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Don't hardcode the number of bands

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This commit is contained in:
Jean-Marc Valin 2021-10-20 17:20:32 -04:00
parent b5b1d5013e
commit a9bf6cee8a
2 changed files with 5 additions and 3 deletions
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2
dnn/lpcnet.c
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@ -89,7 +89,7 @@ void run_frame_network(LPCNetState *lpcnet, float *gru_a_condition, float *gru_b
int pitch; int pitch;
float rc[LPC_ORDER]; float rc[LPC_ORDER];
/* Matches the Python code -- the 0.1 avoids rounding issues. */ /* Matches the Python code -- the 0.1 avoids rounding issues. */
pitch = (int)floor(.1 + 50*features[18]+100); pitch = (int)floor(.1 + 50*features[NB_BANDS]+100);
pitch = IMIN(255, IMAX(33, pitch)); pitch = IMIN(255, IMAX(33, pitch));
net = &lpcnet->nnet; net = &lpcnet->nnet;
RNN_COPY(in, features, NB_FEATURES); RNN_COPY(in, features, NB_FEATURES);

6
dnn/training_tf2/train_lpcnet.py
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@ -100,6 +100,8 @@ batch_size = args.batch_size
quantize = args.quantize is not None quantize = args.quantize is not None
retrain = args.retrain is not None retrain = args.retrain is not None
lpc_order = 16
if quantize: if quantize:
lr = 0.00003 lr = 0.00003
decay = 0 decay = 0
@ -133,7 +135,7 @@ with strategy.scope():
feature_file = args.features feature_file = args.features
pcm_file = args.data # 16 bit unsigned short PCM samples pcm_file = args.data # 16 bit unsigned short PCM samples
frame_size = model.frame_size frame_size = model.frame_size
nb_features = 36 nb_features = model.nb_used_features + lpc_order
nb_used_features = model.nb_used_features nb_used_features = model.nb_used_features
feature_chunk_size = 15 feature_chunk_size = 15
pcm_chunk_size = frame_size*feature_chunk_size pcm_chunk_size = frame_size*feature_chunk_size
@ -160,7 +162,7 @@ features = np.lib.stride_tricks.as_strided(features, shape=(nb_frames, feature_c
#features = features[:, :, :nb_used_features] #features = features[:, :, :nb_used_features]
periods = (.1 + 50*features[:,:,18:19]+100).astype('int16') periods = (.1 + 50*features[:,:,nb_used_features-2:nb_used_features-1]+100).astype('int16')
#periods = np.minimum(periods, 255) #periods = np.minimum(periods, 255)
# dump models to disk as we go # dump models to disk as we go