[FFmpeg-devel] [PATCH] avfilter: add arnndn filter
Paul B Mahol
onemda at gmail.com
Wed Oct 2 22:18:40 EEST 2019
Signed-off-by: Paul B Mahol <onemda at gmail.com>
---
doc/filters.texi | 11 +
libavfilter/Makefile | 1 +
libavfilter/af_arnndn.c | 1543 ++++++++++++++++++++++++++++++++++++++
libavfilter/allfilters.c | 1 +
4 files changed, 1556 insertions(+)
create mode 100644 libavfilter/af_arnndn.c
diff --git a/doc/filters.texi b/doc/filters.texi
index e46839bfec..1a75d2eb4a 100644
--- a/doc/filters.texi
+++ b/doc/filters.texi
@@ -2029,6 +2029,17 @@ atrim=end=5,areverse
@end example
@end itemize
+ at section arnndn
+
+Reduce noise from speech using Recurrent Neural Networks.
+
+This filter accepts the following options:
+
+ at table @option
+ at item model, m
+Set train model file to load. This option is always required.
+ at end table
+
@section asetnsamples
Set the number of samples per each output audio frame.
diff --git a/libavfilter/Makefile b/libavfilter/Makefile
index 182fe9df4b..c43fdd662a 100644
--- a/libavfilter/Makefile
+++ b/libavfilter/Makefile
@@ -71,6 +71,7 @@ OBJS-$(CONFIG_APULSATOR_FILTER) += af_apulsator.o
OBJS-$(CONFIG_AREALTIME_FILTER) += f_realtime.o
OBJS-$(CONFIG_ARESAMPLE_FILTER) += af_aresample.o
OBJS-$(CONFIG_AREVERSE_FILTER) += f_reverse.o
+OBJS-$(CONFIG_ARNNDN_FILTER) += af_arnndn.o
OBJS-$(CONFIG_ASELECT_FILTER) += f_select.o
OBJS-$(CONFIG_ASENDCMD_FILTER) += f_sendcmd.o
OBJS-$(CONFIG_ASETNSAMPLES_FILTER) += af_asetnsamples.o
diff --git a/libavfilter/af_arnndn.c b/libavfilter/af_arnndn.c
new file mode 100644
index 0000000000..3a1b4b40ec
--- /dev/null
+++ b/libavfilter/af_arnndn.c
@@ -0,0 +1,1543 @@
+/* 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/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];
+ RNNState rnn;
+ AVTXContext *tx, *txi;
+ av_tx_fn tx_fn, txi_fn;
+} DenoiseState;
+
+typedef struct AudioRNNContext {
+ const AVClass *class;
+
+ char *model_name;
+
+ int channels;
+ DenoiseState *st;
+
+ DECLARE_ALIGNED(32, float, window)[WINDOW_SIZE];
+ float dct_table[NB_BANDS*NB_BANDS];
+
+ RNNModel *model;
+
+ 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 RNNModel *rnnoise_model_from_file(FILE *f)
+{
+ RNNModel *ret;
+ 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 NULL;
+
+ ret = av_calloc(1, sizeof(RNNModel));
+ if (!ret)
+ return NULL;
+
+#define ALLOC_LAYER(type, name) \
+ name = av_calloc(1, sizeof(type)); \
+ if (!name) { \
+ rnnoise_model_free(ret); \
+ return NULL; \
+ } \
+ 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 NULL; \
+ } \
+ 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 NULL; \
+ } \
+ name = values; \
+ for (int i = 0; i < (len); i++) { \
+ if (fscanf(f, "%d", &in) != 1) { \
+ rnnoise_model_free(ret); \
+ return NULL; \
+ } \
+ 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 NULL; \
+ } \
+ 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 NULL; \
+ } \
+ values[j * (len2) * FFALIGN((len0), 4) + i * FFALIGN((len0), 4) + k] = in; \
+ } \
+ } \
+ } \
+ } 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); \
+ INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons); \
+ INPUT_ARRAY(name->bias, name->nb_neurons); \
+ } 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); \
+ INPUT_ARRAY3(name->input_weights, name->nb_inputs, name->nb_neurons, 3); \
+ INPUT_ARRAY3(name->recurrent_weights, name->nb_neurons, name->nb_neurons, 3); \
+ INPUT_ARRAY(name->bias, name->nb_neurons * 3); \
+ } 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);
+
+ return ret;
+}
+
+static int query_formats(AVFilterContext *ctx)
+{
+ AVFilterFormats *formats = NULL;
+ AVFilterChannelLayouts *layouts = NULL;
+ static const enum AVSampleFormat sample_fmts[] = {
+ AV_SAMPLE_FMT_FLTP,
+ AV_SAMPLE_FMT_NONE
+ };
+ int ret, sample_rates[] = { 48000, -1 };
+
+ formats = ff_make_format_list(sample_fmts);
+ if (!formats)
+ return AVERROR(ENOMEM);
+ ret = ff_set_common_formats(ctx, formats);
+ if (ret < 0)
+ return ret;
+
+ layouts = ff_all_channel_counts();
+ if (!layouts)
+ return AVERROR(ENOMEM);
+
+ ret = ff_set_common_channel_layouts(ctx, layouts);
+ if (ret < 0)
+ return ret;
+
+ formats = ff_make_format_list(sample_rates);
+ if (!formats)
+ return AVERROR(ENOMEM);
+ return ff_set_common_samplerates(ctx, formats);
+}
+
+static int config_input(AVFilterLink *inlink)
+{
+ AVFilterContext *ctx = inlink->dst;
+ AudioRNNContext *s = ctx->priv;
+ int ret;
+
+ s->channels = inlink->channels;
+
+ 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.model = s->model;
