train_lpcnet.py

#!/usr/bin/python3
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'''

# Train a LPCNet model (note not a Wavenet model)

import lpcnet
import sys
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 h5py

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()

# use this option to reserve GPU memory, e.g. for running more than
# one thing at a time.  Best to disable for GPUs with small memory
config.gpu_options.per_process_gpu_memory_fraction = 0.44

set_session(tf.Session(config=config))

nb_epochs = 120

# Try reducing batch_size if you run out of memory on your GPU
batch_size = 64

model, _, _ = lpcnet.new_lpcnet_model(training=True)

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.summary()

feature_file = sys.argv[1]
pcm_file = sys.argv[2]     # 16 bit unsigned short PCM samples
frame_size = model.frame_size
nb_features = 55
nb_used_features = model.nb_used_features
feature_chunk_size = 15
pcm_chunk_size = frame_size*feature_chunk_size

# u for unquantised, load 16 bit PCM samples and convert to mu-law

data = np.fromfile(pcm_file, dtype='uint8')
nb_frames = len(data)//(4*pcm_chunk_size)

features = np.fromfile(feature_file, dtype='float32')

# limit to discrete number of frames
data = data[:nb_frames*4*pcm_chunk_size]
features = features[:nb_frames*feature_chunk_size*nb_features]

features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))

sig = np.reshape(data[0::4], (nb_frames, pcm_chunk_size, 1))
pred = np.reshape(data[1::4], (nb_frames, pcm_chunk_size, 1))
in_exc = np.reshape(data[2::4], (nb_frames, pcm_chunk_size, 1))
out_exc = np.reshape(data[3::4], (nb_frames, pcm_chunk_size, 1))
del data

print("ulaw std = ", np.std(out_exc))

features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
features = features[:, :, :nb_used_features]
features[:,:,18:36] = 0

fpad1 = np.concatenate([features[0:1, 0:2, :], features[:-1, -2:, :]], axis=0)
fpad2 = np.concatenate([features[1:, :2, :], features[0:1, -2:, :]], axis=0)
features = np.concatenate([fpad1, features, fpad2], axis=1)


periods = (.1 + 50*features[:,:,36:37]+100).astype('int16')

in_data = np.concatenate([sig, pred, in_exc], axis=-1)

del sig
del pred
del in_exc

# dump models to disk as we go
checkpoint = ModelCheckpoint('lpcnet30_384_10_G16_{epoch:02d}.h5')

#Set this to True to adapt an existing model (e.g. on new data)
adaptation = False

if adaptation:
    #Adapting from an existing model
    model.load_weights('lpcnet24c_384_10_G16_120.h5')
    sparsify = lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))
    lr = 0.0001
    decay = 0
else:
    #Training from scratch
    sparsify = lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))
    lr = 0.001
    decay = 5e-5

model.compile(optimizer=Adam(lr, amsgrad=True, decay=decay), loss='sparse_categorical_crossentropy')
model.save_weights('lpcnet30_384_10_G16_00.h5');
model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify])