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C implementation of FARGAN
This commit is contained in:
Jean-Marc Valin
parent
9e76a7bfb8
commit
35cb8d7f66
11 changed files with 487 additions and 6 deletions
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@ -9,7 +9,7 @@ set -e
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srcdir=`dirname $0`
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test -n "$srcdir" && cd "$srcdir"
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dnn/download_model.sh f68e31d
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dnn/download_model.sh 9e76a7b
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echo "Updating build configuration files, please wait...."
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220
dnn/fargan.c
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220
dnn/fargan.c
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@ -0,0 +1,220 @@
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/* Copyright (c) 2023 Amazon */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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||||
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||||
- Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
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- Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
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||||
|
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
|
||||
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifdef HAVE_CONFIG_H
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#include "config.h"
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#endif
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#include "fargan.h"
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#include "os_support.h"
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#include "freq.h"
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#include "fargan_data.h"
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#include "lpcnet.h"
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#include "pitch.h"
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#include "nnet.h"
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#include "lpcnet_private.h"
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#define FARGAN_FEATURES (NB_FEATURES)
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static void compute_fargan_cond(FARGANState *st, float *cond, const float *features, int period)
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{
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FARGAN *model;
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float dense_in[NB_FEATURES+COND_NET_PEMBED_OUT_SIZE];
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float conv1_in[COND_NET_FCONV1_IN_SIZE];
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float conv2_in[COND_NET_FCONV2_IN_SIZE];
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model = &st->model;
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celt_assert(FARGAN_FEATURES+COND_NET_PEMBED_OUT_SIZE == model->cond_net_fdense1.nb_inputs);
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celt_assert(COND_NET_FCONV1_IN_SIZE == model->cond_net_fdense1.nb_outputs);
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celt_assert(COND_NET_FCONV2_IN_SIZE == model->cond_net_fconv1.nb_outputs);
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OPUS_COPY(&dense_in[NB_FEATURES], &model->cond_net_pembed.float_weights[IMAX(0,IMIN(period-32, 224))*COND_NET_PEMBED_OUT_SIZE], COND_NET_PEMBED_OUT_SIZE);
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OPUS_COPY(dense_in, features, NB_FEATURES);
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compute_generic_dense(&model->cond_net_fdense1, conv1_in, dense_in, ACTIVATION_TANH);
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compute_generic_conv1d(&model->cond_net_fconv1, conv2_in, st->cond_conv1_state, conv1_in, COND_NET_FCONV1_IN_SIZE, ACTIVATION_TANH);
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compute_generic_conv1d(&model->cond_net_fconv2, cond, st->cond_conv2_state, conv2_in, COND_NET_FCONV2_IN_SIZE, ACTIVATION_TANH);
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}
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static void fargan_deemphasis(float *pcm, float *deemph_mem) {
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int i;
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for (i=0;i<FARGAN_SUBFRAME_SIZE;i++) {
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pcm[i] += FARGAN_DEEMPHASIS * *deemph_mem;
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*deemph_mem = pcm[i];
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}
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}
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static void run_fargan_subframe(FARGANState *st, float *pcm, const float *cond, int period)
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{
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int i, pos;
