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Remove trailing whitespace in dnn

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Marcus Asteborg 2023-06-22 05:27:54 -07:00
parent 26ab10d0c8
commit f36685fc97
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GPG key ID: F69798291D4CE42A
37 changed files with 231 additions and 246 deletions
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14
dnn/training_tf2/lpcnet.py
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@ -186,7 +186,7 @@ class SparsifyGRUB(Callback):
w[0] = p
layer.set_weights(w)
class PCMInit(Initializer):
def __init__(self, gain=.1, seed=None):
@ -264,20 +264,20 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_s
lpcoeffs = diff_rc2lpc(name = "rc2lpc")(cfeat)
else:
lpcoeffs = Input(shape=(None, lpc_order), batch_size=batch_size)
real_preds = diff_pred(name = "real_lpc2preds")([pcm,lpcoeffs])
weighting = lpc_gamma ** np.arange(1, 17).astype('float32')
weighted_lpcoeffs = Lambda(lambda x: x[0]*x[1])([lpcoeffs, weighting])
tensor_preds = diff_pred(name = "lpc2preds")([pcm,weighted_lpcoeffs])
past_errors = error_calc([pcm,tensor_preds])
embed = diff_Embed(name='embed_sig',initializer = PCMInit())
cpcm = Concatenate()([tf_l2u(pcm),tf_l2u(tensor_preds),past_errors])
cpcm = GaussianNoise(.3)(cpcm)
cpcm = Reshape((-1, embed_size*3))(embed(cpcm))
cpcm_decoder = Reshape((-1, embed_size*3))(embed(dpcm))
rep = Lambda(lambda x: K.repeat_elements(x, frame_size, 1))
quant = quant_regularizer if quantize else None
@ -305,7 +305,7 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_s
rnn2.trainable=False
md.trainable=False
embed.Trainable=False
m_out = Concatenate(name='pdf')([tensor_preds,real_preds,ulaw_prob])
if not flag_e2e:
model = Model([pcm, feat, pitch, lpcoeffs], m_out)
@ -315,7 +315,7 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_s
model.rnn_units2 = rnn_units2
model.nb_used_features = nb_used_features
model.frame_size = frame_size
if not flag_e2e:
encoder = Model([feat, pitch], cfeat)
dec_rnn_in = Concatenate()([cpcm_decoder, dec_feat])
@ -330,7 +330,7 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_s
decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
else:
decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])
# add parameters to model
set_parameter(model, 'lpc_gamma', lpc_gamma, dtype='float64')
set_parameter(model, 'flag_e2e', flag_e2e, dtype='bool')