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Adds end-to-end LPC training

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Making LPC computation and prediction differentiable
This commit is contained in:
Krishna Subramani 2021-07-29 03:36:13 -04:00 • committed by Jean-Marc Valin
parent cba0ecd483
commit c1532559a2
11 changed files with 357 additions and 17 deletions
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14
dnn/training_tf2/test_lpcnet.py
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@ -31,8 +31,10 @@ import numpy as np
from ulaw import ulaw2lin, lin2ulaw
import h5py
# Flag for synthesizing e2e (differentiable lpc) model
flag_e2e = False
model, enc, dec = lpcnet.new_lpcnet_model()
model, enc, dec = lpcnet.new_lpcnet_model(training = False, flag_e2e = flag_e2e)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
#model.summary()
@ -70,10 +72,16 @@ fout = open(out_file, 'wb')
skip = order + 1
for c in range(0, nb_frames):
cfeat = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]])
if not flag_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
a = features[c, fr, nb_features-order:]
if not flag_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)