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cleanup

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This commit is contained in:
Jean-Marc Valin 2022-02-07 15:14:56 -05:00
parent fd45fba905
commit 7a7913f388
1 changed files with 7 additions and 39 deletions
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46
dnn/lpcnet_plc.c
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@ -32,10 +32,6 @@
#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);
}
@ -71,15 +67,17 @@ static void compute_plc_pred(PLCNetState *net, float *out, const float *in) {
}
#if 1
/* In this causal version of the code, the DNN model implemented by compute_plc_pred()
returns the predicted features from frame t+1, using the input features from frame t.*/
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");*/
@ -106,15 +104,11 @@ LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
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];
@ -126,13 +120,6 @@ LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
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;
@ -141,9 +128,6 @@ LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
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;
@ -154,35 +138,17 @@ LPCNET_EXPORT int lpcnet_plc_conceal(LPCNetPLCState *st, short *pcm) {
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;
@ -200,6 +166,9 @@ LPCNET_EXPORT int lpcnet_plc_conceal(LPCNetPLCState *st, short *pcm) {
#else
/* In this non-causal version of the code, the DNN model implemented by compute_plc_pred()
returns the predicted features from frame t, using the input features from frame t.*/
LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
int i;
float x[FRAME_SIZE];
@ -212,7 +181,6 @@ LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
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;