ref: d816477c58e7a229c74b910e426e2dbf14a03eb8
dir: /dnn/training_tf2/test_lpcnet.py/
#!/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
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
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
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''
import lpcnet
import sys
import numpy as np
from ulaw import ulaw2lin, lin2ulaw
import h5py
filename = sys.argv[1]
with h5py.File(filename, "r") as f:
units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape)
units2 = min(f['model_weights']['gru_b']['gru_b']['recurrent_kernel:0'].shape)
cond_size = min(f['model_weights']['feature_dense1']['feature_dense1']['kernel:0'].shape)
e2e = 'rc2lpc' in f['model_weights']
model, enc, dec = lpcnet.new_lpcnet_model(training = False, rnn_units1=units, rnn_units2=units2, flag_e2e = e2e, cond_size=cond_size, batch_size=1)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
#model.summary()
feature_file = sys.argv[2]
out_file = sys.argv[3]
frame_size = model.frame_size
nb_features = 36
nb_used_features = model.nb_used_features
features = np.fromfile(feature_file, dtype='float32')
features = np.resize(features, (-1, nb_features))
nb_frames = 1
feature_chunk_size = features.shape[0]
pcm_chunk_size = frame_size*feature_chunk_size
features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
periods = (.1 + 50*features[:,:,18:19]+100).astype('int16')
model.load_weights(filename);
order = 16
pcm = np.zeros((nb_frames*pcm_chunk_size, ))
fexc = np.zeros((1, 1, 3), dtype='int16')+128
state1 = np.zeros((1, model.rnn_units1), dtype='float32')
state2 = np.zeros((1, model.rnn_units2), dtype='float32')
mem = 0
coef = 0.85
fout = open(out_file, 'wb')
skip = order + 1
for c in range(0, nb_frames):
if not e2e:
cfeat = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]])
else:
cfeat,lpcs = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]])
for fr in range(0, feature_chunk_size):
f = c*feature_chunk_size + fr
if not e2e:
a = features[c, fr, nb_features-order:]
else:
a = lpcs[c,fr]
for i in range(skip, frame_size):
pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1])
fexc[0, 0, 1] = lin2ulaw(pred)
p, state1, state2 = dec.predict([fexc, cfeat[:, fr:fr+1, :], state1, state2])
#Lower the temperature for voiced frames to reduce noisiness
p *= np.power(p, np.maximum(0, 1.5*features[c, fr, 19] - .5))
p = p/(1e-18 + np.sum(p))
#Cut off the tail of the remaining distribution
p = np.maximum(p-0.002, 0).astype('float64')
p = p/(1e-8 + np.sum(p))
fexc[0, 0, 2] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))
pcm[f*frame_size + i] = pred + ulaw2lin(fexc[0, 0, 2])
fexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i])
mem = coef*mem + pcm[f*frame_size + i]
#print(mem)
np.array([np.round(mem)], dtype='int16').tofile(fout)
skip = 0