rnn(s, &st->rnn[0], g, &vad_prob, features);
pitch_filter(X, P, Ex, Ep, Exp, g);
for (int i = 0; i < NB_BANDS; i++) {
float alpha = .6f;
g[i] = FFMAX(g[i], alpha * st->lastg[i]);
st->lastg[i] = g[i];
}
interp_band_gain(gf, g);
for (int i = 0; i < FREQ_SIZE; i++) {
X[i].re *= gf[i];
X[i].im *= gf[i];
}
}
frame_synthesis(s, st, out, X);
memcpy(history, in, FRAME_SIZE * sizeof(*history));
return vad_prob;
}
typedef struct ThreadData {
AVFrame *in, *out;
} ThreadData;
static int rnnoise_channels(AVFilterContext *ctx, void *arg, int jobnr, int nb_jobs)
{
AudioRNNContext *s = ctx->priv;
ThreadData *td = arg;
AVFrame *in = td->in;
AVFrame *out = td->out;
const int start = (out->ch_layout.nb_channels * jobnr) / nb_jobs;
const int end = (out->ch_layout.nb_channels * (jobnr+1)) / nb_jobs;
for (int ch = start; ch < end; ch++) {
rnnoise_channel(s, &s->st[ch],
(float *)out->extended_data[ch],
(const float *)in->extended_data[ch],
ctx->is_disabled);
}
return 0;
}
static int filter_frame(AVFilterLink *inlink, AVFrame *in)
{
AVFilterContext *ctx = inlink->dst;
AVFilterLink *outlink = ctx->outputs[0];
AVFrame *out = NULL;
ThreadData td;
out = ff_get_audio_buffer(outlink, FRAME_SIZE);
if (!out) {
av_frame_free(&in);
return AVERROR(ENOMEM);
}
av_frame_copy_props(out, in);
td.in = in; td.out = out;
ff_filter_execute(ctx, rnnoise_channels, &td, NULL,
FFMIN(outlink->ch_layout.nb_channels, ff_filter_get_nb_threads(ctx)));
av_frame_free(&in);
return ff_filter_frame(outlink, out);
}
static int activate(AVFilterContext *ctx)
{
AVFilterLink *inlink = ctx->inputs[0];
AVFilterLink *outlink = ctx->outputs[0];
AVFrame *in = NULL;
int ret;
FF_FILTER_FORWARD_STATUS_BACK(outlink, inlink);
ret = ff_inlink_consume_samples(inlink, FRAME_SIZE, FRAME_SIZE, &in);
if (ret < 0)
return ret;
if (ret > 0)
return filter_frame(inlink, in);
FF_FILTER_FORWARD_STATUS(inlink, outlink);
FF_FILTER_FORWARD_WANTED(outlink, inlink);
return FFERROR_NOT_READY;
}
static int open_model(AVFilterContext *ctx, RNNModel **model)
{
AudioRNNContext *s = ctx->priv;
int ret;
FILE *f;
if (!s->model_name)
return AVERROR(EINVAL);
f = avpriv_fopen_utf8(s->model_name, "r");
if (!f) {
av_log(ctx, AV_LOG_ERROR, "Failed to open model file: %s\n", s->model_name);
return AVERROR(EINVAL);
}
ret = rnnoise_model_from_file(f, model);
fclose(f);
if (!*model || ret < 0)
return ret;
return 0;
}
static av_cold int init(AVFilterContext *ctx)
{
AudioRNNContext *s = ctx->priv;
int ret;
s->fdsp = avpriv_float_dsp_alloc(0);
if (!s->fdsp)
return AVERROR(ENOMEM);
ret = open_model(ctx, &s->model[0]);
if (ret < 0)
return ret;
for (int i = 0; i < FRAME_SIZE; i++) {
s->window[i] = sin(.5*M_PI*sin(.5*M_PI*(i+.5)/FRAME_SIZE) * sin(.5*M_PI*(i+.5)/FRAME_SIZE));
s->window[WINDOW_SIZE - 1 - i] = s->window[i];
}
for (int i = 0; i < NB_BANDS; i++) {
for (int j = 0; j < NB_BANDS; j++) {
s->dct_table[j][i] = cosf((i + .5f) * j * M_PI / NB_BANDS);
if (j == 0)
s->dct_table[j][i] *= sqrtf(.5);
}
}
return 0;
}
static void free_model(AVFilterContext *ctx, int n)
{
AudioRNNContext *s = ctx->priv;
rnnoise_model_free(s->model[n]);
s->model[n] = NULL;
for (int ch = 0; ch < s->channels && s->st; ch++) {
av_freep(&s->st[ch].rnn[n].vad_gru_state);
av_freep(&s->st[ch].rnn[n].noise_gru_state);
av_freep(&s->st[ch].rnn[n].denoise_gru_state);
}
}
static int process_command(AVFilterContext *ctx, const char *cmd, const char *args,
char *res, int res_len, int flags)
{
AudioRNNContext *s = ctx->priv;
int ret;
ret = ff_filter_process_command(ctx, cmd, args, res, res_len, flags);
if (ret < 0)
return ret;
ret = open_model(ctx, &s->model[1]);
if (ret < 0)
return ret;
FFSWAP(RNNModel *, s->model[0], s->model[1]);
for (int ch = 0; ch < s->channels; ch++)
FFSWAP(RNNState, s->st[ch].rnn[0], s->st[ch].rnn[1]);
ret = config_input(ctx->inputs[0]);
if (ret < 0) {
for (int ch = 0; ch < s->channels; ch++)
FFSWAP(RNNState, s->st[ch].rnn[0], s->st[ch].rnn[1]);
FFSWAP(RNNModel *, s->model[0], s->model[1]);
return ret;
}
free_model(ctx, 1);
return 0;
}
static av_cold void uninit(AVFilterContext *ctx)
{
AudioRNNContext *s = ctx->priv;
av_freep(&s->fdsp);
free_model(ctx, 0);
for (int ch = 0; ch < s->channels && s->st; ch++) {
av_tx_uninit(&s->st[ch].tx);
av_tx_uninit(&s->st[ch].txi);
}
av_freep(&s->st);
}
static const AVFilterPad inputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_AUDIO,
.config_props = config_input,
},
};
#define OFFSET(x) offsetof(AudioRNNContext, x)
#define AF AV_OPT_FLAG_AUDIO_PARAM|AV_OPT_FLAG_FILTERING_PARAM|AV_OPT_FLAG_RUNTIME_PARAM
static const AVOption arnndn_options[] = {
{ "model", "set model name", OFFSET(model_name), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, AF },
{ "m", "set model name", OFFSET(model_name), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, AF },
{ "mix", "set output vs input mix", OFFSET(mix), AV_OPT_TYPE_FLOAT, {.dbl=1.0},-1, 1, AF },
{ NULL }
};
AVFILTER_DEFINE_CLASS(arnndn);
const AVFilter ff_af_arnndn = {
.name = "arnndn",
.description = NULL_IF_CONFIG_SMALL("Reduce noise from speech using Recurrent Neural Networks."),
.priv_size = sizeof(AudioRNNContext),
.priv_class = &arnndn_class,
.activate = activate,
.init = init,
.uninit = uninit,
FILTER_INPUTS(inputs),
FILTER_OUTPUTS(ff_audio_default_filterpad),
FILTER_QUERY_FUNC(query_formats),
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL |
AVFILTER_FLAG_SLICE_THREADS,
.process_command = process_command,
};
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