diff --git a/dnn/lpcnet_plc.c b/dnn/lpcnet_plc.c
index 5e9ac8f72d2ee9003dbbbb834c0d3be9123250bb..a764fb42d3b74af3341896a332d17d89cf21b2e1 100644
--- a/dnn/lpcnet_plc.c
+++ b/dnn/lpcnet_plc.c
@@ -69,7 +69,7 @@ static void compute_plc_pred(PLCNetState *net, float *out, const float *in) {
#if 1
/* In this causal version of the code, the DNN model implemented by compute_plc_pred()
- returns the predicted features from frame t+1, using the input features from frame t.*/
+ needs to generate two feature vectors to conceal the first lost packet.*/
LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
int i;
@@ -83,7 +83,11 @@ LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
/*fprintf(stderr, "skip update\n");*/
if (st->blend) {
short tmp[FRAME_SIZE-TRAINING_OFFSET];
- lpcnet_synthesize_tail_impl(&st->lpcnet, tmp, FRAME_SIZE-TRAINING_OFFSET, 0);
+ float zeros[2*NB_BANDS+NB_FEATURES+1] = {0};
+ RNN_COPY(zeros, plc_features, 2*NB_BANDS);
+ zeros[2*NB_BANDS+NB_FEATURES] = 1;
+ compute_plc_pred(&st->plc_net, st->features, zeros);
+ lpcnet_synthesize_impl(&st->lpcnet, &st->features[0], tmp, FRAME_SIZE-TRAINING_OFFSET, 0);
for (i=0;i<FRAME_SIZE-TRAINING_OFFSET;i++) {
float w;
w = .5 - .5*cos(M_PI*i/(FRAME_SIZE-TRAINING_OFFSET));
@@ -104,11 +108,6 @@ LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
preemphasis(x, &st->enc.mem_preemph, x, PREEMPHASIS, FRAME_SIZE);
compute_frame_features(&st->enc, x);
process_single_frame(&st->enc, NULL);
- if (st->skip_analysis <= 1) {
- RNN_COPY(&plc_features[2*NB_BANDS], st->enc.features[0], NB_FEATURES);
- plc_features[2*NB_BANDS+NB_FEATURES] = 1;
- compute_plc_pred(&st->plc_net, st->features, plc_features);
- }
if (st->skip_analysis) {
float lpc[LPC_ORDER];
float gru_a_condition[3*GRU_A_STATE_SIZE];
@@ -117,6 +116,9 @@ LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
run_frame_network(&st->lpcnet, gru_a_condition, gru_b_condition, lpc, st->enc.features[0]);
st->skip_analysis--;
} else {
+ RNN_COPY(&plc_features[2*NB_BANDS], st->enc.features[0], NB_FEATURES);
+ plc_features[2*NB_BANDS+NB_FEATURES] = 1;
+ compute_plc_pred(&st->plc_net, st->features, plc_features);
for (i=0;i<FRAME_SIZE;i++) st->pcm[PLC_BUF_SIZE+i] = pcm[i];
RNN_COPY(output, &st->pcm[0], FRAME_SIZE);
lpcnet_synthesize_impl(&st->lpcnet, st->enc.features[0], output, FRAME_SIZE, FRAME_SIZE);
@@ -138,7 +140,7 @@ LPCNET_EXPORT int lpcnet_plc_conceal(LPCNetPLCState *st, short *pcm) {
int update_count;
update_count = IMIN(st->pcm_fill, FRAME_SIZE);
RNN_COPY(output, &st->pcm[0], update_count);
- if (st->pcm_fill > FRAME_SIZE) compute_plc_pred(&st->plc_net, st->features, zeros);
+ compute_plc_pred(&st->plc_net, st->features, zeros);
lpcnet_synthesize_impl(&st->lpcnet, &st->features[0], output, update_count, update_count);
RNN_MOVE(st->pcm, &st->pcm[FRAME_SIZE], PLC_BUF_SIZE);
