ref: fd45fba905b580cd001637c3b9459e44323f6f0c
dir: /dnn/lpcnet_plc.c/
/* Copyright (c) 2021 Amazon */
/*
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.
*/
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include "lpcnet_private.h"
#include "lpcnet.h"
#include "plc_data.h"
#define PLC_DUMP_FEATURES 0
#define PLC_READ_FEATURES 0
#define PLC_DNN_PRED 1
LPCNET_EXPORT int lpcnet_plc_get_size() {
return sizeof(LPCNetPLCState);
}
LPCNET_EXPORT void lpcnet_plc_init(LPCNetPLCState *st) {
lpcnet_init(&st->lpcnet);
lpcnet_encoder_init(&st->enc);
RNN_CLEAR(st->pcm, PLC_BUF_SIZE);
st->pcm_fill = PLC_BUF_SIZE;
st->skip_analysis = 0;
st->blend = 0;
st->loss_count = 0;
}
LPCNET_EXPORT LPCNetPLCState *lpcnet_plc_create() {
LPCNetPLCState *st;
st = calloc(sizeof(*st), 1);
lpcnet_plc_init(st);
return st;
}
LPCNET_EXPORT void lpcnet_plc_destroy(LPCNetPLCState *st) {
free(st);
}
static void compute_plc_pred(PLCNetState *net, float *out, const float *in) {
float zeros[3*PLC_MAX_RNN_NEURONS] = {0};
float dense_out[PLC_DENSE1_OUT_SIZE];
_lpcnet_compute_dense(&plc_dense1, dense_out, in);
compute_gruB(&plc_gru1, zeros, net->plc_gru1_state, dense_out);
compute_gruB(&plc_gru2, zeros, net->plc_gru2_state, net->plc_gru1_state);
_lpcnet_compute_dense(&plc_out, out, net->plc_gru2_state);
}
#if 1
LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
int i;
float x[FRAME_SIZE];
short output[FRAME_SIZE];
#if PLC_DNN_PRED
float plc_features[2*NB_BANDS+NB_FEATURES+1];
for (i=0;i<FRAME_SIZE;i++) x[i] = pcm[i];
burg_cepstral_analysis(plc_features, x);
#endif
st->enc.pcount = 0;
if (st->skip_analysis) {
/*fprintf(stderr, "skip update\n");*/
if (st->blend) {
short tmp[FRAME_SIZE-TRAINING_OFFSET];
lpcnet_synthesize_tail_impl(&st->lpcnet, tmp, FRAME_SIZE-TRAINING_OFFSET, 0);
for (i=0;i<FRAME_SIZE-TRAINING_OFFSET;i++) {
float w;
w = .5 - .5*cos(M_PI*i/(FRAME_SIZE-TRAINING_OFFSET));
pcm[i] = (int)floor(.5 + w*pcm[i] + (1-w)*tmp[i]);
}
st->blend = 0;
RNN_COPY(st->pcm, &pcm[FRAME_SIZE-TRAINING_OFFSET], TRAINING_OFFSET);
st->pcm_fill = TRAINING_OFFSET;
} else {
RNN_COPY(&st->pcm[st->pcm_fill], pcm, FRAME_SIZE);
st->pcm_fill += FRAME_SIZE;
}
/*fprintf(stderr, "fill at %d\n", st->pcm_fill);*/
}
/* Update state. */
/*fprintf(stderr, "update state\n");*/
for (i=0;i<FRAME_SIZE;i++) x[i] = pcm[i];
preemphasis(x, &st->enc.mem_preemph, x, PREEMPHASIS, FRAME_SIZE);
compute_frame_features(&st->enc, x);
process_single_frame(&st->enc, NULL);
#if PLC_DNN_PRED
if (st->skip_analysis <= 1) {
RNN_COPY(&plc_features[2*NB_BANDS], st->enc.features[0], NB_FEATURES);
plc_features[2*NB_BANDS+NB_FEATURES] = 1;
compute_plc_pred(&st->plc_net, st->features, plc_features);
}
#else
RNN_COPY(st->features, st->enc.features[0], NB_TOTAL_FEATURES);
#endif
if (st->skip_analysis) {
float lpc[LPC_ORDER];
float gru_a_condition[3*GRU_A_STATE_SIZE];
float gru_b_condition[3*GRU_B_STATE_SIZE];
/* FIXME: backtrack state, replace features. */
run_frame_network(&st->lpcnet, gru_a_condition, gru_b_condition, lpc, st->enc.features[0]);
st->skip_analysis--;
} else {
for (i=0;i<FRAME_SIZE;i++) st->pcm[PLC_BUF_SIZE+i] = pcm[i];
RNN_COPY(output, &st->pcm[0], FRAME_SIZE);
