v good results from 200415a, once epochs=05 thing fixed

src/700c_train.sh

@@ -16,7 +16,7 @@ train2=train_8k
 test1=all_8k
 test2=all_speech_subset_8k
 datestamp=$1
-epochs=05
+epochs=5
 log=${1}.txt
 train=${datestamp}_train
 
@@ -35,8 +35,9 @@ experiment() {
     
     c2sim ${train}.sw --ten_ms_centre ${train}_10ms.sw --rateKWov ${train}.f32 ${1}
     sw2packedulaw --frame_size 80 ${train}_10ms.sw ${train}.f32 ${train}_10ms.pulaw
-    
+
     train_lpcnet.py ${train}.f32 ${train}_10ms.pulaw ${datestamp}_${2} --epochs ${epochs} --frame_size 80
+    
     dump_lpcnet.py ${datestamp}_${2}_${epochs}.h5
     cp nnet_data.c src
     make test_lpcnet

src/train_lpcnet.py

@@ -62,6 +62,8 @@ parser.add_argument('packed_ulaw_file', help='file of 4 multiplexed ulaw samples
 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
@@ -71,6 +73,10 @@ 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              
@@ -129,11 +135,15 @@ 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 ceptrals
+# 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.any(periods >= 0), "pitch embedding < 0"
-assert np.any(periods < 256), "pitch embeddeding > 255"
+assert np.all(periods >= 40), "pitch embedding < 40"
+assert np.all(periods < 256), "pitch embeddeding > 255"
 
 """
 # plot pitch to sanity check
@@ -150,9 +160,8 @@ del in_exc
 
 # dump models to disk as we go
 #checkpoint = ModelCheckpoint('lpcnet20h_384_10_G16_{epoch:02d}.h5')
-checkpoint = ModelCheckpoint(prefix + '_{epoch:02d}.h5')
+checkpoint = ModelCheckpoint(prefix + '_{epoch:d}.h5')
 
 # use this to reload a partially trained model
-#model.load_weights('lpcnet_190203_07.h5')
 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))])