st->pcm_fill -= update_count;
@@ -167,7 +169,7 @@ LPCNET_EXPORT int lpcnet_plc_conceal(LPCNetPLCState *st, short *pcm) {
#else
/* In this non-causal version of the code, the DNN model implemented by compute_plc_pred()
- returns the predicted features from frame t, using the input features from frame t.*/
+ is always called once per frame. We process audio up to the current position minus TRAINING_OFFSET. */
LPCNET_EXPORT int lpcnet_plc_update(LPCNetPLCState *st, short *pcm) {
int i;
diff --git a/dnn/training_tf2/train_plc.py b/dnn/training_tf2/train_plc.py
index 18bfd0b35fec092cef4a4e520bc0a3b25e13c465..547e70acb6fe53af8fdc1ec4880e498f6d1c615d 100644
--- a/dnn/training_tf2/train_plc.py
+++ b/dnn/training_tf2/train_plc.py
@@ -49,7 +49,6 @@ parser.add_argument('--lr', metavar='<learning rate>', type=float, help='learnin
parser.add_argument('--decay', metavar='<decay>', type=float, help='learning rate decay')
parser.add_argument('--band-loss', metavar='<weight>', default=1.0, type=float, help='weight of band loss (default 1.0)')
parser.add_argument('--loss-bias', metavar='<bias>', default=0.0, type=float, help='loss bias towards low energy (default 0.0)')
-parser.add_argument('--non-causal', dest='non_causal', action='store_true', help='train non-causal model')
parser.add_argument('--logdir', metavar='<log dir>', help='directory for tensorboard log files')
@@ -98,18 +97,10 @@ if args.decay is not None:
if retrain:
input_model = args.retrain
-delay = not args.non_causal
-
def plc_loss(alpha=1.0, bias=0.):
def loss(y_true,y_pred):
- if delay:
- mask = .2 + .8*y_true[:,1:,-1:]
- y_true = y_true[:,1:,:-1]
- y_pred = y_pred[:,:-1,:]
- else:
- mask = y_true[:,:,-1:]
- y_true = y_true[:,:,:-1]
-
+ mask = y_true[:,:,-1:]
+ y_true = y_true[:,:,:-1]
e = (y_pred - y_true)*mask
e_bands = tf.signal.idct(e[:,:,:-2], norm='ortho')
l1_loss = K.mean(K.abs(e)) + bias*K.mean(K.maximum(e[:,:,:1], 0.)) + alpha*K.mean(K.abs(e_bands) + bias*K.maximum(e_bands, 0.))
@@ -118,13 +109,8 @@ def plc_loss(alpha=1.0, bias=0.):
def plc_l1_loss():
def L1_loss(y_true,y_pred):
- if delay:
- mask = y_true[:,1:,-1:]
- y_true = y_true[:,1:,:-1]
- y_pred = y_pred[:,:-1,:]
- else:
- mask = y_true[:,:,-1:]
- y_true = y_true[:,:,:-1]
+ mask = y_true[:,:,-1:]
+ y_true = y_true[:,:,:-1]
e = (y_pred - y_true)*mask
l1_loss = K.mean(K.abs(e))
return l1_loss
@@ -132,14 +118,8 @@ def plc_l1_loss():
def plc_band_loss():
def L1_band_loss(y_true,y_pred):
- mask = y_true[:,1:,-1:]
- if delay:
- mask = y_true[:,1:,-1:]
- y_true = y_true[:,1:,:-1]
- y_pred = y_pred[:,:-1,:]
- else:
- mask = y_true[:,:,-1:]
- y_true = y_true[:,:,:-1]
+ mask = y_true[:,:,-1:]
+ y_true = y_true[:,:,:-1]
e = (y_pred - y_true)*mask
e_bands = tf.signal.idct(e[:,:,:-2], norm='ortho')
l1_loss = K.mean(K.abs(e_bands))