shithub: opus

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clean excitation

--- a/dnn/test_wavenet_audio.py

+++ b/dnn/test_wavenet_audio.py

@@ -66,7 +66,7 @@

 out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))

-model.load_weights('wavenet4a1_13.h5')

+model.load_weights('wavenet4a3_30.h5')

 order = 16

--- a/dnn/train_wavenet_audio.py

+++ b/dnn/train_wavenet_audio.py

@@ -35,17 +35,18 @@

 feature_chunk_size = 15

 pcm_chunk_size = frame_size*feature_chunk_size

-data = np.fromfile(pcm_file, dtype='int16')

-data = np.minimum(127, lin2ulaw(data/32768.))

+udata = np.fromfile(pcm_file, dtype='int16')

+data = np.minimum(127, lin2ulaw(udata/32768.))

 nb_frames = len(data)//pcm_chunk_size

 features = np.fromfile(feature_file, dtype='float32')

 data = data[:nb_frames*pcm_chunk_size]

+udata = udata[:nb_frames*pcm_chunk_size]

 features = features[:nb_frames*feature_chunk_size*nb_features]

 in_data = np.concatenate([data[0:1], data[:-1]]);

-noise = np.concatenate([np.zeros((len(data)//3)), np.random.randint(-2, 2, len(data)//3), np.random.randint(-1, 1, len(data)//3)])

+noise = np.concatenate([np.zeros((len(data)*2//5)), np.random.randint(-2, 2, len(data)//5), np.random.randint(-1, 1, len(data)*2//5)])

 in_data = in_data + noise

 in_data = np.maximum(-127, np.minimum(127, in_data))

@@ -77,7 +78,7 @@

 in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))

 in_data = (in_data.astype('int16')+128).astype('uint8')

-out_data = np.reshape(lin2ulaw((32768*ulaw2lin(data)-upred)/32768), (nb_frames, pcm_chunk_size, 1))

+out_data = np.reshape(lin2ulaw((udata-upred)/32768), (nb_frames, pcm_chunk_size, 1))

 out_data = np.maximum(-127, np.minimum(127, out_data))

 out_data = (out_data.astype('int16')+128).astype('uint8')

 features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))

@@ -93,7 +94,7 @@

 # f.create_dataset('data', data=in_data[:50000, :, :])

 # f.create_dataset('feat', data=features[:50000, :, :])

-checkpoint = ModelCheckpoint('wavenet4a1_{epoch:02d}.h5')

+checkpoint = ModelCheckpoint('wavenet4a3_{epoch:02d}.h5')

 #model.load_weights('wavernn1c_01.h5')

 model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])

-- 

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