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float fwc0_in[SIG_NET_INPUT_SIZE];
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float gru1_in[SIG_NET_FWC0_CONV_OUT_SIZE+2*FARGAN_SUBFRAME_SIZE];
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float gru2_in[SIG_NET_GRU1_OUT_SIZE+2*FARGAN_SUBFRAME_SIZE];
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float gru3_in[SIG_NET_GRU2_OUT_SIZE+2*FARGAN_SUBFRAME_SIZE];
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float pred[FARGAN_SUBFRAME_SIZE+4];
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float prev[FARGAN_SUBFRAME_SIZE];
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float pitch_gate[4];
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float gain;
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float gain_1;
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float skip_cat[10000];
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float skip_out[SIG_NET_SKIP_DENSE_OUT_SIZE];
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FARGAN *model;
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celt_assert(st->cont_initialized);
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model = &st->model;
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compute_generic_dense(&model->sig_net_cond_gain_dense, &gain, cond, ACTIVATION_LINEAR);
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gain = exp(gain);
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gain_1 = 1.f/(1e-5 + gain);
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pos = PITCH_MAX_PERIOD-period-2;
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for (i=0;i<FARGAN_SUBFRAME_SIZE+4;i++) {
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pred[i] = MIN32(1.f, MAX32(-1.f, gain_1*st->pitch_buf[IMAX(0, pos)]));
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pos++;
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if (pos == PITCH_MAX_PERIOD) pos -= period;
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}
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for (i=0;i<FARGAN_SUBFRAME_SIZE;i++) prev[i] = MAX32(-1.f, MIN16(1.f, gain_1*st->pitch_buf[PITCH_MAX_PERIOD-FARGAN_SUBFRAME_SIZE+i]));
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OPUS_COPY(&fwc0_in[0], &cond[0], FARGAN_COND_SIZE);
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OPUS_COPY(&fwc0_in[FARGAN_COND_SIZE], pred, FARGAN_SUBFRAME_SIZE+4);
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OPUS_COPY(&fwc0_in[FARGAN_COND_SIZE+FARGAN_SUBFRAME_SIZE+4], prev, FARGAN_SUBFRAME_SIZE);
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compute_generic_conv1d(&model->sig_net_fwc0_conv, gru1_in, st->fwc0_mem, fwc0_in, SIG_NET_INPUT_SIZE, ACTIVATION_TANH);
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celt_assert(SIG_NET_FWC0_GLU_GATE_OUT_SIZE == model->sig_net_fwc0_glu_gate.nb_outputs);
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compute_glu(&model->sig_net_fwc0_glu_gate, gru1_in, gru1_in);
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compute_generic_dense(&model->sig_net_gain_dense_out, pitch_gate, gru1_in, ACTIVATION_SIGMOID);
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for (i=0;i<FARGAN_SUBFRAME_SIZE;i++) gru1_in[SIG_NET_FWC0_GLU_GATE_OUT_SIZE+i] = pitch_gate[0]*pred[i+2];
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OPUS_COPY(&gru1_in[SIG_NET_FWC0_GLU_GATE_OUT_SIZE+FARGAN_SUBFRAME_SIZE], prev, FARGAN_SUBFRAME_SIZE);
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compute_generic_gru(&model->sig_net_gru1_input, &model->sig_net_gru1_recurrent, st->gru1_state, gru1_in);
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compute_glu(&model->sig_net_gru1_glu_gate, gru2_in, st->gru1_state);
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for (i=0;i<FARGAN_SUBFRAME_SIZE;i++) gru2_in[SIG_NET_GRU1_OUT_SIZE+i] = pitch_gate[1]*pred[i+2];
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OPUS_COPY(&gru2_in[SIG_NET_GRU1_OUT_SIZE+FARGAN_SUBFRAME_SIZE], prev, FARGAN_SUBFRAME_SIZE);
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compute_generic_gru(&model->sig_net_gru2_input, &model->sig_net_gru2_recurrent, st->gru2_state, gru2_in);
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compute_glu(&model->sig_net_gru2_glu_gate, gru3_in, st->gru2_state);
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for (i=0;i<FARGAN_SUBFRAME_SIZE;i++) gru3_in[SIG_NET_GRU2_OUT_SIZE+i] = pitch_gate[2]*pred[i+2];
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OPUS_COPY(&gru3_in[SIG_NET_GRU2_OUT_SIZE+FARGAN_SUBFRAME_SIZE], prev, FARGAN_SUBFRAME_SIZE);
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compute_generic_gru(&model->sig_net_gru3_input, &model->sig_net_gru3_recurrent, st->gru3_state, gru3_in);
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compute_glu(&model->sig_net_gru3_glu_gate, &skip_cat[SIG_NET_GRU1_OUT_SIZE+SIG_NET_GRU2_OUT_SIZE], st->gru3_state);
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OPUS_COPY(skip_cat, gru2_in, SIG_NET_GRU1_OUT_SIZE);
