shithub: opus

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Bring test_lpcnet.py up-to-date

--- a/dnn/training_tf2/lpcnet.py

+++ b/dnn/training_tf2/lpcnet.py

@@ -313,7 +313,7 @@

     if not flag_e2e:

         encoder = Model([feat, pitch], cfeat)

-        dec_rnn_in = Concatenate()([cpcm, dec_feat])

+        dec_rnn_in = Concatenate()([cpcm_decoder, dec_feat])

     else:

         encoder = Model([feat, pitch], [cfeat,lpcoeffs])

         dec_rnn_in = Concatenate()([cpcm_decoder, dec_feat])

@@ -324,5 +324,5 @@

     if flag_e2e:

         decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])

     else:

-        decoder = Model([pcm, dec_feat, dec_state1, dec_state2, lpcoeffs], [dec_ulaw_prob, state1, state2])

+        decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2])

     return model, encoder, decoder

--- a/dnn/training_tf2/test_lpcnet.py

+++ b/dnn/training_tf2/test_lpcnet.py

@@ -31,16 +31,22 @@

 from ulaw import ulaw2lin, lin2ulaw

 import h5py

-# Flag for synthesizing e2e (differentiable lpc) model

-flag_e2e = False

+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, flag_e2e = flag_e2e)

+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[1]

-out_file = sys.argv[2]

+

+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

@@ -56,7 +62,7 @@

-model.load_weights('lpcnet38Sn_384_02.h5');

+model.load_weights(filename);

 order = 16

@@ -72,13 +78,13 @@

 skip = order + 1

 for c in range(0, nb_frames):

-    if not flag_e2e:

+    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 flag_e2e:

+        if not e2e:

             a = features[c, fr, nb_features-order:]

         else:

             a = lpcs[c,fr]

-- 

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