#!/usr/bin/python3
'''Copyright (c) 2018 Mozilla

   Redistribution and use in source and binary forms, with or without
   modification, are permitted provided that the following conditions
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   - Redistributions of source code must retain the above copyright
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   notice, this list of conditions and the following disclaimer in the
   documentation and/or other materials provided with the distribution.

   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''

# Train a LPCNet 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 argparse
import os
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
import matplotlib.pyplot as plt

# less verbose tensorflow ....
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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))

# Try reducing batch_size if you run out of memory on your GPU
batch_size = 32
# with of feature records used for training
nb_features = 55

parser = argparse.ArgumentParser(description='LPCNet training')
parser.add_argument('feature_file', help='.f32 file of float features')
parser.add_argument('packed_ulaw_file', help='file of 4 multiplexed ulaw samples per speech sample')
parser.add_argument('prefix', help='.h5 file prefix to easily identify each experiment')
parser.add_argument('--frame_size', type=int, default=160, help='frames size in samples')
parser.add_argument('--epochs', type=int, default=20, help='Number of training epochs')
parser.add_argument('--no_pitch_embedding', action='store_true', help='disable pitch embedding')
parser.add_argument('--load_h5', help='disable pitch embedding')
args = parser.parse_args()

nb_epochs = args.epochs

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

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

if args.load_h5:
    print("loading: %s" % (args.load_h5))
    model.load_weights(args.load_h5)

feature_file = args.feature_file
pcm_file = args.packed_ulaw_file           
prefix = args.prefix              
frame_size = model.frame_size
nb_used_features = model.nb_used_features
feature_chunk_size = 15 # time window for conv1d/receptive field
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

"""
# plot ulaw signals to sanity check
testf=10
print(sig.shape)
#plt.plot(sig[testf,:],label="sig")
#plt.plot(pred[testf,:],label="pred")
plt.plot(in_exc[testf,:],label="in_exc")
plt.plot(out_exc[testf,:],label="out_exc")
plt.legend()
plt.show()
"""

features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
features = features[:, :, :nb_used_features]
# 0..37 features total
# 0..17 cepstrals, 36 = pitch, 37 = pitch gain, 38 = lpc-gain
# nb_used_features=38, so 0...37, so lpc-gain not used
features[:,:,18:36] = 0   # zero out 18..35, so pitch and pitch gain being fed in, lpc gain ignored

"""
# plot features to sanity check
print(features.shape)
testf=10
plt.plot(features[testf,:,37:38])
plt.show()
"""

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)

# pitch feature uses as well as cepstrals
periods = (.1 + 50*features[:,:,36:37]+100).astype('int16')
print(periods.shape)
if args.no_pitch_embedding:
    print("no_pitch_embedding")
    periods[:] = 0
# sanity check training data aginst pitch embedding range
assert np.all(periods >= 40), "pitch embedding < 40"
assert np.all(periods < 256), "pitch embeddeding > 255"

"""
# plot pitch to sanity check
print(features.shape, periods.shape)
plt.plot(periods.reshape(-1)[:1000])
plt.show()
"""

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('lpcnet20h_384_10_G16_{epoch:02d}.h5')
checkpoint = ModelCheckpoint(prefix + '_{epoch:d}.h5')

# use this to reload a partially trained model
model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy')
model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))])