eden-emu/opus
15
0
Fork 0
mirror of https://github.com/xiph/opus.git synced 2025-06-01 16:17:42 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

excitation model

Browse source
This commit is contained in:
Jean-Marc Valin 2018-06-25 16:26:47 -04:00
parent cc1c52a63b
commit b65031ef64
2 changed files with 23 additions and 14 deletions
Download patch file Download diff file Expand all files Collapse all files

33
dnn/train_lpcnet.py
View file

@ -6,6 +6,7 @@ import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from ulaw import ulaw2lin, lin2ulaw
import keras.backend as K
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
@ -21,22 +22,30 @@ model.compile(optimizer=Adam(0.0008), loss='sparse_categorical_crossentropy', me
model.summary()
pcmfile = sys.argv[1]
chunk_size = int(sys.argv[2])
feature_file = sys.argv[2]
nb_features = 54
nb_used_features = 38
feature_chunk_size = 15
pcm_chunk_size = 160*feature_chunk_size
data = np.fromfile(pcmfile, dtype='int16')
#data = data[:100000000]
data = data/32768
nb_frames = (len(data)-1)//chunk_size
data = np.fromfile(pcmfile, dtype='int8')
nb_frames = len(data)//pcm_chunk_size
in_data = data[:nb_frames*chunk_size]
#out_data = data[1:1+nb_frames*chunk_size]//256 + 128
out_data = lin2ulaw(data[1:1+nb_frames*chunk_size]) + 128
features = np.fromfile(feature_file, dtype='float32')
in_data = np.reshape(in_data, (nb_frames, chunk_size, 1))
out_data = np.reshape(out_data, (nb_frames, chunk_size, 1))
data = data[:nb_frames*pcm_chunk_size]
features = features[:nb_frames*feature_chunk_size*nb_features]
checkpoint = ModelCheckpoint('wavernn1f_{epoch:02d}.h5')
in_data = np.concatenate([data[0:1], data[:-1]])/16.;
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('lpcnet1b_{epoch:02d}.h5')
#model.load_weights('wavernn1c_01.h5')
model.compile(optimizer=Adam(0.002, amsgrad=True, decay=1e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.compile(optimizer=Adam(0.002, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.fit(in_data, out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])