lpcnet_synthesize_impl(&st->lpcnet, st->enc.features[0], output, FRAME_SIZE, FRAME_SIZE);
#if PLC_READ_FEATURES
for (i=0;i<NB_FEATURES;i++) scanf("%f", &st->features[i]);
#endif
#if PLC_DUMP_FEATURES
for (i=0;i<NB_FEATURES;i++) printf("%f ", st->enc.features[0][i]);
printf("1\n");
#endif
RNN_MOVE(st->pcm, &st->pcm[FRAME_SIZE], PLC_BUF_SIZE);
}
st->loss_count = 0;
return 0;
}
static const float att_table[10] = {0, 0, -.2, -.2, -.4, -.4, -.8, -.8, -1.6, -1.6};
LPCNET_EXPORT int lpcnet_plc_conceal(LPCNetPLCState *st, short *pcm) {
#if PLC_READ_FEATURES || PLC_DUMP_FEATURES
int i;
#endif
short output[FRAME_SIZE];
float zeros[2*NB_BANDS+NB_FEATURES+1] = {0};
st->enc.pcount = 0;
/* If we concealed the previous frame, finish synthesizing the rest of the samples. */
/* FIXME: Copy/predict features. */
while (st->pcm_fill > 0) {
/*fprintf(stderr, "update state for PLC %d\n", st->pcm_fill);*/
int update_count;
update_count = IMIN(st->pcm_fill, FRAME_SIZE);
RNN_COPY(output, &st->pcm[0], update_count);
#if PLC_DNN_PRED
if (st->pcm_fill > FRAME_SIZE) compute_plc_pred(&st->plc_net, st->features, zeros);
#endif
#if PLC_READ_FEATURES
for (i=0;i<NB_FEATURES;i++) scanf("%f", &st->features[i]);
#endif
#if PLC_DUMP_FEATURES
for (i=0;i<NB_FEATURES+1;i++) printf("%f ", 0.);
printf("\n");
#endif
lpcnet_synthesize_impl(&st->lpcnet, &st->features[0], output, update_count, update_count);
RNN_MOVE(st->pcm, &st->pcm[FRAME_SIZE], PLC_BUF_SIZE);
st->pcm_fill -= update_count;
st->skip_analysis++;
}
lpcnet_synthesize_tail_impl(&st->lpcnet, pcm, FRAME_SIZE-TRAINING_OFFSET, 0);
#if PLC_DNN_PRED
compute_plc_pred(&st->plc_net, st->features, zeros);
if (st->loss_count >= 10) st->features[0] = MAX16(-10, st->features[0]+att_table[9] - 2*(st->loss_count-9));
else st->features[0] = MAX16(-10, st->features[0]+att_table[st->loss_count]);
if (st->loss_count > 4) st->features[NB_FEATURES-1] = MAX16(-.5, st->features[NB_FEATURES-1]-.1*(st->loss_count-4));
#endif
#if PLC_READ_FEATURES
for (i=0;i<NB_FEATURES;i++) scanf("%f", &st->features[i]);
#endif
#if PLC_DUMP_FEATURES
for (i=0;i<NB_FEATURES+1;i++) printf("%f ", 0.);
printf("\n");
#endif
lpcnet_synthesize_impl(&st->lpcnet, &st->features[0], &pcm[FRAME_SIZE-TRAINING_OFFSET], TRAINING_OFFSET, 0);
{
int i;
float x[FRAME_SIZE];
/* FIXME: Can we do better? */
for (i=0;i<FRAME_SIZE;i++) x[i] = pcm[i];
preemphasis(x, &st->enc.mem_preemph, x, PREEMPHASIS, FRAME_SIZE);
compute_frame_features(&st->enc, x);
process_single_frame(&st->enc, NULL);
}
st->loss_count++;
st->blend = 1;
return 0;
}
#else
LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
int i;
float x[FRAME_SIZE];
short pcm_save[FRAME_SIZE];
float plc_features[2*NB_BANDS+NB_FEATURES+1];
RNN_COPY(pcm_save, pcm, FRAME_SIZE);
for (i=0;i<FRAME_SIZE;i++) x[i] = pcm[i];
burg_cepstral_analysis(plc_features, x);
st->enc.pcount = 0;
if (st->loss_count > 0) {
LPCNetState copy;
/* Handle blending. */
short tmp[FRAME_SIZE-TRAINING_OFFSET];
float zeros[2*NB_BANDS+NB_FEATURES+1] = {0};
RNN_COPY(zeros, plc_features, 2*NB_BANDS);
zeros[2*NB_BANDS+NB_FEATURES] = 1;
compute_plc_pred(&st->plc_net, st->features, zeros);
