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Switching to neural pitch estimator

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Remove old pitch estimator and retrain all models
This commit is contained in:
Jean-Marc Valin 2023-10-02 02:23:41 -04:00
parent da7f4c6c99
commit f0ec990dba
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GPG key ID: 531A52533318F00A
13 changed files with 103 additions and 137 deletions
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31
dnn/pitchdnn.c
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@ -9,7 +9,7 @@
#include "lpcnet_private.h"
int compute_pitchdnn(
float compute_pitchdnn(
PitchDNNState *st,
const float *if_features,
const float *xcorr_features
@ -18,35 +18,40 @@ int compute_pitchdnn(
float if1_out[DENSE_IF_UPSAMPLER_1_OUT_SIZE];
float downsampler_in[NB_XCORR_FEATURES + DENSE_IF_UPSAMPLER_2_OUT_SIZE];
float downsampler_out[DENSE_DOWNSAMPLER_OUT_SIZE];
float conv1_tmp1[NB_XCORR_FEATURES + 2] = {0};
float conv1_tmp2[NB_XCORR_FEATURES + 2] = {0};
float conv1_tmp1[(NB_XCORR_FEATURES + 2)*8] = {0};
float conv1_tmp2[(NB_XCORR_FEATURES + 2)*8] = {0};
float output[DENSE_FINAL_UPSAMPLER_OUT_SIZE];
int i;
int pos=0;
float maxval=-1;
float sum=0;
float count=0;
PitchDNN *model = &st->model;
/* IF */
compute_generic_dense(&model->dense_if_upsampler_1, if1_out, if_features, ACTIVATION_TANH);
compute_generic_dense(&model->dense_if_upsampler_2, &downsampler_in[NB_XCORR_FEATURES], if1_out, ACTIVATION_TANH);
/* xcorr*/
OPUS_COPY(&conv1_tmp1[1], xcorr_features, NB_XCORR_FEATURES);
compute_conv2d(&model->conv2d_1, &conv1_tmp2[1], st->xcorr_mem1, conv1_tmp1, NB_XCORR_FEATURES, ACTIVATION_TANH);
compute_conv2d(&model->conv2d_1, &conv1_tmp1[1], st->xcorr_mem2, conv1_tmp2, NB_XCORR_FEATURES, ACTIVATION_TANH);
compute_conv2d(&model->conv2d_1, downsampler_in, st->xcorr_mem3, conv1_tmp1, NB_XCORR_FEATURES, ACTIVATION_TANH);
compute_conv2d(&model->conv2d_1, &conv1_tmp2[1], st->xcorr_mem1, conv1_tmp1, NB_XCORR_FEATURES, NB_XCORR_FEATURES+2, ACTIVATION_TANH);
compute_conv2d(&model->conv2d_2, &conv1_tmp1[1], st->xcorr_mem2, conv1_tmp2, NB_XCORR_FEATURES, NB_XCORR_FEATURES+2, ACTIVATION_TANH);
compute_conv2d(&model->conv2d_3, downsampler_in, st->xcorr_mem3, conv1_tmp1, NB_XCORR_FEATURES, NB_XCORR_FEATURES, ACTIVATION_TANH);
compute_generic_dense(&model->dense_downsampler, downsampler_out, downsampler_in, ACTIVATION_TANH);
compute_generic_gru(&model->gru_1_input, &model->gru_1_recurrent, st->gru_state, downsampler_out);
compute_generic_dense(&model->dense_final_upsampler, output, st->gru_state, ACTIVATION_LINEAR);
for (i=0;i<DENSE_FINAL_UPSAMPLER_OUT_SIZE;i++) {
for (i=0;i<180;i++) {
if (output[i] > maxval) {
pos = i;
maxval = output[i];
}
}
return (1.f/60.f)*pos - 1.5;
for (i=IMAX(0, pos-2); i<=IMIN(179, pos+2); i++) {
float p = exp(output[i]);
sum += p*i;
count += p;
}
/*printf("%d %f\n", pos, sum/count);*/
return (1.f/60.f)*(sum/count) - 1.5;
/*return 256.f/pow(2.f, (1.f/60.f)*i);*/
}
@ -55,7 +60,11 @@ void pitchdnn_init(PitchDNNState *st)
{
int ret;
OPUS_CLEAR(st, 1);
#ifndef USE_WEIGHTS_FILE
ret = init_pitchdnn(&st->model, pitchdnn_arrays);
#else
ret = 0;
#endif
celt_assert(ret == 0);
/* FIXME: perform arch detection. */
}