eden-emu/opus
15
0
Fork 0
mirror of https://github.com/xiph/opus.git synced 2025-05-30 23:27:42 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 4

Switching to neural pitch estimator

Browse source 
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
No known key found for this signature in database
GPG key ID: 531A52533318F00A
13 changed files with 103 additions and 137 deletions
Download patch file Download diff file Expand all files Collapse all files

27
dnn/nnet.c
View file

@ -408,22 +408,22 @@ void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const f
storing the output as [ out_channels x len2 ].
We assume that the output dimension along the ksize1 axis is 1,
i.e. processing one frame at a time. */
void conv2d_float(float *out, const float *weights, int in_channels, int out_channels, int ktime, int kheight, const float *in, int len2)
void conv2d_float(float *out, const float *weights, int in_channels, int out_channels, int ktime, int kheight, const float *in, int height, int hstride)
{
int i;
int in_stride;
in_stride = len2+kheight-1;
OPUS_CLEAR(out, out_channels*len2);
in_stride = height+kheight-1;
for (i=0;i<out_channels;i++) {
int m;
OPUS_CLEAR(&out[i*hstride], height);
for (m=0;m<in_channels;m++) {
int t;
for (t=0;t<ktime;t++) {
int h;
for (h=0;h<kheight;h++) {
int j;
for (j=0;j<len2;j++) {
out[i*len2 + j] += weights[i*in_channels*ktime*kheight + m*ktime*kheight + t*kheight + h] *
for (j=0;j<height;j++) {
out[i*hstride + j] += weights[i*in_channels*ktime*kheight + m*ktime*kheight + t*kheight + h] *
in[t*in_channels*in_stride + m*in_stride + j + h];
}
}
@ -432,26 +432,31 @@ void conv2d_float(float *out, const float *weights, int in_channels, int out_cha
}
}
#define MAX_CONV2D_INPUTS 2048
#define MAX_CONV2D_INPUTS 8192
void compute_conv2d(const Conv2dLayer *conv, float *out, float *mem, const float *in, int len2, int activation)
void compute_conv2d(const Conv2dLayer *conv, float *out, float *mem, const float *in, int height, int hstride, int activation)
{
int i;
const float *bias;
float in_buf[MAX_CONV2D_INPUTS];
int time_stride;
celt_assert(in != out);
time_stride = conv->in_channels*(len2+conv->kheight);
time_stride = conv->in_channels*(height+conv->kheight-1);
celt_assert(conv->ktime*time_stride <= MAX_CONV2D_INPUTS);
OPUS_COPY(in_buf, mem, (conv->ktime-1)*time_stride);
OPUS_COPY(&in_buf[(conv->ktime-1)*time_stride], in, time_stride);
OPUS_COPY(mem, &in_buf[time_stride], (conv->ktime-1)*time_stride);
bias = conv->bias;
conv2d_float(out, conv->float_weights, conv->in_channels, conv->out_channels, conv->ktime, conv->kheight, in_buf, len2);
conv2d_float(out, conv->float_weights, conv->in_channels, conv->out_channels, conv->ktime, conv->kheight, in_buf, height, hstride);
if (bias != NULL) {
for (i=0;i<conv->out_channels*len2;i++) out[i] += bias[i];
for (i=0;i<conv->out_channels;i++) {
int j;
for (j=0;j<height;j++) out[i*hstride+j] += bias[i];
}
}
for (i=0;i<conv->out_channels;i++) {
compute_activation(&out[i*hstride], &out[i*hstride], height, activation);
}
compute_activation(out, out, conv->out_channels*len2, activation);
}