lpcnet_synthesize_impl(&st->lpcnet, st->features, &st->pcm[FRAME_SIZE-TRAINING_OFFSET], TRAINING_OFFSET, 0);
copy = st->lpcnet;
{
short rev[FRAME_SIZE];
for (i=0;i<FRAME_SIZE;i++) rev[i] = pcm[FRAME_SIZE-i-1];
lpcnet_synthesize_tail_impl(&st->lpcnet, rev, FRAME_SIZE, FRAME_SIZE);
lpcnet_synthesize_tail_impl(&st->lpcnet, rev, TRAINING_OFFSET, 0);
for (i=0;i<TRAINING_OFFSET;i++) {
float w;
w = .5 - .5*cos(M_PI*i/(TRAINING_OFFSET));
st->pcm[FRAME_SIZE-1-i] = (int)floor(.5 + w*st->pcm[FRAME_SIZE-1-i] + (1-w)*rev[i]);
}
}
st->lpcnet = copy;
lpcnet_synthesize_tail_impl(&st->lpcnet, pcm, FRAME_SIZE-TRAINING_OFFSET, FRAME_SIZE-TRAINING_OFFSET);
for (i=0;i<FRAME_SIZE;i++) x[i] = st->pcm[i];
preemphasis(x, &st->enc.mem_preemph, x, PREEMPHASIS, FRAME_SIZE);
compute_frame_features(&st->enc, x);
process_single_frame(&st->enc, NULL);
}
for (i=0;i<FRAME_SIZE;i++) x[i] = pcm[i];
preemphasis(x, &st->enc.mem_preemph, x, PREEMPHASIS, FRAME_SIZE);
compute_frame_features(&st->enc, x);
process_single_frame(&st->enc, NULL);
if (st->loss_count == 0) {
RNN_COPY(&plc_features[2*NB_BANDS], st->enc.features[0], NB_FEATURES);
plc_features[2*NB_BANDS+NB_FEATURES] = 1;
compute_plc_pred(&st->plc_net, st->features, plc_features);
lpcnet_synthesize_impl(&st->lpcnet, st->enc.features[0], &st->pcm[FRAME_SIZE-TRAINING_OFFSET], TRAINING_OFFSET, TRAINING_OFFSET);
lpcnet_synthesize_tail_impl(&st->lpcnet, pcm, FRAME_SIZE-TRAINING_OFFSET, FRAME_SIZE-TRAINING_OFFSET);
}
RNN_COPY(&pcm[FRAME_SIZE-TRAINING_OFFSET], pcm, TRAINING_OFFSET);
RNN_COPY(pcm, &st->pcm[TRAINING_OFFSET], FRAME_SIZE-TRAINING_OFFSET);
RNN_COPY(st->pcm, pcm_save, FRAME_SIZE);
st->loss_count = 0;
return 0;
}
static const float att_table[10] = {0, 0, -.2, -.2, -.4, -.4, -.8, -.8, -1.6, -1.6};
LPCNET_EXPORT int lpcnet_plc_conceal(LPCNetPLCState *st, short *pcm) {
int i;
float x[FRAME_SIZE];
float zeros[2*NB_BANDS+NB_FEATURES+1] = {0};
st->enc.pcount = 0;
compute_plc_pred(&st->plc_net, st->features, zeros);
if (st->loss_count >= 10) st->features[0] = MAX16(-10, st->features[0]+att_table[9] - 2*(st->loss_count-9));
else st->features[0] = MAX16(-10, st->features[0]+att_table[st->loss_count]);
if (st->loss_count > 4) st->features[NB_FEATURES-1] = MAX16(-.5, st->features[NB_FEATURES-1]-.1*(st->loss_count-4));
if (st->loss_count == 0) {
RNN_COPY(pcm, &st->pcm[FRAME_SIZE-TRAINING_OFFSET], TRAINING_OFFSET);
lpcnet_synthesize_impl(&st->lpcnet, st->features, &st->pcm[FRAME_SIZE-TRAINING_OFFSET], TRAINING_OFFSET, TRAINING_OFFSET);
lpcnet_synthesize_tail_impl(&st->lpcnet, &pcm[TRAINING_OFFSET], FRAME_SIZE-TRAINING_OFFSET, 0);
} else {
lpcnet_synthesize_impl(&st->lpcnet, st->features, pcm, TRAINING_OFFSET, 0);
lpcnet_synthesize_tail_impl(&st->lpcnet, &pcm[TRAINING_OFFSET], FRAME_SIZE-TRAINING_OFFSET, 0);
RNN_COPY(&st->pcm[FRAME_SIZE-TRAINING_OFFSET], pcm, TRAINING_OFFSET);
for (i=0;i<FRAME_SIZE;i++) x[i] = st->pcm[i];
preemphasis(x, &st->enc.mem_preemph, x, PREEMPHASIS, FRAME_SIZE);
compute_frame_features(&st->enc, x);
process_single_frame(&st->enc, NULL);
}
RNN_COPY(st->pcm, &pcm[TRAINING_OFFSET], FRAME_SIZE-TRAINING_OFFSET);
st->loss_count++;
return 0;
}
#endif