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Using PyTorch model (same architecture for now)

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Jean-Marc Valin 2024-01-17 15:15:22 -05:00
parent e699263660
commit 1ddfcfd48c
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GPG Key ID: 5E5DD9A36F9189C8
7 changed files with 22 additions and 13 deletions
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2
autogen.sh
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@ -9,7 +9,7 @@ set -e
srcdir=`dirname $0`
test -n "$srcdir" && cd "$srcdir"
dnn/download_model.sh 88477f4
dnn/download_model.sh e699263
echo "Updating build configuration files, please wait...."

16
dnn/lpcnet_plc.c
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@ -62,7 +62,7 @@ int lpcnet_plc_init(LPCNetPLCState *st) {
lpcnet_encoder_init(&st->enc);
st->loaded = 0;
#ifndef USE_WEIGHTS_FILE
ret = init_plc_model(&st->model, lpcnet_plc_arrays);
ret = init_plcmodel(&st->model, plcmodel_arrays);
if (ret == 0) st->loaded = 1;
#else
ret = 0;
@ -76,7 +76,7 @@ int lpcnet_plc_load_model(LPCNetPLCState *st, const unsigned char *data, int len
WeightArray *list;
int ret;
parse_weights(&list, data, len);
ret = init_plc_model(&st->model, list);
ret = init_plcmodel(&st->model, list);
opus_free(list);
if (ret == 0) {
ret = lpcnet_encoder_load_model(&st->enc, data, len);
@ -108,14 +108,14 @@ void lpcnet_plc_fec_clear(LPCNetPLCState *st) {
static void compute_plc_pred(LPCNetPLCState *st, float *out, const float *in) {
float zeros[3*PLC_MAX_RNN_NEURONS] = {0};
float dense_out[PLC_DENSE1_OUT_SIZE];
float tmp[PLC_DENSE_IN_OUT_SIZE];
PLCModel *model = &st->model;
PLCNetState *net = &st->plc_net;
celt_assert(st->loaded);
_lpcnet_compute_dense(&st->model.plc_dense1, dense_out, in, st->arch);
compute_gruB(&st->model.plc_gru1, zeros, net->plc_gru1_state, dense_out, st->arch);
compute_gruB(&st->model.plc_gru2, zeros, net->plc_gru2_state, net->plc_gru1_state, st->arch);
_lpcnet_compute_dense(&st->model.plc_out, out, net->plc_gru2_state, st->arch);
compute_generic_dense(&model->plc_dense_in, tmp, in, ACTIVATION_TANH, 0);
compute_generic_gru(&model->plc_gru1_input, &model->plc_gru1_recurrent, net->gru1_state, tmp, 0);
compute_generic_gru(&model->plc_gru2_input, &model->plc_gru2_recurrent, net->gru2_state, net->gru1_state, 0);
compute_generic_dense(&model->plc_dense_out, out, net->gru2_state, ACTIVATION_LINEAR, 0);
}
static int get_fec_or_pred(LPCNetPLCState *st, float *out) {

5
dnn/lpcnet_private.h
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@ -41,6 +41,11 @@ struct LPCNetEncState{
float burg_cepstrum[2*NB_BANDS];
};
typedef struct {
float gru1_state[PLC_GRU1_STATE_SIZE];
float gru2_state[PLC_GRU2_STATE_SIZE];
} PLCNetState;
#define PLC_BUF_SIZE ((CONT_VECTORS+5)*FRAME_SIZE)
struct LPCNetPLCState {
PLCModel model;

4
dnn/nnet.c
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@ -64,9 +64,9 @@ void compute_generic_dense(const LinearLayer *layer, float *output, const float
}
#ifdef ENABLE_OSCE
#define MAX_RNN_NEURONS_ALL IMAX(IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_NEURONS), DRED_MAX_RNN_NEURONS), OSCE_MAX_RNN_NEURONS)
#define MAX_RNN_NEURONS_ALL IMAX(IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_UNITS), DRED_MAX_RNN_NEURONS), OSCE_MAX_RNN_NEURONS)
#else
#define MAX_RNN_NEURONS_ALL IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_NEURONS), DRED_MAX_RNN_NEURONS)
#define MAX_RNN_NEURONS_ALL IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_UNITS), DRED_MAX_RNN_NEURONS)
#endif
void compute_generic_gru(const LinearLayer *input_weights, const LinearLayer *recurrent_weights, float *state, const float *in, int arch)

2
dnn/nnet.h
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@ -142,7 +142,7 @@ int parse_weights(WeightArray **list, const unsigned char *data, int len);
extern const WeightArray lpcnet_arrays[];
extern const WeightArray lpcnet_plc_arrays[];
extern const WeightArray plcmodel_arrays[];
extern const WeightArray rdovaeenc_arrays[];
extern const WeightArray rdovaedec_arrays[];
extern const WeightArray fwgan_arrays[];

4
dnn/torch/plc/plc_dataset.py
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@ -40,6 +40,10 @@ class PLCDataset(torch.utils.data.Dataset):
lost_offset = np.random.randint(0, high=self.lost.shape[0]-self.sequence_length)
lost = self.lost[lost_offset:lost_offset+self.sequence_length]
#randomly add a few 10-ms losses so that the model learns to handle them
lost = lost * (np.random.rand(lost.shape[-1]) > .02).astype('float32')
#randomly break long consecutive losses so we don't try too hard to predict them
lost = 1 - ((1-lost) * (np.random.rand(lost.shape[-1]) > .1).astype('float32'))
lost = np.reshape(lost, (features.shape[0], 1))
lost_mask = np.tile(lost, (1,features.shape[-1]))
in_features = features*lost_mask

2
dnn/write_lpcnet_weights.c
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@ -81,7 +81,7 @@ int main(void)
FILE *fout = fopen("weights_blob.bin", "w");
write_weights(pitchdnn_arrays, fout);
write_weights(fargan_arrays, fout);
write_weights(lpcnet_plc_arrays, fout);
write_weights(plcmodel_arrays, fout);
write_weights(rdovaeenc_arrays, fout);
write_weights(rdovaedec_arrays, fout);
#ifdef ENABLE_OSCE