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OPUS_COPY(&skip_cat[SIG_NET_GRU1_OUT_SIZE], gru3_in, SIG_NET_GRU2_OUT_SIZE);
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OPUS_COPY(&skip_cat[SIG_NET_GRU1_OUT_SIZE+SIG_NET_GRU2_OUT_SIZE+SIG_NET_GRU3_OUT_SIZE], gru1_in, SIG_NET_FWC0_CONV_OUT_SIZE);
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for (i=0;i<FARGAN_SUBFRAME_SIZE;i++) skip_cat[SIG_NET_GRU1_OUT_SIZE+SIG_NET_GRU2_OUT_SIZE+SIG_NET_GRU3_OUT_SIZE+SIG_NET_FWC0_CONV_OUT_SIZE+i] = pitch_gate[3]*pred[i+2];
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OPUS_COPY(&skip_cat[SIG_NET_GRU1_OUT_SIZE+SIG_NET_GRU2_OUT_SIZE+SIG_NET_GRU3_OUT_SIZE+SIG_NET_FWC0_CONV_OUT_SIZE+FARGAN_SUBFRAME_SIZE], prev, FARGAN_SUBFRAME_SIZE);
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compute_generic_dense(&model->sig_net_skip_dense, skip_out, skip_cat, ACTIVATION_TANH);
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compute_glu(&model->sig_net_skip_glu_gate, skip_out, skip_out);
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compute_generic_dense(&model->sig_net_sig_dense_out, pcm, skip_out, ACTIVATION_TANH);
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for (i=0;i<FARGAN_SUBFRAME_SIZE;i++) pcm[i] *= gain;
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OPUS_MOVE(st->pitch_buf, &st->pitch_buf[FARGAN_SUBFRAME_SIZE], PITCH_MAX_PERIOD-FARGAN_SUBFRAME_SIZE);
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OPUS_COPY(&st->pitch_buf[PITCH_MAX_PERIOD-FARGAN_SUBFRAME_SIZE], pcm, FARGAN_SUBFRAME_SIZE);
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fargan_deemphasis(pcm, &st->deemph_mem);
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}
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void fargan_cont(FARGANState *st, const float *pcm0, const float *features0)
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{
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int i;
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float cond[COND_NET_FCONV2_OUT_SIZE];
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float x0[FARGAN_CONT_SAMPLES];
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float dummy[FARGAN_SUBFRAME_SIZE];
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int period=0;
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/* Pre-load features. */
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for (i=0;i<5;i++) {
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const float *features = &features0[i*NB_FEATURES];
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st->last_period = period;
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period = (int)floor(.5+256./pow(2.f,((1./60.)*((features[NB_BANDS]+1.5)*60))));
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compute_fargan_cond(st, cond, features, period);
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}
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x0[0] = 0;
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for (i=1;i<FARGAN_CONT_SAMPLES;i++) {
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x0[i] = pcm0[i] - FARGAN_DEEMPHASIS*pcm0[i-1];
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}
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OPUS_COPY(&st->pitch_buf[PITCH_MAX_PERIOD-FARGAN_FRAME_SIZE], x0, FARGAN_FRAME_SIZE);
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st->cont_initialized = 1;
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for (i=0;i<FARGAN_NB_SUBFRAMES;i++) {
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run_fargan_subframe(st, dummy, &cond[i*FARGAN_COND_SIZE], st->last_period);
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OPUS_COPY(&st->pitch_buf[PITCH_MAX_PERIOD-FARGAN_SUBFRAME_SIZE], &x0[FARGAN_FRAME_SIZE+i*FARGAN_SUBFRAME_SIZE], FARGAN_SUBFRAME_SIZE);
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}
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st->deemph_mem = pcm0[FARGAN_CONT_SAMPLES-1];
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}
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void fargan_init(FARGANState *st)
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{
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int ret;
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OPUS_CLEAR(st, 1);
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ret = init_fargan(&st->model, fargan_arrays);
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celt_assert(ret == 0);
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/* FIXME: perform arch detection. */
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}
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int fargan_load_model(FARGANState *st, const unsigned char *data, int len) {
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WeightArray *list;
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int ret;
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parse_weights(&list, data, len);
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ret = init_fargan(&st->model, list);
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free(list);
