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Adding sampling

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This commit is contained in:
Jean-Marc Valin 2018-11-26 11:04:41 -05:00
parent 91c9524af3
commit 575d8d6fa4
3 changed files with 38 additions and 5 deletions
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12
dnn/lpcnet.c
View file

@ -33,6 +33,9 @@
#define NB_FEATURES 38
#define PITCH_GAIN_FEATURE 37
#define PDF_FLOOR 0.002
#define FRAME_INPUT_SIZE (NB_FEATURES + EMBED_PITCH_OUT_SIZE)
#define SAMPLE_INPUT_SIZE (2*EMBED_SIG_OUT_SIZE + EMBED_EXC_OUT_SIZE + FEATURE_DENSE2_OUT_SIZE)
@ -79,11 +82,10 @@ void run_frame_network(NNetState *net, float *condition, float *lpc, const float
RNN_CLEAR(lpc, LPC_ORDER);
}
int run_sample_network(NNetState *net, const float *condition, int last_exc, int last_sig, int pred)
void run_sample_network(NNetState *net, float *pdf, const float *condition, int last_exc, int last_sig, int pred)
{
float in_a[SAMPLE_INPUT_SIZE];
float in_b[GRU_A_STATE_SIZE+FEATURE_DENSE2_OUT_SIZE];
float pdf[DUAL_FC_OUT_SIZE];
compute_embedding(&embed_sig, &in_a[0], last_sig);
compute_embedding(&embed_sig, &in_a[EMBED_SIG_OUT_SIZE], pred);
compute_embedding(&embed_exc, &in_a[2*EMBED_SIG_OUT_SIZE], last_exc);
@ -93,8 +95,6 @@ int run_sample_network(NNetState *net, const float *condition, int last_exc, int
RNN_COPY(&in_b[GRU_A_STATE_SIZE], condition, FEATURE_DENSE2_OUT_SIZE);
compute_gru(&gru_b, net->gru_b_state, in_b);
compute_mdense(&dual_fc, pdf, net->gru_b_state);
/* FIXME: Do the actual sampling here. */
return 0;
}
void generate_samples(LPCNetState *lpcnet, short *output, const float *features, int pitch, int N)
@ -102,6 +102,7 @@ void generate_samples(LPCNetState *lpcnet, short *output, const float *features,
int i;
float condition[FEATURE_DENSE2_OUT_SIZE];
float lpc[LPC_ORDER];
float pdf[DUAL_FC_OUT_SIZE];
run_frame_network(&lpcnet->nnet, condition, lpc, features, pitch);
for (i=0;i<N;i++)
{
@ -115,7 +116,8 @@ void generate_samples(LPCNetState *lpcnet, short *output, const float *features,
pred = (int)floor(.5f + sum);
last_sig_ulaw = lin2ulaw(lpcnet->last_sig[0]);
pred_ulaw = lin2ulaw(pred);
exc = run_sample_network(&lpcnet->nnet, condition, lpcnet->last_exc, last_sig_ulaw, pred_ulaw);
run_sample_network(&lpcnet->nnet, pdf, condition, lpcnet->last_exc, last_sig_ulaw, pred_ulaw);
exc = sample_from_pdf(pdf, DUAL_FC_OUT_SIZE, MAX16(0, 1.5f*features[PITCH_GAIN_FEATURE] - .5f), PDF_FLOOR);
output[i] = pred + ulaw2lin(exc);
RNN_MOVE(&lpcnet->last_sig[1], &lpcnet->last_sig[0], LPC_ORDER-1);
lpcnet->last_sig[0] = output[i];

30
dnn/nnet.c
View file

@ -30,6 +30,7 @@
#include "config.h"
#endif
#include <stdlib.h>
#include <math.h>
#include "opus_types.h"
#include "arch.h"
@ -236,3 +237,32 @@ void compute_embedding(const EmbeddingLayer *layer, float *output, int input)
}
}
int sample_from_pdf(const float *pdf, int N, float exp_boost, float pdf_floor)
{
int i;
float sum, norm;
float r;
float tmp[DUAL_FC_OUT_SIZE];
celt_assert(N <= DUAL_FC_OUT_SIZE);
sum = 0;
/* Decrease the temperature of the sampling. */
for (i=0;i<N;i++)
{
tmp[i] = pow(pdf[i], 1.f+exp_boost);
sum += tmp[i];
}
norm = 1.f/sum;
/* Convert tmp to a CDF while subtracting the floor */
tmp[0] = MAX16(0, norm*tmp[0] - pdf_floor);
for (i=1;i<N;i++)
{
tmp[i] = tmp[i-1] + MAX16(0, norm*tmp[i] - pdf_floor);
}
/* Do the sampling (from the cdf). */
r = tmp[N-1] * ((float)rand()/RAND_MAX);
for (i=0;i<N-1;i++)
{
if (r > tmp[i]) return r;
}
return N-1;
}

1
dnn/nnet.h
View file

@ -89,5 +89,6 @@ void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const f
void compute_embedding(const EmbeddingLayer *layer, float *output, int input);
int sample_from_pdf(const float *pdf, int N, float exp_boost, float pdf_floor);
#endif /* _MLP_H_ */