ref: 36a0bf8c75ed54f521f044599275a8c04d990cdf
dir: /dnn/train_lpcnet.py/
#!/usr/bin/python3
'''Copyright (c) 2018 Mozilla
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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()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.summary()
exc_file = sys.argv[1] # not used at present
feature_file = sys.argv[2]
pred_file = sys.argv[3] # LPC predictor samples. Not used at present, see below
pcm_file = sys.argv[4] # 16 bit unsigned short PCM samples
frame_size = 160
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
udata = np.fromfile(pcm_file, dtype='int16')
data = lin2ulaw(udata)
nb_frames = len(data)//pcm_chunk_size
features = np.fromfile(feature_file, dtype='float32')
# limit to discrete number of frames
data = data[:nb_frames*pcm_chunk_size]
udata = udata[:nb_frames*pcm_chunk_size]
features = features[:nb_frames*feature_chunk_size*nb_features]
# Noise injection: the idea is that the real system is going to be
# predicting samples based on previously predicted samples rather than
# from the original. Since the previously predicted samples aren't
# expected to be so good, I add noise to the training data. Exactly
# how the noise is added makes a huge difference
in_data = np.concatenate([data[0:1], data[:-1]]);
noise = np.concatenate([np.zeros((len(data)*1//5)), np.random.randint(-3, 3, len(data)*1//5), np.random.randint(-2, 2, len(data)*1//5), np.random.randint(-1, 1, len(data)*2//5)])
#noise = np.round(np.concatenate([np.zeros((len(data)*1//5)), np.random.laplace(0, 1.2, len(data)*1//5), np.random.laplace(0, .77, len(data)*1//5), np.random.laplace(0, .33, len(data)*1//5), np.random.randint(-1, 1, len(data)*1//5)]))
in_data = in_data + noise
in_data = np.clip(in_data, 0, 255)
features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
# Note: the LPC predictor output is now calculated by the loop below, this code was
# for an ealier version that implemented the prediction filter in C
upred = np.fromfile(pred_file, dtype='int16')
upred = upred[:nb_frames*pcm_chunk_size]
# Use 16th order LPC to generate LPC prediction output upred[] and (in
# mu-law form) pred[]
pred_in = ulaw2lin(in_data)
for i in range(2, nb_frames*feature_chunk_size):
upred[i*frame_size:(i+1)*frame_size] = 0
for k in range(16):
upred[i*frame_size:(i+1)*frame_size] = upred[i*frame_size:(i+1)*frame_size] - \
pred_in[i*frame_size-k:(i+1)*frame_size-k]*features[i, nb_features-16+k]
pred = lin2ulaw(upred)
in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
in_data = in_data.astype('uint8')
# LPC residual, which is the difference between the input speech and
# the predictor output, with a slight time shift this is also the
# ideal excitation in_exc
out_data = lin2ulaw(udata-upred)
in_exc = np.concatenate([out_data[0:1], out_data[:-1]]);
out_data = np.reshape(out_data, (nb_frames, pcm_chunk_size, 1))
out_data = out_data.astype('uint8')
in_exc = np.reshape(in_exc, (nb_frames, pcm_chunk_size, 1))
in_exc = in_exc.astype('uint8')
features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
features = features[:, :, :nb_used_features]
features[:,:,18:36] = 0
pred = np.reshape(pred, (nb_frames, pcm_chunk_size, 1))
pred = pred.astype('uint8')
periods = (50*features[:,:,36:37]+100).astype('int16')
in_data = np.concatenate([in_data, pred], axis=-1)
# dump models to disk as we go
checkpoint = ModelCheckpoint('lpcnet9_384_10_G16_{epoch:02d}.h5')
#model.load_weights('wavenet4f2_30.h5')
model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, 0.1)])