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ddd5669e79
Removing one of the 2d conv layers for pitch estimation reduces complexity without noticeable degradation. FARGAN model has more adversarial training. Also, no need for the double precision in the low-pass filter.
69 lines
2.1 KiB
C
69 lines
2.1 KiB
C
#ifdef HAVE_CONFIG_H
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#include "config.h"
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#endif
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#include <math.h>
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#include "pitchdnn.h"
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#include "os_support.h"
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#include "nnet.h"
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#include "lpcnet_private.h"
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float compute_pitchdnn(
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PitchDNNState *st,
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const float *if_features,
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const float *xcorr_features
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)
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{
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float if1_out[DENSE_IF_UPSAMPLER_1_OUT_SIZE];
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float downsampler_in[NB_XCORR_FEATURES + DENSE_IF_UPSAMPLER_2_OUT_SIZE];
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float downsampler_out[DENSE_DOWNSAMPLER_OUT_SIZE];
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float conv1_tmp1[(NB_XCORR_FEATURES + 2)*8] = {0};
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float conv1_tmp2[(NB_XCORR_FEATURES + 2)*8] = {0};
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float output[DENSE_FINAL_UPSAMPLER_OUT_SIZE];
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int i;
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int pos=0;
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float maxval=-1;
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float sum=0;
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float count=0;
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PitchDNN *model = &st->model;
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/* IF */
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compute_generic_dense(&model->dense_if_upsampler_1, if1_out, if_features, ACTIVATION_TANH);
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compute_generic_dense(&model->dense_if_upsampler_2, &downsampler_in[NB_XCORR_FEATURES], if1_out, ACTIVATION_TANH);
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/* xcorr*/
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OPUS_COPY(&conv1_tmp1[1], xcorr_features, NB_XCORR_FEATURES);
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compute_conv2d(&model->conv2d_1, &conv1_tmp2[1], st->xcorr_mem1, conv1_tmp1, NB_XCORR_FEATURES, NB_XCORR_FEATURES+2, ACTIVATION_TANH);
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compute_conv2d(&model->conv2d_2, downsampler_in, st->xcorr_mem2, conv1_tmp2, NB_XCORR_FEATURES, NB_XCORR_FEATURES, ACTIVATION_TANH);
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compute_generic_dense(&model->dense_downsampler, downsampler_out, downsampler_in, ACTIVATION_TANH);
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compute_generic_gru(&model->gru_1_input, &model->gru_1_recurrent, st->gru_state, downsampler_out);
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compute_generic_dense(&model->dense_final_upsampler, output, st->gru_state, ACTIVATION_LINEAR);
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for (i=0;i<180;i++) {
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if (output[i] > maxval) {
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pos = i;
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maxval = output[i];
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}
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}
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for (i=IMAX(0, pos-2); i<=IMIN(179, pos+2); i++) {
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float p = exp(output[i]);
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sum += p*i;
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count += p;
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}
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/*printf("%d %f\n", pos, sum/count);*/
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return (1.f/60.f)*(sum/count) - 1.5;
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/*return 256.f/pow(2.f, (1.f/60.f)*i);*/
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}
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void pitchdnn_init(PitchDNNState *st)
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{
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int ret;
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OPUS_CLEAR(st, 1);
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#ifndef USE_WEIGHTS_FILE
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ret = init_pitchdnn(&st->model, pitchdnn_arrays);
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#else
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ret = 0;
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#endif
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celt_assert(ret == 0);
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/* FIXME: perform arch detection. */
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}
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