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if (ret == 0) return 0;
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else return -1;
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}
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static void fargan_synthesize_impl(FARGANState *st, float *pcm, const float *features)
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{
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int subframe;
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float cond[COND_NET_FCONV2_OUT_SIZE];
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int period;
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celt_assert(st->cont_initialized);
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period = (int)floor(.5+256./pow(2.f,((1./60.)*((features[NB_BANDS]+1.5)*60))));
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compute_fargan_cond(st, cond, features, period);
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for (subframe=0;subframe<FARGAN_NB_SUBFRAMES;subframe++) {
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float *sub_cond;
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sub_cond = &cond[subframe*FARGAN_COND_SIZE];
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run_fargan_subframe(st, &pcm[subframe*FARGAN_SUBFRAME_SIZE], sub_cond, st->last_period);
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}
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st->last_period = period;
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}
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void fargan_synthesize(FARGANState *st, float *pcm, const float *features)
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{
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fargan_synthesize_impl(st, pcm, features);
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}
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void fargan_synthesize_int(FARGANState *st, opus_int16 *pcm, const float *features)
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{
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int i;
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float fpcm[FARGAN_FRAME_SIZE];
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fargan_synthesize(st, fpcm, features);
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for (i=0;i<LPCNET_FRAME_SIZE;i++) pcm[i] = (int)floor(.5 + MIN32(32767, MAX32(-32767, 32768.f*fpcm[i])));
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}
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68
dnn/fargan.h
Normal file
68
dnn/fargan.h
Normal file
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@ -0,0 +1,68 @@
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/* Copyright (c) 2023 Amazon */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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|
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- Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
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- Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
|
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
|
||||
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
||||
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifndef FARGAN_H
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#define FARGAN_H
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#include "freq.h"
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#include "fargan_data.h"
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#include "pitchdnn.h"
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#define FARGAN_CONT_SAMPLES 320
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#define FARGAN_NB_SUBFRAMES 4
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#define FARGAN_SUBFRAME_SIZE 40
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#define FARGAN_FRAME_SIZE (FARGAN_NB_SUBFRAMES*FARGAN_SUBFRAME_SIZE)
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#define FARGAN_COND_SIZE (COND_NET_FCONV2_OUT_SIZE/FARGAN_NB_SUBFRAMES)
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#define FARGAN_DEEMPHASIS 0.85f
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#define SIG_NET_INPUT_SIZE (FARGAN_COND_SIZE+2*FARGAN_SUBFRAME_SIZE+4)
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#define SIG_NET_FWC0_STATE_SIZE (2*SIG_NET_INPUT_SIZE)
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typedef struct {
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FARGAN model;
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int arch;
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int cont_initialized;
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float deemph_mem;
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float pitch_buf[PITCH_MAX_PERIOD];
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float cond_conv1_state[COND_NET_FCONV1_STATE_SIZE];
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float cond_conv2_state[COND_NET_FCONV2_STATE_SIZE];
