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FFmpeg/libavfilter/af_arnndn.c
Andreas Rheinhardt 8be701d9f7 avfilter/avfilter: Add numbers of (in|out)pads directly to AVFilter 
Up until now, an AVFilter's lists of input and output AVFilterPads
were terminated by a sentinel and the only way to get the length
of these lists was by using avfilter_pad_count(). This has two
drawbacks: first, sizeof(AVFilterPad) is not negligible
(i.e. 64B on 64bit systems); second, getting the size involves
a function call instead of just reading the data.

This commit therefore changes this. The sentinels are removed and new
private fields nb_inputs and nb_outputs are added to AVFilter that
contain the number of elements of the respective AVFilterPad array.

Given that AVFilter.(in|out)puts are the only arrays of zero-terminated
AVFilterPads an API user has access to (AVFilterContext.(in|out)put_pads
are not zero-terminated and they already have a size field) the argument
to avfilter_pad_count() is always one of these lists, so it just has to
find the filter the list belongs to and read said number. This is slower
than before, but a replacement function that just reads the internal numbers
that users are expected to switch to will be added soon; and furthermore,
avfilter_pad_count() is probably never called in hot loops anyway.

This saves about 49KiB from the binary; notice that these sentinels are
not in .bss despite being zeroed: they are in .data.rel.ro due to the
non-sentinels.

Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
2021-08-20 12:53:58 +02:00

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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 <float.h>
#include "libavutil/avassert.h"
#include "libavutil/avstring.h"
#include "libavutil/float_dsp.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;
s->channels = inlink->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];
if (!st->tx)
ret = av_tx_init(&st->tx, &st->tx_fn, AV_TX_FLOAT_FFT, 0, WINDOW_SIZE, NULL, 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, NULL, 0);
if (ret < 0)
return ret;
}
return 0;
}
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(float));
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(float));
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->channels * jobnr) / nb_jobs;
const int end = (out->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);
}
out->pts = in->pts;
td.in = in; td.out = out;
ff_filter_execute(ctx, rnnoise_channels, &td, NULL,
FFMIN(outlink->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 = av_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,
},
};
static const AVFilterPad outputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_AUDIO,
},
};
#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."),
.query_formats = query_formats,
.priv_size = sizeof(AudioRNNContext),
.priv_class = &arnndn_class,
.activate = activate,
.init = init,
.uninit = uninit,
FILTER_INPUTS(inputs),
FILTER_OUTPUTS(outputs),
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL |
AVFILTER_FLAG_SLICE_THREADS,
.process_command = process_command,
};
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