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Adding pitch

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This commit is contained in:
Jean-Marc Valin 2018-06-26 16:40:55 -04:00
parent 617e462be3
commit fd9002e98e
3 changed files with 23 additions and 6 deletions
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5
dnn/denoise.c
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@ -46,7 +46,7 @@
#define WINDOW_SIZE (2*FRAME_SIZE)
#define FREQ_SIZE (FRAME_SIZE + 1)
#define PITCH_MIN_PERIOD 20
#define PITCH_MIN_PERIOD 32
#define PITCH_MAX_PERIOD 256
#define PITCH_FRAME_SIZE 320
#define PITCH_BUF_SIZE (PITCH_MAX_PERIOD+PITCH_FRAME_SIZE)
@ -321,7 +321,7 @@ static void frame_analysis(DenoiseState *st, signed char *iexc, float *lpc, kiss
float g_1;
_celt_autocorr(x, ac, NULL, 0, LPC_ORDER, WINDOW_SIZE);
/* -40 dB noise floor. */
ac[0] += ac[0]*1e-4;
ac[0] += ac[0]*1e-4 + 320/12;
/* Lag windowing. */
for (i=1;i<LPC_ORDER+1;i++) ac[i] *= (1 - 6e-5*i*i);
e = _celt_lpc(lpc, rc, ac, LPC_ORDER);
@ -582,7 +582,6 @@ int main(int argc, char **argv) {
preemphasis(x, &mem_preemph, x, PREEMPHASIS, FRAME_SIZE);
compute_frame_features(st, iexc, X, P, Ex, Ep, Exp, features, x);
pitch_filter(X, P, Ex, Ep, Exp, g);
#if 1
fwrite(features, sizeof(float), NB_FEATURES, stdout);
fwrite(iexc, sizeof(signed char), FRAME_SIZE, fexc);

2
dnn/lpcnet.py
View file

@ -16,7 +16,7 @@ nb_used_features = 37
def new_wavernn_model():
pcm = Input(shape=(None, 1))
pcm = Input(shape=(None, 2))
feat = Input(shape=(None, nb_used_features))
rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))

22
dnn/train_lpcnet.py
View file

@ -7,6 +7,7 @@ from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from ulaw import ulaw2lin, lin2ulaw
import keras.backend as K
import h5py
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
@ -23,10 +24,11 @@ model.summary()
pcmfile = sys.argv[1]
feature_file = sys.argv[2]
frame_size = 160
nb_features = 54
nb_used_features = lpcnet.nb_used_features
feature_chunk_size = 15
pcm_chunk_size = 160*feature_chunk_size
pcm_chunk_size = frame_size*feature_chunk_size
data = np.fromfile(pcmfile, dtype='int8')
nb_frames = len(data)//pcm_chunk_size
@ -38,13 +40,29 @@ features = features[:nb_frames*feature_chunk_size*nb_features]
in_data = np.concatenate([data[0:1], data[:-1]])/16.;
features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
pitch = 1.*data
pitch[:320] = 0
for i in range(2, nb_frames*feature_chunk_size):
period = int(50*features[i,36]+100)
period = period - 4
pitch[i*frame_size:(i+1)*frame_size] = data[i*frame_size-period:(i+1)*frame_size-period]
in_pitch = np.reshape(pitch/16., (nb_frames, pcm_chunk_size, 1))
in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
out_data = np.reshape(data, (nb_frames, pcm_chunk_size, 1))
out_data = (out_data.astype('int16')+128).astype('uint8')
features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
features = features[:, :, :nb_used_features]
checkpoint = ModelCheckpoint('lpcnet1c_{epoch:02d}.h5')
in_data = np.concatenate([in_data, in_pitch], axis=-1)
#with h5py.File('in_data.h5', 'w') as f:
# f.create_dataset('data', data=in_data[:50000, :, :])
# f.create_dataset('feat', data=features[:50000, :, :])
checkpoint = ModelCheckpoint('lpcnet1e_{epoch:02d}.h5')
#model.load_weights('wavernn1c_01.h5')
model.compile(optimizer=Adam(0.002, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])