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float fwc0_mem[SIG_NET_FWC0_STATE_SIZE];
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float gru1_state[SIG_NET_GRU1_STATE_SIZE];
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float gru2_state[SIG_NET_GRU2_STATE_SIZE];
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float gru3_state[SIG_NET_GRU3_STATE_SIZE];
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int last_period;
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} FARGANState;
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void fargan_init(FARGANState *st);
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int fargan_load_model(FARGANState *st, const unsigned char *data, int len);
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void fargan_cont(FARGANState *st, const float *pcm0, const float *features0);
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void fargan_synthesize(FARGANState *st, float *pcm, const float *features);
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void fargan_synthesize_int(FARGANState *st, opus_int16 *pcm, const float *features);
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#endif /* FARGAN_H */
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@ -37,6 +37,7 @@
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#include "freq.h"
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#include "os_support.h"
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#include "fwgan.h"
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#include "fargan.h"
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#ifdef USE_WEIGHTS_FILE
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# if __unix__
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@ -86,6 +87,7 @@ void free_blob(unsigned char *blob, int len) {
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#define MODE_PLC 4
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#define MODE_ADDLPC 5
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#define MODE_FWGAN_SYNTHESIS 6
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#define MODE_FARGAN_SYNTHESIS 7
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void usage(void) {
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fprintf(stderr, "usage: lpcnet_demo -features <input.pcm> <features.f32>\n");
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@ -115,6 +117,7 @@ int main(int argc, char **argv) {
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if (strcmp(argv[1], "-features") == 0) mode=MODE_FEATURES;
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else if (strcmp(argv[1], "-synthesis") == 0) mode=MODE_SYNTHESIS;
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else if (strcmp(argv[1], "-fwgan-synthesis") == 0) mode=MODE_FWGAN_SYNTHESIS;
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else if (strcmp(argv[1], "-fargan-synthesis") == 0) mode=MODE_FARGAN_SYNTHESIS;
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else if (strcmp(argv[1], "-plc") == 0) {
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mode=MODE_PLC;
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plc_options = argv[2];
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@ -210,6 +213,32 @@ int main(int argc, char **argv) {
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for (i=0;i<LPCNET_FRAME_SIZE;i++) pcm[i] = (int)floor(.5 + MIN32(32767, MAX32(-32767, 32768.f*fpcm[i])));
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fwrite(pcm, sizeof(pcm[0]), LPCNET_FRAME_SIZE, fout);
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}
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} else if (mode == MODE_FARGAN_SYNTHESIS) {
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FARGANState fargan;
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size_t ret, i;
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float in_features[5*NB_TOTAL_FEATURES];
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float zeros[320] = {0};
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fargan_init(&fargan);
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#ifdef USE_WEIGHTS_FILE
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fargan_load_model(fwgan, data, len);
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#endif
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/* uncomment the following to align with Python code */
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/*ret = fread(&in_features[0], sizeof(in_features[0]), NB_TOTAL_FEATURES, fin);*/
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for (i=0;i<5;i++) {
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||||
ret = fread(&in_features[i*NB_FEATURES], sizeof(in_features[0]), NB_TOTAL_FEATURES, fin);
|
||||
}
|
||||
fargan_cont(&fargan, zeros, in_features);
|
||||
while (1) {
|
||||
float features[NB_FEATURES];
|
||||
float fpcm[LPCNET_FRAME_SIZE];
|
||||
opus_int16 pcm[LPCNET_FRAME_SIZE];
|
||||
ret = fread(in_features, sizeof(features[0]), NB_TOTAL_FEATURES, fin);
|
||||
if (feof(fin) || ret != NB_TOTAL_FEATURES) break;
|
||||