+ st->rnn.vad_gru_state = av_calloc(sizeof(float), FFALIGN(s->model->vad_gru_size, 16));
+ st->rnn.noise_gru_state = av_calloc(sizeof(float), FFALIGN(s->model->noise_gru_size, 16));
+ st->rnn.denoise_gru_state = av_calloc(sizeof(float), FFALIGN(s->model->denoise_gru_size, 16));
+ if (!st->rnn.vad_gru_state ||
+ !st->rnn.noise_gru_state ||
+ !st->rnn.denoise_gru_state)
+ return AVERROR(ENOMEM);
+
+ ret = av_tx_init(&st->tx, &st->tx_fn, AV_TX_FLOAT_FFT, 0, WINDOW_SIZE, NULL, 0);
+ if (ret < 0)
+ return ret;
+
+ 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];
+
+ for (int i = 0; i < FREQ_SIZE; i++)
+ x[i] = in[i];
+
+ 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]);
+
+ 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);
+}
+
+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 int second_check[16] = {0, 0, 3, 2, 3, 2, 5, 2, 3, 2, 3, 2, 5, 2, 3, 2};
+static const 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 = 0.f;
+
+ for (int j = 0; j < NB_BANDS; j++) {
+ sum += in[j] * s->dct_table[j * NB_BANDS + i];
+ }
+ 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;
+ 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);
+
+ for (int i = 0; i < rnn->model->input_dense_size; i++)
+ noise_input[i] = dense_out[i];
+ for (int i = 0; i < rnn->model->vad_gru_size; i++)
+ noise_input[i + rnn->model->input_dense_size] = rnn->vad_gru_state[i];
+ for (int i = 0; i < INPUT_SIZE; i++)
+ noise_input[i + rnn->model->input_dense_size + rnn->model->vad_gru_size] = input[i];
+
+ compute_gru(s, rnn->model->noise_gru, rnn->noise_gru_state, noise_input);
+
+ for (int i = 0; i < rnn->model->vad_gru_size; i++)
+ denoise_input[i] = rnn->vad_gru_state[i];
+ for (int i = 0; i < rnn->model->noise_gru_size; i++)
+ denoise_input[i + rnn->model->vad_gru_size] = rnn->noise_gru_state[i];
+ for (int i = 0; i < INPUT_SIZE; i++)
+ denoise_input[i + rnn->model->vad_gru_size + rnn->model->noise_gru_size] = input[i];
+
+ 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)
+{
+ AVComplexFloat X[FREQ_SIZE];
+ AVComplexFloat P[WINDOW_SIZE];
+ float x[FRAME_SIZE];
+ float Ex[NB_BANDS], Ep[NB_BANDS];
+ float Exp[NB_BANDS];
+ float features[NB_FEATURES];
+ float g[NB_BANDS];
+ float gf[FREQ_SIZE];
+ float vad_prob = 0;
+ 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) {
+ compute_rnn(s, &st->rnn, 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);
+
+ 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]);
+ }
+
+ 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;
+ ctx->internal->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 av_cold int init(AVFilterContext *ctx)
+{
+ AudioRNNContext *s = ctx->priv;
+ FILE *f;
+
+ s->fdsp = avpriv_float_dsp_alloc(0);
+ if (!s->fdsp)
+ return AVERROR(ENOMEM);
+
+ if (!s->model_name)
+ return AVERROR(EINVAL);
+ f = av_fopen_utf8(s->model_name, "r");
+ if (!f)
+ return AVERROR(EINVAL);
+
+ s->model = rnnoise_model_from_file(f);
+ fclose(f);
+ if (!s->model)
+ return AVERROR(EINVAL);
+
+ 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[i*NB_BANDS + j] = cosf((i + .5f) * j * M_PI / NB_BANDS);
+ if (j == 0)
+ s->dct_table[i*NB_BANDS + j] *= sqrtf(.5);
+ }
+ }
+
+ return 0;
+}
+
+static av_cold void uninit(AVFilterContext *ctx)
+{
+ AudioRNNContext *s = ctx->priv;
+
+ av_freep(&s->fdsp);
+ rnnoise_model_free(s->model);
+ s->model = NULL;
+
+ if (s->st) {
+ for (int ch = 0; ch < s->channels; ch++) {
+ av_freep(&s->st[ch].rnn.vad_gru_state);
+ av_freep(&s->st[ch].rnn.noise_gru_state);
+ av_freep(&s->st[ch].rnn.denoise_gru_state);
+ 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,
+ },
+ { NULL }
+};
+
+static const AVFilterPad outputs[] = {
+ {
+ .name = "default",
+ .type = AVMEDIA_TYPE_AUDIO,
+ },
+ { NULL }
+};
+
+#define OFFSET(x) offsetof(AudioRNNContext, x)
+#define AF AV_OPT_FLAG_AUDIO_PARAM|AV_OPT_FLAG_FILTERING_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 },
+ { NULL }
+};
+
+AVFILTER_DEFINE_CLASS(arnndn);
+
+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,
+ .inputs = inputs,
+ .outputs = outputs,
+ .flags = AVFILTER_FLAG_SLICE_THREADS,
+};
diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c
index 1a26129069..83125f0dba 100644
--- a/libavfilter/allfilters.c
+++ b/libavfilter/allfilters.c
@@ -64,6 +64,7 @@ extern AVFilter ff_af_apulsator;
extern AVFilter ff_af_arealtime;
extern AVFilter ff_af_aresample;
extern AVFilter ff_af_areverse;
+extern AVFilter ff_af_arnndn;
extern AVFilter ff_af_aselect;
extern AVFilter ff_af_asendcmd;
extern AVFilter ff_af_asetnsamples;
--
2.17.1
More information about the ffmpeg-devel
mailing list