OPUS_COPY(features, in_features, NB_FEATURES);
|
||||
fargan_synthesize(&fargan, fpcm, features);
|
||||
for (i=0;i<LPCNET_FRAME_SIZE;i++) pcm[i] = (int)floor(.5 + MIN32(32767, MAX32(-32767, 32768.f*fpcm[i])));
|
||||
fwrite(pcm, sizeof(pcm[0]), LPCNET_FRAME_SIZE, fout);
|
||||
}
|
||||
} else if (mode == MODE_PLC) {
|
||||
opus_int16 pcm[FRAME_SIZE];
|
||||
int count=0;
|
||||
|
|
|
|||
10
dnn/nnet.c
10
dnn/nnet.c
|
|
@ -143,6 +143,16 @@ void compute_generic_gru(const LinearLayer *input_weights, const LinearLayer *re
|
|||
state[i] = h[i];
|
||||
}
|
||||
|
||||
void compute_glu(const LinearLayer *layer, float *output, const float *input)
|
||||
{
|
||||
int i;
|
||||
float act2[MAX_INPUTS];
|
||||
celt_assert(layer->nb_inputs == layer->nb_outputs);
|
||||
compute_linear(layer, act2, input);
|
||||
compute_activation(act2, act2, layer->nb_outputs, ACTIVATION_SIGMOID);
|
||||
for (i=0;i<layer->nb_outputs;i++) output[i] = input[i]*act2[i];
|
||||
}
|
||||
|
||||
void compute_gated_activation(const LinearLayer *layer, float *output, const float *input, int activation)
|
||||
{
|
||||
int i;
|
||||
|
|
|
|||
|
|
@ -146,6 +146,7 @@ void compute_generic_dense(const LinearLayer *layer, float *output, const float
|
|||
void compute_generic_gru(const LinearLayer *input_weights, const LinearLayer *recurrent_weights, float *state, const float *in);
|
||||
void compute_generic_conv1d(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int activation);
|
||||
void compute_generic_conv1d_dilation(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int dilation, int activation);
|
||||
void compute_glu(const LinearLayer *layer, float *output, const float *input);
|
||||
void compute_gated_activation(const LinearLayer *layer, float *output, const float *input, int activation);
|
||||
|
||||
void compute_activation(float *output, const float *input, int N, int activation);
|
||||
|
|
@ -176,6 +177,7 @@ extern const WeightArray lpcnet_plc_arrays[];
|
|||
extern const WeightArray rdovaeenc_arrays[];
|
||||
extern const WeightArray rdovaedec_arrays[];
|
||||
extern const WeightArray fwgan_arrays[];
|
||||
extern const WeightArray fargan_arrays[];
|
||||
extern const WeightArray pitchdnn_arrays[];
|
||||
|
||||
int linear_init(LinearLayer *layer, const WeightArray *arrays,
|
||||
|
|
|
|||
112
dnn/torch/fargan/dump_fargan_weights.py
Normal file
112
dnn/torch/fargan/dump_fargan_weights.py
Normal file
|
|
@ -0,0 +1,112 @@
|
|||
import os
|
||||
import sys
|
||||
import argparse
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
|
||||
sys.path.append(os.path.join(os.path.split(__file__)[0], '../weight-exchange'))
|
||||
import wexchange.torch
|
||||
|
||||
import fargan
|
||||
#from models import model_dict
|
||||
|
||||
unquantized = [ 'cond_net.pembed', 'cond_net.fdense1', 'sig_net.cond_gain_dense', 'sig_net.gain_dense_out' ]
|
||||
|
||||
unquantized2 = [
|
||||
'cond_net.pembed',
|
||||
'cond_net.fdense1',
|
||||
'cond_net.fconv1',
|
||||
'cond_net.fconv2',
|
||||
'cont_net.0',
|
||||
'sig_net.cond_gain_dense',
|
||||
'sig_net.fwc0.conv',
|
||||
'sig_net.fwc0.glu.gate',
|
||||
'sig_net.dense1_glu.gate',
|
||||
'sig_net.gru1_glu.gate',
|
||||
'sig_net.gru2_glu.gate',
|
||||
'sig_net.gru3_glu.gate',
|
||||
'sig_net.skip_glu.gate',
|
||||
'sig_net.skip_dense',
|
||||
'sig_net.sig_dense_out',
|
||||
'sig_net.gain_dense_out'
|
||||
]
|
||||
|
||||
description=f"""
|
||||
This is an unsafe dumping script for FARGAN models. It assumes that all weights are included in Linear, Conv1d or GRU layer
|
||||
and will fail to export any other weights.
|
||||
|
||||
Furthermore, the quanitze option relies on the following explicit list of layers to be excluded:
|
||||
{unquantized}.
|
||||
|
||||
Modify this script manually if adjustments are needed.
|
||||
"""
|
||||
|
||||
parser = argparse.ArgumentParser(description=description)
|
||||
parser.add_argument('weightfile', type=str, help='weight file path')
|
||||
parser.add_argument('export_folder', type=str)
|
||||
parser.add_argument('--export-filename', type=str, default='fargan_data', help='filename for source and header file (.c and .h will be added), defaults to fargan_data')
|
||||
parser.add_argument('--struct-name', type=str, default='FARGAN', help='name for C struct, defaults to FARGAN')
|
||||
parser.add_argument('--quantize', action='store_true', help='apply quantization')
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = parser.parse_args()
|
||||
|
||||
print(f"loading weights from {args.weightfile}...")
|
||||
saved_gen= torch.load(args.weightfile, map_location='cpu')
|
||||
saved_gen['model_args'] = ()
|
||||
saved_gen['model_kwargs'] = {'cond_size': 256, 'gamma': 0.9}
|
||||
|
||||
model = fargan.FARGAN(*saved_gen['model_args'], **saved_gen['model_kwargs'])
|
||||
model.load_state_dict(saved_gen['state_dict'], strict=False)
|
||||
def _remove_weight_norm(m):
|
||||
try:
|
||||
torch.nn.utils.remove_weight_norm(m)
|
||||
except ValueError: # this module didn't have weight norm
|
||||
return
|
||||
model.apply(_remove_weight_norm)
|
||||
|
||||
|
||||
print("dumping model...")
|
||||
quantize_model=args.quantize
|
||||
|
||||
output_folder = args.export_folder
|
||||
os.makedirs(output_folder, exist_ok=True)
|
||||
|
||||
writer = wexchange.c_export.c_writer.CWriter(os.path.join(output_folder, args.export_filename), model_struct_name=args.struct_name)
|
||||
|
||||
for name, module in model.named_modules():
|
||||
|
||||
if quantize_model:
|
||||
quantize=name not in unquantized
|
||||
scale = None if quantize else 1/128
|
||||
else:
|
||||
quantize=False
|
||||
scale=1/128
|
||||
|
||||
if isinstance(module, nn.Linear):
|
||||
print(f"dumping linear layer {name}...")
|
||||
wexchange.torch.dump_torch_dense_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale)
|
||||
|
||||
elif isinstance(module, nn.Conv1d):
|
||||
print(f"dumping conv1d layer {name}...")
|
||||
wexchange.torch.dump_torch_conv1d_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale)
|
||||
|
||||
elif isinstance(module, nn.GRU):
|
||||
print(f"dumping GRU layer {name}...")
|
||||
wexchange.torch.dump_torch_gru_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale, recurrent_scale=scale)
|
||||
|
||||
elif isinstance(module, nn.GRUCell):
|
||||
print(f"dumping GRUCell layer {name}...")
|
||||
wexchange.torch.dump_torch_grucell_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale, recurrent_scale=scale)
|
||||
|
||||
elif isinstance(module, nn.Embedding):
|
||||
print(f"dumping Embedding layer {name}...")
|
||||
wexchange.torch.dump_torch_embedding_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale)
|
||||
#wexchange.torch.dump_torch_embedding_weights(writer, module)
|
||||
|
||||
else:
|
||||
print(f"Ignoring layer {name}...")
|
||||
|
||||
writer.close()
|
||||
|
|
@ -31,5 +31,6 @@ from .torch import dump_torch_conv1d_weights, load_torch_conv1d_weights
|
|||
from .torch import dump_torch_conv2d_weights, load_torch_conv2d_weights
|
||||
from .torch import dump_torch_dense_weights, load_torch_dense_weights
|
||||
from .torch import dump_torch_gru_weights, load_torch_gru_weights
|
||||
from .torch import dump_torch_grucell_weights
|
||||
from .torch import dump_torch_embedding_weights, load_torch_embedding_weights
|
||||
from .torch import dump_torch_weights, load_torch_weights
|
||||
|
|
|
|||
|
|
@ -61,6 +61,30 @@ def dump_torch_gru_weights(where, gru, name='gru', input_sparse=False, recurrent
|
|||
np.save(os.path.join(where, 'bias_hh_rzn.npy'), b_hh)
|
||||
|
||||
|
||||
def dump_torch_grucell_weights(where, gru, name='gru', input_sparse=False, recurrent_sparse=False, quantize=False, scale=1/128, recurrent_scale=1/128):
|
||||
|
||||
w_ih = gru.weight_ih.detach().cpu().numpy().copy()
|
||||
w_hh = gru.weight_hh.detach().cpu().numpy().copy()
|
||||
if hasattr(gru, 'bias_ih') and gru.bias_ih is not None:
|
||||
b_ih = gru.bias_ih.detach().cpu().numpy().copy()
|
||||
else:
|
||||
b_ih = None
|
||||
if hasattr(gru, 'bias_hh') and gru.bias_hh is not None:
|
||||
b_hh = gru.bias_hh.detach().cpu().numpy().copy()
|
||||
else:
|
||||
b_hh = None
|
||||
|
||||
if isinstance(where, CWriter):
|
||||
return print_gru_layer(where, name, w_ih, w_hh, b_ih, b_hh, format='torch', input_sparse=input_sparse, recurrent_sparse=recurrent_sparse, quantize=quantize, scale=scale, recurrent_scale=recurrent_scale)
|
||||
else:
|
||||
os.makedirs(where, exist_ok=True)
|
||||
|
||||
np.save(os.path.join(where, 'weight_ih_rzn.npy'), w_ih)
|
||||
np.save(os.path.join(where, 'weight_hh_rzn.npy'), w_hh)
|
||||
np.save(os.path.join(where, 'bias_ih_rzn.npy'), b_ih)
|
||||
np.save(os.path.join(where, 'bias_hh_rzn.npy'), b_hh)
|
||||
|
||||
|
||||
|
||||
def load_torch_gru_weights(where, gru):
|
||||
|
||||
|
|
@ -165,11 +189,20 @@ def load_torch_conv2d_weights(where, conv):
|
|||
conv.bias.set_(torch.from_numpy(b))
|
||||
|
||||
|
||||
def dump_torch_embedding_weights(where, emb):
|
||||
os.makedirs(where, exist_ok=True)
|
||||
def dump_torch_embedding_weights(where, embed, name='embed', scale=1/128, sparse=False, diagonal=False, quantize=False):
|
||||
|
||||
w = emb.weight.detach().cpu().numpy().copy()
|
||||
np.save(os.path.join(where, 'weight.npy'), w)
|
||||
print("quantize = ", quantize)
|
||||
w = embed.weight.detach().cpu().numpy().copy().transpose()
|
||||
b = np.zeros(w.shape[0], dtype=w.dtype)
|
||||
|
||||
if isinstance(where, CWriter):
|
||||
return print_dense_layer(where, name, w, b, scale=scale, format='torch', sparse=sparse, diagonal=diagonal, quantize=quantize)
|
||||
|
||||
else:
|
||||
os.makedirs(where, exist_ok=True)
|
||||
|
||||
np.save(os.path.join(where, 'weight.npy'), w)
|
||||
np.save(os.path.join(where, 'bias.npy'), b)
|
||||
|
||||
|
||||
def load_torch_embedding_weights(where, emb):
|
||||
|
|
@ -187,6 +220,8 @@ def dump_torch_weights(where, module, name=None, verbose=False, **kwargs):
|
|||
return dump_torch_dense_weights(where, module, name, **kwargs)
|
||||
elif isinstance(module, torch.nn.GRU):
|
||||
return dump_torch_gru_weights(where, module, name, **kwargs)
|
||||
elif isinstance(module, torch.nn.GRUCell):
|
||||
return dump_torch_grucell_weights(where, module, name, **kwargs)
|
||||
elif isinstance(module, torch.nn.Conv1d):
|
||||
return dump_torch_conv1d_weights(where, module, name, **kwargs)
|
||||
elif isinstance(module, torch.nn.Conv2d):
|
||||
|
|
@ -209,4 +244,4 @@ def load_torch_weights(where, module):
|
|||
elif isinstance(module, torch.nn.Embedding):
|
||||
load_torch_embedding_weights(where, module)
|
||||
else:
|
||||
raise ValueError(f'dump_torch_weights: layer of type {type(module)} not supported')
|
||||
raise ValueError(f'dump_torch_weights: layer of type {type(module)} not supported')
|
||||
|
|
|
|||
|
|
@ -8,6 +8,8 @@ dnn/lpcnet.h \
|
|||
dnn/burg.h \
|
||||
dnn/common.h \
|
||||
dnn/freq.h \
|
||||
dnn/fargan.h \
|
||||
dnn/fargan_data.h \
|
||||
dnn/fwgan.h \
|
||||
dnn/fwgan_data.h \
|
||||
dnn/kiss99.h \
|
||||
|
|
|
|||
|
|
@ -1,6 +1,8 @@
|
|||
LPCNET_SOURCES = \
|
||||
dnn/burg.c \
|
||||
dnn/freq.c \
|
||||
dnn/fargan.c \
|
||||
dnn/fargan_data.c \
|
||||
dnn/fwgan.c \
|
||||
dnn/fwgan_data.c \
|
||||
dnn/kiss99.c \
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue